Intelligent Computing and Innovation on Data Science: Proceedings of ICTIDS 2021 (Lecture Notes in Networks and Systems, 248) 9811631522, 9789811631528
This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Scienc
98 75 16MB
English Pages 618 [590] Year 2021
Organizing Committee and Key Members
Conference Committee Members
Patron
Honorary Chair
Conference Advisors
Convenor
Program Chair
Publication Chairs
Scientific Advisory Chair
International Advisory Board Members
Publicity Chairs
Technical Program Committee Members
Invited Speakers
Preface and Acknowledgements
Contents
Editors and Contributors
1 Recent Trends in Potential Security Solutions for SD-WAN: A Systematic Review
Abstract
1 Introduction
2 Significant Characteristics of SD-WAN
2.1 Life cycle Orchestration and Automation
2.2 Continuous Self-Learning
2.3 Consistent Quality of Experience (QoEx)
2.4 End-To-End Micro-Segmentation
2.5 Local Internet Breakout for Cloud Application
3 Enhancement of Security Standards with SD-WAN Framework
3.1 Delegating Network Security
3.2 Flowchecker
3.3 Cognition
3.4 Resonance
3.5 Secure Forensics
3.6 Tualatin
3.7 NetSecCloud
3.8 Cloud Watcher
3.9 Flow Tags
3.10 Securing Distributed Control
4 Results and Discussion—Comparison of Performance Evaluation
4.1 Packet Delivery Ratio (PDR)
5 Conclusion
References
3 SVM- and K-NN-Based Paddy Plant Insects Recognition
Abstract
1 Introduction
1.1 Paddy Plant Insects
1.1.1 Grasshopper
1.1.2 Planthopper
1.1.3 Stem Borer
2 Review of Literatures
2.1 Approaches of Paddy Plant Insects Recognition
3 Proposed Research Work
3.1 SVM-Based IR2PI
3.2 k-NN-Based IR2PI
4 Performance Comparison of SVM- and k-NN-Based IR2PI Systems
5 Major Contributions
6 Conclusions
References
4 Comparative Study on Challenges and Detection of Brain Tumor Using Machine Learning Algorithm
Abstract
1 Introduction
2 Literature Survey
3 Challenges in Detection and Diagnosis of Tumor
4 Proposed Treatment Method
4.1 MRI Medical Image Dataset
4.2 Image Processing Techniques
4.3 Random Forest Classifier for Classification
5 Results and Discussion
6 Conclusion and Future Works
References
5 Deep Learning in Image Recognition for Easy Sketching Techniques
Abstract
1 Introduction
2 Related Work
3 Methodology
4 Conclusion
References
6 Deep Learning in Image Signal Processing for Minimal Method by Using Kernel DBN
Abstract
1 Introduction
2 Related Works
3 Methodology
4 Conclusion
References
8 Bone Age Measurement-Based on Dental Radiography, Employing a New Model
Abstract
1 Introduction
2 Problem Statement
3 Literature Review
4 Proposed Model
4.1 CNN
4.1.1 KNN
4.2 CNN-KNN on Dental Images for Bone Age Measurement
4.2.1 Image Pre-processing and Data Augmentation
4.2.2 Hybrid CNN-KNN
5 Discussion
6 Conclusion
7 Future Work
Acknowledgements
References
9 Climatic Analysis for Agriculture Cultivation in Geography Using Big Data Analytics
Abstract
1 Introduction
2 Prediction
2.1 Climatic Prediction
2.2 Soil Prediction
2.3 Wind and Water Prediction
3 Clustering
3.1 Division Clustering
3.2 Hierarchy and Fuzzy Clustering
3.3 Clustering Methods with Model
4 Conclusion
References
10 Implementation and Performance Analysis of Various Models of PCNN for Medical Image Segmentation
Abstract
1 Introduction
2 Related Work
2.1 PCNN Model
2.2 Intersecting Cortical Model PCNN (ICM-PCNN)
2.3 Unit-Linking PCNN (UL-PCNN)
3 Experimental Results
3.1 Experimental Results with Basic PCNN Model for Input 1
3.2 Experimental Results with Unit-Linking PCNN for Input 1
3.3 Experimental Results with Intersecting Cortical Model (ICM) for Input 1
3.4 Experimental Results for Basic PCNN Model for Input 2
3.5 Experimental Results for Intersecting Cortical Model Input 2
3.6 Experimental Results for Unit-Linking PCNN Model for Input 2
3.7 Experimental Results for Basic PCNN Model for Input 3
3.8 Experimental Results for Intersecting Cortical Model for Input 3
3.9 Experimental Results for Unit-Linking PCNN Model for Input 3
4 Analyzing the Best-Segmented Values for Three Kinds of Input Images
4.1 Basic PCNN Model
4.2 Intersecting Cortical Model
4.3 Unit-Linking PCNN Model
5 Conclusion
References
12 Combined Minimum Spanning Tree and Particle Swarm Optimization for the Design of the Cable Layout in Offshore Wind Farms
Abstract
1 Introduction
2 The Methodology of the Optimization Approach
2.1 Minimum Spanning Tree
2.2 Clustering of Wind Turbines with k-means
2.3 Particle Swarm Optimization
3 Optimization of the Internal Power Collection Network
3.1 First Case Study
3.2 Second Case Study
4 Conclusions and Future Work
References
13 Biomedical Scan Image Retrieval Using Higher Order Neural Networks
Abstract
1 Introduction
2 Related Work
3 Proposed Work
3.1 Feature Extraction
3.2 The PCNN’s Neuron Model.
3.3 UL-PCNN Model
4 Experimentation and Results
5 Conclusion
References
14 Soil Category Classification Using Convolutional Neural Network Long Short Wide Memory Method
Abstract
1 Introduction
2 Related Work
3 Convolutional Neural Network
4 Long Short-Term Memory (LSTM)
5 Proposed Work
6 Experimental Results
7 Conclusion
References
15 An Empirical Study on Selected Emerging Technologies: Strengths and Challenges
Abstract
1 Introduction
2 Trends of Emerging Technologies
2.1 Selected Emerging Technologies
2.2 Overview of the Selected Technologies
2.2.1 Smart Eye Technology
2.2.2 3D Searching
2.2.3 Blockchain
2.2.4 Polymer Memory
2.2.5 Artificial Passenger
3 Significance of the Selected Technologies in the Society
4 Conclusion
References
16 Energy Harvesting: A Panacea to the Epileptic Power Supply in Nigeria
Abstract
1 Introduction
1.1 Power Supply in Nigeria
1.2 Current Situation in the Power Sector
2 Overview of Energy Harvesting Technology
2.1 Solar Cells
2.2 Thermoelectric Elements
2.3 Electromagnetic Energy
2.4 Application Area of Energy Harvesting
3 Solution to Epileptic Power Supply in Nigeria
4 Conclusion
References
17 Forecasting of Inflation Rate Contingent on Consumer Price Index: Machine Learning Approach
Abstract
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Model Design
4 Result Analysis
5 Conclusion and Future Work
References
18 Face Detection and Recognition System
Abstract
1 Introduction
2 Related Works
3 Proposed Methodology
3.1 Histogram of Oriented Gradients (HOG)
3.2 Classifier: Support Vector Machine (SVM)
3.3 K Nearest Neighbor (KNN)
4 Experimental Results
5 Conclusions
References
19 Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period
Abstract
1 Introduction
2 Literature Review
3 Proposed Method
4 Case Study
5 Conclusion and Future Work
References
20 Encoding and Refuge Shelter by Embracing Steganography with Hybrid Methods in Image Reduction Processing
Abstract
1 Introduction
2 Related Works
3 Methodology
3.1 Data Encryption Standards (DES)
3.2 Formulas
3.3 3DES
3.4 MD5—Message-Digest 5
4 Results and Discussion
4.1 Experimental Results
5 Conclusion and Future Enhancement
References
21 Bigdata Analysis Using Machine Learning Algorithm in Predicting the Cardiovascular Disease
Abstract
1 Introduction
2 Literature Review
3 Data and Description
4 Work Results
5 Conclusion
References
22 Anomaly Detection in Business Process Event Using KNN Algorithm
Abstract
1 Introduction
2 Related Works
3 Proposed System
3.1 Modules Description
3.1.1 Business Process Management
3.1.2 Process Mining
Infrequent Behavior
Frequent Behavior
KNN Algorithm
4 Experimentation
5 Conclusion
References
23 Comparison of Multidimensional Hyperspectral Image with SIFT Image Mosaic Methods for Mosaic Better Accuracy
Abstract
1 Introduction
2 Related Works
3 Methodology
3.1 Multidimensional Hyperspectral
3.1.1 Definition of the Framework
3.1.2 Registration Picture
3.1.3 Selection of the Reference Image
3.1.4 Function Identification and Connection
3.1.5 Matrix Fusion Transform
3.2 SIFT
3.2.1 Detection of Harris Corner
3.2.2 Point Matching
4 Conclusion
References
24 The Role of Innovativeness in Mediating the Relationship Between Overall Quality and User Satisfaction Among the Financial Information Systems in Yemen
Abstract
1 Introduction
2 Literature Review
2.1 Overall Quality (QUL)
2.2 User Satisfaction (SAT)
2.3 Innovativeness (INN)
3 Research Method
4 Result of Analysising Data
4.1 Measurement Model Assessment
4.2 Structural Model Assessment
4.2.1 Direct Effect Hypotheses
4.2.2 Moderating Effect Hypotheses
5 Discussion
6 Implications
7 Conclusion
References
25 Mobile Banking Adoption—Extending Technology Acceptance Model with Transaction Convenience and Perceived Risk: A Conceptual Framework
Abstract
1 Introduction
2 Hypotheses Development
2.1 Perceived Ease of Use, Perceived Usefulness and Transaction Convenience
2.2 Transaction Convenience and Intention to Use M-banking
2.3 Perceived Ease of Use, Perceived Usefulness and Intention to Use M-banking
2.4 Perceived Risk, Intention to Use and Actual Usage
3 Operationalization of Variables
4 Conclusion
References
26 Drone—an Assistive Device for Aquacare Monitoring
Abstract
1 Introduction
2 Literature Review
3 Methodology
3.1 Hardware Components
3.1.1 Frame of Drone
4 Results and Discussion
5 Conclusion
References
27 Big Data Background on the Bank Account for Progress of Income Improvement on Customers on Cloud Accounting
Abstract
1 Introduction
2 Literature Survey
3 Methodology
4 Conclusion
References
28 Hyper-Personalization of Mobile Applications for Cloud Kitchen Operations
Abstract
1 Introduction
1.1 Hyper-Personalization of Food (HPF)
1.2 Cloud Kitchen Operation
1.3 Mobile App Options
2 Materials and Methods
3 Results and Analysis
3.1 Correlation
3.2 Linear Regression
4 Conclusion
References
29 Exploring Intention to Use E-Government: The Role of Technology Acceptance Model with Self-Efficacy and System Quality
Abstract
1 Introduction
2 Related Work
3 Conceptual Model and Proposed Hypotheses
4 Result and Discussion
5 Conclusion
References
30 Analysis of E-learner’s Opinion Using Automated Sentiment Analysis in E-learning and Comparison with Naive Bayes Classification, Random Forest and K-Nearest Neighbour Algorithms
Abstract
1 Introduction
2 Related Works
3 Methodology
3.1 Machine Learning
3.2 Bayesian Classification (BSC)
3.3 NBC
4 Process Flow and Experimentation
4.1 Process Flow
4.2 Experimentation
4.2.1 Random Forest
4.2.2 KNN
4.2.3 Performance Measures
Recall
Precision
5 Conclusion
References
31 Car Damage Detection and Cost Evaluation Using MASK R-CNN
Abstract
1 Introduction
2 Related Works
3 Proposed Methods
3.1 Mask R-CNN
4 Transfer Learning
5 Dataset Description
6 Result and Discussion
7 Conclusion and Future Work
References
32 Layered Architecture for End-To-End Security, Trust, and Privacy for the Internet of Things
Abstract
1 Introduction
1.1 Paper Contributions
1.2 Paper Organization
2 Background and Motivations
3 Related Works
3.1 Layered Architecture for IoT
3.2 End-To-End IoT Security
4 Proposed Layered Architecture
4.1 End-To-End Security at Different Layers
5 Conclusion and Future Works
References
33 Service Rating Prediction in Location-Based Services
Abstract
1 Introduction
2 Proposed System
3 Service Rating Prediction
4 Experiments and Results
4.1 Dataset
4.2 Experiments and Analysis
4.2.1 Experimental Setup
4.2.2 Results
4.2.3 Summary
5 Conclusions
6 Future Work
References
34 Analytics of e-Commerce Platforms Based on User-Experience (UX)
Abstract
1 Introduction
2 Review of Literature
3 Proposed Methodology
3.1 Popularity of Online Shopping
3.1.1 Data Analysis
3.1.2 Extraction Method: Principal Component Analysis: 3 Components Extracted
3.2 Analysis and Interpretation
3.2.1 Data Visualization
4 The Role of UX in E-Commerce Platforms
5 Conclusion
References
35 Towards a Threat Model for Unmanned Aerial Vehicles
Abstract
1 Introduction
2 Related Work
3 UAV Architectures
3.1 Background
3.2 Unmanned Aircraft Vehicle
3.3 Ground Control Station (GCS)
3.4 The Data Connection
4 Threat Modeling Process for UAV
4.1 The Attacker Models
4.2 Assets
4.3 Access Entry Points
4.4 Vulnerabilities and Threats
4.5 Mitigation Strategies
5 Conclusion
References
36 Numerical Analysis of Strut-Based Scramjet Combustor with Ramps Under Non-reacting Flow Field
Abstract
1 Introduction
2 Computational Method
2.1 Numerical Modeling
2.2 Boundary Conditions
2.3 Grid Independence Study
3 Validation
4 Result and Discussion
4.1 Wall Static Pressure
4.2 Total Pressure Loss
5 Conclusion
References
37 Qualitative Assessment of Machine Learning Classifiers for Employee Performance Prediction
Abstract
1 Introduction
1.1 Machine Learning Algorithms
2 Literature Review
3 Methodology
3.1 Data Set
3.2 Data Analysis
3.3 Feature Selection and Reduction
3.4 Training and Testing
4 Implementation of Machine Learning Classifiers
5 Experimental Results and Discussion
5.1 Receiver Operating Characteristic Curve (ROC)
5.2 Performance Assessment of Machine Learning Algorithms
6 Conclusion and Future Work
References
38 Developing Smart Application for Screening and Reducing Maternal and Neonatal Mortality Birth Preparedness
Abstract
1 Introduction
2 Methods of the Research
3 Results and Discussion
4 Discussion
5 Conclusion
Acknowledgements
References
39 Role of Machine Learning Approaches in Remaining Useful Prediction: A Review
Abstract
1 Introduction
1.1 RUL Estimation
1.2 Machine Learning Approaches
1.3 Physics-Based Model Approach
1.4 Knowledge-Based and Expert Model Approach
2 Data-Driven Model Approaches
2.1 Supervised Learning Approach
2.2 Unsupervised Learning Approach
2.3 Reinforcement Learning Approach
3 Machine Learning on RUL Prediction
3.1 Review of Machine Learning Techniques
4 Conclusion
References
40 An Extensive Review on Malware Classification Based on Classifiers
Abstract
1 Introduction
2 Malware Analysis
2.1 Machine Learning for Malware Classification
2.2 Deep Learning for Malware Classification
3 Experiment
3.1 Dataset
3.2 Decision Tree
3.3 Random Forest
3.4 Naïve Bayes
3.5 K-Nearest Neighbor
3.6 Support Vector Machine
4 Performance Metrics
4.1 Confusion Matrix
5 Result and Discussion
6 Conclusion and Future Work
References
41 Video-Based Deep Face Recognition Using Partial Facial Information
Abstract
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 The VGG Face Model
3.2 Feature Extraction Using VGG Face Model
3.3 Classification
4 Experiment Result
5 Conclusions
References
42 Early Prediction of Cardio Vascular Disease by Performing Associative Classification on Medical Datasets and Using Genetic Algorithm
Abstract
1 Introduction
2 Related Work
3 Background Work
3.1 Associative Classification
3.2 Genetic Algorithm
3.3 Cardiovascular Diseases
4 Cardio Vascular Prediction System
5 Experimental Results and Discussion
6 Conclusion
References
43 Classification of Benign and Malignant Lung Cancer Nodule Using Artificial Neural Network
Abstract
1 Introduction
2 Related Works
3 Proposed System
3.1 Image Acquisition
3.2 Pre-processing
3.3 Segmentation
3.4 Features Extraction
3.5 Classification
4 Discussions
5 Conclusion
References
44 Proposing an Algorithm for UAVs Interoperability: MAVLink to STANAG 4586 for Securing Communication
Abstract
1 Introduction
2 Background
3 Identified Problem
4 Proposed Solution
5 Conclusion and Future Work
References
45 Multi-class Segmentation of Organ at Risk from Abdominal CT Images: A Deep Learning Approach
Abstract
1 Introduction
2 Literature Review
3 SegTHOR Dataset
4 Proposed Methodology
5 Discussion
6 Conclusion
References
46 Cybersecurity for Data Science: Issues, Opportunities, and Challenges
Abstract
1 Introduction
2 Related Work
2.1 Cybersecurity
2.2 Common Cybersecurity Attacks and Mitigation Ways
2.3 Data Science/Cybersecurity Data
2.4 Machine Learning for Cybersecurity
3 Proposed Framework
4 Results and Discussion
5 Conclusion and Future Works
References
47 Cost-Effective Anomaly Detection for Blockchain Transactions Using Unsupervised Learning
Abstract
1 Introduction
1.1 Problem Identification and Objectives
2 Related Works
3 Cost-Effective Anomaly Detection (CEAD) for Blockchain Transactions
3.1 Overview
3.2 Cluster Formation
3.3 Erasure Coding Using Forward Error Correction (FEC) Technique
3.4 Block Propagation Using FEC
3.5 Feature Extraction Technique
3.6 Anomaly Detection Model
4 Experimental Results
4.1 Experimental Setup
4.2 Results
5 Conclusion
References
48 Understanding of Data Preprocessing for Dimensionality Reduction Using Feature Selection Techniques in Text Classification
Abstract
1 Introduction
2 Data Preprocessing: Data Cleaning and Dimensionality Reduction
3 Feature Selection Methods
4 Related Work
4.1 Filter Methods
4.2 Wrapper Methods
4.3 Embedded Methods
5 Conclusion
References
49 A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques
Abstract
1 Introduction
2 CBES and CFES Image Encryption Schemes
3 Quality Measures for Image Encryption Schemes
4 Conclusion
References
50 A Review on Chaotic Scheme-Based Image Encryption Techniques
Abstract
1 Introduction
2 Image Security and Its Issues
3 Recent Studies in Image Encryption Techniques
4 Spatial Domain
5 Frequency Domain
6 Other Contributions, Results, and Discussions
7 Conclusion
References
51 FANET: Efficient Routing in Flying Ad Hoc Networks (FANETs) Using Firefly Algorithm
Abstract
1 Introduction
1.1 Flying Ad Hoc Network (FANET)
1.2 Firefly Algorithm
1.3 Routing Protocols
2 Related Work
3 Proposed Methodology
4 Simulation and Results
5 Conclusions and Future Scope
References
52 Energy-Efficient Model for Recovery from Multiple Cluster Nodes Failure Using Moth Flame Optimization in Wireless Sensor Networks
Abstract
1 Introduction
2 Literature Survey
3 Proposed Framework
3.1 Moth Flame Optimization Overview
3.2 Intra-Partition Phase
3.3 Inter-Partition Phase
4 Simulation Results
5 Conclusion
References
53 Analyzing DistilBERT for Sentiment Classification of Banking Financial News
Abstract
1 Introduction
1.1 Text Sentiment Classification
1.2 Why DistilBERT?
2 Related Work
2.1 Text Representation
2.2 Classifiers
3 Experimental Set up
3.1 Data
3.2 Embeddings and Classifiers
4 Results and Discussion
4.1 DistilBERT Fine-Tuned on Banking News-Events Sentiments for Classification with Machine Learning Classifiers
4.2 Results of Banking News-Events Sentiments Classification Using Machine Learning Classifiers with TF-IDF
5 Conclusion and Future direction
References
54 An Enhanced Cos-Neuro Bio-Inspired Approach for Document Clustering
Abstract
1 Introduction
1.1 Document Representation
2 Related Study
3 Problem Definition
4 Proposed Approach: Cos-Neuro Bio-Inspired Document Clustering (CNBDC)
4.1 Similarity Measure: Cosine Similarity
4.2 The Clustering Approach: K-Means
4.3 Optimization Approach: Bee Swarm Optimization (BSO)
4.4 Artificial Neural Network (ANN)
5 Simulations and Results
5.1 The CACM (Collection of ACM) Dataset
6 Conclusion and Future Work
References
55 Improved Decision Tree Method in E-Learning System for Predictive Student Performance System During COVID 19
Abstract
1 Introduction
2 Related Works
3 Proposed Method
3.1 C4.5 Algorithm
3.2 Fuzzy Decision Tree
3.3 Experimentation
4 Conclusion
References
56 Improving Content Delivery on User Behavior Using Data Analytics
Abstract
1 Introduction
2 Background Study
3 System Model
3.1 Regional Interest
3.2 Temporal Shift
3.3 Patterns
3.4 Behavior Analysis
4 Results and Discussion
5 Conclusion
References
57 Improved Nature-Inspired Algorithms in Cloud Computing for Load Balancing
Abstract
1 Introduction
2 Related Works
3 Proposed Method
3.1 Load Balancing
3.1.1 Load Balancing Goals
3.1.2 Improved Load Balancing Algorithms
3.2 Improved Ant Colony Optimization
3.3 Normal Honeybee
3.4 Experimentation
4 Conclusion
References
58 Multipoint Data Transmission Using Li-Fi with LGS Formation
Abstract
1 Introduction
2 Related Work
3 Proposed System
3.1 Laser Block
3.2 Glass Prism Block
3.3 Solar Panel Block
4 Multipoint Text Transmission
5 Multipoint File Transmission
6 Conclusion and Future Scope
References
59 Disease Prediction and Diagnosis Model for IoT–Cloud-Based Critical Healthcare System
Abstract
1 Introduction
2 Related Works
3 Proposed Model
3.1 Overview and System Model
3.2 Construction of Knowledge and Information Bases
3.3 Detection of Abnormal Activities Using DNN
3.3.1 RF Algorithm
3.3.2 DNN Model
4 Experimental Setup
5 Conclusion
References
60 Predication of Dairy Milk Production Using Machine Learning Techniques
Abstract
1 Introduction
2 Related Works
3 Proposed Solution
3.1 Overview
3.2 Estimation of Health Condition of Cows
3.3 Estimation of Feed Intake Capacity (FIC)
3.4 Estimation of Expected Relative Milk Yield (ERMY)
3.5 Estimation of Wood’s Parameters
3.6 Artificial Butterfly Optimization (ABO) Algorithm
4 Experimental Results
5 Conclusion
References
Author Index
507136_1_En_61_Chapter_OnlinePDF.pdf
61 Correction to: Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period
Correction to: Chapter “Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period” in: S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_19
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Lecture Notes in Networks and Systems 248
Sheng-Lung Peng Sun-Yuan Hsieh Suseendran Gopalakrishnan Balaganesh Duraisamy Editors
Intelligent Computing and Innovation on Data Science Proceedings of ICTIDS 2021
Lecture Notes in Networks and Systems Volume 248
Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.
More information about this series at http://www.springer.com/series/15179
Sheng-Lung Peng Sun-Yuan Hsieh Suseendran Gopalakrishnan Balaganesh Duraisamy •
•
•
Editors
Intelligent Computing and Innovation on Data Science Proceedings of ICTIDS 2021
123
Editors Sheng-Lung Peng Creative Technologies and Product Design National Taipei University of Business Taipei, Taiwan Suseendran Gopalakrishnan Department of Information Technology Vels Institute of Science, Technology & Advanced Studies Chennai, Tamil Nadu, India
Sun-Yuan Hsieh Computer Science and Information Engineering National Cheng Kung University Tainan City, Taiwan Balaganesh Duraisamy Faculty of Computer Science and Multimedia Lincoln University College Petaling Jaya, Malaysia, Malaysia
ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-16-3152-8 ISBN 978-981-16-3153-5 (eBook) https://doi.org/10.1007/978-981-16-3153-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021, corrected publication 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Organizing Committee and Key Members
Conference Committee Members Patron Datuk Dr. Hajjah Bibi Florina Abdullah, Pro Chancellor, Lincoln University College, Malaysia
Honorary Chair Prof. Amiya Bhaumik, Vice Chancellor and CEO, Lincoln University College, Malaysia
Conference Advisors Hafizah Che Hassan, Deputy Vice Chancellor (Academic), Lincoln University College, Malaysia
Convenor B. Duraisamy, Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia
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Organizing Committee and Key Members
Program Chair K. B. Swamy, Director International Affairs, Lincoln University College, Malaysia
Publication Chairs Sheng-Lung Peng, Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan Souvik Pal, Department of Computer Science and Engineering, Global Institute of Management and Technology, India G. Suseendran, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India B. Duraisamy, Dean, Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia Ali Abdulbaqi Ameen, Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia Midhun chakkaravarthy, Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia Vivekanandam, Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia
Scientific Advisory Chair D. Akila, Department of Information Technology, VISTAS, Chennai, India
International Advisory Board Members Srinath Doss, Botho University, Botswana Hanaa Hachimi, Associate Professor in Applied Mathematics and Computer Science, Ibn Tofail University, Kenitra, Morocco Anirban Das, University of Engineering and Management, India Debashis De, Maulana Abul Kalam Azad University of Technology, India Mario Jose Diván, National University of La Pampa, Argentina Sanjeevikumar Padmanaban, Aalborg University, Denmark K. Rohini, VELS Institute of Science, Technology and Advanced Studies, Chennai, India Dac-Nhuong Le, Haiphong University, Haiphong, Vietnam
Organizing Committee and Key Members
vii
Kusum Yadav, University of Hail, Kingdom of Saudi Arabia Noor Zaman, Taylors University, Malaysia
Publicity Chairs Ashish Mishra, Gyan Ganga Institute of Technology and Sciences, Jabalpur, India. Dinesh Rajassekharan, Lincoln University College, Malaysia Che Asma Noor Akma, Lincoln University College, Malaysia Ali Akbary, Lincoln University College, Malaysia Mohd Nabil Amin, Lincoln University College, Malaysia Yahya-Imam Munir Kolapo, Lincoln University College, Malaysia Osama Isaac, Lincoln University College, Malaysia Mohammed Saleh Nusari, Lincoln University College, Malaysia
Technical Program Committee Members B. Mahalaksmi, VELS Institute of Science, Technology & Advanced Studies Chennai, India T. Nagarathinam, MASS College of Arts and Science, Thanjavur, India Divya Midhun Chakkaravarthy, Centre of Post Graduate Studies Lincoln University College, Malaysia S. Gopinathan, Department of Computer Science, University of Madras, Chennai, India. V. R. Elangovan, Department of Computer Science, A. M. Jain College, Chennai, India. R. Amutha, Tiruvalluvar University College Arts & Science, Tamilnadu, India J. Deny, Kalasalingam Academy of Research and Education, Srivilliputtur, India Sayed Ameeddnuddin Irfan, Universiti Teknologi Petronas, Malaysia K. R. Karthikeyan, Caledonian College of Engineering, Sultanet of Oman D. Senthil, Nazareth College of Arts and Science, Chennai, India K. Kavitha, Mother Teresa University, India Anand Paul, Kyungpook National University, South Korea E. Ramaraj, Alagappa University, India M. Thiyagaraj, Nazareth College of Arts and Science, India T. Nathiya, New Prince Shri Bhvani Arts and Science College, India S. Elavarasi, New Prince Shri Bhvani Arts and Science College, India
Invited Speakers
Sheng-Lung Peng, Professor, Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan Souvik Pal, Associate Professor and Head, Department of Computer Science and Engineering, Global Institute of Management and Technology, India Noor Zaman, Associate Professor, School of Computing and Information Technology, Taylors University, Malaysia Hanaa Hachimi, Associate Professor, Applied Mathematics and Computer Science, Secretary General of Sultan Moulay Slimane university USMS of Beni Mellal, Morocco Mustansar Ali Ghazanfar, Assistant Professor, University of East London, London Uttam Ghosh, Assistant Professor, Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA Kanad Basu, Assistant Professor, Department of Electrical and Computer Engineering, University of Texas at Dallas, USA Ahmed A. Elngar, Assistant Professor, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Egypt
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Preface and Acknowledgements
The main aim of this proceedings book is to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of intelligent ecosystems, data sciences, and IoT systems. ICTIDS 2021 is a multidisciplinary conference organized with the objective of bringing together academic scientists, professors, research scholars, and students working in various fields of engineering and technology. In ICTIDS 2021, we have the opportunity to meet some of the world’s leading researchers, to learn about some innovative research ideas and developments around the world, and to become familiar with emerging trends in science- and technology. The conference will provide the authors, research scholars, and listeners with opportunities for national and international collaborations and networking among universities and institutions for promoting research and developing the technologies globally. This conference aims to promote translation of basic research into institutional and industrial researches and convert applied investigation into real-time application. The 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021) has been organized by Lincoln University College, Malaysia, on February 10 and 20, 2021 in online mode (Microsoft Teams Platform). The conference brought together researchers from all regions around the world working on a variety of fields and provided a stimulating forum for them to exchange ideas and report on their researches. The proceedings of ICTIDS 2021 consists of 60 best selected papers which were submitted to the conferences and peer reviewed by conference committee members and international reviewers. The presenters have presented through virtual screen. Many distinguished scholars and eminent speakers have joined from different countries like India, Malaysia, Bangladesh, Sri Lanka, Pakistan, Morocco, UAE, Vietnam, and Taiwan to share their knowledge and experience and to explore better ways of educating our future leaders. This conference became a platform to share the knowledge domain among different countries research culture. The main and foremost pillar of any academic conference is the authors and the researchers. So, we are thankful to the authors for choosing this conference platform to present their works in this pandemic situation. xi
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We are sincerely thankful to the Almighty for supporting and standing at all times with us, whether it is good or tough times, and giving ways to concede us. Starting from the call for papers till the finalization of chapters, all the team members have given their contributions amicably, which is a positive sign of significant team works. The editors and conference organizers are sincerely thankful to all the members of Springer, especially Mr. Aninda Bose, for the providing constructive inputs and allowing an opportunity to finalize this conference proceedings. We are also thankful to Dr. Thomas Ditzinger, Prof. William Achauer, and Prof. Anil Chandy for their support. We are also thankful to Silky Abhay Sinha for coordinating this project. We are thankful to all the reviewers who hail from different places in and around the globe who shared their support and stood firm toward quality chapter submission in this pandemic situation. Finally, we would like to wish you to have good success in your presentations and social networking. Your strong supports are critical to the success of this conference. We hope that the participants not only enjoyed the technical program in conference but also found eminent speakers and delegates in the virtual platform. Wishing you a fruitful and enjoyable ICTIDS 2021. Taipei, Taiwan Tainan City, Taiwan Chennai, India Petaling Jaya, Malaysia
Sheng-Lung Peng Sun-Yuan Hsieh Suseendran Gopalakrishnan Balaganesh Duraisamy
Contents
Recent Trends in Potential Security Solutions for SD-WAN: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Rose Varuna and R. Vadivel SVM- and K-NN-Based Paddy Plant Insects Recognition . . . . . . . . . . . T. Nagarathinam and V. R. Elangovan Comparative Study on Challenges and Detection of Brain Tumor Using Machine Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Magesh, V. R. Niveditha, Ambeshwar Kumar, R. Manikandan, and P. S. Rajakumar Deep Learning in Image Recognition for Easy Sketching Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hannah Vijaykumar, T. Nusrat Jabeen, R. Nirmala, S. Gayathri, and G. Suseendran Deep Learning in Image Signal Processing for Minimal Method by Using Kernel DBN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Punitha, D. Sasirekha, R. S. Dhanalakshmi, K. Aruna Devi, and G. Suseendran Bone Age Measurement-Based on Dental Radiography, Employing a New Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatemeh Sharifonnasabi, N. Z. Jhanjhi, Jacob John, and Prabhakaran Nambiar
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Climatic Analysis for Agriculture Cultivation in Geography Using Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Anita and S. Shakila
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Implementation and Performance Analysis of Various Models of PCNN for Medical Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . T. Vignesh, K. K. Thyagharajan, L. Balaji, and G. Kalaiarasi
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Combined Minimum Spanning Tree and Particle Swarm Optimization for the Design of the Cable Layout in Offshore Wind Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chakib El Mokhi and Adnane Addaim Biomedical Scan Image Retrieval Using Higher Order Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. K. Thyagharajan, T. Vignesh, I. Kiruba Raji, and G. Nirmala
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Soil Category Classification Using Convolutional Neural Network Long Short Wide Memory Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 K. Anandan, R. Shankar, and S. Duraisamy An Empirical Study on Selected Emerging Technologies: Strengths and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Munir Kolapo Yahya-Imam and Murtadho M. Alao Energy Harvesting: A Panacea to the Epileptic Power Supply in Nigeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Munir Kolapo Yahya-Imam and Murtadho M. Alao Forecasting of Inflation Rate Contingent on Consumer Price Index: Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Shampa Islam Momo, Md Riajuliislam, and Rubaiya Hafiz Face Detection and Recognition System . . . . . . . . . . . . . . . . . . . . . . . . . 145 Tanjim Mahmud, Sajib Tripura, Umme Salma, Jannat Fardoush, Sultana Rokeya Naher, Juel Sikder, and Md Faisal Bin Abdul Aziz Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period . . . . . . . . . 157 S. Sirajudeen, Balaganesh Duraisamy, Haleema, and V. Ajantha Devi Encoding and Refuge Shelter by Embracing Steganography with Hybrid Methods in Image Reduction Processing . . . . . . . . . . . . . . 167 U. Reethika and S. Srinivasan Bigdata Analysis Using Machine Learning Algorithm in Predicting the Cardiovascular Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 D. R. Krithika and K. Rohini Anomaly Detection in Business Process Event Using KNN Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 B. Sowmia and G. Suseendran Comparison of Multidimensional Hyperspectral Image with SIFT Image Mosaic Methods for Mosaic Better Accuracy . . . . . . . . . . . . . . . 201 G. Suseendran, E. Chandrasekaran, Souvik Pal, V. R. Elangovan, and T. Nagarathinam
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The Role of Innovativeness in Mediating the Relationship Between Overall Quality and User Satisfaction Among the Financial Information Systems in Yemen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Dhoha Younis, Divya Midhunchakkaravarthy, Ali Ameen, Balaganesh Duraisamy, and Midhunchakkaravarthy Janarthanan Mobile Banking Adoption—Extending Technology Acceptance Model with Transaction Convenience and Perceived Risk: A Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Wiwit Apit Sulistyowati, Ibrahim Alrajawy, Osama Isaac, and Ali Ameen Drone—an Assistive Device for Aquacare Monitoring . . . . . . . . . . . . . . 229 A. Josephin Arockia Dhivya, Jaya Rubi, R. J. Hemalatha, and T. R. Thamizhvani Big Data Background on the Bank Account for Progress of Income Improvement on Customers on Cloud Accounting . . . . . . . . . . . . . . . . . 237 Rajeswari Purushothaman and G. Suseendran Hyper-Personalization of Mobile Applications for Cloud Kitchen Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 H. M. Moyeenudin, G. Bindu, and R. Anandan Exploring Intention to Use E-Government: The Role of Technology Acceptance Model with Self-Efficacy and System Quality . . . . . . . . . . . 257 Agung Yulianto, Osama Isaac, Ibrahim Alrajawy, Ali Ameen, and Wiwit Apit Sulistyowati Analysis of E-learner’s Opinion Using Automated Sentiment Analysis in E-learning and Comparison with Naive Bayes Classification, Random Forest and K-Nearest Neighbour Algorithms . . . . . . . . . . . . . . 265 P. Rajesh and G. Suseendran Car Damage Detection and Cost Evaluation Using MASK R-CNN . . . . 279 J. D. Dorathi Jayaseeli, Greeta Kavitha Jayaraj, Mehaa Kanakarajan, and D. Malathi Layered Architecture for End-To-End Security, Trust, and Privacy for the Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Kazi Masum Sadique and Paul Johannesson Service Rating Prediction in Location-Based Services . . . . . . . . . . . . . . 299 Sneha Prakash and R. Gunasundari Analytics of e-Commerce Platforms Based on User-Experience (UX) . . . 309 Sakthi Kumaresh, Riya Haran, and Michelle Maria Jarret Towards a Threat Model for Unmanned Aerial Vehicles . . . . . . . . . . . . 319 Wiam I. Alluhybi and Omar H. Alhazmi
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Numerical Analysis of Strut-Based Scramjet Combustor with Ramps Under Non-reacting Flow Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 A. Antony Athithan, S. Jeyakumar, Kalaiarasan Sekar, and Mukil Alagirisamy Qualitative Assessment of Machine Learning Classifiers for Employee Performance Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 P. Sujatha and R. S. Dhivya Developing Smart Application for Screening and Reducing Maternal and Neonatal Mortality Birth Preparedness . . . . . . . . . . . . . . . . . . . . . . 351 Desi Sarli, Faridah Mohd Said, Ali Ameen, and Imam Gunawan Role of Machine Learning Approaches in Remaining Useful Prediction: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Kalaiarasan Sekar, Shahani Aman Shah, A. Antony Athithan, and A. Mukil An Extensive Review on Malware Classification Based on Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 P. M. Kavitha and B. Muruganantham Video-Based Deep Face Recognition Using Partial Facial Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 B. Vivekanandam, Midhunchakkaravarthy, and Balaganesh Duraisamy Early Prediction of Cardio Vascular Disease by Performing Associative Classification on Medical Datasets and Using Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Murtaza Saadique Basha Classification of Benign and Malignant Lung Cancer Nodule Using Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 D. Napoleon and I. Kalaiarasi Proposing an Algorithm for UAVs Interoperability: MAVLink to STANAG 4586 for Securing Communication . . . . . . . . . . . . . . . . . . . 413 Navid Ali Khan, N. Z. Jhanjhi, Sarfraz Nawaz Brohi, and Zahrah A. Almusaylim Multi-class Segmentation of Organ at Risk from Abdominal CT Images: A Deep Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Muhammad Ibrahim Khalil, Mamoona Humayun, N. Z. Jhanjhi, M. N. Talib, and Thamer A. Tabbakh Cybersecurity for Data Science: Issues, Opportunities, and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 Mamoona Humayun, N. Z. Jhanjhi, M. N. Talib, Mudassar Hussain Shah, and G. Suseendran
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Cost-Effective Anomaly Detection for Blockchain Transactions Using Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 M. Deepa and D. Akila Understanding of Data Preprocessing for Dimensionality Reduction Using Feature Selection Techniques in Text Classification . . . . . . . . . . . 455 Varun Dogra, Aman Singh, Sahil Verma, Kavita, N. Z. Jhanjhi, and M. N. Talib A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 Gopal Ghosh, Divya Anand, Kavita, Sahil Verma, N. Z. Jhanjhi, and M. N. Talib A Review on Chaotic Scheme-Based Image Encryption Techniques . . . 473 Gopal Ghosh, Divya Anand, Kavita, Sahil Verma, N. Z. Jhanjhi, and M. N. Talib FANET: Efficient Routing in Flying Ad Hoc Networks (FANETs) Using Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Manjit Kaur, Aman Singh, Sahil Verma, Kavita, N. Z. Jhanjhi, and M. N. Talib Energy-Efficient Model for Recovery from Multiple Cluster Nodes Failure Using Moth Flame Optimization in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 Sowjanya Ramisetty, Divya Anand, Kavita, Sahil Verma, N. Z. Jhanjhi, and Mamoona Humayun Analyzing DistilBERT for Sentiment Classification of Banking Financial News . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 Varun Dogra, Aman Singh, Sahil Verma, Kavita, N. Z. Jhanjhi, and M. N. Talib An Enhanced Cos-Neuro Bio-Inspired Approach for Document Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Vaishali Madaan, Kundan Munjal, Sahil Verma, N. Z. Jhanjhi, and Aman Singh Improved Decision Tree Method in E-Learning System for Predictive Student Performance System During COVID 19 . . . . . . . . . . . . . . . . . . 525 T. Varun and G. Suseendran Improving Content Delivery on User Behavior Using Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 T. Thirumalaikumari and C. Shanthi
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Improved Nature-Inspired Algorithms in Cloud Computing for Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 D. Akila, Amiya Bhaumik, Balaganesh Duraisamy, G. Suseendran, and Souvik Pal Multipoint Data Transmission Using Li-Fi with LGS Formation . . . . . . 559 Balaganesh Duraisamy Disease Prediction and Diagnosis Model for IoT–Cloud-Based Critical Healthcare System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 D. Akila and Balaganesh Duraisamy Predication of Dairy Milk Production Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 G. Suseendran and Balaganesh Duraisamy Correction to: Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Sirajudeen, Balaganesh Duraisamy, Haleema, and V. Ajantha Devi
C1
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589
Editors and Contributors
About the Editors Sheng-Lung Peng is a Professor and the director (head) of the Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan. He received the BS degree in Mathematics from National Tsing Hua University, and the MS and PhD degrees in Computer Science from the National Chung Cheng University and National Tsing Hua University, Taiwan, respectively. He is an honorary Professor of Beijing Information Science and Technology University, China, and a visiting Professor of Ningxia Institute of Science and Technology, China. He is also an adjunct Professor of Mandsaur University, India. He serves as the secretary general of the ACM-ICPC Contest Council for Taiwan and the regional director of the ICPC Asia Taipei-Hsinchu site. He is a director of Institute of Information and Computing Machinery, of Information Service Association of Chinese Colleges and of Taiwan Association of Cloud Computing. He is also a supervisor of Chinese Information Literacy Association, of Association of Algorithms and Computation Theory. Dr. Peng has edited several special issues at journals, such as Soft Computing, Journal of Internet Technology, Journal of Real-Time Image Processing, International Journal of Knowledge and System Science, MDPI Algorithms, and so on. He is also a reviewer for more than 10 journals such as IEEE Access and Transactions on Emerging Topics in Computing, IEEE/ACM Transactions on Networking, Theoretical Computer Science, Journal of Computer and System Sciences, Journal of Combinatorial Optimization, Journal of Modelling in Management, Soft Computing, Information Processing Letters, Discrete Mathematics, Discrete Applied Mathematics, Discussiones Mathematicae Graph Theory, and so on. His research interests are in designing and analyzing algorithms for Bioinformatics, Combinatorics, Data Mining, and Networks areas in which he has published over 100 research papers.
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Sun-Yuan Hsieh received the Ph.D. degree in computer science from National Taiwan University, Taipei, Taiwan, in June 1998. He then served the compulsory two-year military service. From August 2000 to January 2002, he was an assistant professor at the Department of Computer Science and Information Engineering, National Chi Nan University. In February 2002, he joined the Department of Computer Science and Information Engineering, National Cheng Kung University, and now he is a chair professor. His awards include the 2007 K. T. Lee Research Award, President's Citation Award (American Biographical Institute) in 2007, Engineering Professor Award of Chinese Institute of Engineers (Kaohsiung Branch) in 2008, National Science Council’s Outstanding Research Award in 2009, IEEE Outstanding Technical Achievement Award (IEEE Tainan Section) in 2011, Outstanding Electronic Engineering Professor Award of Chinese Institute of Electrical Engineers in 2013, and Outstanding Engineering Professor Award of Chinese Institute of Engineers in 2014. He is Fellow of the British Computer Society (BCS) and Fellow of Institution of Engineering and Technology (IET). Suseendran Gopalakrishnan completed his Postdoctoral Research Fellow in Computer Science, Lincoln University College, Malaysia and Ph.D., degree in Information Technology-Mathematics from Presidency College, University of Madras, India. He has obtained DOEACC ‘O’ Level AICTE Ministry of Information Technology, India and Microsoft Certified Database Administrator in additional qualification. He is currently working as Assistant Professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies, Chennai, India, a well-known university. He has more than ten years of teaching experience in both UG /PG and research levels. His research interests include Wireless Sensor Network, Ad-hoc networks, IoT, Data Mining, Cloud Computing, Image Processing, Knowledgebased systems, and Web Information Exploration. He has produced 4 M.Phil. Scholars and 8 Ph.D., Research Scholars awarded under his Guidance and Supervision. He has published more than 106 research papers in various international journals such as Science Citation Index, IEEE Access, Springer Book Chapter, Scopus, and UGC referred journals. He has presented 30 papers at various international conferences. He has edited/authored 18 books, received 15 awards, and has been acknowledged globally as a top reviewer by Publons (Web of Science). He has been invited to be a resource person/keynote plenary speaker at many reputed national and international universities and colleges. Balaganesh Duraisamy is working as Dean, Faculty Computer Science and Multimedia, Lincoln University College, Malaysia. He is the one of the key members of Lincoln University College. He has 19 years of professional teaching experience which include overseas experience in India, Oman and Malaysia. He has In-depth knowledge of Information technology and la56tfvtest wireless fidelity. His Familiar research area is Malware detection, web mining and open source technology. He has given training in mobile application, open source ERP, software testing, and MYSQL. He has developed software applications “Timetable
Editors and Contributors
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Automation”, “Online Exam”. He has ability to explain technical concepts and ideas in a clear and precise way. He serves as Editor/Editorial Board Member/Technical Committee/Reviewer of International Journal such Arab/Poland/Europe/USA journals. He servers as International Committee Members towards International Conference conducted in association with Springer and Scopus. He is a one of the editors of Springer (Scopus, ISI indexing). Also, the editor of two book chapter (“Internet-ofRobotic-Things and Ubliquitous Computing” and “The Internet of Medical Things (IoMT): Healthcare Transformation” which is Wiley Press Publication.
Contributors Adnane Addaim EMI, Mohamed V University, Rabat, Morocco V. Ajantha Devi AP3 Solutions, Chennai, TN, India D. Akila Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, India; Department of Computer Science, Lincoln University College, Petaling Jaya, Malaysia Mukil Alagirisamy Faculty of Engineering, Lincoln University College, Petaling Jaya, Malaysia Murtadho M. Alao Faculty of Business and Accounting, Lincoln College of Science, Management, and Technology, Abuja, Nigeria Omar H. Alhazmi Department of Computer Science, Taibah University, Medina, Saudi Arabia Wiam I. Alluhybi Princeton University, Princeton, NJ, USA Zahrah A. Almusaylim King Abdulaziz City for Science and Technology (KACST), Riyad, Kingdom of Saudi Arabia Ibrahim Alrajawy Student of Lincoln University College, Lincoln University College, Petaling Jaya, Malaysia Ali Ameen Student of Lincoln University College, Lincoln University College, Petaling Jaya, Malaysia; Lincoln University College, Petaling Jaya, Malaysia Divya Anand School of Computer Science and Engineering, Lovely Professional University, Phagwara, India K. Anandan Department of Computer Science, Chikkanna Government Arts College, Tiruppur, Tamilnadu, India
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R. Anandan School of Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India M. Anita Department of Computer Science, The Government Arts College, Trichy, Tamil Nadu, India A. Antony Athithan Faculty of Engineering, Lincoln University College, Petaling Jaya, Malaysia; Faculty of Engineering and Built Environment, Lincoln University College, Kuala Lumpur, Malaysia Md Faisal Bin Abdul Aziz Department of CSE, Comilla University, Comilla, Bangladesh Balaganesh Duraisamy Dean Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia L. Balaji Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India Murtaza Saadique Basha Department of MCA, C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Ranipet, Tamil Nadu, India Amiya Bhaumik Lincoln University College, Petaling Jaya, Malaysia G. Bindu School of Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India Sarfraz Nawaz Brohi School of Information Technology, Monash University, Bandar Sunway, Malaysia E. Chandrasekaran Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India M. Deepa Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai, India K. Aruna Devi Valliammal College for Women, Chennai, India R. S. Dhanalakshmi Department of Computer Applications, Anna Adarsh College For Women, Chennai, India A. Josephin Arockia Dhivya Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India R. S. Dhivya School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, India Varun Dogra School of Computer Science and Engineering, Lovely Professional University, Phagwara, India
Editors and Contributors
J. D. Dorathi Jayaseeli SRM Kattankulathur, India
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and
Technology,
S. Duraisamy Department of Computer Science, Chikkanna Government Arts College, Tiruppur, Tamilnadu, India Chakib El Mokhi ENSA, Ibn Tofail University, Kenitra, Morocco V. R. Elangovan Department of Computer Applications, Agurchand Manmull Jain College, Meenambakkam, Chennai, India Jannat Fardoush Department of CSE, University of Chittagong, Chittagong, Bangladesh S. Gayathri Shri Krishnaswamy College for Women, Chennai, India Gopal Ghosh School of Computer Science and Engineering, Lovely Professional University, Phagwara, India R. Gunasundari Department of Computer Science, Karapagham Academy of Higher Education, Coimbatore, India Imam Gunawan STMIK Jaya Nusa, Padang, Indonesia Rubaiya Hafiz Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh Haleema Adjunct Faculty, University of Stirling, Stirling, UAE Riya Haran Department of Computer Applications, M.O.P. Vaishnav College for Women (Autonomous), Chennai, Tamil Nadu, India R. J. Hemalatha Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India Mamoona Humayun Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka, Kingdom of Saudi Arabia Osama Isaac Student of Lincoln University College, Lincoln University College, Petaling Jaya, Malaysia T. Nusrat Jabeen Anna Adarsh College for Women, Chennai, India Midhunchakkaravarthy Janarthanan Lincoln University College, Petaling Jaya, Malaysia Michelle Maria Jarret Department of Computer Applications, M.O.P. Vaishnav College for Women (Autonomous), Chennai, Tamil Nadu, India Greeta Kavitha Jayaraj SRM Kattankulathur, India
Institute
of
Science
and
Technology,
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Editors and Contributors
S. Jeyakumar Faculty of Engineering, Lincoln University College, Petaling Jaya, Malaysia; Aeronautical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, India N. Z. Jhanjhi School of Computer Science and Engineering (SCE), Taylor’s University, Subang Jaya, Selangor, Malaysia Paul Johannesson Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden Jacob John Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia G. Kalaiarasi CSE Department, Sathyabama Institute of Science and Technology, Chennai, India I. Kalaiarasi Department Coimbatore, India
of
Computer
Science,
Bharathiar
University,
Mehaa Kanakarajan SRM Institute of Science and Technology, Kattankulathur, India Manjit Kaur School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Kavita Department of Computer Science and Engineering, Chandigarh University, Mohali, India P. M. Kavitha Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India Muhammad Ibrahim Khalil Department of Computer Science, Bahria University, Islamabad, Pakistan Navid Ali Khan School of Computer Science and Engineering, SCE, Taylo’s University, Selangor, Malaysia I. Kiruba Raji Department of CSE, RMD Engineering College, Kavaraipettai, India D. R. Krithika Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, Chennai, India Ambeshwar Kumar SASTRA Deemed University, Thanjavur, Tamil Nadu, India Sakthi Kumaresh Department of Computer Science, M.O.P. Vaishnav College for Women (Autonomous), Chennai, Tamil Nadu, India Vaishali Madaan Maharishi Markandeshwar University, Mullana, India S. Magesh Maruthi Technocrat E Services, Chennai, Tamil Nadu, India
Editors and Contributors
xxv
Tanjim Mahmud Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati, Bangladesh D. Malathi SRM Institute of Science and Technology, Kattankulathur, India R. Manikandan SASTRA Deemed University, Thanjavur, Tamil Nadu, India Divya Midhunchakkaravarthy Lincoln University College, Petaling Jaya, Malaysia Midhunchakkaravarthy Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia Shampa Islam Momo Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh H. M. Moyeenudin School of Hotel and Catering Management, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India A. Mukil Faculty of Engineering and Built Environment, Lincoln University College, Kuala Lumpur, Malaysia Kundan Munjal Apex Institute of Technology, Chandigarh University, Mohali, India; University College of Engineering, Punjabi University, Patiala, India B. Muruganantham Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India T. Nagarathinam PG and Research Department of Computer Science, MASS College of Arts and Science, Kumbakonam, India Sultana Rokeya Naher Department of CSE, University of Information Technology and Sciences, Dhaka, Bangladesh Prabhakaran Nambiar Department of Oral Biology and Biomedical Sciences, Faculty of Dentistry, Mahsa University, Saujana Putra, Malaysia; Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia D. Napoleon Department Coimbatore, India
of
Computer
Science,
Bharathiar
University,
G. Nirmala Department of CSE, RMD Engineering College, Kavaraipettai, India R. Nirmala Shri Krishnaswamy College for Women, Chennai, India V. R. Niveditha Dr M.G.R Educational and Research Institute, Chennai, India Souvik Pal Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishnanagar, India
xxvi
Editors and Contributors
Sneha Prakash Department of Computer Science, Karapagham Academy of Higher Education, Coimbatore, India A. Punitha Department of Computer Applications, Queen Marys College, Chennai, India Rajeswari Purushothaman Department of Computer Science, College of Arts and Science, King Khalid University, Ahadh Rufaidha, Kingdom of Saudi Arabia P. S. Rajakumar Dr M.G.R Educational and Research Institute, Chennai, India P. Rajesh Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai, India Sowjanya Ramisetty Department of Computer Science and Engineering, KG Reddy College of Engineering and Technology, Hyderabad, India; Lovely Professional University, Phagwara, India U. Reethika Vivekanandha College of Arts and Science for Women (Autonomous), Tiruchengode, Namakkal, Tamilnadu, India Md Riajuliislam Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh K. Rohini Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, Chennai, India W. Rose Varuna Department of Information Technology, Bharathiar University, Tamilnadu, India Jaya Rubi Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India Kazi Masum Sadique Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden Faridah Mohd Said Lincoln University College, Petaling Jaya, Malaysia Umme Salma Department of CSE, Bangladesh University, Dhaka, Bangladesh Desi Sarli STIKes Alifah Padang, Padang, Indonesia D. Sasirekha Department of Computer Applications, Anna Adarsh College For Women, Chennai, India Kalaiarasan Sekar Faculty of Engineering, Lincoln University College, Petaling Jaya, Malaysia; Faculty of Engineering and Built Environment, Lincoln University College, Kuala Lumpur, Malaysia Mudassar Hussain Shah Department of Communication and Media Studies, University of Sargodha, Sargodha, Pakistan
Editors and Contributors
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Shahani Aman Shah Unikl MIAT, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia S. Shakila Department of Computer Science, The Government Arts College, Trichy, Tamil Nadu, India R. Shankar Department of Computer Science, Chikkanna Government Arts College, Tiruppur, Tamilnadu, India C. Shanthi Department of computer science, Vels Institute of Science and Technology & Advanced Studies, Chennai, India Fatemeh Sharifonnasabi School of Computer Science and Engineering, SCE, Taylor’s University, Subang Jaya, Malaysia Juel Sikder Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati, Bangladesh Aman Singh School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Sirajudeen S. PhD Scholar, Lincoln University College, Malaysia B. Sowmia Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, Chennai, India S. Srinivasan Vivekanandha College of Arts and Science for Women (Autonomous), Tiruchengode, Namakkal, Tamilnadu, India P. Sujatha Department of Information Technology, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, India Wiwit Apit Sulistyowati Universitas Swadaya Gunung Jati, Cirebon, Indonesia; Student, Lincoln University College, Petaling Jaya, Malaysia G. Suseendran Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, India; Department of Computer Science, Lincoln University College, Petaling Jaya, Malaysia; Department of Information Technology, Jeppiaar Engineering College, Chennai, India Thamer A. Tabbakh Material Research Science Institute, King Abdulaziz City for Science and Technology (KACST), Riyad, Kingdom of Saudi Arabia M. N. Talib Papua New Guinea University of Technology, Lae, Papua New Guinea T. R. Thamizhvani Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India
xxviii
Editors and Contributors
T. Thirumalaikumari Department of computer science, Vels Institute of Science and Technology & Advanced Studies, Chennai, India K. K. Thyagharajan Department of ECE, RMD Engineering College, Kavaraipettai, India Sajib Tripura Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati, Bangladesh R. Vadivel Department of Information Technology, Bharathiar University, Tamilnadu, India T. Varun Department of Management Studies, Anna University, Chennai, India Sahil Verma Department of Computer Science and Engineering, Chandigarh University, Mohali, India T. Vignesh Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India Hannah Vijaykumar Anna Adarsh College for Women, Chennai, India B. Vivekanandam Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia Munir Kolapo Yahya-Imam Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia Dhoha Younis Lincoln University College, Petaling Jaya, Malaysia Agung Yulianto Universitas Swadaya Gunung Jati, Cirebon, Indonesia
Recent Trends in Potential Security Solutions for SD-WAN: A Systematic Review W. Rose Varuna and R. Vadivel
Abstract Software-defined networking (SDN) is a valuable network management approach that facilitates the network configuration programmatically. It enriches the performance of the monitoring schemes and data transmission across the network. The innovations in the SD-WAN have embarked due to the advancement in network applications. Various operational scenarios and applications have necessitated long-range data transmission, whereas the transmission network is designed with the assistance of SD-WAN with a new perspective. The data transmission in wide area network has used multi-protocol label switching (MPLS), and it has a configuration issue. To overcome the drawbacks, the software-defined technique is incorporated into the WAN, and it is an adequate replacement for the MPLS. During data transmission, SD-WAN has faced various threats and vulnerabilities in the transmission channel. Numerous researches and solutions are articulated for SD-WAN network security. In this review article, SD-WAN characteristics, architectural design, attacks, and potential solutions are demystified.
Keywords Configuration management Network security Network service And centralized control
Data leakage
1 Introduction The SDN is an emerging wireless network paradigm that has altered the networking system’s management and control aspects by establishing the transmission channels as flexible and straightforward. SDN’s key intent is to partition the control and management plane from the data plane by initiating essential protocol into the data transmission system [1]. The features of SDN improve the performance and scalability of the network. The traditional WAN is implemented in the data centers, which are connected with W. Rose Varuna (&) R. Vadivel Department of Information Technology, Bharathiar University, Coimbatore-46, Tamilnadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_1
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Internet
Broadband 4G-LTE Branch
Branch
Datacenter
Branch
Branch
MPLS
Branch
Branch
Fig. 1 SD-WAN architecture
the branches. SDN technology is embedded in the network system to ensure reliable connectivity [2]. SD-WAN provides effective data transmission architecture and is widely used in the data transmission paradigm. The overall architecture of SD-WAN is illustrated in Fig. 1. The overall schema of SD-WAN is shown in Fig. 1. The SD-WAN is a virtual architecture of WAN that permits the enterprise to leverage transport service, and it includes LTE, MPLS, and broadband service, which enables secure data connection among the user with the application. The traffic is directed securely and intelligently across the network using a centralized control function of SD-WAN. The network established with SD-WAN has attained the best user experience, reduced cost, increased agility, productivity, and performance. SD-WAN has numerous benefits in the long-range data transmission, and it faces several security issues [3]. Various researchers have developed multiple security solutions, which are discussed in the subsequent section. In this paper, the significant characteristics of SD-WAN are discussed in Sect. 2, and numerous network threats and potential solutions for various security attacks elucidated in Sect. 3. The review paper is concluded in Sect. 4.
2 Significant Characteristics of SD-WAN In the traditional WAN, the Internrt cloud service is available in the absence of routers, that require a backhauling approach for handling the traffic issues and advanced security standards to ensure data security over the network. The transmission delay caused by the backhaul ruins the data transmission performance, resulting in a productivity loss and poor user experience. Unlike WAN, the SD-WAN
Recent Trends in Potential Security Solutions…
3
architecture is formulated to support the applications hosted at on-premise data centers, private or public clouds. The SD-WAN supports and enables the cloud-first enterprise to transmit a higher application quality of experience (QoEx) for the application user. SD-WAN permits the application-aware routing scheme in the transmission area that uses an intelligent system. By the application needs, appropriate security policies and QoS are equipped with the application [4]. The features of SD-WAN simplify the traditional WAN. The network is also established with adequate bandwidth, lower cost, and a seamless on-ramp to the cloud environment with enriched application performance without any breach in privacy and security [5]. Some of the significant characteristics are described below.
2.1
Life cycle Orchestration and Automation
SD-WAN provides provisioning schemes with the zero-touch method, and the end-to-end orchestration and the WAN functions are not provided. SD-WAN supports the configuration system centrally and enables the needs within a duration of the requested time. Data transmission in SD-WAN is highly automatic, and the environment is adaptive to the frequent changes in the transmission that make it scalable.
2.2
Continuous Self-Learning
SD-WAN operates through programmed templates and predefined rules, namely IP rules, which deliver optimal performance in the application under certain conditions of the network or alter the congestion during the occurrence of any impairments. The continuous monitoring and self-learning process has made the SD-WAN decide and automatic response generation in real-time data transmission in the network.
2.3
Consistent Quality of Experience (QoEx)
The main advantage of an SD-WAN solution is the capability to actively utilize the multiple forms of transport in the WAN. The intelligent monitoring, performance of the application, and QoEX to the application user are assured in SD-WAN. It rectifies the drawbacks of WAN, namely packet loss, jitter, and latency, to deliver the data to the highest levels of the application by enhancing the transport service. Business-driven SD-WAN offers sub-second failover that prevents interruptions in critical applications, namely video and voice communications.
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W. Rose Varuna and R. Vadivel
End-To-End Micro-Segmentation
SD-WAN has an end-to-end and comprehensive security system with a virtual private network's equivalent abilities (VPN). It should enforce stateful firewall and end-to-end spanning of micro-segmentation across LAN-WAN-cloud and LAN-WAN-data center. Consistent and centrally configured security policies are required in SD-WAN. SD-WAN is subjected to DoS attack, worm attack, malicious attack, and port scan attack. The operational efficiency of the network is improved, and the overall attack and the security breach are minimized.
2.5
Local Internet Breakout for Cloud Application
The SD-WAN adapts to the cloud environment’s frequent changes and provides an automated definition and Internet Protocol (IP) address updates. This removes the productivity issues and application interruption. The general difference between SD-WAN and SDN is described based on usage. The SD-WAN is an alternate approach of MPLS, and it is highly cost-effective [6]. The SD-WAN connects the geographically scattered location in a secure and scalable way. The SDN is mainly incorporated in the data centers and the telecommunication system that enables on-demand service and reduces operating costs. Both SD-WAN and SDN are similar in architecture and methodology, which make network performance more intelligent. Some of the difference between the versions is elucidated in Table 1.
Table 1 Difference between SDN and SD-WAN SDN
SD-WAN
SDN is mainly incorporated in the data centers It implements centralized control and orchestration
SD-WAN is incorporated in data centers and branch office Zero-touch provisioning is offered, and centralized orchestration controls implemented The technology matured as well as adopted easily and rapidly SD-WAN poses physical, virtual, and cloud appliances Effective transmission is attained by minimal WAN transport, cost of infrastructure, and operational efficiency
The maturity of technology takes more time, and it takes more time to adopt the maturity In SDN, specialization and commodity switching hardware is different Effective transmission is attained by operational efficiency
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3 Enhancement of Security Standards with SD-WAN Framework In the SD-WAN, the network transmission is secured with an encryption scheme, and everything in the system has to be authenticated to perform any task [7]. It has a unique mechanism for exchanging the key, and it is easy to manage. Secure communication is established among the network [8]. The SD-WAN system is developed with an advanced firewall protection system and built-in foundational security shown in Fig. 2. The SD-WAN is capable of deep recognition that permits granular data control over traffic, and it is routed via security service [9]. The data loss prevention and scanning of virus schemes are allowed in SD-WAN [10]. The security threats and the formulated solution are analyzed and described as follows. The SD-WAN architecture with the data center and the integration of the firewall is shown in Fig. 2.
3.1
Delegating Network Security
The SD-WAN is susceptible to various DoS attacks due to the separation of data and control planes. The DoS attack interrupts the switch flow of the information that resides in the table or controller. The security-based protocol ensures the security policies in the network [11]. The bottlenecks in the network administration are removed effectively with the assistance of security protocols [12].
Untrusted Traffic - MPLS
Data Center
SDWAN
Cloud Service Trusted Traffic - LTE
Fig. 2 Firewall integrated SD-WAN
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Flowchecker
The configuration problem during data transmission is occurred due to the flow of conflict rule. A binary decision diagram (BDD) is developed to rectify the issues raised by the misconfiguration. The BDD analyzes the misconfiguration in the intra-switch and repairs effectively.
3.3
Cognition
The level of security in SD-WAN is enriched by implementing the cognitive functions at the application plane. The cognitive module is implemented through a multi-objective dynamic optimization approach in the application plane. The cognition improves the detection and protection mechanism.
3.4
Resonance
The resonance improves the process of rule updation and analysis of traffic. In resonance, the dynamic access control system's mechanism is incorporated into the transmission system that secures the network and is established based on the high-level security policies. The network response-ability is improved, and the resonance rectifies the network attack.
3.5
Secure Forensics
SD-WAN is incorporated in the forensic system to examine the faults and intrusion such as collusion and data exfiltration among the compromised nodes in the transmission network [13]. The Web is monitored, and activity is tracked using the lightweight middleboxes, whereas it prevents intrusion. The security standards are improved in the architecture of the network and the security middleboxes. The service policies are revised to meet security requirements.
3.6
Tualatin
The tenant’s cloud infrastructures are provided with high network security standards to safeguard data transmission across the cloud environment. The secured workload system is implemented within the transmission area, and fine-grained protection is ensured in the dynamic network.
Recent Trends in Potential Security Solutions…
3.7
7
NetSecCloud
The system-specific network security is provided in the environment of cloud service. The logically centralized database system affords the latest security services and system-specific network security. NetSecCloud provides a secure environment and improves scalable multi-tenancy.
3.8
Cloud Watcher
The data transmission is monitored in the cloud environment. The data transmission from the application plane to the control plane is accomplished by the network security strategy [14].
3.9
Flow Tags
In the data transmission network, consistent security rules, security policies, and network security standards are implemented that ensure protective data transmission with the incidence of middleboxes. The transmitting packets are assigned by the tags that are flow tags, which are added with the middleboxes, and it provides the accurate context of information about the network.
3.10
Securing Distributed Control
The distributed data control model secures data transmission in SD-WAN against various security issues. The malicious interruption into the system and transmission threats are the primary network issues. The data flow installation rules are securely transmitted by the signature algorithm [15].
4 Results and Discussion—Comparison of Performance Evaluation 4.1
Packet Delivery Ratio (PDR)
Packet delivery ratio (PDR) is the success rate of a packet transmitted from the source to the destination node. The algorithm with the best PDR rate is efficient. The PDR value of the SD-WAN security algorithms MPLS and MPLS [6]-SRS is
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Fig. 3 Comparison packet delivery ratio
CO M P ARISO N O F P ACK ET DELIVERY RATIO
200
1.25
150 NO OF NODES
0.76
0.78 1.21
100
MPLS-SRS 0.87 1.09
50
0.91 1.33
PDR
1.15 1.52
MPLS
250
compared and depicted in Fig. 3. The PDR is denoted in percentage and noted for a different number of nodes.
5 Conclusion The SD-WAN is installed with definite trust boundary and security principles where the communication is established within the trustable region. SD-WAN’s deployment strategy reflects the network’s characteristics and performance, whereby research solutions have addressed some security threats initiated by SD-WAN. Secure SD-WAN architecture limits the hidden damage from malicious applications. In this review, two sides of SD-WAN security coins elaborated: the enrichment of network security and the security issues with their possible solutions. The performance of the existing approaches in terms of PDR is compared, and the security is highly effective in MPLS-SRS. In this article, the major limitation of the SD-WAN is discussed, which is the security aspects. The review is concluded that the work on improvement to network security through SD-WAN is more effective. The commercially developed applications evidence these security aspects.
References 1. Yang Z, Cui Y, Li B, Liu Y, Xu Y (2019) Software-defined wide area network (SD-WAN): architecture, advances and opportunities. In: 28th International conference on computer communication and networks (ICCCN) IEEE, pp 1–9 2. Vdovin L, Likin P, Vilchinskii A (2014) Network utilization optimizer for SD-WAN. International science and technology conference (modern networking technologies) (MoNeTeC) IEEE, pp 1–4 3. Hou X, Muqing W, Bo L, Yifeng L (2019) Multi-controller deployment algorithm in hierarchical architecture for SDWAN. IEEE Access 7:65839–65851
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4. Lemeshko O, Yeremenko O, Hailan AM, Yevdokymenko M, Shapovalova A (2020) Policing based traffic engineering fast reroute in SD-WAN architectures: approach development and investigation. In: International conference on new trends in information and communications technology applications, pp 29–43. Springer, Cham 5. Manel M, Habib Y (2017) An efficient MPLS-based source routing scheme in software-defined wide area networks (SD-WAN). In: IEEE/ACS 14th international conference on computer systems and applications (AICCSA) IEEE, pp 1205–1211 6. Baghaei N, Hunt R (2004) Security performance of loaded IEEE 802.11 b wireless networks. Comput Commun 27(17):1746–1756 7. Lanka V, Mohan HK (2019) Network in a box-automation of secure SD-WAN with service chaining in Juniper NFX. In: 11th international conference on communication systems and networks (comsnets) IEEE, pp 527–529 8. Rajendran A (2016) Security analysis of a software defined wide area network solution. Master’s Thesis Espoo 9. Tanwar S, Mehrotra D, Nagpal R (2020) Identifying the best network security using EDAS. In: 10th International conference on cloud computing, data science and engineering (confluence) IEEE, pp 445–449 10. Steinke M, Adam I, Hommel W (2018) Multi-tenancy-capable correlation of security events in 5G networks. In: IEEE conference on network function virtualization and software defined networks (NFV-SDN) IEEE, pp 1–6 11. Yaguache FR, Ahola K (2019) Containerized services orchestration for edge computing in software-defined Wide Area Networks. Int J Comput Netw Commun 11(5):113–132 12. Luciani C (2019) From MPLS to SD-WAN: opportunities, limitations and best practices. Master in information and network engineering, KTH Royal Institute of technology, School of Electrical Engineering and Computer Science, Sweden 13. Awasthi A (2020) SDWAN (software defined-WAN) technology evaluation and implementation. Global J Comput Sci Technol 20(1):1–16 14. Holmes K, Ortiz J, Woolley B (2020) Solving whole-of-state approach using secure SD-WAN. https://www.govtech.com/webinars/Solving-Whole-of-State-Approach-UsingSecure-SD-WAN-12806 15. Mohamed A (2019) Current trends in using the software-defined WAN. Third international scientific and technical conference on computer and informational systems and technologies, Ukraine
SVM- and K-NN-Based Paddy Plant Insects Recognition T. Nagarathinam and V. R. Elangovan
Abstract India is one of the agricultural countries where 70% of the population depends on agriculture. The agriculture fields, paddy farming play a vital role. The worth and magnitude of farming yields are pretentious through ecological limits like rainfall, temperature and climate parameter, away from managing person beings. One more important organic parameter that influences yields harvest is the insects bytes that can be controlled to better crop yield. This work aims to develop an automated agricultural image-based plant-insect recognition system. The BAG and SURF features are extracted and passed over the classifiers SVM and k-NN. The performance of IR2PI using k-NN is better than IR2PI using SVM with an accuracy of 95%. Keywords BAG
SURF SVM k-NN IR2PI
1 Introduction This work investigates the paddy plant insect recognition algorithms accessible for handheld assistance to lead farmers and fertilizer industries. The paddy plant-insect images from agricultural fields are collected, labeled and grouped into the image dataset. The work deals with some modules, namely feature extraction and recognition. Both the BAG and SURF features are extracted from the insect–plant image. Then, the parts are passed over the classifiers k-NN and SVM for recognition of insects such as grasshopper, plant hopper and stem borer.
T. Nagarathinam (&) Department of Computer Science, MASS College of Arts and Science, Kumbakonam, India V. R. Elangovan Department of Computer Applications, Agurchand Manmull Jain College, Meenambakkam, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_3
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T. Nagarathinam and V. R. Elangovan
Paddy Plant Insects
This work focuses on recognizing three major insects on images of paddy plant, namely grasshopper, planthopper and stem borer. Grasshopper recognition is highly significant because they cause a viral infection called ‘tungro’. They are primarily found in rain-fed and irrigated wetland environments. Planthoppers can be found in areas with high humidity. They damage the crops resulting in the yellowing of the paddy plants known as ‘hopper’ burn. Stem borers are found in aquatic lands where there is non-stop flooding. Extreme boring through the cover can destroy the yields. This work aims to clear the insects early by recognizing their class using digital image processing techniques to pertain to required insecticides and pesticides.
1.1.1
Grasshopper
Short-horned grasshopper and oriental traveling locust both infect the rice crop and cause feeding damage. Feeding damage is formed by short-horned grasshoppers cut out areas on leaves and cut-off panicles. They both provide on-leaf margins.
1.1.2
Planthopper
Two species of planthopper infect rice. These are the brown planthopper (BPH) and the white-backed planthopper (WBPH). The high population of planthoppers causes the leaves to turn from orange–yellow to brown and put together to dry. This disorder, named hopper burn, destroys the plant.
1.1.3
Stem Borer
Six classes of stem borer hit paddy. They are white, striped, gold-fringed, darkheaded and the pink stem borer. Among these, the pink stem borer is the least important. It is polyphagous and chooses sugarcane to rice. The six classes can demolish paddy at any stage from plantlet to maturity.
2 Review of Literatures 2.1
Approaches of Paddy Plant Insects Recognition
Qing et al. [1] have proposed that paddy planthoppers’ quantitative study in fields is significant to levy the population density and construct forecasting assessments. This article illustrates a handheld gadget used for capturing planthopper images on
SVM- and K-NN-Based Paddy Plant Insects Recognition
13
rice stems and an automatic method for counting rice planthoppers based on image processing. The assessor can utilize the smartphone to manage the camera set lying on the stick’s frontage by WiFi and take pictures of planthoppers on paddy stems. There are three layers used for detecting the count of planthoppers on paddy stems; layer one is AdaBoost classifier, which uses Haar features for detection of planthoppers; the author uses SVM as a second layer for organizing HOG features, the third layer of detection is the threshold judgment of the three features, and they achieved detection rate of 85.2%. Sudbrink et al. used remote sensing technologies to detect late-season insect infestations and wild host plants of stained plant bugs in the Mississippi delta. Groundwork outcome points out that those spider mite infestations are visible from healthy and stressed cotton through aerial videography. Qing et al. [2] have developed a rice light-trap insect imaging system to automate rice pest identification. The system uses two cameras to get more image features from the light trap. The authors also proposed a method for removing pest images from the background trap image. Nearly, 156 shapes, textures and color-based features are extracted, and the same is passed into the support vector machine with radial basis kernel function with 97.5% accuracy. Venugoban and Ramanan [3] used computer vision techniques to identify insect pests automatically in the paddy fields. The developed system improves the precision and increases the speed of identifying insects compared with the manual method. The manual process is also tricky due to pest variation in color, size and differences in patterns. To address the challenges, differences in viewpoints, noise, rotation and cluttered background adopt the gradient-based features in paddy field insect classifications. To evaluate this system through SIFT [4] and SURF [5] descriptors, the bag-of-words approach [6] and the HOG [7] features are used. The testing outcome illustrates that HOG features shows eight percentage higher than SIFT along with an overall accuracy of 90%. Mundada and Gohokar [8] developed a system to identify insect images in greenhouse trips using the gray scales preprocessing technique captured by the camera. The entropy, mean, standard deviation, contrast, energy, correlation and eccentricity features are extracted from the images, and the parts were inputted to the SVM for classifications. Finally, the authors prove the rapid system performance of the system. Samanta and Ghosh [9] developed a system for tea pest classification using CFS and IBPLN. They created a database consisting of 609 instances that fit into eight classes illustrated by 11 attributes (signs and symptoms), every one of which was nominal [10, 11]. The artificial neural networks were used for classification, and the results were compared with the original and reduced feature set. They also illustrated that the correlation-based feature selection reduces the feature vector. The combinations of correlation-based feature selection and incremental back propagation neural networks can be used for other classification problems.
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3 Proposed Research Work This work’s primary goal is to develop an image recognition system that can recognize the paddy plant insects from the agricultural image dataset. The recognition process involves two stages, namely feature extraction and classification. The features are extracted using speeded-up robust features (SURF) and block average Gabor (BAG) features. Finally, the parts are classified using k-nearest neighbors (k-NN) and support vector machine (SVM). The evidence from the models is evaluated using precision, recall, accuracy and F-score measures. The block diagram of the proposed paddy plant-insect recognition system is illustrated in Fig. 1. Three types of paddy plant-recognized insects are given below. 1. Grasshopper 2. Planthopper 3. Stem borer. Paddy plant-insect recognition deals with multiple images in a complex environment. Three different insect images are considered for training, namely grasshopper, planthopper and stem borer; for each type, 60 samples are used. The SURF and BAG features are extracted from the insect image, and the classifiers SVM and k-NN are used to recognize the insect from the paddy plant images. Each category’s performance is evaluated by the metrics such as recall, precision, accuracy and F-score.
3.1
SVM-Based IR2PI
In the training phase, paddy plant-insect features are extracted using SURF and inputted into the SVM model. In the testing phase, 64 SURF features are removed
1. GrassHopper 2. Plant Hopper 3. Stem Borer 1. Gross
SURF
Image BAG featur
Hopper
MODELI NG USING SVM &KNN
Fig. 1 Proposed method for paddy insects recognition system
Recogniz ed
2. Plant Hopper 3. Stem Borer
SVM- and K-NN-Based Paddy Plant Insects Recognition
15
from the test, paddy plant insects are given as input to the SVM model, and the distance among every feature vector and the hyperplane of the SVM is acquired. The identification of a test object is determined based on the most significant distance.
3.2
k-NN-Based IR2PI
In the training phase, paddy plant insect features are extracted using BAG from the three insect categories’ input images. The input image size is 30 30. Gabor filter produces same size as the input image size. High dimension and high redundancy is a problem for Gabor. Block average Gabor is used to overcome this issue, the image is divided into 3 3 blocks, and the average value of each block is taken as a feature vector. For recognition, the nine-dimensional feature vectors are extracted and are given as input to the k-NN to classification.
4 Performance Comparison of SVM- and k-NN-Based IR2PI Systems The SVM- and k-NN-based IR2PI systems are evaluated through the recall, precision, accuracy and F-score using (4.1–4.4) by composing the confusion matrix. • True Positive (TP): While training the model with training images, it correctly recognizes the insects (positive) and tests the model with testing images; it correctly identifies the insects. • False Positive (FP): While training the model with training images, it correctly recognizes the insects (negative), but while testing the model with testing images, it incorrectly recognizes the insects (positive). • True Negative (TN): While training the model with training images, it incorrectly recognizes the insects (negative) and tests the model with testing images; it incorrectly recognizes the insects. • False Negative (FN): While train the model with training images, it correctly recognizes the insects (positive), but while testing the model with testing images, it incorrectly identifies the insects (negative). • Precision (P): In other words, it is the positive predictive value (PPV). It is the ratio between the TP and the amount of TP and FP. P¼
TP TP þ FP
ð1Þ
where PPV—positive predictive value or precision, TP—true positive, and FP— false positive.
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• Recall (R): It is also called sensitivity or true positive rate (TPR). It is defined as the hit rate between the TP along with TP and FN. R¼
TP TP þ FN
ð2Þ
• Accuracy (A): It is the number of true outcomes (both TP and TN) A¼
TP + TN TP þ FN + FP + TN
ð3Þ
• F-Score: It is a measure of the test’s accuracy: F ¼ Score ¼ 2
PR PþR
ð4Þ
Predicted performance results obtained from different pre-trained SVM-based IR2PI and k-NN-based IR2PI models are given in Table 1. The performance table for the SVM- and k-NN-based IR2PI system is given in Table 2. The performance comparison chart is shown in Fig. 2. The TP and FN comparison of insect's recognition is given in Fig. 3. The SVM and K-NN disease-wise accuracy comparison char is given in Fig. 4. The SVM- and K-NN-based insect recognition
Table 1 Predicted performance result obtained from different pre-trained SVM-based IR2PI and k-NN based IR2PI models Model
INS ECTS name
Confusion matrix and performance results TP TN FP FN Precision Recall Accuracy
SVM-based IR2PI
Grasshopper 37 75 Planthopper 37 78 Stem borer 36 77 SVM-based performance result (mean) k-NN-based Grasshopper 38 77 IR2PI Planthopper 39 79 Stem borer 38 79 k-NN-based performance result (mean)
5 2 3
3 3 4
3 1 1
2 1 2
93 96 94 94 93 95 97 95
92 92 90 91 95 95 95 95
88 95 92 91.6 96 98 97 97
F-Score 92 94 92 92 94 95 96 95
Table 2 Performance table for SVM- and k-NN-based IR2PI system S.No
Approach
Classifiers
Precision (%)
Recall (%)
Accuracy (%)
F-score (%)
1
SVM-based (IR2PI) k-NN-based (IR2PI)
SVM
94
91
91.6
92
k-NN
95
95
97
95
2
SVM- and K-NN-Based Paddy Plant Insects Recognition
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PERFORMANCE IN %
100
80
60
40
20
PRECISION RECALL
0
ACCURACY
1
2
3
4
5
6
F-SCORE
SVM AND k-NN BASED PERFORMANCE COMPARISON
Fig. 2 SVM- and K-NN-based performance comparisons chart
40 GRASS HOPPER TP & FN(SVM) PLANT HOPPER TP & FN(SVM) STEM BORER TP & FN(SVM) GRASS HOPPER TP & FN(KNN) PLANT HOPPER TP & FN(KNN) STEM BORER TP & FNKNN)
35 30 25 20 15 10 5 0
1
2
DISEASEWISE ACCURAY VALUES IN %
Fig. 3 SVM- and K-NN-based TP and FN comparisons chart
100 90 80 70
GH ACCURACY (SVM & K-NN) PH ACCURACY (SVM & K-NN) SB ACCURACY (SVM & K-NN)
60 50 40 30 20 10 0
1
2
Fig. 4 SVM- and K-NN-based disease-wise accuracy comparisons charts
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graph is given in Fig. 5. The TP and FP comparison of insect's recognition is shown in Fig. 6a, b, respectively. The SVM- and k-NN-based FP and FN comparison graph is given in Fig. 7. The overall performance comparison chart for SVM-k-NN-based IR2PI models is given in Fig. 8.
5 Major Contributions In this work, a novel plant-insect recognition system was proposed. The summary of significant contributions is highlighted below: • The classification of the paddy plant-insect recognition system was explored effectively in the agriculture field. • The features are trained and tested with both a supervised and unsupervised environment.
98
PERFORMANCE IN %
97 96 95 94 93 92 91
PRECISION RECALL ACCURACY F-SCORE
90 89 88
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
SVM AND k-NN BASED INSECTS RECOGNITION PERFORMANCE COMPARISC
39
S V M & K NN B A S E D FA LS E P OS ITIV E V A LUE S
SVM & KNN BASED TRUE POSITIVE VALUES
Fig. 5 SVM and K-NN insects’ recognition performance comparison graph F
a 38.5
38
37.5
37
36.5 TP 36
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
5 FP
b
4.5 4 3.5 3 2.5 2 1.5 1
1
1.5
2
Fig. 6 a, b SVM- and K-NN-based TP and FP comparison graphs
2.5
3
3.5
4
4.5
5
5.5
6
SVM & k-NN BASED FP AND FN VALUES
SVM- and K-NN-Based Paddy Plant Insects Recognition
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5 FP FN
4.5 4 3.5 3 2.5 2 1.5 1
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
1. SVM BASED & 2. k-NN BASED PERFORMANCE IN %
Fig. 7 SVM- and K-NN-based FP and FN comparison graph
100 90 80 70 60 50 40 30 20 10 0
1 PRECISION
RECALL
2 ACCURACY
F-SCORE
Fig. 8 Comparison chart for the SVM- and k-NN-based IR2PI system
• All experimental results are presented to demonstrate the applicability of such strategies under real-time agricultural images to benefit farmers and fertilizer industries.
6 Conclusions The IR2PI uses images for three classes, namely grasshopper, planthopper and stem borer from paddy have been taken for consideration. A total 300 plant-insect images (three classes) were used for developing the system. 180 images (each class contains 60 images) were used for training the classifier, and 120 images (each class contains 40 images) were used to test the classifier. The different types of insects in
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paddy plant images are detected from the input images. In this work, SURF and BAG features are extracted. Both the extracted features are applied to achieve recognition performance using SVM and k-NN. The k-NN-based IR2PI with BAG features provides better accuracy in recognition, and the approach reports a higher accuracy of 95%. The SVM established IR2PI with SURF features reports an accuracy of 91.6%. The performance of IR2PI using k-NN is better than IR2PI using SVM. Overall performance of the proposed approach on insect recognition is satisfactory under this environment. The proposed method was analyzed and tested with standard metrics such as recall, precision, accuracy and F-score.
References 1. Qing Y et al (2014) Automated counting of rice planthoppers in paddy fields based on image processing. J Integr Agri 13(8):1736–1745 2. Qing Y et al (2020) Development of an automatic monitoring system for rice light-trap pests based on machine vision. J Integr Agric 19(10):2500–2513. https://doi.org/10.1016/S20953119(20)63168-9 3. Venugoban K, Ramanan A (2014) Image classification of paddy field insect pests using gradient-based features. Int J Mach Learning Comput 4(1) 4. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 91–110 5. Bay H, Ess A, Tuytelaars A et al (2008) Speeded-up robust features (SURF) Preprint submitted to Elsevier 6. Csurka et al (2011) Bag-of-visual-words approach to abnormal image detection in wireless capsule endoscopy videos. In: International symposium on visual computing, advances in visual computing, pp 320–327 7. Dalal N, Trigg’s B (2005) Histograms of oriented gradients for human detection. IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), ISBN:0–7695–2372–2. https://doi.org/10.1109/CVPR.2005.177 8. Mundada RG, Dr. Gohokar VV (2013) Detection and classification of pests in greenhouse using image processing. OSR J Electron Commun Eng (IOSR-JECE) 5(6):57–63. e-ISSN:2278–2834, p-ISSN:2278–8735. www.iosrjournals.org 9. Samanta RK, Ghosh I (2012) Tea insect pests classification based on artificial neural networks. Int J Comput Eng Sci (IJCES) 2(6). ISSN:2250:3439. https://sites.goole.com/site/ ijcesjournal. http://www.ijces.com/ 10. Suseendran G, Chandrasekaran E, Akila D, Balaganesh D (2020) Automatic seed classification by multi-layer neural network with spatial-feature extraction. J Critical Rev 7 (2):587–590 11. Akila D, Jeyalaksshmi S, Doss GSS (2020) Prognostication of domestic animals in india using arima model. J Critical Rev 7(5):643–647
Comparative Study on Challenges and Detection of Brain Tumor Using Machine Learning Algorithm S. Magesh, V. R. Niveditha, Ambeshwar Kumar, R. Manikandan, and P. S. Rajakumar
Abstract In the human body, the brain is the most central and multi-faceted organ. It is built up of more than 100 billion nerves that can communicate with trillions of associations in the human body. In a recent technology scenario, numerous efforts and promising results are obtained in healthcare systems. The brain tumor is the most complex and challenging disease and cannot get a cure quickly. The two forms of brain tumor are benign and malignant. The most distressing state of the tumor is malignant, whose patient survival rate is still tricky. Malignant happens because the brain is a critical and complex part of the body. Early detection and diagnosis of tumors increase the patient’s survival rate. Distinct techniques and algorithms have been developed to detect and diagnose the tumor. However, still, it is a challenging task to recognize and predict in earlier stages accurately. This article represents a new methodology to handle the challenges that occur during brain tumor detection using machine learning algorithms. The problems of brain tumor identification and evaluation have been addressed in the study. It is time-consuming to limit the current system, and the accuracy is not efficient. In the proposed treatment model, the random forest classifier technique identifies the tumor with measured precision in less machine time. Our findings concluded that the proposed system has a high tumor detection accuracy rate, disease diagnosis rate, and disease diagnosis time has been measured in less computational time for tumor detection. Keywords Peak signal noise ratio (PSNR) Random forest classifier Challenges
Brain tumor Benign Malignant
S. Magesh Maruthi Technocrat E Services, Chennai, Tamil Nadu, India V. R. Niveditha P. S. Rajakumar Dr M.G.R Educational and Research Institute, Chennai, India A. Kumar R. Manikandan (&) SASTRA Deemed University, Thanjavur, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_4
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1 Introduction The brain is various structures compare to any other known system in the world. The average weight of the brain is a three-pound combination of fats and protein. It is made up of two cell, glia, and neuron; it contains billions of cells that provide physical protection to neurons and help keep them healthy. The brain tumor is one of the most dangerous and challenging diseases. It is challenging due to its location, shape, and image intensities, which does not provide the tumor’s precise and accurate position. Several efforts and promising results are getting into the healthcare system due to machine learning algorithms. The brain tumor category has been graded from grade I (benign) to grade IV (high malignancy). In the detailed collection of the patient’s brain scan to classify the tumor, the role of difficulties in identifying brain tumors was discussed. Brain tumor treatment is essential. Any decision is linked to the consequences of surgery, chemotherapy, radiotherapy, and the likelihood of even healthy brain cells being injured by tumor control. The most shattering type of cancer is a grade IV high malignancy tumor. The patient’s life is at substantial risk, the survival rate of the last decade of the year. To escape a neuron’s death, brain cancer cells adopted multiple techniques to rebuild and change immunity. This generates the challenges to kill the cancer cell in prescribed computational time. Image fusion is accomplished between the spatial-spectral classification map resulting from the KNN-supervised and hierarchical K-means map unmonitored technique to obtain the ultimate definitive classification table. Finally, to fuse all images with more significant effects, a plurality voting (MV) procedure is used. A principal component analysis (PCA) algorithm is altered for dimensionality reduction. For a different subset of a specified dataset, the random forest classifier algorithm includes several decision trees. It takes the average to increase the dataset’s precision. This article discusses the challenges in brain tumor diagnosis using the random forest-based segmentation method to segment the tumor and locate the tumor’s position with the highest accuracy rate. Section 2 covers the literature review with the following challenges in detecting and diagnosing a brain tumor in Sect. 3. Section 4 considers the proposed treatment approach with results and conclusion in Sect. 5 follow up with future scope and references.
2 Literature Survey Scientists and clinical experts in the healthcare system have identified the concept of the procedure used for brain tumor segmentation and diagnosis. Nowadays, many relevant algorithms have been developed to solve the critical health care problem. The challenge faces in terms of detecting the brain tumor in less
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computational time is vital. Researchers and scientists are still searching for the best and optimal algorithm to find the best accuracy in detecting the tumor. Grundy et al. [1] clarified the challenges of brain and spinal tumor issues during clinical practice. According to patient age, the author illustrates the advancement in recognizing childhood CNS tumor by improving germ cell tumor; low-grade tumor is higher or lower by histological and biological component according to patient age to consider the risk. Lyon et al. [2] focused on immunotherapies to treat the brain tumor, discussing challenges during the tumor’s detection. This is the fast advancement of cancer therapy in the possible cure of the disease. Still, the tumor cannot be identified at the beginning of the point. Tandel et al. [3] explored brain tumor classification using an in-depth learning approach. Tumor detection, rating assessment, and determination of scientists and clinical experts in the healthcare system have identified the concept of the procedure used for brain tumor segmentation and diagnosis care. A critical feature of the process of diagnosis is detection and accurate grade estimate. Amin et al. [4] brain tumor recognition using machine learning algorithm to fragment the tumor area analyzed with different performance. Machine learning algorithms have been extensively used in the detection of a tumor with a high accuracy rate. The decision tree shows the highest accuracy based on entropy, morphological, SIFT, and texture features to detect brain tumor [5] efficiently. It can accommodate an extensive database, track thousands of variables quickly, calculate the significance of the variable used in image segmentation, balance the error rate of unbalanced datasets, and create a generic error estimator that is non-biased. The random forest algorithm retrieves all features from MRI data, and the tumor has eventually been removed. It is seen after passing through multiple image filters [6]. CNN has been used effectively to identify tumor datasets according to the extracted characteristics [7]. It was evident from the above literature review that various approaches and tools were used to detect and fragment the brain tumor. To have better performance, the convolutional neural network was used. The issues of tumor identification and diagnosis were addressed in the next part. The suggested method is used with minimal computational time to detect the tumor.
3 Challenges in Detection and Diagnosis of Tumor In this section, the detailed discussion of challenges occurs in our understanding and ability to successfully detect and diagnose patients with brain tumors; the brain tumor is the deadliest disease in all cancer forms. Brain tumor has proven challenging, but limited progress has been shown in research to diagnose the brain tumor. The tumor is frequently situated behind the blood-brain barrier (BBB); an organization of tight junctions and proteins that protect the perception of conditions
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in the general bloodstream from tantalizing neuronal tissues, often preventing systemic sensitivity chemotherapy. As per the need to consider the study’s actual condition to cope with brain tumor diagnosis, the main problems were highlighted in various ways. • To disclose an operative therapy for the treatment of brain tumors, researcher must redesign the research pipeline. From fundamental neurobiology to clinical trials, any factor utilized in treating brain tumors needs close examination to promote science creation. The scientific group moves outside the limits of conventional penalties for research. It appeals to the maximum degree of competence required in the biological and physical sciences. • The guide provides an appreciation of the properties and roles of the tumor micro system (TME) to gain a comprehensive knowledge of brain tumor pathology and management. In conjunction with neurosurgery, preclinical models should be established that have opportunities for novel treatments to be explored to penetrate the analysis into meaningful areas for diagnosing the disorder. • Brain tumor recognition is mainly based on microscopic anatomy and immunohistochemistry. This method empowers the extensive categorization of the type of tumor and a certain degree of extrapolation, but it fails to manipulate clinical trials. In contrast, intra-tumor heterogeneity and the frequently insufficient quantities of tumor information accessible for examination are likely to misunderstand some tumor grades [8]. The task of approving and combining novel diagnostic modalities is immense. Still, it also can improve the precision of the classification of brain tumors significantly. Antagonistic medicine, radiotherapy, and chemotherapy may cure brain tumors found in infants. However, it has been considered challenging to pursue this strategy in people with brain tumors, most of which have progressed following treatment alone. Medulloblastomas offer a good illustration of how a complete view of tumor biology should drive treatment intensity changes.
4 Proposed Treatment Method A thorough discussion of the potential therapy for brain tumor diagnosis has been addressed in this portion. Because of the variety of tumor type, scale, location, presentation, scan parameters, etc., segmentation of brain tumor MRI images is a challenging task. In segmenting and placing the tumor in the brain, machine learning algorithms achieve improved efficiency. The learning algorithms have achieved great success in detecting and diagnosing the disease, given the broad range of machine learning applications in the enterprise, especially in the medical fields. The random forest segmentation process analyzes the probability that the tumor tissue or the healthy brain from which the
Comparative Study on Challenges and Detection
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tumor masks are removed may belong. A random forest is a standard method in machine learning where classification trees are trained in partnership, each time on a random subset of knowledge. The algorithm is also intuitive, capable of solving multi-class issues. When adding more trees to the woodland, it does not over-fit. Most research has been undertaken in recent years on diagnostic MRI photographs to automate brain tumors’ detection. The study attempts to separate the tumor center and distinguish the tumor tissue from the normal tissue, offering a more accurate survival calculation. The arbitrary forest classification aims to estimate the form of likelihood tissue based on the tumor function measured. We are looking at five distinct tumor groups, one regular tumor type, and four irregular tumor forms. Randomly chosen parts from training set data referring to the tissue class are stored during the training process. During the calculation of the featured scope, three-fold cross-validation was used on training datasets. This begins with an initial feature collection of 275 characteristics. Even only 52 are integrated into the final model following sequential forward group. The texture characteristics can be assumed to contribute primarily to identify heterogeneous structures such as tissue enhancement. In comparison, the abnormality characteristics provide evidence on homogeneous areas. Starting with an empty feature set, the random forest algorithm starts. Forecasters are successively added to the model until no further development is obtained. This model adds two parameters as an indicator for model performance: overall accuracy across all groups and tumor class accuracy. Lastly, the combination of these two characteristics is defined in the final model [9]. In Fig. 1, the suggested random forest classifier-based tumor section model description is observed to classify the tumor images further for preformation evaluation is discussed.
Fig. 1 Random forests classifier-based tumor detection
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4.1
S. Magesh et al.
MRI Medical Image Dataset
The MRI brain picture utilizes a dominant magnetic field, radio waves, and a computer to produce accurate photographs of the brain and other cranial features that are sharper and more detailed than different imaging approaches. Our body consists of millions of atoms of hydrogen that are electrical. The image is obvious, and even the minimum irregularity can be seen. The dataset is taken from the Kaggle directory; it comprises 500 photographs of the human brain with 280 irregular brain images and 220 regular brain images.
4.2
Image Processing Techniques
In the suggested therapy method, before progressing to the next stage of segmentation to remove the pictures’ noise, the input brain images are preprocessed. If MRI images are directly forwarded to the segmentation, then accuracy is not appropriate, and the result might be inaccurate. To overcome the issue, the image should proceed with the preprocessing method to sharping the images. The discrepancy between the input image and the output image is given in Fig. 2. We have discussed brain tumor images; after reducing the noise, the filtered images are shown as output images. In MATLAB 2020, a code was executed.
4.3
Random Forest Classifier for Classification
The machine learning algorithm achieves a good response in terms of research relevance. It can have high efficiency and the potential to process several MRI
(a) Input Image
(b) Output Image
Fig. 2 The discrepancy between the input image and the output image
Comparative Study on Challenges and Detection
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datasets. The random forest classifier constructs an extensive array of binary decision trees based on two random processes. To accomplish the bootstrap sequence, the training array is initially sampled at random with a substitute. Randomization is then enacted throughout the building phase of trees. In each node, only a small portion, the randomly chosen features, is used to check for the best split [10, 11]. The proposed system’s derived features have been added as hand-crafted features to help the machine-learned features and boost segmentation. By converting the picture with a Gabor filter bank, characteristics are obtained. The original region of interest was the possible tumor area identified by the feature exaction production (ROI). By analyzing various tree depths and the number of trees on training datasets and testing the classification precision using fivefold cross-validation, RF parameters were tuned [12]. In the bootstrap package, the training set and bootstrap set have the same dimension, N, and an illustration of a training set that is around 2/3 new is used. At the same time, the remainder consists of repeated steps. 1/3 of the training samples from the bootstrap kit are left out. Such instances generate the out-of-bag (OOB) collection. Each tree is then installed on its OOB set on a bootstrap package of its own and evaluated. The mean classification error on the OOB sets on all the trees in the field is the total OOB error. For classification, each tree independently produces a decision, after which all choices are combined to generate the production. Photos with rare tumors are grouped into four types of a malignant tumors [13].
5 Results and Discussion In this section, the performance evaluation method is used to evaluate the proposed approach. The performance of the proposed RF classifier for the MRI dataset is illustrated in the above section. The classified brain tumor images are carried out to analyze the simulation using MATLAB in terms of PSNR value, mean square error, accuracy, sensitivity, and specificity [14]. PSNR operated to estimate the brain MR image features reassembled from the processed brain MR image with ‘m’ pixels per sample. It is measured using equation one shown below. PSNR ¼ 20 log 10
2m 1 MSE
ð1Þ
Table 1 indicates the consistency of brain image testing in PSNR in the experimental results. These PSNR specify that our methodology is better than the current technique to extract brain images’ purpose. The de-noising effect of traditional strategies is diminished as the noise level gets higher. Still, with increased noise, the proposed approach leads to improved implementation [15].
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Table 1 Results in terms of PSNR (in dB) for brain tumor images Image name
Noise level
Conventional method
Proposed method
Brain sample image 1
5 10 15 20 5 10 15 20 5 10 15 20
29.63 25.30 26.60 23.09 26.07 25.01 22.08 21.90 28.60 23.87 21.69 22.64
36.32 32.70 30.09 29.05 40.09 35.80 32.06 30.70 35.90 33.10 32.67 30.81
Brain sample image 2
Brain sample image 3
Mean square error ‘MSE’ is a measure of brain MR images’ reliability to compare two brain MR images and achieve a comparison score. It is given in Eq. 2 below: MSE ¼
X X 2 1 f ði; jÞ f R ði; jÞ Pði; jÞ Qði; jÞ
ð2Þ
From Eq. 2, the squared difference between real value ‘f ði; jÞ’ and the predicted values. ‘f R ði; jÞ’ are used for ‘Pði; jÞ’ and ‘Qði; jÞ’ prediction. Brain tumor classification accuracy [16, 17] is the correct classification ratio to the total number of classification results. Classification of MR images was carried with different MR images. Brain tumor classification accuracy is measured using Eq. 3; it is shown below: Accuracyð%Þ ¼
Correct cases 100 Total number
ð3Þ
The definition is defined by the right and accurate description of the type of brain tumor. It is seen using formulas calculated in Eq. 4. Specificity ¼
True negative case True negative case þ False positive case
ð4Þ
Sensitivity is defined as the percentage of the total relevant results or output correctly detected by the applied algorithm. As seen in Eq. 5, the reckoning is,
Comparative Study on Challenges and Detection
Sensitivity ¼
True positives case True positives case þ False negatives case
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ð5Þ
The final stage of classification is the review, as seen above, of the findings received. The proposed solution process utilizing the machine learning algorithm performs well relative to the current techniques based on the experiment.
6 Conclusion and Future Works In this comparative review, we explored the challenges of tumor identification and diagnosis. This paper’s critical contribution is to concentrate on improvement, function extraction, tumor description, and the proposed solution’s assessment. A delineation of various tumor compartments is given by random forest grouping and dedicated voxel clustering. For the segmentation of the improving tissue, the tumor center, and the entire pathological area, high-grade glioma produces improved outcomes. Based on detailed test results, it is checked that random forest machine learning algorithms have a better outcome. The optical consistency of the noisy image is significantly increased. Deep learning algorithms for segmentation and brain tumor enhancement can be added to future work to gain greater PSNR and more transparency in images of other techniques.
References 1. Grundy R, Walker D (2010) Brain and spinal tumours: contemporary challenges in clinical practice. Paediatr Child Health 20(3):117–122 2. Lyon JG, Mokarram N, Saxena T, Carroll SL, Bellamkonda RV (2017) Engineering challenges for brain tumour immunotherapy. Adv Drug Deliv Rev 114:19–32 3. Tandel GS, Biswas M, Kakde OG, Tiwari A, Suri HS, Turk M, …, Madhusudhan BK (2019) A review on a deep learning perspective in brain cancer classification. Cancers 11(1):111 4. Amin J, Sharif M, Raza M, Saba T, Anjum MA (2019) Brain tumour detection using statistical and machine learning method. Comput Methods Programs Biomed 177:69–79 5. Hussain L, Saeed S, Awan IA, Idris A, Nadeem MSA, Chaudhry QUA (2019) Detecting brain tumour using machines learning techniques based on different features extracting strategies. Curr Med Imaging 15(6):595–606 6. Hatami T, Hamghalam M, Reyhani-Galangashi O, Mirzakuchaki S (2019) A machine learning approach to brain tumours segmentation using adaptive random forest algorithm. In: 2019 5th Conference on knowledge-based engineering and ınnovation (KBEI), pp 076–082. IEEE 7. Ker J, Bai Y, Lee HY, Rao J, Wang L (2019) Automated brain histology classification using machine learning. J Clin Neurosci 66:239–245 8. Aldape K, Brindle KM, Chesler L, Chopra R, Gajjar A, Gilbert MR, …, Jones DT (2019) Challenges to curing primary brain tumours. Nat Rev Clin Oncol 16(8):509–520 9. Bonte S, Goethals I, Van Holen R (2018) Machine learning-based brain tumour segmentation on limited data using local texture and abnormality. Comput Biol Med 98:39–47
30
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10. Zhang L, Zhang H, Rekik I, Gao Y, Wang Q, Shen D (2018) Malignant brain tumor classification using the random forest method. In: Joint IAPR ınternational workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR), pp 14–21. Springer, Cham 11. Lefkovits L, Lefkovits S, Vaida MF (2016) Brain tumour segmentation based on random forest. Mem Sci Sections Rom Acad 39(1):83–93 12. Soltaninejad M, Zhang L, Lambrou T, Yang G, Allinson N, Ye X (2019) MRI brain tumor segmentation using random forests and fully convolutional networks. arXiv:1909.06337 13. Doss S, Paranthaman J, Gopalakrishnan S, Duraisamy A, Pal S, Duraisamy B, …, Le DN (2021) Memetic optimization with cryptographic encryption for secure medical data transmission in IoT-based distributed systems. CMC-Comput Mater Contınua 66(2):1577– 1594 14. Manikandan P, Sekaran R, Suseendran G, Jabeen TN, Raveendran AP, Manikandan R (2020) An efficient detection and segmentation of brain tumor using robust active shape model. J Crit Rev 7(9):2020 15. Gupta D, Ahmad M (2018) Brain MR image de-noising based on wavelet transform. Int J Adv Technol Eng Explor 5(38):11–16 16. Kumar A, Manikandan R (2020) Recognition of brain tumour using fully convolutional neural network-based classifier. In: International conference on ınnovative computing and communications, pp 587–597. Springer, Singapore 17. Kumar A, Manikandan R, Rahim R (2020) A study on brain tumour detection and segmentation using deep learning techniques. J Comput Theor Nanosci 17(4):1925–1930
Deep Learning in Image Recognition for Easy Sketching Techniques Hannah Vijaykumar, T. Nusrat Jabeen, R. Nirmala, S. Gayathri, and G. Suseendran
Abstract Inventing learning algorithms and developing information systems mean that the advancement of AI is feasible in many areas due to the growth in the volume of data. This paper discusses the learning models technique of an in-depth sketch classifier. For a basic approach, we use the depth convolution layer and use the deep neural network. The results would reveal that around one-fifth of the equation has been changed. To train and validate the network, we are using Google Quick Dataset; it can have 98% precision in ten categories and 85% precision in ten categories and for 100 types, accuracy can be improved by STM32F429I showing Production Board for Discovery. The software is capable of applying the grouping of sketches in real time. Keywords Deep learning
Sketch classification Image recognition
1 Introduction An algorithm in machine learning is used to make it possible for computers to predict or categorize mathematical models. This approach involves the raw data, and human-labeled ground reality suggests a vital volume to train the details to conform and choose matching algorithm models. Sketching is a standard form of input that has acquired more significant popularity in computer graphics and human–computer interface cultures [1]. Assume we obtain a series of random images containing the repeating events of 2D objects belonging to an unexplained visual group, defined as a series of H. Vijaykumar T. N. Jabeen Anna Adarsh College for Women, Chennai, India R. Nirmala S. Gayathri Shri Krishnaswamy College for Women, Chennai, India G. Suseendran (&) Department of Information Technology, Vels Institute of Science, Technology & Advanced Studies, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_5
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subimages sharing the same spatial and geometric properties [2]. To date, people have developed several subspace learning methods that minimize dimensionality by mapping the initial high-dimensional data into a low-dimensional subspace [3]. Since 2013, algorithms for deep learning have been increasingly popular. In many image recognition operations, matching images is an essential step. One of the most promising approaches to achieving low error rates is the flexible matching of the image to the specified references, especially in the presence of high image uncertainty [4]. Embedded sketching classification recognition technology uses an embedded device to collect sketching information, isolate and interpret information characteristics, understand and identify. It depends on the background and way of thought of people as shown in Fig. 1. It makes the computer familiar with the previous experience with human classification knowledge [5]. The emergence provides hardware such as notebook PCs and portable PDAs. Easy means to grab the input pen from these instruments are a mix of display, pen tracker and computing unit. It is easy to catch and process sketches digitally as they are drawn using special set of drawing and processing tool. The problem is that even though it seems so easy and intuitive for humans to understand drawings, in reality, it is a great challenge for your computer. One of the most essential steps in sketch identification is converting the original digitized strokes of the pen in the drawing to the ordinary geometric objects using a point detection function [1]. Parents expect more gadgets in comparison to traditional toys. Promote the production of infant behavior and reasoning. There is a range of computer items for pre-school education, such as an interactive drawing board, term cards, puzzle games and other interactive learning assets. One of the most common applications
Fig. 1 Embedded system
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of children’s plays in this century was interactive goods. More parents of pre-school children are rewarded for using interactive products faster and earlier. Their children can use, as it works well, their smartphone or another digital device [6]. In this task, on the STM32F429I Discovery Board, we are adding a sketch recognition system. The touch control system provides a direct, fast and efficient means of communicating with the application more intuitive than the conventional button and rod interface. Communication mechanisms are more appealing than interfaces involving an interpretation of the input unit. The touchpad can be used directly for finger or stylus drawing, a deep neural network can also be used to reach a high accuracy, and it is easy to use in real time for children.
2 Related Work Dong et al. [7] demonstrate that machine learning, the foundation of artificial intelligence, is also the underlying explanation for machines’ intelligence and artificial intelligence has been widely used in the industry via the improvement in the capacity of computers to interpret data, deep learning. In the area of machine learning, it has been illustrated that in more and more theoretical science and research, more scholars have joined the applied deep learning science. And object identification and picture recognition are very important uses for classification. Second, this article compares deep learning with conventional machine learning methods and then implements a method of deep growth learning, researching and evaluating the deep network structure training such as a deep faith network and a neural convolution network. And the recursive neural network exposes the operation of the deep learning in recognition and classification of image and promotes deep learning in the identification and classification of pictures. The issues faced with the implementation of identification and classification and corresponding solutions shall be discussed. Finally, the comparative study of in-depth picture learning recognition and classification was summarized and discussed. Pramanik et al. [8] represent a novel illustration of a facial drawing or a face-sketch identification technique focused on facial characteristics extraction. We often use a collection of geometric facet features including eyebrows, nose, forehead, mouth and so on and the relation between their length and width to identify a face-sketch because images and drawings cannot be comparable as they belong to two distinct modes. First, the facial features/components are obtained in this method, and the length, distance, area, etc. ratios are measured and evaluated as vector features of each image. The medium vector function is then calculated and subtracted from any vector feature to centralize the vector feature. Also, in the next step, the function vector for the input sample is determined similarly. To classify the face-sketch probe, the KNN classifier is used. The recommended solution is checked experimentally to stabilize against the eye, with natural light and neutral voice without occlusions. A total of 60 men’s and women’s face images from
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separate facial databases were used for experiment. For law enforcement and digital entertainment, it has practical applications. Rajput et al. [9] suggested an efficient face-to-face portrait sketch algorithm. The framework collects face images from the database based on a query drawn by an artist using specific details present in facial areas identified as the histogram (HOG) descriptor attribute. The HOG features are taken from and retained as information bases in a photo-face training pack. When applying a KNN classifier, the sketch’s HOGs are measured and compared with the knowledge base to obtain the best picture face if the sketch is given. In our study, we presented facial pictures in front with natural light and neutral expression. No occurrences are inferred in the frame. Studies of facial drawings and portraits were carried out in the CUHK student archive. Zhang et al. [10] illustrated that new methods could only randomly color the line sketch as a result, and the main task is a failure: A style is specific in conversion, applying a painting style to an anime picture. In this article, with an auxiliary generative adversarial network (AC-GAN) classifier [11], we have residual integrated U-net combined in the grayscale sketch style. In terms of both the quality of the art style and the coloring, the whole process is automated and simple, and the results are credible. Ebeid et al. [12] illustrate that certain critical biometric technologies are considered facial recognition for personal authentication. Face-sketch identification is a particularly crucial case in forensic applications. In this document, we propose an unattended method of face-sketch recognition by synthesizing a single-frame pseudo-sketch. The first uncontrolled technology is the technique suggested for the identification of face drawings. The photo-skizze synthesis process is proposed in two main phases: edge detection and fur detection, relevant to its gray-like color. The picture of the artist is contrasted with the pseudo-sketch created in the identification process. LDA and PCA make a way to retrieve sketch properties of the photos. During the classification point, the closest neighbor classification with the Euclidean distance is used. To test the utility of the proposed technique, we use the CUHK index. To test the utility of the proposed technique, we use the CUHK index, contrasting the effects of the synthesized drawings, up-to-date methods. The test results demonstrate that the suggested approach produces a simple synthesis definition that describes individuals more reliably than other systems. Moreover, the proposed approach achieves a reconnaissance rate in the reconnaissance stage of 1-nearest neighbor (rank 1: first match), which varies from 81% with PCA to 94.3% with LDA. The highest identification score is 98.3% for the closest neighbor, which is above all state-of-the-art approaches.
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3 Methodology Artificial neural networks have been widely used for the study and identification of records. Many attempts have been made to identify isolated handwritten and typed characters with commonly recognized achievements. In this job, a complete model of the neural network is used. The batch normalizations and the entire link layer are not used for this analysis to minimize the total volume or scale images to different resolutions, as shown below [13]. The layer parameter of the original network with the conventional transformation is 1,04,976 single-precision floating-point numbers. This suggests that there are approximately 10 M multiplications and modifications to this calculation. A total of 5–15 STM cycles are required for each of the multi-adds and other CPU loadings. They are developing board access to memory, based on the meeting’s instructions and time [14]. We plan the network for the following requirements to limit the number of parameters and computing: Reduce the filter unit size, and increase the pooling layer quantity [15]. Reduce input frame count. Minimize the complexity of the kernel. Coincide with each layer’s parameters, and layer number calculation and layer details are shown in Table 1. Therefore, the model architecture in this work is a separate layer of integration in the mobile network. It could reduce the cost of measurement for each layer to (1). M is the output layer number, D is the kernel size, or the signal convolution layer. Since organized color and depth photos of the region with good segmentation are used, baseline assessments for different approaches are carried out in the database [16]. 1 1 þ M D2s
ð1Þ
Table 1 Convolutionary network Layer
Sizein
Sizeout
Kernel
param
FLOPS
Con1 Con2 Poo1 Con3 Poo2 Con4 Con5 Con6 Con7 Total
1*28*28 16*28*28 16*28*28 16*14*14 32*14*14 32*7*7 64*7*7 64*5*5 64*3*3 –
16*28*28 16*28*28 16*14*14 32*14*14 32*7*7 64*7*7 64*5*5 64*3*3 10*1*1 –
16,1*3*3 16,16*3*3 2*2 32,16*3*3 2*2 64,32*3*3 64,64*3*3 64,64*3*3 10,64*3*3 –
144 2304 – 4608 18,432 36,864 36,864 5760 104,976
113 K 1.8 M 900 K – 900 K 1.8 M 900 K 51 K 6.5 M
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Table 2 Basic model structure Layer
Sizein
Sizeout
Kernel
param
FLOPS
Con1 Con1 Con2 Poo1 Con3 Con3 Poo2 Con4 Con4 Con5 Con5 Con6 Con6 Con7 Total Con7 Total
16*28*28 16*28*28 16*28*28 16*14*14 16*14*14 32*14*14 32*7*7 32*7*7 64*7*7 64*5*5 64*5*5 64*3*3 64*3*3 10*1*1
16,1*3*3 1,16*3*3 16,16*1*1 2*2 16,1*3*3 32,16*1*1 2*2 32,1*3*3 64,32*1*1 64,1*3*3 64,64*1*1 64,64*3*3 64,64*1*1 10,64*3*3
100 class
64*3*3
100*1*1
100,64*3*3
144 144 256 – 4608 512 – 288 2048 576 4096 576 4096 5760 18,640 57,600 70,480
113 K 113 K 200 K
10 class
1*28*28 16*28*28 16*28*28 16*28*28 16*14*14 16*14*14 32*14*14 32*7*7 32*7*7 32*7*7 64*5*5 64*5*5 64*3*3 64*3*3
depth point depth point depth point depth point
900 K 100 K 14 K 100 K 28 k 200 k 14 K 100 K 51 K 1M 510 K 1.5 M
The model of reduction from Table 2 shows the parameter of the cost of computing can be divided into ten groups and decreased to one-fifth the simple model. The experimental environment is used by the Nvidia GeForce GTX 1060ti graphics card with TensorFlow system framework. We have selected five types, orange, bike, cat, cow and bird, to be validated; it is the blueprint [11]. First of all, the model must be verified that it can accommodate the exchanged sketch-related functionality. Stochastic regression is the gradient descent optimizer with a study rate of 0.05 with a batch size of 600, randomly divided in each group. Train data and test data collection are of 20:1 ratio. We are using a loss function called Softmax cross-entropy [17]. X loss in cross entropy ¼ yxgt log yx ð2Þ x
Table 3 and Fig. 2 show the confusion matrix rate for the sketching images in percentage values.
Table 3 5 Category confusion matrix
Categories
Orange
Bike
Cat
Cow
Bird
Orange Bike Cat Cow Bird
0.988 0.003 0.005 00 0.006
00 0.975 00 00 0.003
0.009 0.005 0.983 0.011 0.02
0.002 0 0.003 0.907 0.089
0.002 00 0.003 0.905 0.885
Deep Learning in Image Recognition for Easy Sketching Techniques Fig. 2 5 Category confusion matrix
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CATEGORY 1.2 Orange
1
VALUE
0.8
Bike
0.6 0.4
Cat
0.2
Cow
0
Bird Orange
Bike
Cat
Cow
Bird
Category
Table 4 Recognition rate for the sketching
Recognition rate in percentage Orange Bike Cat Cow Bird
96.3 95.6 93.4 92.4 96.1
Recognition rate in percentage
Fig. 3 Recognition rate for the sketching
97
Percentage
96 95
Recognion rate in percentage
94 93 92 91 90 Orange Bike
Cat
Cow
Bird
Types
Table 4 and Fig. 3 show the recognition rate for the sketching images in percentage values. When applying the convolution system embedded device, we have got to match the interface and requirement from the Planning Council. Images are 28 28 pixels in a dataset, while 800 480 area on a touch screen. Therefore, the drawing area’s design is 280 280 pixels, and the bilinear interpolation is employed process to scale to 28 pixels 28 pixels [18].
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Fig. 4 STM32F429I board
In Fig. 4, the STM32F429I Discovery Board, we are adding a sketch recognition system. The touch control system provides a direct, fast and efficient means of communication with the application more intuitive than the conventional button and rod interface.
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In g, two independent convolution operations are written, one is the regular two-dimensional convolution and the other is the deep-wise convolution. You should write the pseudo-code of the convolutionary layer as in Algorithm 1. And that is the layer of deep-wise convolution that is written down. Only the normal layer, the highest pooling layer and the layer initialization feature can be appended to the point layer. 1 1 is capacity of the kernel by settings [19].
4 Conclusion We introduced the idea of the deep learning model sketch classifier technique. The key aim is to help children understand and reap the benefits of schooling. We are using a profound learning model which streamlines parameters. To obtain a highly productive performance. Moreover, we create a complete architecture on the interconnected system and show a presentation in real time. We supply ten divisions for grouping purposes. Recognition standards are above 98%. This is roughly average. The sketch is difficult to draw, albeit constrained by the precision of the touchpad-forming surface, but it can be resolved when it is used on a product with a wider screen and stylus drawing.
References 1. Li H, Shao H, Cai J, Wang X (2010) Hierarchical primitive shape classification based on cascade feature point detection for early processing of on-line sketch recognition. In: 2010 2nd International conference on computer engineering and technology, vol 2, pp V2–397. IEEE
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2. Todorovic S, Ahuja N (2008) Unsupervised category modeling, recognition, and segmentation in images. IEEE Trans Pattern Anal Mach Intell 30(12):2158–2174 3. Du H, Wang S, Zhao J, Xu N (2010) Two-dimensional neighborhood preserving embedding for face recognition. In: 2010 2nd IEEE international conference on information management and engineering, pp 500–504. IEEE 4. Keysers D, Deselaers T, Gollan C, Ney H (2007) Deformation models for image recognition. IEEE Trans Pattern Anal Mach Intell 29(8):1422–1435 5. Sun Y, An Y (2010) Research on the embedded system of facial expression recognition based on HMM. In: 2010 2nd IEEE international conference on information management and engineering, pp 727–731. IEEE 6. Manikandan P, Sekaran R, Suseendran G, Jabeen TN, Raveendran AP, Manikandan R (2020) An efficient detection and segmentation of brain tumor using robust active shape model. J Crit Rev 7(9):2020 7. Dong YN, Liang GS (2019) Research and discussion on image recognition and classification algorithm based on deep learning. In: 2019 International conference on machine learning, big data and business intelligence (MLBDBI), pp 274–278. IEEE 8. Pramanik S, Bhattacharjee D (2012) Geometric feature based face-sketch recognition. In: International conference on pattern recognition, informatics and medical engineering (PRIME-2012), pp 409–415. IEEE 9. Rajput GG, Geeta B (2018) Face photo recognition from sketch images using HOG descriptors. In: 2018 Second international conference on inventive communication and computational technologies (ICICCT), pp 555–558. IEEE 10. Zhang L, Ji Y, Lin X, Liu C (2017) Style transfer for anime sketches with enhanced residual u-net and auxiliary classifier gan. In: 2017 4th IAPR Asian conference on pattern recognition (ACPR), pp 506–511. IEEE 11. Hu C, Li D, Song YZ, Xiang T, Hospedales TM (2018) Sketch-a-classifier: sketch-based photo classifier generation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9136–9144 12. Abdel-Aziz HGM, Ebeid HM, Mostafa MG (2016) An unsupervised method for face photo-sketch synthesis and recognition. In: 2016 7th International conference on information and communication systems (ICICS), pp 221–226. IEEE 13. Doss S, Paranthaman J, Gopalakrishnan S, Duraisamy A, Pal S, Duraisamy B, …, Le DN (2021) Memetic optimization with cryptographic encryption for secure medical data transmission in IoT-based distributed systems. CMC-Comput Mater Continua 66(2):1577–1594 14. Varun T, Suseendran G (2021) Reforming fixed asset database in university using SQL database management system. In: 2021 2nd International conference on computation, automation and knowledge management (ICCAKM), pp 180–184. IEEE 15. Alzubi JA, Alzubi OA, Suseendran G, Akila D (2019) + A novel chaotic map encryption methodology for image cryptography and secret communication with steganography. Int J Recent Technol Eng 8(1C2):1122–1128 16. Liu J, Furusawa K, Tateyama T, Iwamoto Y, Chen YW (2019) An improved hand gesture recognition with two-stage convolution neural networks using a hand color image and its pseudo-depth image. In: 2019 IEEE international conference on image processing (ICIP), pp 375–379. IEEE 17. Marinai S, Gori M, Soda G (2005) Artificial neural networks for document analysis and recognition. IEEE Trans Pattern Anal Mach Intell 27(1):23–35 18. Cheng P, Hao J, Guo Y (2012) Convolutional codes in two-way relay networks with rate diverse network coding. In: 2012 World congress on information and communication technologies, pp 1014–1018. IEEE 19. Lei F, Yang Z, Lei H, He Y, Xie L (2011) Embedded implementation of barcode recognition system in ammeter image. In: 2011 International conference on multimedia technology, pp 5390–5393. IEEE
Deep Learning in Image Signal Processing for Minimal Method by Using Kernel DBN A. Punitha, D. Sasirekha, R. S. Dhanalakshmi, K. Aruna Devi, and G. Suseendran
Abstract The use of profound certification networks in advanced vision applications has the potential to be beneficial. Deep learning accelerator in-sensor is energy efficient. However, their adverse impact was severely underestimated by precision. The conventional vision pipeline undermines the accuracy of standard post-ISP datasets-trained machine learning algorithms. For example, in a car detection case, the off-the-shelf Faster RCNN algorithm’s detection accuracy is decreased by 59%. Our approach increases accuracy by 24–59% for the problem of vehicle recognition. Combine the kernel process with the deep conviction network by the researcher. It is an algorithm to preserve their advantages and compensate for their disadvantages, and add deep learning to the kernel to improve performance. The PolSAR Image Classification has recently been applied to a deep belief network (DBN) that can use several unmarked pixels in the image data model. Relative to the conventional edition Improve Image Signal Processing ISP, energy usage and reaction time 28% and 32%. Keywords Deep learning: kernel algorithm processing
Deep belief network Image signal
A. Punitha Department of Computer Applications, Queen Marys College, Mylapore, Chennai, India D. Sasirekha R. S. Dhanalakshmi Department of Computer Applications, Anna Adarsh College For Women, Chennai, India K. A. Devi Valliammal College for Women, Chennai, India G. Suseendran (&) Department of Information Technology, Vels Institute of Science, Technology & Advanced Studies, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_6
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1 Introduction In several respects, deep learning, the new machine learning algorithm model, has reached state-of-the-art effectiveness. Domains for apps, please. However, deep belief networks’ (DBNs) high energy, processing, and memory specifications limit their application to embedded devices. By enabling the simultaneous execution of primitive analysis processes, vision applications offer fertile ground for achieving energy savings. Several in-sensor and near-sensor accelerators use these capabilities [1] that use parallel readout capabilities of the image sensor to allow effective convolution operations. Many industry sectors need quality management and non-destructive inspection of vital systems for safe service [2]. Computational sophistication required for the image applications for production is large and complicated. This is complexity arises from the fact that it requires a considerable amount of space. Several data are to be displayed in digital format if this is the case. The data are to be analyzed in real time after the device is built on the fastest uniprocessor available that would always struggle to reach speed requirement. This is because this is the case in which the processor’s speed constrains the process speed and the bandwidth of the memory and U0 [3]. Recently, a DBN is processed in a variety of unmarked pixels. The picture information model has been added to the image classification in PolSAR [4–6]. Some image signal processing algorithms are used in a wide range variety of implementations, including target tracking, edge position, and object segmentation with statistically active contours and grids [7]. Hinton has suggested DBN because it had used commonly in the fields of prediction and so on Tao et al. [8]. In the area of learning, deep learning has grown exponentially. In recent years, the academy and the company Identified that the efficiency has been greatly enhanced in various traditional recognition and regression [3], [9] studies. The DBN learning model exemplifies the classic deep neural network (DNN) challenges the usefulness of feature learning and its unique network design and teaching procedure throughout the training phase, as is typical of significant things. Phases such as the absence of gradients, overfitting, and partial position must be handled correctly Gal and Saniie [10], e.g., MR image recognition, in particular segmentation and analysis, is used widely in biological and clinical science to advance our knowledge of the different diseases of the flesh of man [11]. To optimize the image sensor, deep learning accelerators are used before ISP and add an implementation challenge. As DBNs are typically educated on distributions that vary from the image produced, RAW pictures are learned already processed by the ISP. This is a sensor. This conundrum of training disparities and data dissemination targets usually known as covariate shift, drastically reduces the precision of application for in-sensor accelerator pipelines; in the instances we looked at, accuracy was reduced by 59%. Training networks on pre-ISP data (RAW images) will be a potential alternative. However, that would entail creating new ones, huge databases of RAW images (expensive tasks; see Sect. 4), or small training data, thereby decreasing accuracy.
Deep Learning in Image Signal Processing …
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On the other hand, our proposed solution requires sensor accelerators to be used directly by professional DBNs beyond the shelf. It is possible to ensure precision by using our method. When removing the ISP, this increases detection accuracy by 24–59% and cuts device reaction time by 32% and 28% device energy in ISPs with near-sensor accelerators. The DBN is used in PolSAR and various applications to research it in this paper deeply.
2 Related Works Hou et al. [4] proposed a new semi-supervised POLSAR form to classify photos using a DNN to minimize the size of the PolSAR tensor and take into account the distinct properties of the PolSAR data input from the DBN, using a multilinear principal component analysis (MPCA) to extract tensor measurements. Each pixel of PolSAR data is collected in-depth to help the community benefit. For Polsar data, the pixel as a tensor and its neighbourhood simple function have proven ineffective at distinguishing complicated terrain. Therefore, we merge various PolSAR data features representing each spatial structure of PolSAR to obtain more significant details. The tests’ findings demonstrate that the description is highly reliable and relies on the suggested methodology, similar to the standard grouping process. The pay-as-you-go technique is utilised because it has good performance in cloud field services output on less virtual machine resources, according to Tao et al. [8]. So how is it predicting cloud service output under virtual computer services and parallel demands has been a hot problem of analysis. Targeting the cloud service dilemma output forecast, as a significant predictor for measuring cloud service quality, their paper takes time to react. It proposes the DBN method estimation of the cloud service response times. This method defines a mapping model based on DBN between cloud response time and competitive requests and virtual machine resources. The response time of the cloud provider is determined based on a mapping model. The experiments’ efficacy of the proposed solution is displayed. Yi et al. [10] illustrated that to increase the accuracy of the charging of electricity integrated energy service (IES) forecasting and enhancing electrical neural network, DBN has been suggested as a load forecasting method focused on hierarchical NAR. Secondly, the regular topology of the IES is used to determine the method of calculation of the net load. A complex NAR neural network model will be developed based on the historic net load information. Provide net load time array modules. The DBN pre-training effects are then used for network initialization. Finalize DNN and parameters. The net load modules for instability predict the back-propagation (BP) algorithm. The estimated value of the net load is eventually produced in conjunction with a two-part forecast result. The case review is used to verify the study’s effectiveness suggested by the model. Sinha et al. [12] mentioned that classification is an effective way to investigate hyperspectral imaging (HSI). The deep learn algorithm has been applied, and there are some partial results obtained in HSI output. DBN is a regular network.
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Framework for deep learning combines the advantages of education. Successful learning comes from both unsupervised and supervised study. A broad classification of results' efficiency. This paper proposes an optimized DBN hybrid classification system with principal component analysis (PCA) that considers both spatial and spectral HSI characteristic definitions. Experimental findings demonstrate the use of the PCA-DBN process—a bright outlook for the HSI category. The DNN is utilised to develop a fault diagnostic model, as proven by Zhang et al. [13]. The objective function falls naturally into a defect diagnostic model. Local limit since the DBN bias and weights is arbitrarily initialized during the learning and testing processes. They are influencing productivity in computing. The defect diagnostic method is based on a genetic algorithm (GA-DBN)-configured deep faith network to fix the error. The device uses the Boltzmann computer’s minimal reconstruction error for shaping the genetic algorithm fitness function to increase weight and bias in the network, thus improving network consistency and accuracy. The model's output is tested in the trial due to the reconstruction error, representation precision, and time-consuming length. The findings are compared to those of the back GE spread.
3 Methodology The extraction features three functions. Definition optimization and parameter are performed using the in-depth analysis, algorithm, and kernel learning philosophy. The deep learning architecture is primarily used to enhance extraction efficiency, and kernel programs are also used as classification systems. Deep learning and kernel deployments are implemented simultaneously. The matrices passed between them should be adjusted and optimized with the parameters accordingly. We combine the fundamental algorithms and hypothesis used in the kernel’s profound learning in this scenario [14]. DNN: The DNN could classify more optical patterns than shallow patterns since they are considered scientifically possible. Theoretically, a deep learning system displays a robust learning capacity and discovers the essence sets in data from samples. We draw the essential line that the experimental research findings are a nearly in-depth learning algorithm method for the unsupervised pre-training level. The space parameters are less constrained, matching the capture form of the input distribution in the area as the individual is in charge of regularization [15]. DBN: The DBN layers are hidden components. This is the layer that has layer links rather than layer units. DBN learns how artifacts can be reconstructed and protected by feedback vectors as DBN input function detector. The subjects are taught in an unsupervised manner.
Deep Learning in Image Signal Processing …
Pc Ffit ¼ ER
a¼1
45
Pd
b¼1 ðu1ab
M
u2ab Þ2
ð1Þ
where m is no. of sample data, n is the no. of sample data dimension of the sample results, u1 is the rebuilt price for b the sample’s dimensional data. u2 is the actual value of the b data on the dimension of the model. The DBN-based DNN training process can be carried out in two phases: uncontrolled and pre-training and supervised fine-tuning. A general framework for the DBN is shown in Fig. 1. The first step involves increasingly greedily executing the RBM pre-training layer. Initialization of the DNN weights to solve the problem by stochastic initialization weights in the preparation step to reduce conventional DNN optimal local solution [16]. Application kernal: The kernel approach is beneficial in several applications. The boundary between choices is difficult to learn with only a few parameters since the classifier can be produced using a high-dimension kernel reproduction projection with Hilbert vacuum data. Here deep kernel architecture is shown in Fig. 2. Kernel methods are kernel-based features. SVM, A multi-core learning are the most common implementation process, a RAW and ISP-processed image vehicle detection scenario, PolSAR, etc. [17]. Support vector machine (SVM) is a supervised learning model for testing data used today of the original SVM to address linear issues that may be nonlinear. Tasks are done with kernel’s assistance. SVM may be used to distinguish and define handwritten characters as images [18]. Vector machines help yield lower peak recognition rates, but they can do that. Training evidence is equal to profound learning approaches in terms of conditioning to hit considerably fewer near-peak values. To learn the ideal kernel, a variety of seeds must be implemented. This demonstrates the ability to learn multiple keys and skills to learn several cores. They are combining heterogeneous data with the basic configuration of the framework. Fig. 1 General framework of the DBN
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Fig. 2 Deep kernel architecture
Since the RAW and the image processed by the ISP are the same, both the scenes’ configuration and the geometry of the same scene are caught as shown in Fig. 3. Their key contradictions are attributable to local transformation [10]. Gamma compression operations modify the distribution of pixel data. Since computer vision algorithms are trained in ISP-processed images, representations are mastered. Based on the delivery of the transformed knowledge, although a case may be made for RAW image training networks, there are a few obstacles: (1) RAW image datasets are not freely accessible (partially discussed in this paper), (2) the RAW image by the release of such a dataset; the specification is not standardized, and the migration to and from another format is not standardized. Formats like JPEG and PNG are proprietary, and (3) there are vast proportions of RAW files, making them quick to transfer [19]. Pbinn ða; bÞ ¼
þu 1 X Pðt a þ i; s b þ iÞ 2u þ 1 i¼u
ð2Þ
Equation 2 The binning procedure for the subsampling of a picture s*s Using u*u binning window, it can be formulated. Since DBN-based object detection algorithms use small coevolutionary kernels, high-resolution images increase processing time and energy proportionally, as
Fig. 3 RAW image, proposed method image, and the final image
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Table 1 Accuracy rating of the images Input image
RAW image
Proposed image
Final image
34.5
55.3
87.5
89.9
Fig. 4 Accuracy rating of the images
Accuracy Rang Acc. rating in %
Accuracy rating in percentage
100 80 60 40 20 0
Accu racy Ra ng
IMAGES
shown in Table 1 and Fig. 4. Even on the 4 GB NVIDIA GTX 1660 GPU, ideas outweigh the available memory by more than 1000–1000. Previous experiments on in-sensor accelerators have neglected the effect of image size because the assessments on the images were too limited for realistic object recognition. This DBN preparation method is split into two steps: One is pre-training, and that is an entirely unsupervised form of learning; the other is a method of learning. Fine-tune: The output layer is applied to the actual application after the top of the RBM. A BP at the fine-tuning point algorithms is used to perfect the entire parameter network to execute the grouping mission. Algorithm 1 PolSAR-DBN based classification Input: POLSAR pixel set ɵ; POLSAR named set pixel ɵ l = {(Ii l, yi l), I = 1, 2,…, N}; hidden unit numbers, n1 and n2, in two hidden layers, h1 and h2, respectively; group numbers. 1. Preprocess all pixels in ɵ and ɵ1 by splitting each dimension’s corresponding standard deviation; 2. The first WBRBM is conditioned according to (12)–(16) with an unlabeled pixel collection ɵ, leading to the parameters {b1, W1, c1}; 3. Bring the pixels in ɵ into the qualified WBRBM visible units and sample data, and train the second RBM with the sampled data from the hidden modules; then get the second RBM parameter, i.e., {b2, W2, c2}; then assign the W-DBN parameters to {b1, W1, (1/2)(c1 + b2), W2, c2}; 4. Flip the weight signs corresponding to the first three recognizable units in W1; initialize the discriminative network with {W1, (1/2) (c1 + b2); W2, c2; W3, c3}, allocated randomly to W3 and c3;
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5. Fine-tune the BP algorithm discriminative network with the named pixel collection ɵ 1; 6. Define the POLSAR image as a whole, i.e., all ɵ pixels, pixel by pixel. Output: Product of classification Yc = {y1 c, y2 c, …, yn c}. Thus, two actual PolSAR datasets were included. The first data array is extracted as shown in Fig. 5, September 16, 2018, from PolSAR’s multi-look data subset from the AIRSAR network. That’s 3(a) of it. The scene comes from Flevoland, The Netherlands, with the original camera size being 210–330 pixels. The photograph displays the global map of truth 3(b) in the paragraphs that follow. The second set of images, bought from RADARSAT-2, is compiled from a subset of PolSAR C-band image data from a single-look set. The photo covers the West of Xia, Yeah, China. We also choose from this Gif 2 of the small pixel pictures of 512–512 pixels. Both areas of the Weihe River and Jingkun Highway have been named [20]. From graph 1 and Table 1, We will reduce their miscalculation rate by 24% and reduce device power usage by 29% and DBNN test inactivity by 32%. So we can conclude that the DBN is greater than DNN, as shown in Fig. 6 and Table 2. The PolSAR Semi-Supervised Image Recognition Algorithm Using a Deep Network of Beliefs is the most recent version of the PolSAR algorithm. PolSAR Details generates the Tensioner Dimension using a Multilinear Analysis of the Concept Variable, which integrates different aspects of the DBN Input Function PolSAR data.
Fig. 5 PolSAR image from Netherlands 2018 a PolSAR image in RGB and b from image (a) truth map ground
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PERCENTAGE
Fig. 6 Difference in DNN and DBN
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35 30 25 20 15 10 5 0
DIFFERENCE BETWEEN DNN and DBN
DBN
DNN
Error rate
Table 2 Difference between DNN and DBN
DNN DBN
Energy Consumpon TYPES
Analysis
Error rate
Energy consumption
Analysis
25 24
30 29
30 32
The outcome experiment shows that our approach has increased precision and accuracy—consistency of the region and success of the traditional trategies for grouping.
4 Conclusion Deep learning, the latest model of the kernel-based version of DBN in various fields like support vector machinery (SVM), is a supervised learning model for testing data used to study classification and regression with related learning algorithms. Some methodology allows sensor accelerators to be used outside the shelf explicitly by trained DBNs. For a wide range of existing datasets, ISP-processed photos with no loss of accuracy. It is possible to ensure precision by using our method. This increases detection accuracy by 24–59% more significant than the normal DNN and cuts device reaction time by 32% and 28% device energy. The DBN is used in PolSAR and various applications and is deeply researched about it in this paper.
References 1. Zilong Z, Wei Q (2018) Intelligent fault diagnosis of rolling bearing using one-dimensional multi-scale deep convolutional neural network based health state classification. In: 2018 IEEE 15th International conference on networking, sensing and control (ICNSC), pp 1–6. IEEE 2. Madabhushi A, Udupa JK (2005) Interplay between intensity standardization and inhomogeneity correction in MR image processing. IEEE Trans Med Imaging 24(5):561–576
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3. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26 4. Hou B, Guo X, Hou W, Wang S, Zhang X, Jiao L (2018) PolSAR image classification based on DBN and tensor dimensionality reduction. In: IGARSS 2018–2018 IEEE international geoscience and remote sensing symposium, pp 8448–8450. IEEE 5. Yu D, Deng L (2010) Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Process Mag 28(1):145–154 6. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237 7. Purwins H, Li B, Virtanen T, Schlüter J, Chang SY, Sainath T (2019) Deep learning for audio signal processing. IEEE J Select Topics Signal Process 13(2):206–219 8. Tao C, Wang X, Gao F, Wang M (2020) Fault diagnosis of photovoltaic array based on deep belief network optimized by genetic algorithm. Chinese J Electr Eng 6(3):106–114 9. Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recogn 58:121– 134 10. Gal P, Saniie J (2016) Reconfigurable accelerator design platform for ultrasonic signal processing and imaging applications. In: 2016 IEEE international ultrasonics symposium (IUS), pp 1–4. IEEE 11. Li T, Zhang J, Zhang Y (2014) Classification of hyperspectral image based on deep belief networks. In: 2014 IEEE international conference on image processing (ICIP), pp 5132–5136. IEEE 12. Sinha A, Karmakar A, Maiti K, Halder P (2001) A reconfigurable architecture for a class of digital signal/image processing applications. In: 2001 IEEE Pacific rim conference on communications, computers and signal processing (IEEE Cat. No. 01CH37233), vol 1, pp 71–74. IEEE 13. Zhang X, Pan X, Wang S (2017) Fuzzy DBN with rule-based knowledge representation and high interpretability. In: 2017 12th International conference on intelligent systems and knowledge engineering (ISKE), pp 1–7. IEEE 14. Petersson H, Gustafsson D, Bergstrom D (2016) Hyperspectral image analysis using deep learning—a review. In: 2016 Sixth international conference on image processing theory, tools and applications (IPTA), pp 1–6. IEEE 15. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85– 117 16. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156 17. Xu G, Liu M, Jiang Z, Söffker D, Shen W (2019) Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning. Sensors 19(5):1088 18. Li J, Xi B, Li Y, Du Q, Wang K (2018) Hyperspectral classification based on texture feature enhancement and deep belief networks. Remote Sensing 10(3):396 19. Jing L, Zhao M, Li P, Xu X (2017) A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 111:1–10 20. Ghasemi F, Mehridehnavi A, Perez-Garrido A, Perez-Sanchez H (2018) Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discov Today 23(10):1784–1790
Bone Age Measurement-Based on Dental Radiography, Employing a New Model Fatemeh Sharifonnasabi, N. Z. Jhanjhi, Jacob John, and Prabhakaran Nambiar
Abstract Bone age measurement is a process for evaluating skeletal maturity levels to estimate one’s actual age. This evaluation is generally done by contrasting the radiographic image of one’s wrist or dentition with an existing uniform map, which contains a series of age-recognized images at any point of its development. Manual methods are based on the analysis of specific areas of hand bone images or dental structures. Both approaches are vulnerable to observer uncertainty and are time-consuming, so this approach is a subjective approximation of age. As a result, an automated model is needed to estimate one’s age accurately. This framework aims to develop a new Fatemeh Ghazal Sharifonnasabi (FGS) model for accurate measurement of bone age (± 1 year) or less than that with dental radiography. This study will use a new image processing technique, which involves creating a histogram of dental orthopantomogram (OPG) X-rays. In the machine, learning classification can be grouped as the training and testing phase. The training phase is used to extract all the images’ features for the classification model. The convolutional neural network (CNN) and K-nearest neighbour (KNN) classifications are ideal for this problem, based on the available literature.
F. Sharifonnasabi (&) N. Z. Jhanjhi School of Computer Science and Engineering, SCE, Taylor’s University, 47500 Subang Jaya, Malaysia e-mail: [email protected] J. John Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: [email protected] P. Nambiar Department of Oral Biology and Biomedical Sciences, Faculty of Dentistry, Mahsa University, Saujana Putra, Malaysia e-mail: [email protected] P. Nambiar Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_8
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Keywords Dental age measurement Panoramic images network Dental imaging Image processing techniques
Conventional neural
1 Introduction Bone age measurement is an index of medical image processing (MIP) techniques. MIP is a multidisciplinary discipline at the intersection of software engineering, data designing, computer science, information engineering, material science, arithmetic, medicine, and electrical engineering. It introduces statistical and scientific approaches to deal with clinical image problems and uses them for biomedical analysis and clinical consideration. The fundamental reason for use MIP is to take out the critical information from clinical data and hidden knowledge in clinical pictures. While firmly identified with the field of clinical imaging, MIP centres on the computational investigation of images, not their inception [1]. The methods can be categorized into a few general classifications: image classification, image recording, physiological sketching image-based, and this study also is part of MIP research, which proposing the bone age measurement (BAM) framework [2]. As an individual grows from the earliest stages of maturity and evolves as a grown-up, skeletal bones change fit as a fiddle. The development of a person’s skeletal framework can be evaluated with the assistance of a bone age concentrate by the clinical specialist. One significant clinical issue is the estimation of bone age to assess the chronological age, otherwise called real age, in years dependent on their date of birth. In the past, the irregularities found in the skeletal development or even the discharge issues in juvenile can be uncovered through age disparity that it has committed a unique spot to itself in instructive and clinical examination [3]. In the realm of criminological science, bone age estimation is an ability that can aid the forensic age estimation (FAE) [4]. In 1895, Rontgen discovered X-ray, and his findings made another explicit aspect of age measurement for living individuals. For skeleton radiographs, the innovation was utilized to enhance dental emission during criminological medication [5]. Further, it may be essential to assign the identity of a person about catastrophes and legacy. In such cases, an individual may look for help from a measurable clinical establishment. These legal specialists can assess age by inspecting the example of teeth eruption and bone development. Since the population in Europe was accurate, the estimation of the survivors’ ages was not required until recently. Hence, the reports can be used to confirm the age of a national. In the last two decades, European nations have experienced a sudden massive invasion of immigrants from other countries. Many of these people do not carry documentation of their chronological age. This issue has also become much more complicated for certain immigrants with a minor’s appearance [6]. European laws and courts of justice today have provisions to deal specifically with the cases related to children. The number of minor immigrants entering the European nations each year is unknown. The legal framework for the registration of under-aged immigrants in
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Germany and Spain and European countries faces a fundamental issue. An automated model for exact age can minimize this problem in many aspects, including immigration institutions [7]. This study aims to develop a new model for measuring bone age based on dental radiographic images for living individuals and to increase the accuracy of existing research. All previous studies lead to age accuracy, and this study seeks to achieve it over the years. The method, design, and implementation of the new model will be discussed in this paper’s conceptual framework. The article is organized in the following steps: Sect. 2 presents problem statement and limitations. Section 3 presents review of the general literature of the current problem. Section 4 depicts the proposed model for unravelling the distinguished issues. Section 5 presents the discussion. At long last, the paper closes in Sect. 6 and presents future work in Sect. 7.
2 Problem Statement In dental age measurement, physicians use odontometrics to estimate and investigate tooth size and utilize natural science to examine human phenotypic assorted variety. The purpose behind its utilization is the dental investigation, the structure, and the course of action of the teeth. There is a distinct attribute that can be found in human teeth utilizing an odontometer. Typically, this evaluation is made by contrasting a dental radiographic image with a current standard chart, which involves a series of known age images for each growth point. At present, most research that has been done so far in estimating age through teeth is minimal. Considering the multiple related legal and forensic issues that need bone age measurement, estimating age in forensic medicine and clinical dentistry is particularly significant. Modelling an accurate method for measuring age in the medical field can help accelerate personal identification and make it cost-effective. Furthermore, there are limits in computerized methods for BAM within the healthcare setting, with a precision to the range of (± 1 year) or less than (± 1 year) for dental age estimation techniques, due to limitations in image analysis and image processing techniques [8]. This study’s incentive is based on the hypothesis of a predictive hybrid CNN-KNN model for measuring bone age using dental radiography overcomes the bone age measurement process more accurately and making skeletal maturation more objective. This study’s next contribution is that the hybrid (FGS) model can measure minors’ age accurately for the mentioned aspects.
3 Literature Review This section presents a review of literature related to measuring bone age in clinical approaches by previous research. An automatic age system will eliminate the human observer’s role and scale back the sound judgement of analysis that is the
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main reason behind quality in the investigation. Most age measurement approach is supported by skeletal maturity estimation within the left-hand radiographical joint X-ray images [9]. This estimation causes complications with some bones that shift over time and a few bones that overlap over time, making dental panoramic images the best skeleton area source for age estimation. Various studies have been performed on OPG dental images of teeth. Identification processes were created for feature extraction from radiographic images [8]. Different research estimated the age using radiographic images of the teeth using numerous methods with contrasting age accuracy to detect the period up to (± 1 year) only [10]. The estimation of age was done by measuring different field areas, and generation was done by measuring other range variety [11]. Measurement of age was performed depending on odontometrics on the tooth’s size by previous researchers. Various applications and measurements were performed on teeth and to determine the age of dental comparative summary of the literature was mentioned in this section are below in Table 1, different studies were performed using image processing and artificial intelligence techniques classified in terms of techniques and accuracies analysed.
4 Proposed Model In medicine, artificial intelligence (AI) techniques, especially computer vision, are becoming increasingly common. Deep learning uses convolutional neural networks (CNN) that are of considerable ability to assist physicians in several areas. In dermatology, CNN is used to diagnose skin cancer. In the field of ophthalmology, CNN helps to identify different types of retinopathy. It is also essential in oral and dental diseases, radiography, and radiology as it can be used to diagnose abnormalities in chest X-rays [12]. While there is evidence that CNN can identify pathologies, it also has anatomical structures in medical images with similar or even higher accuracy than medicine. Experts also point to potential bias in these programs’ underlying algorithms, with potential risks of limited resistance and generalization [13]. Hence, CNN algorithms are the best solution for implementing the proposed model [14]. Another methodology is proposed to overcome the various concerns in the problem statement, which is to upgrade the proposed model’s efficiency for these dental images. This method is the classification of the hybrid CNN-KKN model technique that consists of two approaches, as shown in Fig. 1, the CNN section and the KNN section. In this section, for a better understanding of the CNN-KKN hybrid model technique, the CNN and KNN have explained in Sect. 4.1 and combinations of them presented in Sect. 4.2.
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Table 1 Summary of literature review Author
Year
Country
Dental X-rays
Technical approach
Accuracy results and limitations
Avuçlu
2020
Turkey
Dental X-ray
Hemalatha
2020
India
Avuçlu, E.B.
2019
Turkey
Dental X-ray Dental X-ray
The results showed highest estimates of age and sex were 95%, respectively. The accuracy rate was 89% by years only. The accuracy was 90% by years.
Tao, J.
2019
China
Dental X-ray
C# Programming language Fuzzy neural network Neural network multilayer Multilayer perceptron
Farhadian, M.
2019
Iran
Dental X-ray
Kim, J.
2019
N/A
Dental X-ray
Alshahrani, N.F.
2018
Saudi
Dental X-ray
Tao, J.
2018
China
Dental X-ray
Tuan, T.M.
2017
China
Dental X-ray
De Tobel, J.
2017
China
Dental X-ray
Franklin, D.
2015
Australia
Dental X-ray
Erbudak, H.Ö.
2014
Turkey
Dental X-ray
Patil, S.K.
2014
India
Dental X-ray
Linear regression models Kaval’s method
Velemínská, J.
2013
Czech
Dental X-ray
Data mining methods
Multilayer perceptron networks Convolutional neural networks Cameriere’s method Demirjian’s method, Willem’s Fuzzy clustering
Algorithms available in MATLAB R2017a Regression models
The results for the Chinese populations are more accurate dental age. The result showed a more accurate estimation of age in forensic research. The result was evaluated and the model has promised in developing the actual age. The generated model is more accurate compared with the previous studies. The results are more accurate with the proposed model for china populations. The proposed work has better accuracy, the lack of a large data set was the main limitations. The result showed less accuracy of 51% accurate using the MATLAB algorithm. Prediction accuracy was 90% for predicting the age by the number of years only. Measurement ratios showed no significance or correlation with age. The result was not adequate. The model was not capable of the Indian samples. The results showed 52% accuracy in girls and 62% accuracies in boys by years.
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Fig. 1 FGS model generation
4.1
CNN
The construction of convolutional neural networks generally contains three different layers. Convolutional, pooling, and fully connected are the layers. Every layer follows distinct rules for propagating the forward signal and error. No specific restrictions are implemented on how to systemize the configuration of each layer. However, apart from current development, CNNs are typically made in two parts. The first component typically carries out feature extraction, which uses convolutional and pooling layers [12]. The use of fully connected layers is the second component, called classification. Figure 1 demonstrates the whole of this process.
4.1.1
KNN
An undeviating and straightforward classifier in machine learning techniques is KNN; the classification of KNNs is upon the plurality of votes of neighbouring k-classes. Classification is acceded by identifying the nearest neighbours as the sample of the question and using those neighbours to distinguish the class of problem [15]. To calculate the two classes’ similarity, the distance between two points is calculated according to some criteria such as Euclidean distance (shown in Fig. 2).
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Fig. 2 Euclidean distance
4.2
CNN-KNN on Dental Images for Bone Age Measurement
The set of activities that are done to categorize images includes four steps: • The image capturing and data collection as creating the dataset for train data and test data • Image pre-processing and normalization • Feature extracting • Classification.
4.2.1
Image Pre-processing and Data Augmentation
For a better resolution of OPG X-ray images, a series of pre-processing and normalization operations are used. Initially, the images were removed from the extra margins and cropped. Adjusting the image is then used to increase the contrast of the images and highlight the images’ edges. It is also done to normalize the image with zero base and variance 1. We also need a lot of data from OPG X-ray images to train CNN better. If any images are not available, data augmentation is used. In this study, data mirroring and data duplication have been used to increase the number of samples.
4.2.2
Hybrid CNN-KNN
The architecture used in this methodology includes an inputs layer that consists of 2 convolutional layers, a fully connected layer, and a KNN layer for classification. An input image of a dimension with zero centre normalization and a 224 * 224 is the first layer of this model. The convolutional layer is the second layer of this model. This layer contains 16 convolutional filters with a kernel size of 5, and the input image is added to 2 paddings. In this step, low-level characteristics like edges, blobs, forms, and others are acquired. The previous convolutional layer’ conv1’ is
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extended to a nonlinear activation function, ReLU, and a cross-channel normalization with 5 channels per element. A max-pooling layer with step 2 and 2 paddings downsample the prototype. There are two such convolutional blocks in the proposed model. The first and second convolutional blocks are identical. However, the 32 filters with a kernel size of 5 and 2 paddings make up the convolutional layer. Then, a fully connected layer is used to attach all the neurons in the layer. In the CNN model, to minimize the next layer's features, 50 per cent of dropout layers are linked amongst fully connected layers. The softmax probability layer is the last layer to determine the probability of class incidence. The number of groups in this classification issue is 9, which is like 15–23 years. The last fully related layer is updated to include 9 classification tasks with bias learning rate features and weight learning rate factors. But in our proposed CNN-KNN model, the KNN model was replaced with softmax on fully connected for classification. Thus, that fully connected layer neuron is considered as the backbone of the KNN classification. In this case, the KNN input is assumed to be 200. The hybrid CNN-KNN pseudo-code proposed algorithm is explained in detail in Table 2:
Table 2 Proposed pseudo-code algorithm for hybrid CNN-KKN
Model CNN_KNN models { Size=224 Batch size=16 conv1 = Sequential ( Conv2d (3, 16, kernel size=5, padding=2), BatchNorm2d (16), ReLU (), MaxPool2d (2)) conv2 = Sequential ( Conv2d (16, 32, kernel size=5, padding=2), BatchNorm2d (32), ReLU (), MaxPool2d (2)) FC = Linear (56 * 56 * 32, 36) knn = KNeighborsClassifier(n_neighbors=k) loop counter=1; Epoch=iteration. Initial error rate; While (loop counter < Epoch or error rate > Threshold) { convolution1 = Conv1 (x) convolution2 = Conv2 (convolution1) Fully connected = FC (convolution2) Predicted=knn.fit (Fully connected, targets) error rate=Calculate Loss (CrossEntropyLoss)) Return predicted. } }
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The model has been trained with a 0.0001 learning rate using a stochastic gradient descent momentum optimizer. With a small size of 16 images, the model is trained for ten epochs. In KNN, K is two, and the Euclidean distance metric is the distance.
5 Discussion Past investigations are introduced in Table 1, and 14 mechanized methodologies were observed and assessed in dental age estimation with prerequisites for BAM. The past literature intangibly shows that a significant move towards computerized techniques is unavoidable. Mechanized age estimation procedures will assist set aside with timing and money just as quickens the human ID measure. Machine system frameworks for estimating bone age are depicted in 14 orders. In the 14 frameworks, the standard cycle is image preparation, extractions of features, processing the images, and results. As expressed in the past examination, different techniques for age predictions in juvenescence have been estimated for a considerable length of time. Be that as it may, there are constraints, such as error, inadequate strategies, and the disappointment of some framework to perform dental age estimation just as zero accuracies in estimating the adolescent’s age as minor’s age [16]. Criminal procedures in which children give uncommon assurance to casualties and are likewise answerable for the crime. In all cases, official courtrooms and open organizations require scientific specialists to have an FAE-specific report. This report requires the person’s particular age in question (the minor). Also, there is no robust computational methodology for BAM in the healthcare providers, with exactness inside the scope of (± a half) year for dental age assessment procedures, because of constraints in image examination and image handling strategies between past explores.
6 Conclusion This investigation aims to develop a new model used in BAM to increase the existing model’s performance accuracy and prevent CNN overload situations. Image classification methods can also train data for better production and quicker outcomes when using new tasks coming to the data centre. Lastly, using suitable classifications like the hybrid CNN-KNN combination generates an image processing model with great accuracy (minors age) and lesser training time.
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7 Future Work In the following work, more criteria will be considered to maximize accuracy. During the implementation, a combination of radiographic techniques and various machine learning techniques will be evaluated to improve the precision of the data set training and help with forensic issues related to the accuracy of age judgement that can be improved. Acknowledgements We acknowledge the School of Computer Science and Engineering, Taylor’s University Malaysia’s support, for providing a scholarship and the facilities to complete this work.
References 1. Zhao C et al (2018) Versatile framework for medical image processing and analysis with application to automatic bone age assessment. J Electr Comput Eng 2018 2. Ahmad M, Zaman N, Jung LT, Ilyas M, Rohaya DA (2014) An integrated approach for medical image enhancement using wavelet transforms and image filtering. Life Sci J 11 (6):445–449 3. Botha D, Lynnerup N, Steyn M (2019) Age estimation using bone mineral density in South Africans. Forensic Sci Int 297:307–314 4. Gambier A et al (2019) Contribution of third molar eruption to the estimation of the forensic age of living individuals. Int J Legal Med 133(2):625–632 5. Sharma A, Rai A (2020) An Improved DCNN-based classification and automatic age estimation from multi-factorial MRI data. Adv Comput, Commun Computat Sci. Springer, pp 483–495 6. Cole AL, Webb L, Cole T (1988) Bone age estimation: a comparison of methods. Br J Radiol 61(728):683–686 7. Müller L-SO et al (2019) Bone age for chronological age determination—statement of the European Society of Paediatric Radiology musculoskeletal task force group. Pediatr Radiol 49 (7):979–982 8. Avuçlu E, Başçiftçi F (2020) The determination of age and gender by implementing new image processing methods and measurements to dental x-ray images. Measurement 149:106985 9. Marouf M et al (2020) Automated hand x-ray based gender classification and bone age assessment using convolutional neural network. In: 2020 3rd International conference on computing, mathematics and engineering technologies (iCoMET). IEEE 10. Jahankhani H et al (2020) Cyber defence in the age of AI, smart societies and augmented humanity. Springer 11. Atallah RR et al (2018) Face recognition and age estimation implications of changes in facial features: a critical review study. IEEE Access 6:28290–28304 12. Janković R (2020) Machine learning models for cultural heritage image classification: comparison based on attribute selection. Information 11(1):12 13. Ngoc VTN et al (2020) The combination of adaptive convolutional neural network and bag of visual words in automatic diagnosis of third molar complications on dental x-Ray images. Diagnostics 10(4):209 14. Sun Y et al (2020) Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans Cybernet 50(9):3840–3854
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15. Chen Y et al (2020) Fast density peak clustering for large scale data based on kNN. Knowl-Based Syst 187:104824 16. Kim J et al (2019) Development and validation of deep learning-based algorithms for the estimation of chronological age using panoramic dental x-ray images
Climatic Analysis for Agriculture Cultivation in Geography Using Big Data Analytics M. Anita and S. Shakila
Abstract Human and their surroundings are dependent on climatic conditions. Climate condition management needs a vast and huge effort due to various factors in every place under the eclipse. Mainly, agriculture cultivation prediction is very complex and dependent very much on its environment and climatic conditions. This complexity needs a large number of tracking and maintenance systems that can store and analyze. Big Data can accommodate this large dataset and can be used for climate analytical science. Climate conditions depend on the soil, water, trees, carbon-dioxide content in the air, and solar energy. This climatic condition mostly affects the farmers. Carbon usage, soil quality, and crops are the main factors for any farming productivity. Predicting climate, carbon usage, and soil quality can provide the health of any farming activities. This prediction accuracy level can be closed by applying machine learning techniques to Big Data. Keywords Agriculture
Climate change Environment Vector model
1 Introduction Climate conditions and carbon usage or carbonization should be in similar proportion for the environment to be safe. This proportion is a disputable number that has the effect of carbon usage. As these are inter-dependent, the prediction is very complicated, so the researchers used vector models for more accuracy. Managing agriculture surely needs agricultural science and has to be accommodated in one system. This system configuration would indeed require very vast space and analytical skills. Climate information systems, soil information systems, crop management, social and public laws information systems, social media facts, satellite collecting information, IoT tools information are needed to be tracked.
M. Anita (&) S. Shakila Department of Computer Science, The Government Arts College, Trichy 620 022, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_9
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Ancient people in India were able to predict these factors by analyzing nature by their fore-fathers. But this prediction method had become extinct, and farmers came out of agriculture due to glaring globalization and unpredictable nature. In these modern days, agriculture is a “Must need” industry for huge production and consumption need. Due to this unpredictable nature and other factors, agriculture industries need Big Data involvement for storing and analyzing the attributes. Machine learning techniques with these Big Data systems and storage can produce our products’ accuracy to yield more [1].
2 Prediction This agriculture result prediction involves climate prediction, soil prediction, and crop cultivation. These three-factor predictions are very much in need of nature’s features. In our modern days, satellites, drones, and other monitoring tools information play a vital role in collecting and up-scaling the data that would need for prediction [2].
2.1
Climatic Prediction
Climate is the key and vital factor for any agricultural-related industry. The changing nature of climate is keenly affecting the yield from these industries. Climate can be classified as location-wise. Similarly, the work of agricultural products can also be classified as location-wise. Location-specific crops can only be produced in that specific location. Climate prediction is the prime factor in yielding profit or loss. Hence, this climate prediction model has to generate more accurate results [3]. Climate change is a concern with Green House Gases in our atmosphere. These gases are covering up the earth’s temperature from Sun. The increase of greenhouse gases is primarily due to fossil oil usage and methane and nitrous oxide by the agricultural industries. There was a report that the global temperature could affect the Metropolitan cities like Chennai and Mumbai which might partially be submerged into the sea by a temperature rise of 2 °C globally [4]. Climate is based on various factors such as the location, atmosphere temperature, pollution, land, sea level, forestation, industry growth, and so on Fig. 1. There are many types of research and classifications based on different factors; one could be growing degree days (GDD). killing degree days (KDD) produces different results found on the products. Growing degree days helped increase the cultivation during April and May, and rainfall in April, and May has adverse effects during the ripening period [5].
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Fig. 1 Daily surface air temperature (F)
y ¼ p þ qx
ð1Þ
y ¼ predictant; x ¼ predictor y ¼ p þ q1x1 þ q2x2 þ . . . þ qnxn The regression coefficient of p on q P P P N pq p q bpq ¼ P P N q2 ð y Þ 2
ð2Þ
ð3Þ
Artificial intelligence and machine learning techniques are the main topics in the current era to find exciting factors or features from any data. Climate is such data which is in huge numbers to get predicted from the historical data. Climate can be predicted, if the data is in a continuous range. There should be several computations on the data points from various sources in the case of discrete data. These calculations use various equations with the data points. The weather data from multiple sources can be tabulated against the years. The regression can be found to predict futuristic values. Using this Table 1, climate prediction can be arrived at with the regression coefficient. This tries to find the climate to be more closure to the actual value Fig. 2. There are many theorems behind the historical and statistical data to come up with data visualizing into clusters and find more significant trends. These trends may get analyzed with qualitative and quantitative measures to get into more accurate values. Climate factors with their corresponding yields have been maintained in a table. Python and other scripting languages can implement different algorithms with various climate features [6]. In the coming sections, further details from soil and water necessary will be discussed.
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Table 1 Preview monthly average for predicting climate Preview monthly average Observed values Average of HadCM3 40 years B2a
HadCM3 A2c
2050 average
HadCM3 B2a
HadRM2
2100 average
January February March April May June July August September October November December Total
105.48 85.84 54.31 73.91 64.19 38.26 9.64 11.23 32.36 54.62 76.22 112.32 718.38
113.75 99.1 62.78 75.735 66.205 54.235 31.91 9.05 40.3 76.945 84.27 112.9 827.18
83.21 106.1 98.52 79.28 44.22 12.52 5.23 54.23 48.35 24.32 26.36 60.87 643.21
52.23 61.25 96.28 107.63 47.26 11.98 4.52 23.07 16.18 13.58 23.52 48.67 506.17
67.72 83.675 97.4 93.455 45.74 12.25 4.875 38.65 32.265 18.95 24.94 54.77 574.69
123.04 108.2 69.83 68.56 73.12 8.53 43.21 10.25 54.86 96.74 112.71 110.24 879.29
122.02 112.36 71.25 77.56 68.22 70.21 54.18 6.87 48.24 99.27 92.32 113.48 935.98
Fig. 2 Preview monthly average
2.2
Soil Prediction
Agricultural industries rely upon the land and its quality based on the soil. Soil quality has to be re-used or re-enhanced based on crop production. Also, pesticides and other salts in the land and soil are vital for agriculture. Hence, the factors that decide the prediction have to be maintained and managed by the data analytics system.
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These factors and the volume needed have to be monitored and analyzed based on the experience's yield and agriculture industries. The monitoring tools like drones, periodical laboratory tests, weather monitoring, and water management based on the soil and locations must be maintained in a bulk dataset. There could have advice from social media and other experienced farmers and industries that have to be maintained and managed Fig. 3. Data collection from these tools and monitoring tools must be managed in the Big Data to analyze the yield. They should also involve training the agricultural industries to monitor the daily routine and document them in a structured manner. There could also be the unstructured format of data that should be stored in an acceptable form. Data science requires machine learning techniques to find the features from the systems. This data needs statistical and operational behaviors of each system/tool involved in the soil quality. Soil quality study analyzes its composition of minerals, salts, and other bio-degradable pesticides that impact agricultural land maintenance. Agricultural soil analysts develop procedures that will increase soil quality and food production [7] Fig. 4.
Fig. 3 Soil horizons
Fig. 4 Clay, sand, and silt trigonometry
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Our research with this background is to help and suggest agricultural soil science to get into farmers and accordingly to India’s future. Big Data methods are used to analyze existing data into patterns. Clustering these patterns would be easy if the clusters are made perfect. Other researchers with partitions around medoids (PAM) and clustering large application algorithms and DBSCAN tools to find the yield results. Multiple linear regression method has been used to forecast the annual crop yield. Table 2 represents the pH value, acidic, base, carbon content, and moisture content in soil from different fields. The mean and median values have been tabulated to find the regression coefficient to get the qualitative value. Some equations are used for finding the soil texture Fig. 5. The regression coefficient can be found with the following equation. P ðð p pÞð y yÞÞ b¼ ð4Þ P ð p pÞ 2
b¼
a ¼ q bp P pq npq
ð5Þ ð6Þ
ðn 1ÞSDðpÞ2
Table 2 Soil quality test summary—mean and median Variable
Unit (kg ha−1)a
pH
–
Land use
Min
NF 3.2 JF 6.4 PF 5.3 N Alkaline permanganate NF 150.34 method JF 168.31 PF 52.62 P Olsen’s method in NF 14.12 alkaline soil JF 19.21 PF 11.45 K Neutral N, ammonia NF 110.34 acetate method JF 157.23 PF 38.61 SOC Soil organic carbon NF 2.28 JF 1.12 PF 3.99 SOM Soil moisture content NF 6.78 JF 3.67 PF 1.58 a represents the regression coefficient of p on q
Max
Mean
Median
SD
4.2 7.4 5.9 180.42 174.45 64.67 16.31 23.24 16.67 132.45 168.83 44.25 4.24 2.46 5.77 8.32 5.67 1.87
3.72 6.91 5.62 165.37 171.37 58.65 15.22 21.22 14.05 121.38 163.02 41.42 3.27 1.78 4.87 7.54 4.66 1.73
3.7 6.9 5.6 165.38 171.38 58.645 15.215 21.225 14.06 121.395 163.03 41.43 3.26 1.79 4.88 7.55 4.67 1.725
0.7071 0.7071 0.4243 21.2698 4.3416 8.5206 1.5486 2.8496 3.6911 15.6341 8.2024 3.9881 1.3859 0.9475 1.2587 1.0889 1.4142 0.2051
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Fig. 5 Soil quality values
As there are other factors with the direction of the wind, location, soil quality, and the rain possibilities that are getting impacted, vector-based linear regression methods should have been used. Our research is to have vector-based data from the fields to find the soil quality and yield prediction.
2.3
Wind and Water Prediction
Agricultural industries are very much dependent on the water bodies for most of the productions. But some products don’t require aquatic bodies for their growth. Some products can only be grown in the less-water area, whereas some crops can be planted only near the aquatic land. The researchers mostly took the crops needed in a place and then predicted water bodies for those products. As per the agriculture expertise, the crops needed limited water bodies cannot be grown in high-water areas. This statement is true for windy areas as well. These features of crops and the water needs have to be collected by the monitoring tools like Network of Field Sensors, Satellite monitoring and Water-flow meter monitoring periodically Fig. 6. This huge data collection can be mined in more significant data storage like Big Data. This statistical data of structured and unstructured has to be analyzed with the help of different algorithms. The artificial intelligence and machine learning techniques have to be implemented with the plots from the various algorithmic outputs. This also needs the vector-oriented approach to abide by the wind directions and amount of wind to get included as the features to find the accuracy [8]. Wind and water are very much connected and inter-related factors for agriculture products/crop yield. The volume, velocity, and variety of the wind and water have to be monitored and maintained with its output value. Future can be predicted with the history and present nature of the wind blow and water flow. Ancient farmers of our old parents did this way to predict the future.
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Fig. 6 Wind and water quality
Corporate or industries needed proof of the concepts through which concepts can be agreed to proceed. This made the technologies and technologists use standards and statistical calculations with various factors to come up with the prediction results and extend accuracy.
3 Clustering Clustering is an activity to segregate the multitude of data in one collection similar to another in the same group than the data in other collections. Clustering can be made in defining the agriculture data points against the factors with crop production. Big Data with structured and unstructured data from satellites has to maintain a large number of datasets. These datasets have to be made as clusters that can be used as a data warehouse. Clustering is used mainly in market research, pattern recognition, data analysis, and image processing. Agriculture data points are more likely the patterns based on the different factors involved in it. Though prediction and clustering are the consecutive steps to conclude, the amount of calculations and applying many algorithms is much more than that of prediction. Clustering methods can be classified as division clustering, hierarchy and fuzzy clustering, and clustering with density.
3.1
Division Clustering
The division is a method of subdividing the statistical dataset into a set of collections based on the observation within a dataset into multiple groups based on the similarity. This clustering method used the mid-value of the dataset. There are specific algorithms to represent objects or medoids among the dataset observation. Clusters can be formed by observing each data to the mean. Each selected medoids and non-selected data points are swapped. The inter-changed dataset that increased the objective function by changing the selected objects with the unselected item has to be iterated until the objective function cannot be decreased. This
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model of dividing the datasets is also called as classical clustering, which observes the medoids from only one cluster. Some algorithms helped with large datasets as well [9].
3.2
Hierarchy and Fuzzy Clustering
Hierarchy clustering is for finding groups in the dataset. No pre-definition of numbers is required in this clustering. The hierarchy clustering is a tree-based representation of the data points. Data points that can be inherited from other data points can be plot at the same hierarchy. This hierarchy clustering data point has to be evaluated and validated based on the measures of output results. At first, algorithms can be applied to the data points which contain hierarchical grouping structure. Other data points which are closure to these inherited data points are getting plotted on the same cluster [10]. Qualitative and quantitative crops in different quality of soil can be found based on this hierarchy clustering method. The fuzzy clustering method can be demonstrated using the combination of soil type, quality, and products. This fuzzy clustering can be used to find the quality of soil location/for the related corps [11].
3.3
Clustering Methods with Model
The classical clustering methods discussed had shown random output results for a similar number of data points. Also, it needed a pre-defined number to define the clusters by the user [12]. Clustering methods with models considers the data from a model that combines more than two clusters. In this method, two or more variables such as the cultivation procedure and the corresponding weather of a specific crop product can plot the clusters. This clustering method needs the model or proof of concept from the data points. These data points have to be associated with each cluster practiced.
4 Conclusion Agriculture and its products are mainly dependent on climate changes and soil prediction. There are many clustering techniques in the researching world, as we discussed above. Nowadays, many countries and corporate companies encourage agriculture to use the results of these researches. These researches have shown better accuracy in agriculture production, but the accuracy is always not the same or not in the increasing trend. Agriculture products cultivation is the ultimate harvest
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by the agriculture industries. These prediction and clustering techniques are very much helpful for these industries. In the future, this prediction may use machine learning techniques that involve artificial intelligence with data science and analytics. Through this technique, the accuracy may get improved in the future.
References 1. Suseendran G, Sarkar B, Pal S, Mahalakshmi B, Rohini K (2020) An approach towards soil erosion monitoring system using satellite images through data mining and pattern mining techniques. J Crit Rev 7(7):721–725 2. Jeyalaksshmi S, Rama V, Suseendran G (2019) Data mining in soil and plant nutrient management, recent advances and future challanges in organic crops. Int J Recent Technol Eng (IJRTE) 213–216 3. Deji W, Bo X, Faquan Z, Jianting L, Guangcai L, Bingyu S (2009) Climate prediction by SVM based on initial conditions. In: 2009 sixth international conference on fuzzy systems and knowledge discovery, August 2009, vol 5. IEEE, pp 578–581 4. Sethi N (2007) Global warming: Mumbai to face the heat. Times of India. Retrieved 03-18 5. Zhu Y, Shi Y, Liu C, Lyu B, Wang Z (2020) Reinspecting the climate-crop yields relationship at a finer scale and the climate damage evaluation: evidence from China. Complexity 2020:3–5 6. Lee H, Moon A (2014) Development of yield prediction system based on real-time agricultural meteorological information. In: 16th international conference on advanced communication technology, February 2014. IEEE, pp 1292–1295 7. Yadav SA, Sahoo BM, Sharma S, Das L (2020) An analysis of data mining techniques to analyze the effect of weather on agriculture. In: 2020 international conference on intelligent engineering and management (ICIEM), June 2020. IEEE, pp 29–32 8. Doss S, Pal S, Akila D, Jeyalaksshmi S, Jabeen TN, Suseendran G (2020) Satellite image remote sensing for identifying aircraft using SPIHT and NSCT. J Crit Rev 7(5):631–634 9. Shah P, Hiremath D, Chaudhary S (2017) Towards development of spark based agricultural information system including geo-spatial data. In: 2017 IEEE international conference on big data (big data), December 2017. IEEE, pp 3476–3481 10. Bajocco S, Vanino S, Raparelli E, Marchetti A, Bascietto M (2019) Mapping phenological heterogeneity of a Mediterranean agricultural landscape. In: 2019 IEEE international workshop on metrology for agriculture and forestry (MetroAgriFor), October 2019. IEEE, pp 185–190 11. Bosma R, Kaymak U, Van Den Berg J, Udo HMJ (2005) Fuzzy modelling of farmer motivations for integrated farming in the Vietnamese Mekong delta. In: IEEE international conference on fuzzy system. IEEE, pp 827–832 12. Usman A (2017) Sustainable development through climate change mitigation and biomass agriculture: India’s perspective. In: 2017 IEEE conference on technologies for sustainability (SusTech), November 2017. IEEE, pp 1–7
Implementation and Performance Analysis of Various Models of PCNN for Medical Image Segmentation T. Vignesh, K. K. Thyagharajan, L. Balaji, and G. Kalaiarasi
Abstract Image segmentation is the process of dividing an image into multiple portions and assigning a label to the portions that have similar characteristics. The pulse-coupled neural network (PCNN) models can segment the objects in an image. This paper analyzes the suitability of PCNN models for high-performance biomedical image segmentation. In this research work, three different PCNN models have used to evaluate the performance of classifying the medical images, specifically traditional PCNN, intersecting cortical model PCNN (ICM-PCNN), and unit linking PCNN (UL-PCNN). Various PCNN models were used to extract the essential features from the images and classify the images. The segmentation results obtained by different PCNN models are compared based on entropy, standard deviation, and correlation. The results of PCNN are considered the best-segmented images as it gives the proper output in lesser iteration with the highest entropy, and corresponding standard deviation and correlation are calculated.
Keywords Pulse-coupled neural network Entropy Correlation Segmentation Biomedical images
T. Vignesh (&) Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India K. K. Thyagharajan Department of ECE, RMD Engineering College, Kavaraipettai, India L. Balaji Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India G. Kalaiarasi CSE Department, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_10
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1 Introduction Medical image analysis requires object segmentation as a preprocess to detect abnormalities in tissue growth and measurement for surgical planning. Image segmentation is also an essential step for the automatic analysis of medical images. In the late 1980s, Eckhorn et al. [1] proposed a neuron model based on the functioning of cats’ visual cortex. This model was improved by Johnson [2] and became a pulse-coupled neural network (PCNN). This model solved image segmentation, object recognition, image fusion, and visual similarity estimation [3]. Kuntimad and Ranganath [4] proposed conditions to provide better segmentation of objects, and they showed that PCNN provided perfect object segmentation even when the pixel intensity values of adjacent regions overlap. Choosing appropriate values for a large number of parameters for segmentation was a challenging problem. This problem was addressed in [5] by proposing the unit linking the PCNN model that reduced the number of the parameters used. To solve this problem, Stewart et al. [6] proposed a multi-value object segmentation method and named it as region growing PCNN. But they set the dynamic threshold and linking strength parameters manually. Adaptive parameter setting based on spatial and grayscale values of the image was used by Bi et al. [7] to set the values for weight matrixes and linking strength b. But they empirically set a constant value to the amplitude of the dynamic threshold and then decremented it linearly. Methods were proposed by Berg et al. [8] and Ma and Qi [9] to automatically set all the parameters of PCNN using evolutionary algorithms. However, these algorithms require heavy training before obtaining the proper values of parameters [10, 11].
2 Related Work 2.1
PCNN Model
Each pixel in an image acts as an external stimulus for a PCNN neuron, as shown in Fig. 1. Each neuron also gets stimulated by the outputs of its neighboring neurons (local stimuli). So, the PCNN forms an array of neurons, and hence it is a two-dimensional neural network. An internal activation system modulates the external stimuli with local stimuli. It accumulates the motivations the series of pulses produced by the array of neurons representing the input image’s features. It can be used for various image processing applications, image segmentation [12, 13], image recognition, and content-based image retrieval applications. The PCNNs are robust against noise, geometric variations, translation, and variation in the pixel intensity values. In Fig. 1, Fij is the internal activation state of the neuron located in the (i, j)th position of the two-dimensional array, Sij is the feeding input which corresponds to the intensity of the pixel at location (i, j), and (Wijkl Yij) is the weighted input
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Fig. 1 Architecture of PCNN neuron
provided to the linking field by the neighboring neurons [14]. The value of Wijkl is generally the reciprocal of the Euclidean distance. Yij is the neuron output, and Tij is the dynamic threshold. The neuron output Yij is set to 1 when the internal activation value is higher than the threshold value, and then the threshold value rapidly decreases. The threshold decreases in each iteration; when it reaches less than the internal state, the output will change to 0 [15]. When this process is repeated for many iterations, a time series corresponding to the object’s pixels will be generated [16]. The firing neurons affect the firing states of their neighbors through the linear linking input. Since the basic PCNN is computation-intensive, we go for other models of PCNN, which are the intersecting cortical model, unit-linking PCNN, and spiking cortical model [17]. All these networks are two-dimensional networks with only one layer [18].
2.2
Intersecting Cortical Model PCNN (ICM-PCNN)
The ICM neuron model is shown in Fig. 2, and it consists of an input field, a pulse generator, and a modulation part. Since the pulse generator part is the same as that of the basic PCNN neuron structure shown in Fig. 1, it is not repeated in Fig. 2. The input section of the neuron has a linear linking field and a feeding field. The linear linking field receives input from the outputs of the adjacent neurons. The feeding input receives an external stimulus. The linear linking input and the feeding input are modulated to obtain the internal activation state. The pulse generator compares the internal activation state with the dynamic threshold. If the internal activation unit's output exceeds the dynamic threshold, the neuron generates a pulse in the output.
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Fig. 2 ICM neuron structure
When ICM is applied for image segmentation, each neuron represents one pixel in the image. This neuron is also connected to 3 3 neighbors. This creates a two-dimensional local-connected network. The outputs of the neurons are normalized. The neuron connected to a pixel that has a high-intensity value will fire first. If a neuron whose neighbors fire first, it is communicated through the linking input and makes that neuron fire in advance. This allows the neurons with spatial proximity and brightness similarity to produce synchronous pluses. These synchronously fired neurons create neuron clusters, and these clusters represent different regions or objects in the image.
2.3
Unit-Linking PCNN (UL-PCNN)
By modifying the linking field of the PCNN model, as shown in Fig. 3, the ULPCNN neuron model is obtained. In this model, the linking area Lij of the basic PCNN is simplified such that its value is based only on the firing status (Y) of the neighboring neurons and the linking coefficient b. This neuron model applies the same pulse generator used in the basic PCNN neuron model. In UL-PCNN, a fired neuron will fire the other neighboring neurons with almost the same gray value.
3 Experimental Results The following images were given as inputs to the three models of PCNN discussed above, and the quality of segmentation was compared using the quality metrics entropy, standard deviation, and correlation [19].
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Fig. 3 UL-PCNN neuron structure
INPUT 1
INPUT 2
INPUT 3
3.1
Experimental Results with Basic PCNN Model for Input 1
From Table 1, the values of the entropy, standard deviation, and correlation are obtained in every iteration, and those values were that satisfied the condition set in the code. Only eight iterations satisfied the conditions, values of entropy are considered, and the corresponding standard deviation and correlation are considered for the basic PCNN model.
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Table 1 Basic PCNN for input 1
3.2
Entropy
Standard deviation
Correlation
0.2322 0.9416 0.9985 0.942 0.2857 0.9395
0.1907 0.4796 0.4995 0.4910 0.2176 0.4789
0.4554 0.8764 0.8082 0.7629 0.1972 0.7077
Experimental Results with Unit-Linking PCNN for Input 1
From Table 2, the values of the entropy, standard deviation, and correlation are obtained in every iteration, and those values satisfied the condition set in the code. Only five iterations satisfied the states, the value of entropy was considered, and the corresponding standard deviation and correlation were considered for the unit-linking PCNN model.
3.3
Experimental Results with Intersecting Cortical Model (ICM) for Input 1
From Table 3, the values of the entropy, standard deviation, and correlation are obtained in every iteration, and those values satisfied the condition set in the code. Only six iterations helped the states, the importance of entropy was considered, and Table 2 UL-PCNN for input 1
Entropy
Standard deviation
Correlation
0.2089 0.3913 0.5929 0.4501 0.4260 0.4031
0.1785 0.2665 0.3504 0.2920 0.2817 0.2717
0.4290 0.4977 0.4356 0.1873 0.0560 0.3856
Table 3 ICM for input 1
Entropy
Standard deviation
Correlation
0.1693 0.1693 0.2911 0.6609 0.8265 0.7783
0.1565 0.1565 0.2202 0.3769 0.4386 0.4209
0.1552 0.1552 0.5159 0.755 0.6536 0.3445
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the corresponding standard deviation and correlation were considered for the intersecting cortical model.
3.4
Experimental Results for Basic PCNN Model for Input 2
From Table 4, the values of the entropy, standard deviation, and correlation were obtained, and those values satisfied the condition set in the code. Only nine iterations satisfied the states, the importance of entropy was considered, and the corresponding standard deviation and correlation were considered for the basic PCNN model.
3.5
Experimental Results for Intersecting Cortical Model Input 2
From Table 5, the values of the entropy, standard deviation, and correlation were obtained, and those values were that satisfied the condition set in the code. Only six iterations satisfied the states, the value of entropy was considered, and the corresponding standard deviation and correlation were considered for the intersecting cortical model.
Table 4 Basic PCNN for input 2
Entropy
Standard deviation
Correlation
0.1443 0.8531 0.9653 0.7915 0.7072 0.1309
0.1417 0.4481 0.4879 0.4258 0.3945 0.1335
0.4626 0.7638 0.5659 0.5580 0.5825 0.1629
Table 5 ICM for input 2
Entropy
Standard deviation
Correlation
0.2044 0.2044 0.1788 0.3610 0.6233 0.9132
0.1761 0.1761 0.1619 0.2529 0.3623 0.4696
0.2408 0.2408 0.5154 0.6601 0.5575 0.4586
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Experimental Results for Unit-Linking PCNN Model for Input 2
From Table 6, the values of the entropy, standard deviation, and correlation were obtained, and those values satisfied the condition set in the code. Only five iterations satisfied the states, the importance of entropy was considered, and the corresponding standard deviation and correlation were considered for unit-linking PCNN model.
3.7
Experimental Results for Basic PCNN Model for Input 3
From Table 7, the values of the entropy, standard deviation, and correlation were obtained, and those values were that satisfied the condition set in the code. Only nine iterations satisfied the conditions, the importance of entropy was considered, and the corresponding standard deviation and correlation were considered for the basic PCNN model.
3.8
Experimental Results for Intersecting Cortical Model for Input 3
From Table 8, the values of the entropy, standard deviation, and correlation were obtained, and those values were that satisfied the condition set in the code. Only Table 6 UL-PCNN for input 2
Entropy
Standard deviation
Correlation
0.1291 0.1962 0.3058 0.5948 0.7114 0.2044
0.1324 0.1716 0.2272 0.3512 0.3961 0.1761
0.4359 0.4311 0.3531 0.3263 0.1580 0.2408
Table 7 Basic PCNN for input 3
Entropy
Standard deviation
Correlation
0.0514 0.3610 0.8924 0.7471 0.9541 0.9470
0.0759 0.2529 0.4622 0.4168 0.4840 0.4815
0.2814 0.5662 0.6118 0.4831 0.7551 0.6621
Implementation and Performance Analysis of Various Models … Table 8 ICM for input 3
Table 9 UL-PCNN for input 3
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Entropy
Standard deviation
Correlation
0.7461 0.7461 0.0986 0.1163 0.3462 0.4687
0.4090 0.4090 0.1123 0.1241 0.2462 0.2999
0.5207 0.5207 0.3818 0.3215 0.5812 0.5785
Entropy
Standard deviation
Correlation
0.0056 0.0319 0.800 0.0982 0.1640 0.1420
0.0211 0.0573 0.0989 0.1120 0.1534 0.1521
0.0948 0.2160 0.3125 0.2849 0.3122 0.2112
seven iterations satisfied the states, the importance of entropy was considered, and the corresponding standard deviation and correlation were considered for the intersecting cortical model.
3.9
Experimental Results for Unit-Linking PCNN Model for Input 3
From Table 9, the values of the entropy, standard deviation, and correlation were obtained, and those values were that satisfied the condition set in the code. Only five iterations satisfied the conditions, the value of entropy was considered, and the corresponding standard deviation and correlation were considered for unit-linking PCNN model.
4 Analyzing the Best-Segmented Values for Three Kinds of Input Images 4.1
Basic PCNN Model
From Table 10, it gives us the values of entropy, standard deviation, and correlation for all the four input images used for the image segmentation. The medical scan images contained tumors in them that were segmented and shown. So, from that, the segmented image, the image with the maximum entropy, was chosen. The
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Table 10 Best-segmented output selection standard for Basic PCNN
Images
Entropy
Standard deviation
Correlation
Input 1 Input 2 Input 3
0.9985 0.9653 0.9985
0.4995 0.4879 0.4996
0.8082 0.5659 0.8245
corresponding standard deviation and correlation were noted. These segmented images give us the best result that can be used for the further process after image segmentation as an input in any processes.
4.2
Intersecting Cortical Model
Table 11 gives us the values of entropy, standard deviation, and correlation for all the four input images used for the image segmentation. The medical scan images contained tumors in them that were segmented and shown. The segmented image, the maximum entropy image, was chosen, and the corresponding standard deviation and correlation were noted. These segmented images give us the best result that can be used for the further process after image segmentation as an input in any process.
4.3
Unit-Linking PCNN Model
From Table 12, it gives us the values of entropy, standard deviation, and correlation for all the four input images used for the image segmentation. The medical scan images contained tumors in them that were segmented and shown. So, from that, the segmented image, the image with the maximum entropy, was chosen. The corresponding standard deviation and correlation were noted. These segmented images give us the best result that can be used for the further process after image segmentation as an input in any process (Fig. 4). Table 11 Best-segmented output selection standard for ICM
Images
Entropy
Standard deviation
Correlation
Input 1 Input 2 Input 3
0.5929 0.9132 0.8025
0.3504 0.4696 0.4298
0.4356 0.4586 0.5164
Table 12 Best-segmented output for selection standard for ULPCNN
Images
Entropy
Standard deviation
Correlation
Input 1 Input 2 Input 3
0.8265 0.7114 0.1640
0.4386 0.3961 0.1534
0.6536 0.1580 0.3122
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Fig. 4 Classification results of various models
5 Conclusion This study first describes standard PCNN models and other important models. The iterative segmentation approach determines the better-segmented image. The performance analysis of various models of PCNN is carried out with the help of MATLAB. The output image with the highest entropy value is selected as the final segmentation result from the obtained results. The corresponding standard deviation and correlation values are also calculated for the output. The two models intersecting cortical model and the unit-linking PCNN models give the results better than the spiking cortical model and other simplified models of PCNN. The results of PCNN are considered as the best-segmented images as it gives the proper output in lesser iteration with the highest entropy, and corresponding standard deviation and correlation are calculated.
References 1. Eckhorn R, Reitboeck HJ, Arndt MT, Dicke P (1990) Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Comput 2(3):293–307 2. Johnson JL, Padgett ML (1999) PCNN models and applications. IEEE Trans Neural Networks 10(3):480–498 3. Thyagharajan KK, Kalaiarasi G (2018) Pulse coupled neural network based near-duplicate detection of images (PCNN–NDD). Adv Electr Comput Eng 18(3):87–96 4. Kuntimad G, Ranganath HS (1999) Perfect image segmentation using pulse coupled neural networks. IEEE Trans Neural Networks 10(3):591–598 5. Gu XD, Guo SD, Yu DH (2002) A new approach for automated image segmentation based on unit-linking PCNN. In: Proceedings of the international conference on machine learning and cybernetics, November 2002, vol 1. IEEE, pp 175–178 6. Stewart RD, Fermin I, Opper M (2002) Region growing with pulse-coupled neural networks: an alternative to seeded region growing. IEEE Trans Neural Networks 13(6):1557–1562 7. Yonekawa M, Kurokawa H (2009) An automatic parameter adjustment method of pulse coupled neural network for image segmentation. In: International conference on artificial neural networks, September 2009. Springer, Berlin, Heidelberg, pp 834–843 8. Berg H, Olsson R, Lindblad T, Chilo J (2008) Automatic design of pulse coupled neurons for image segmentation. Neurocomputing 71(10–12):1980–1993 9. Ma Y, Qi CL (2006) Study of automated PCNN system based on genetic algorithm. J Syst Simul 18(3):722–725
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10. Bi Y, Qiu T, Li X, Guo Y (2004) Automatic image segmentation based on a simplified pulse coupled neural network. In: International symposium on neural networks, August 2004. Springer, Berlin, Heidelberg, pp 405–410 11. Broussard RP, Rogers SK, Oxley ME, Tarr GL (1999) Physiologically motivated image fusion for object detection using a pulse coupled neural network. IEEE Trans Neural Networks 10(3):554–563 12. Zhang J, Hu J (2008) Image segmentation based on 2D Otsu method with histogram analysis. In: International conference on computer science and software engineering, December 2008, vol 6. IEEE, pp 105–108 13. Li J, Li X, Yang B, Sun X (2014) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518 14. Karvonen JA (2004) Baltic sea ice SAR segmentation and classification using modified pulse-coupled neural networks. IEEE Trans Geosci Remote Sens 42(7):1566–1574 15. Kavitha S, Thyagharajan KK (2017) Efficient DWT-based fusion techniques using genetic algorithm for optimal parameter estimation. Soft Comput 21(12):3307–3316 16. Kinser JM, Wyman CL, Kerstiens BL (1998) Spiral image fusion: a 30 parallel channel case. Opt Eng 37(2):492–498 17. Kurokawa H, Kaneko S, Yonekawa M (2008) A color image segmentation using inhibitory connected pulse coupled neural network. In: International conference on neural information processing, November 2008. Springer, Berlin, Heidelberg, pp 776–783 18. Mureşan RC (2003) Pattern recognition using pulse-coupled neural networks and discrete Fourier transforms. Neurocomputing 51:487–493 19. Doss S, Paranthaman J, Gopalakrishnan S, Duraisamy A, Pal S, Duraisamy B, Le DN (2021) Memetic optimization with cryptographic encryption for secure medical data transmission in IoT-based distributed systems. CMC-Computers Mater Continua 66(2):1577–1594
Combined Minimum Spanning Tree and Particle Swarm Optimization for the Design of the Cable Layout in Offshore Wind Farms Chakib El Mokhi
and Adnane Addaim
Abstract Wind energy is gaining more and more interest as a sustainable energy source in the Kingdom of Morocco. So far, all investments in this type of energy source have been focused on onshore applications. However, Morocco is characterized by two sea fronts that extend over a length of 3; 500 km across the Atlantic and the Mediterranean. This is due to the high investment costs and the complicated construction of the offshore wind farm at sea. Only the investment cost for the electrical infrastructure and the offshore substation within a wind farm can reach 15–30% of the entire project’s cumulative investment cost and is considered extremely expensive. To make the offshore wind farms competitive with the onshore ones in terms of investment costs, we have developed an algorithm based on clustering, minimum spanning tree and particle swarm optimization, which significantly reduces the total cable length in the design phase of the project and consequently minimizes the associated power losses. This paper will present and discuss the optimization approach results on randomly generated wind turbines in an offshore wind farm.
Keywords Wind farm cable layout Branched collector topology Metaheuristic optimization Particle swarm optimization Minimum spanning tree
1 Introduction The available primary energy sources are perceived as unsustainable and are expected to be used up in a possible future because of limited natural resources. Additionally, conventional forms of energy sources like fossil fuels are leading to C. El Mokhi (&) ENSA, Ibn Tofail University, Kenitra, Morocco e-mail: [email protected] A. Addaim EMI, Mohamed V University, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_12
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climate change due to their high level of usage by both industry and society, which is caused by global warming. For this reason, many researchers are competing to provide alternative solutions to conventional sources of energy, such as wind energy and solar energy, as a replacement for traditional forms of energy sources. Particularly, wind energy is favored as an attractive renewable energy source because of its advantages in cleanliness, safety and high yield. Therefore, wind-based energy adoption as a zero-carbon renewable energy option has significantly grown in recent decades. Worldwide, the total capacity of wind energy has increased by a factor of 98:5 and reached 591 GW in 2018 compared to 6 GW in 1996 (Fig. 1). Onshore wind farms commonly cover, in fact, large areas of land. For example, the required land area for a single 3:6 MW onshore wind turbine (WT) maybe around 0:37 km2 , which would mean that 54 turbines will cover roughly 20 km2 [1, 2]. Consequently, the energy companies are switching to offshore wind farms as an excellent opportunity to escape land leasing costs, which typically account for 10– 18% of the associated operation and maintenance expenditure required for an onshore wind farm (WF) project [1, 2]. Besides these specific benefits, offshore wind farms can provide a higher energy yield thanks to the prevailing breezes at sea that could be of high intensity for the whole day, allowing the wind turbines to produce higher amounts of energy. Nevertheless, a challenge in the installation projects of wind farms comes from the heavyweight associated with the WT's nacelle, which usually contained the generator, the step-up transformer on the grid side and the converter as well as the equipment for control and monitoring. For instance, the typical nacelle’s weight of the 5 MW WT is roughly 300 t, and on the other hand, the rotor unit weighs approximately 120 t. Roughly around 20% of the overall capital costs, in average, could be related to the installation of each offshore wind farm, a figure which is regarded to be exceedingly high [1, 2]. Table 1 shows the potential breakdown of the initial capital expenditure for an offshore wind farm (OWF) [3]. The purchase of wind turbines represents a significant part of the Fig. 1 Global cumulative onshore and offshore capacity
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Table 1 Standard initial cost allocation of an OWF [3] Purchase of offshore WTs Substation and electrical infrastructure Installation of inner electrical distribution Offshore substation Network connection Foundations Component transport and installation Other Overall WT cost
30 − 50 15 − 30 2−5 2−4 15 − 18 15 − 25 0 − 30 8 1800 − 2650
investment costs, as recognized. The costs for the foundations and the electrical infrastructure as well represent an important factor. Nevertheless, the respective percentage of the individual concepts’ total cost may change depending on the wind farm characteristics (WF). In general, the design and the building of an offshore wind farm is in fact a complicated process consisting of many stages. The process involves the accurate selection of a location with the adequate wind performance spectrum. However, for the planning of an OWF, wind weather records are not sufficient. Additionally, to ensure the technical and the economic viability of the offshore project, it is important to consider the optimal placement of each WT, to choose the optimal placement of the required transformer substation as well as to optimize the power network interconnecting the turbines with each other and with the substation, allowing the energy generated to be collected. Offshore wind farms are distinctly more expensive to install than onshore wind farms with an identical capacity. However, offshore wind energy has the advantages of higher wind speeds when compared to onshore installations and regular wind speeds for more stable and continuous power production [1, 2]. Also, offshore wind farms do not have any visual impact on the landscape, as they are invisible from a distance of around ten kilometers from the coast. Regarding these advantages, it is worthwhile in Morocco to switch to offshore wind farms instead of onshore type, particularly that Morocco has two seafronts stretching over a length of 3; 500 km across the Atlantic Ocean and the Mediterranean Sea, which means a potential capacity of 135 GW offshore wind farms that can be built. This figure means as well that approximately 130 million households can be supplied with electricity. The population of Morocco is about 35 million, corresponding to 17 million homes. This means that the large amount of energy produced by offshore wind farms can be exported to Europe and Africa since Morocco is located geographically in North Africa close to Europe. To make offshore wind farms more competitive with onshore wind farms from the viewpoint of investment cost, this paper proposes an algorithm based on
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Fig. 2 Typical architecture of an offshore wind farm
clustering with k-means, minimum spanning tree (MST) in the graph theory and particle swarm optimization (PSO) that significantly minimizes the overall cable length of the internal system for power collection already in the design phase of the project which consequently leads to the minimization of the associated power losses. Figure 2 shows the typical architecture of an offshore wind farm, which includes • • • •
wind turbines, which can currently generate up to 8 MW per unit, an offshore platform for the energy conversion (substation), an export cable from the offshore substation to the mainland station, connections between various wind turbines (feeders) and the export cable from the internal power collection network.
2 The Methodology of the Optimization Approach 2.1
Minimum Spanning Tree
The graph theory defines the spanning tree as a subgraph of an undirected graph that comprises all vertices in this graph and allows only one path or edge between every two vertices. However, the spanning tree is not unique for a given map, which signifies several possible ways to formulate a tree graph that interconnects all nodes [4]. Consequently, the minimum spanning tree on this basis can be defined to be a spanning tree with minimal weights and can be expressed in Eq. (1): GT ðV; ET ; W T Þ
GT 2 G; ET 2 E; W T 2 W
ð1Þ
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Fig. 3 Branched topology
Whereas G denotes the undirected weighted graph, and GT indicates the subgraph in G representing one potential spanning tree of G. V denotes the vertices in the graph G. E represents all possible paths or the edges linking V, while E T are all edges connecting V in GT . W denotes the weight of each edge in G, and W T stands for the weight of each edge in GT . For a given graph G, the minimum spanning tree can be described as a GT with minimum total W T . The minimum spanning tree algorithm can be projected onto an offshore wind farm so that the wind turbines can be imagined as vertices and the cables connecting them as edges. The MST allows representing the cable of the power collector system routed like the branched topology shown in Fig. 3.
2.2
Clustering of Wind Turbines with k-means
For two points in a graph connected by an edge in a minimum spanning tree, at least one is the nearest neighbor of the other, meaning that the connections possess a locality property [4]. Therefore, in the partition step, the subsets of vertices or wind turbines are expected to maintain this locality. Since k-means can effectively divide some of the local neighboring set of nodes into the same cluster, with k-means, can the wind turbines within the offshore wind farm be partitioned. The number of clusters can be from the user-determined [4]. The k-means enable the optimization approach based on particle swarm optimization and minimum spanning tree to quickly reach an optimum for the power collection system’s cable routing.
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Particle Swarm Optimization
Particle swarm optimization (PSO) is a famous metaheuristic approach of optimization originally developed by Eberhardt and Kennedy [5]. The PSO is composed of a group of individuals who are influenced by their neighborhood thanks to a simple communicative process. In [6], Eberhardt and Kennedy present a detailed model describing the swarm intelligence using three elementary operations. These operations incorporate adaptation within a population and hence the optimization as an outcome of the evaluation, followed by comparison and then imitation. Specifically, they perceive the swarm in two main modules; the “low-level” part is describing the interactional comportment and communication-related procedures between the parties, followed by the ensuing “high-level” part describing the rather complex arrangements and rules, leading in this case to the resulting strategy of emergence in helping to find an optimum. The evaluation process is one of the absolutely necessary conditions for any purposeful action because it will be impossible to move forward in the sense of improvement without the option to assess the current state. Meanwhile, the second key operation is the comparison. In this way, highly sophisticated organisms are capable of comparing their actual state with their historical states. Still, they can use other individuals’ existing knowledge through communication and then compare these to their own. The other crucial operation is imitation, meaning that beings can extract decisions from two previously mentioned operations through imitating “good behavior”. PSO applies all those mentioned above three different operations to a given particle swarm in such a way as to exploit the subsequent emergence force for the optimization. By treating the preexisting swarm like a naturally occurring fish or birds swarm which is moving at specific velocities, then each particle is modeled by its own position inside the available search area and its velocity. All of them constantly readjust their positions, likewise velocities, by matching their routes concerning the best available places, the best-positioned particle within the swarm and its present position. Effectively, each individual within the swarm is influenced by other particles' experiences within the same swarm. Equation (2) describes the part of any given particle throughout a single iteration concerning its preceding part [5, 7–9]: pi ¼ pi1 þ vi
ð2Þ
where vi is the velocity of the particle, which can be described by Eq. (3) [1, 5, 8]: vi ¼ K 1 vi þ 1 þ K 2 ðpb pi1 Þ þ K 3 ðgb pi 1Þ þ K 4 e
ð3Þ
where K 1 , K 2 , K 3 and K 4 are specific coefficients characterizing the weighting of the individual entries that are defined by customizing the PSO to the given optimization problematic, pb is the most relevant particle’s best position, gb is the
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Fig. 4 Flowchart of the optimization approach
swarm’s best position, and e 2 ½0; 1 is a random generated number [5, 7, 8, 10]. The optimization method using the k-means clustering, the MST and the PSO is shown in Fig. 4.
3 Optimization of the Internal Power Collection Network 3.1
First Case Study
For this case, a set of 37 randomly generated wind turbines are considered on the surface of 20 km2 . The wind turbines are of type Vestas 80–2 MW with a rated capacity of 2 MW, while the rotor diameter is 80 m. Therefore, a minimum distance between each turbine of 560 m is to be respected, and this is equivalent to 7D, where D is the rotor diameter of the offshore wind turbine. In general, at least 7D is considered to avoid the wake effect, which is the turbulence behind an upstream wind turbine that causes a weakening of the wind velocity incoming to the downstream wind turbine. Regarding the substation, the proposed location in [1, 7] was chosen. In this study case, the clustering with k-means was set to 5, which means that the total number of wind turbines is partitioned into five clusters, which means there are five cable branches to be optimized in the power collection network. Each cable
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Fig. 5 Optimized cable routing of an OWF with 37 WTs
Fig. 6 Convergence curve of the optimization approach
branch gets its distinctive color corresponding to the cluster to which it belongs, as shown in Fig. 5. The total length of the cable within the power collection grid is after using the optimization approach around 55:4 km. The initial length was approximately 107 km; as shown in the convergence curve in Fig. 6. The reduction in the cable length, and therefore, economic saving is 48:2%. The optimal cable length was reached already after the 50th iteration. The total computing time for 250 iterations is 30 s.
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Fig. 7 Optimized cable routing of an OWF with 17 WTs
3.2
Second Case Study
In the second case study, 17 randomly generated offshore wind turbines were placed in the same area as in the first case. However, these turbines are supposed to be the Haliade 150 6 MW of the GE company, with a rated power output of 6 MW, while the rotor is 150 m in diameter. This corresponds to a minimum distance between each turbine of 1,050 m to avoid the wake effect. As shown in Fig. 7, the k-means clustering was set to four clusters to get four cable branches in the power collection network. The resulting total cable length for the internal network is 40:3 km, while the original cable length for the generated initial population was approximately 41:3 km. The total saving in cable length was 1 km, corresponding to 2:4%. The optimal cable routing was obtained near the 100th iteration. The computing time for Fig. 8 Convergence curve of the optimization approach
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250 iterations of the optimization approach based on the combination of k-means clustering, minimum spanning tree, and the particle swarm optimization is as well 30 s. In this case study, the result for the cable savings was small, and this can be explained by the lower number of offshore wind turbines used within the offshore wind farm. Thus, the higher the number of turbines, the more the algorithm can optimize the cable length (Fig. 8).
4 Conclusions and Future Work The article focuses on developing an optimization algorithm for the cable routing of the internal power collector network within offshore wind farms using a combination involving k-means clustering, minimum spanning tree, and particle swarm optimization. The power grid incorporates feeders connecting the offshore wind turbines and export cable, which transmits the generated energy to the offshore transformer substation. To validate the proposed optimization methodology’s reliable performance, the algorithm was evaluated on two arbitrarily created offshore wind farms. In particular, the algorithm is appropriate to get the branched design topology. The obtained results are interesting and satisfying in terms of minimizing the cable total length required for the power collection network, which signifies economic savings in the whole investment cost already in the design phase of the project and a minimization of the power losses induced through the cables during the expected operational lifespan of the offshore wind farm. This work can be extended with an additional algorithm, allowing the selection of the adequate cross-sectional area of the used cables in the power collection system.
References 1. El Mokhi C, Addaim A (2020) Optimization of wind turbine interconnections in an offshore wind farm using metaheuristic algorithms. Sustainability 12(5761):1–24 2. Rabiul Islam Md, Guo Y, Zhu J (2014) A review of offshore wind turbine nacelle: technical challenges, and research and developmental trends. Renew Sustain Energy Rev 33:161–176 3. González JS, Payán MB, Santos JR (2013) A new and efficient method for optimal design of large offshore wind power plants. IEEE Trans Power Syst 28(3):3075–3084 4. Zhong C, Malinen M, Miao D (2015) A fast minimum spanning tree algorithm based on K-means. Inf Sci 295:1–19 5. Kiranyaz S, Ince T, Gabbouj M (eds) (2014) Multidimensional particle swarm optimization for machine learning and pattern recognition. Springer-Verlag, Berlin Heidelberg 6. Kennedy J, Eberhart RC, Shi Y (eds) (2001) Swarm intelligence. Morgan Kaufmann, Burlington NJ
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7. El Mokhi C, Addaim A (2020) Optimal substation location of a wind farm using different metaheuristic algorithms. In: Proceedings of the 6th IEEE International Conference on Optimization and Applications (ICOA2020), 20–21 Apr. Beni Mellal, Morocco, pp 1–6 8. Hachimi H, El Hami A, Ellaia R (2012) A new hybrid genetic algorithm and particle swarm optimization. Key Eng Mater 498:115–125 9. Hachimi H, El Hami A, Ellaia R (2013) A new hybrid genetic algorithm with particle swarm optimization and normal boundary intersection method for generating generation the Pareto frontier. J Autom Syst Eng 1–7 10. Zou L (2021) Design of reactive power optimization control for electromechanical system based on fuzzy particle swarm optimization algorithm. Microprocess Microsyst 82:103865
Biomedical Scan Image Retrieval Using Higher Order Neural Networks K. K. Thyagharajan, T. Vignesh, I. Kiruba Raji, and G. Nirmala
Abstract The automatic medical diagnosis that uses computer tomography or magnetic resonance images or any other medical images requires retrieving most similar images from an image repository of previous patients when an image of a particular patient is given as a query. In the existing methods, the researchers have used local bit plane decoded pattern (LBDP), local diagonal extrema pattern (LDEP), local binary pattern (LBP), and local wavelet pattern (LWP) for indexing and retrieval of biomedical images. In this proposed method, the pulse-coupled neural networks (PCNN) and unit-linking PCNN are used to extract the features. The PCNN is a single-layer two-dimensional neural network of pulse-coupled neurons. Each pixel in the image is feeding input to the corresponding neuron in the 2D network. Thus, the feature extraction is done using basic and UL-PCNN. Then to retrieve similar images, the classification is done using the neural networks. The proposed algorithm is tested for image retrieval using the mammographic image analysis society (MIAS) database and the existing approaches.
Keywords Medical diagnosis Feature information Pulse-coupled neural networks (PCNN) Unit-linking pulse-coupled neural networks (UL-PCNN) Classification
K. K. Thyagharajan Department of ECE, RMD Engineering College, Kavaraipettai, India T. Vignesh (&) Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India I. Kiruba Raji G. Nirmala Department of CSE, RMD Engineering College, Kavaraipettaic, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_13
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1 Introduction The biomedical images play a vital role in the management and diagnosis of cancer. The growth of various types of medical images helps in identifying and classifying cancer at the early stages. Simultaneously, the multiple modalities of images make the analysis or diagnosis more challenging day by day. The ultrasound, X-ray, magnetic resonance imaging (MRI), computer tomography (CT), etc., are the different modalities of medical images which are mainly accessible to the physician. Since the diagnostic accuracy is improved significantly by using these medical images, patients are requested from radiology departments to obtain multi-modal medical images for pathological decision making. Hence, a huge number of medical images accumulate in hospitals every year. So, automatic content-based retrieval methods are needed to compare similar images from these repositories and retrieve images of similar characteristics. The biomedical images should be stored and retrieved with a suitable data structure that allows efficient searching, indexing, and retrieval. Biomedical image processing is an analysis and manipulation of digitalized images, significantly to improve their quality. These images are captured for both diagnostic and therapeutic purposes. The biomedical image processing also includes enhancing and displaying images captured through X-ray, CT, and MRI techniques. Biomedical images of one patient are compared with the images already obtained for other patients or the same patient by doctors to get more information about the disease’s progress that will be extremely useful for making decisions during treatment. If the images’ pathological characteristics are similar, they may get the same disease with a high probability. Similar biomedical images should be automatically retrieved from the image repository to make this possible because manual comparison for retrieval will be time-consuming. The images should also be structured for efficient retrieval, and they should also be supported with an expert system with automatic diagnostics and decision support. Content-based biomedical image indexing and retrieval address these points based on visual features such as color, texture, shape, structure, faces, etc., extracted from the biomedical images. The disorders happening with the patient can also be analyzed by retrieving the related reference reports and these images.
2 Related Work The efficiency and accuracy of this type of content-based retrieval of images and reports are heavily dependent on the features extracted from images. These features are also called visual descriptors and are represented as a vector for each image. These feature vectors are used to index and retrieve images [1, 2]. The effectiveness of this type of content-based image retrieval (CBIR) depends on the feature extraction methods adopted. The existing systems for biomedical image retrieval
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are explained in [3–7]. The literature published in [2] provides an extensive and comprehensive review of content-based image retrieval. Image texture descriptors have widely been used to capture repeated patterns in the images, and it represents the fine details of the image. It is more suitable for biomedical image retrieval and disease identification [8, 9]. The feature vectors are created using the image features such as shape [10], texture [11–13], edges [14, 15], and color histograms [16, 17]. for retrieval and classification systems. The feature extraction methods decide the performance, computation cost, and time complexity of an image retrieval system. Quellec et al. [18] presented a brain image retrieval system using optimized wavelet transform-based features. The local binary pattern (LBP) was used by Ojala et al. [19] for extracting and classifying the textures in an image. In this method, the local features were extracted by taking the central pixel's difference with its neighbors and considering it. The feature extraction is greatly affected by illumination changes, and Dubey et al. presented a compensation mechanism in [20] to make the features robust to illumination change. In [21], CT and MRI image features were extracted using the co-occurrence matrices for biomedical image retrieval. Peng et al. [22] extracted texture features based on uniformity of structure and brightness of the image and used them for chest CT image retrieval. Vignesh et al. used LBP for satellite image classification and found that LBP produced higher classification accuracy than other feature extraction techniques [23]. The main disadvantages of the existing methods are the “curse of dimensionality” when local neighbors increase. It is the driving force to propose a new system for scan image retrieval using PCNN. The different stages involved in image retrieval are feature extraction, feature similarity measure, and classification using neural networks. The feature extraction and classification is done by considering images from mammographic image analysis society (MIAS) database. The algorithm is proposed to test the results with high accuracy and computing speed. The proposed method was tested for retrieving CT images and provided promising performance.
3 Proposed Work In the proposed system, we use basic pulse-coupled neural network (PCNN), and unit-linking pulse-coupled neural network (UL-PCNN) is used to extract the features of the image and the classification is done using neural networks.
3.1
Feature Extraction
The feature extraction is done using a basic pulse-coupled neural network and unit-linking model of pulse-coupled neural network. The PCNN is a two-dimensional neural network with pulse-coupled neurons connected laterally in
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a single layer. Each neuron is connected with an image pixel and also with its neighboring neurons. Since each pixel is associated with a PCNN-neuron, the structure and size of the PCNN depend on the input image that will be processed.
3.2
The PCNN’s Neuron Model.
A neuron of PCNN consists of an input part, a linking part, and a pulse generator. The neuron receives the input signals from the feeding and linking inputs. Figure 1 illustrates the traditional neuron model of PCNN [24]. The receptive area of the neuron covers the neighboring pixels of that particular neuron in the input image. The linking part will also receive inputs from neighboring neurons [25]. The feeding input has a shorter time constant than the linking connection’s time constant. The following mathematical equations represent the basic PCNN model: Fij ðnÞ ¼ Fij ðn 1ÞeaF þ
X
Lij ðnÞ ¼ Lij ðn 1Þ eaL þ VL
Mi;j;k;l Yk;l ðn 1Þ
X
Wi;j;k;l Yk;l ðn 1Þ
ð1Þ ð2Þ
Uij ðnÞ ¼ Fij ðnÞ 1 þ bLij ðnÞ
ð3Þ
Tij ðnÞ ¼ Tij ðn 1Þ þ VT Yi;j ðn 1Þ
ð4Þ
Yij ðnÞ ¼ 1 ¼ 0;
Fig. 1 Neuron model of basic PCNN
if Uij ðnÞ [ Tij ðnÞ else
ð5Þ
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Equation 1 describes the feeding field input with a feedback subsystem, where Sij is the (i, j)th element in the input matrix of stimulus signals. Usually, this is the gray value of the image pixel at (i, j) position. Fij(n) is the feedback input to the neuron (i, j) at the nth iteration and Yij(n) is the corresponding output of the neuron. Equation 2 represents the linking subsystem, where Lij(n) is the linking field. Equation 3 describes the modulating subsystem, and Uij(n) is the internal activation value at the nth iteration. Equation 4 describes the dynamic threshold of Tij(n). Equation 5 represents the firing mechanism, where n is the iteration number. M and W is weight matrices for feeding and linking fields, aL, aT, and aF are the attenuation coefficients. VF, VT, and VL are range constant of Fij(n), Lij(n), and Tij(n), respectively, and b is the linking coefficient. A 2D array of PCNN neurons processes the 2D array of pixels of the input image. The gray value of the pixel at location (i, j) is denoted by Sij, and it is used as a stimulus to the neuron at (i, j) location. The input pixels are normalized in the range 0–1 before they are fed to neurons. In the first iteration, the internal activation Uij(n) is compared with the threshold Tij(n). If Uij(n) > Tij(n), the neuron will be fired, and the pulse generator will generate an impulse. After that, the threshold Tij(n) will be increased quickly by the feedback such that Uij(n) Tij(n), and the impulse generator will stop generating impulse. The output of the neuron will be 0. Now, the threshold Tij(n) will start to decrease again exponentially with a decay rate of aT; when Uij(n) > Tij(n), the neuron will also be fired, the output will be 1. This procedure continues, and depending on the number of iterations, a train of impulses will be generated. When a neuron is fired and generates an inspiration, the pulse signal is also transmitted to the adjacent neurons. Similarly, when a neuron flames out with the threshold increase, the adjacent one will flame out quickly. Thus, activation of a neuron may cause adjacent neurons with similar fire characteristics and form clusters of similar neurons. This corresponds to an object or area with similar characters in the image.
3.3
UL-PCNN Model
The unit-linking PCNN model is an improved basic PCNN model structure, and it is less complicated. The improvement is made on the linking channel Lij(n) and the threshold Tij(n) of PCNN. In the basic PCNN, the linking Lij(n) and feeding fields Fij(n) are computation intensive. Unit-linking is introduced to solve this problem, and the feeding field is stimulated only by the pixel value. This means that Lij(n) will be 1 if at least one neighboring neuron gets fired. Otherwise, Lij(n) will be 0. In UL-PCNN, a fired neuron will capture neurons with similar characteristics in the neighborhood. Figure 2 illustrates the UL-PCNN model.
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Fig. 2 UL-PCNN model
The UL-PCNN model is described by following equations: Fij ðnÞ ¼ Sij Lij ðnÞ ¼ 1 ¼0
ð6Þ
if neighbouring neuron fires else
ð7Þ
Uij ðnÞ ¼ Fij 1 þ bLij
ð8Þ
Tij ðnÞ ¼ Tij ðn 1Þ þ VT Yij ðn 1Þ
ð9Þ
Yij ðnÞ ¼ step Uij Tij ¼ 1; ¼ 0;
if Uij [ Tij else
ð10Þ
The mathematical model of the unit-linking PCNN model (UL-PCNN) was presented above and the algorithm for this network is given below. Algorithm Step 1: Get the input image and convert the image to double, and read the image. Step 2: Calculate the size of the image and the mean value of pixels. Step 3: Initialize the feeding input Fij(n), linking input Lij(n), and the weight matrices. Step 4: Update the output pixel and compute internal activation values Uij(n). Step 5: Update the threshold input values Tij(n). Step 6: Calculate the parameters like aF, aL, aT, VF, VL, b. Step 7: Declare a nested for loop • • • •
Initialize weight sums to zero. Initialize the variables to determine rows and columns. Calculations are done for Fij(n), Lij(n), Uij(n), Tij(n). Check whether (Uij(n) > = Tij(n))
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If true Yij = 1 Else Yij = 0 Step 8: Initialize (Z) and calculate the sum of the binary values. Step 9: Determine the number of 1’s by different iterations and end the function.
4 Experimentation and Results The experimental results and the datasets used are presented in this section. Several experiments were carried out to retrieve the most similar image against a query image with high retrieval precision. Here, we use the mammographic image analysis society (MIAS) database. The mammographic image analysis society (MIAS) database contains breast cancer images. 322 samples are obtained with mammograms containing microcalcifications from the mammographic image analysis society (MIAS). This database has images of three characteristics of background tissue such as F-Fatty, G-Fatty glandular, D-Dense glandular, seven classes of abnormality, and two types of the severity of the abnormality. Figure 3 illustrates the various tissue types. The images in the MIAS database are used for feature extraction and classification. Based on this image classification, we can retrieve similar images. The images in MIAS database are classified into three types glandular (G), dense glandular (D), and fatty (F). The result was obtained by giving the images as input and extracting the features from the image. For every image, ten iterations are performed for extracting ten features of the image as shown below. Figures 4, 5 and 6 illustrate the classification value of the respective images. We characterize the MIAS database image patterns and analyze the performance based on the classification using neural networks. We use each image in the database as a query image and retrieve n images x = (x1, x2, … xn) from the database using neural networks. If n belongs to the same category of the query
(a) Fatty
Fig. 3 Various tissue types
(b) Fatty-glandular
(c) Dense-glandular
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mdb002.tiff
OUTPUT 0.3340654373 0.03654098511 0.1504192352 0.09730243683 0.1387615204 0.1415920258 0.1124706268 0.109246254 0.132484436 0.1141881943
Fig. 4 Fatty glandular image classification value
INPUT
mdb003.tiff
OUTPUT 0.3091812134 0.02190971375 0.1486778259 0.126534462 0.1308107376 0.1142272949 0.1402177811 0.1378288269 0.1018943787 0.1449022293
Fig. 5 Dense glandular image classification value
INPUT
mdb010.tiff
OUTPUT
0.2899084091 0.02423763275 0.1902685165 0.07181549072 0.0787858963 0.1651678085 0.1038169861 0.1328983307 0.1072273254 0.07681941986
Fig. 6 Fatty image classification value
image, then the system has correctly retrieved the desire. The classification is done based on three classes: Fatty, fatty glandular, and dense glandular. If an image pertains to fatty class (F), then the result will be 1. If it belongs to fatty glandular (G) class, then the result will be 2, and if the image is related to dense glandular (D) class, then the result will be 3. Based on this classification, we can determine the query image belongs to which class, and hence we can retrieve similar images.
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Through this method, we got good precision and recall. Precision = (Number of images retrieved correctly)/(Total number of images retrieved), Recall = (Number of images retrieved correctly)/(Total number of relevant images in the database). We can determine the average retrieval rate (ARR) and average retrieval precision (ARP) through precision and recall. The higher value of ARP, ARR represents better retrieval performance and vice-versa.
5 Conclusion This paper has presented a novel technique to retrieve similar images against a query image. We have used basic PCNN and UL-PCNN to extract features of the images. Neural networks are used to classify the images; based on this classification, we can retrieve similar images. The proposed system was developed using MATLAB software and then evaluated and tested on the MIAS database. Experimental results proved that the proposed method provides higher accuracy than existing methods with 89.8% accuracy. The ARR and ARP are good and computing speed is also high. Thus, this method provides comparable results with the existing methods. This method's future scope is it can be considered an essential requirement for online applications and satellite image classification.
References 1. Smeulders AW, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380 2. Liu Y, Zhang D, Lu G, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282 3. Akakin HC, Gurcan MN (2012) Content-based microscopic image retrieval system for multi-image queries. IEEE Trans Inf Technol Biomed 16(4):758–769 4. Xu X, Lee DJ, Antani S, Long LR (2008) A spine X-ray image retrieval system using partial shape matching. IEEE Trans Inf Technol Biomed 12(1):100–108 5. Scott G, Shyu CR (2007) Knowledge-driven multidimensional indexing structure for biomedical media database retrieval. IEEE Trans Inf Technol Biomed 11(3):320–331 6. Quddus A, Basir O (2012) Semantic image retrieval in magnetic resonance brain volumes. IEEE Trans Inf Technol Biomed 16(3):348–355 7. Zheng L, Wetzel AW, Gilbertson J, Becich MJ (2003) Design and analysis of a content-based pathology image retrieval system. IEEE Trans Inf Technol Biomed 7(4):249–255 8. Zakeri FS, Behnam H, Ahmadinejad N (2012) Classification of benign and malignant breast masses based on shape and texture features in sonography images. J Med Syst 36(3):1621– 1627 9. Traina AJ, Castanon CA, Traina C (2003) Multiwavemed: a system for medical image retrieval through wavelets transformations. In: Proceedings of 16th IEEE symposium computer-based medical systems, June 2003. IEEE, pp 150–155 10. Larsen ABL, Vestergaard JS, Larsen R (2014) HEp-2 cell classification using shape index histograms with donut-shaped spatial pooling. IEEE Trans Med Imaging 33(7):1573–1580
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11. Murala S, Wu QJ (2013) Local ternary co-occurrence patterns: a new feature descriptor for MRI and CT image retrieval. Neurocomputing 119:399–412 12. Murala S, Wu QJ (2013) Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE J Biomed Health Inform 18(3):929–938 13. Dubey SR, Singh SK, Singh RK (2014) Rotation and illumination invariant interleaved intensity order-based local descriptor. IEEE Trans Image Process 23(12):5323–5333 14. Rahman MM, Bhattacharya P, Desai BC (2007) A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback. IEEE Trans Inf Technol Biomed 11(1):58–69 15. Rahmani R, Goldman SA, Zhang H, Krettek J, Fritts JE (2005) Localized content based image retrieval. In: Proceedings of the 7th ACM SIGMM international workshop on multimedia information retrieval, November 2005, pp 227–236 16. Vu K, Hua KA, Tavanapong W (2003) Image retrieval based on regions of interest. IEEE Trans Knowl Data Eng 15(4):1045–1049 17. Konstantinidis K, Gasteratos A, Andreadis I (2005) Image retrieval based on fuzzy color histogram processing. Optics Commun 248(4–6):375–386 18. Quellec G, Lamard M, Cazuguel G, Cochener B, Roux C (2010) Wavelet optimization for content-based image retrieval in medical databases. Med Image Anal 14(2):227–241 19. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987 20. Dubey SR, Singh SK, Singh RK (2015) A multi-channel based illumination compensation mechanism for brightness invariant image retrieval. Multimedia Tools Appl 74(24):11223– 11253 21. Felipe JC, Traina AJ, Traina C (2003) Retrieval by content of medical images using texture for tissue identification. In: Proceedings of 16th IEEE symposium computer-based medical systems, June 2003. IEEE, pp 175–180 22. Peng SH, Kim DH, Lee SL, Lim MK (2010) Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images. Comput Biol Med 40(11–12):931–942 23. Vigneshl T, Thyagharajan KK (2014) Local binary pattern texture feature for satellite imagery classification. In: International conference on science engineering and management research (ICSEMR), November 2014. IEEE, pp 1–6 24. Vignesh T, Thyagharajan KK, Murugan D. Land use and land cover classification using CIELAB color space, PCNN and SOM 25. Deng X, Yan C, Ma Y (2019) PCNN mechanism and its parameter settings. IEEE Trans Neural Netw Learn Syst 31(2):488–501
Soil Category Classification Using Convolutional Neural Network Long Short Wide Memory Method K. Anandan, R. Shankar, and S. Duraisamy
Abstract Classification of soil reliant on its series is one of the troublesome tasks among farmers. Accurate characterization and conspicuous confirmation of soil would provoke a proper use of farmer land and reasonable respect to developing. Manual classification is repetitive, and time-consuming is to be done by farmers. This paper presents another strategy for impelling the classification soil series using deep learning methods. We develop long short-term wide memory (LSTM) and convolutional neural network (CNN). This approach presented a classification task that evaluated the proposed method’s introduction and differentiated them with another classifier. To implement this method, we used the information in an openly accessible LUCAS topsoil dataset. The proposed classification method gave high proficient accuracy. Keywords Soil
CNN Classification LSTM
1 Introduction Soil is a significant average asset. The fast securing of soil property content and spatial appropriation is of incredible worth and noteworthiness to farming and worldwide change. Nonetheless, the soil test assortment devours many costs, so soil supplement content forecast has become an exciting soil research issue. Obvious light close to infrared (Vis–NIR) spectroscopy examination, with its unique points of interest, for example, fast location, non-ruinous, non-dirtying, and ongoing identification, has broad exploration and application establishments in soil supplement content expectation. In any case, the phantom information is defenseless to impedance from stray light, clamor, pattern float, and different elements, which influence the displaying impact [1–3].
K. Anandan (&) R. Shankar S. Duraisamy Department of Computer Science, Chikkanna Government Arts College, Tiruppur, Tamilnadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_14
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Consequently, it is important to preprocess the unearthly information prior to demonstrating to improve the model’s prescient capacity and vigor. Because of the unpredictable attributes of otherworldly information, albeit conventional numerical demonstrating strategies can play out a specific level of examination and forecast, it is more precise, and more all-inclusive expectation measure faces technological bottlenecks. With AI advancement, numerous new ghastly model relapse expectation calculations have been consistently proposed and applied. Notwithstanding contrasted and customary numerical demonstrating and AI strategies, neural network models have higher computational productivity and more grounded displaying abilities and can freely extricate robust component structures from complex ghostly information for learning. LeCun et al. implemented the deep learning computational model for a large dataset [4]. We made extensive use of the Land Use Cover Area Frame Statistical Survey Soil dataset, which was generously given. This incorporates hyper-spooky data with data from soil surface assessments across Europe. We look at the introduction of a couple of CNN models for representing soil surfaces using this dataset. The following are our main responsibilities: • • • •
Pre-planning of the uninhibitedly available dataset. A difference in these techniques is used to manage the order task. A new development and use of three own CNN methods. The comprehensive appraisal of every system.
2 Related Work Customary AI strategies are fruitful in distinguishing impedance and yet have downsides since the traditional AI framework needs to make test attributes falsely. Their prosperity relies upon their consistency. Analysts have executed profound learning procedures to take care of this issue [5]. They implemented profound organization trust in interruption ID and performed in a way that is better than numerous regular AI [6, 7]. In the interruption discovery field, a predetermined number of explorations contemplates have researched profound learning; however, none of them have effectively misused the full force of profound learning procedures [6]. Among the various ways to deal with profound learning, the convolutional neural network (CNN) performed essentially in PC vision, for example, faces and item acknowledgment. The AI algorithms used have been trained on a particular dataset. For hyperspectral picture order, Zhao et al. (2017) recommend using pre-prepared organizations, a technique known as exchange learning [8]. In exchange learning, it is accepted that the prepared highlights of a neural organization are tantamount between various picture datasets. Subsequently, this methodology is efficient and empowers preparing on more modest datasets. Since the neural organization is usually planned with another dataset already, the above is possible (pre-prepared).
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For a 1D CNN, Liu et al. [9] propose move learning (2018a). Using the LUCAS soil dataset, they use the CNN to estimate the occurrence of mud content in the dirt. We go over this strategy in detail in Sect. 4 and compare it to other techniques we used in our characterization mission. The European Commission has played out the LUCAS soil information assortment workout [10]. The new improvements in computerized soil planning [11] and the latest information assortment exercise of LUCAS soil in 2009 permit assessing soil properties with more refreshed (and definite) input datasets. LUCAS information has been now used to approve heritage maps [12], where the assessment of soil natural carbon at NUTS2 level dependent on LUCAS soil information demonstrated that the current dataset OCTOP [13] indicated a rich undervalue of SOC in Southern Europe while in local and Eastern Europe a net overestimation is noticeable [14]. CNN learns by manipulating the shift in weights according to the goal during training using the backpropagation algorithm. The optimization of an objective function using a backpropagation algorithm is similar to the human brain’s response-based learning. A deep CNN can extract low-, mid-, and high-level features thanks to its multilayered, hierarchical structure. High-level features are created by merging lower and mid-level features (more abstract features). The neocortex in the human brain’s deep and layered learning process, which dynamically learns features from raw data, is similar to CNN’s hierarchical feature extraction capacity. CNN’s ability to extract hierarchical elements is a crucial factor in its performance.
3 Convolutional Neural Network A convolution neural network is a variation of a neural network whose aim is to familiarize the right portrayals of the information’s qualities. There are two basic qualifications between a CNN and an MLP: weight appropriation and gathering. Each CNN layer can comprise a few convolution cores that are utilized to make different component maps. Each contiguous neuron region is connected to a next layer work map neuron. Moreover, all the spatial places of the information sources share the core to make the capacity chart. For different convolution and gathering layers, at least one complete bound layer is utilized for grouping. Because of the utilization of shared loads on CNN, the model can get familiar with the very example that shows up at a particular section area without discrete indicators for every area to prepare. Also, the format can be steady for input interpretation [20]. Grouping layers increments figuring pressure as the number of connections between convolutional layers diminishes [15]. A bunch of n center w = {w1, w2… wn) and their inclinations B = {b1, b2…bn} are converted into input information at each
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CNN layer. Another xk work map is produced by the convolution between the information and each center. The change is depicted by: For each convolutional layer l, xk 1 ¼ rðwk 11 x11 þ bk 11 Þ
ð1Þ
A thin window moves over the contributions all through the CNN learning stage. The inclination and weight esteem through this window can be changed from different information qualities without their place inside the information.
4 Long Short-Term Memory (LSTM) Long-short vast memory is one of the counterfeit recurrent neural network (RNN) models utilized in profound learning. LSTM has input joins, in contrast to ordinary progressing neural networks. It can deal with singular information focuses (e.g., pictures) and whole information streams. LSTM, for instance, alludes to exercises, for example, non-fragmented penmanship acknowledgment, voice acknowledgment, and the recognizable proof of inconsistencies in the network traffic or intrusion detection systems. A LSTM unit’s commonplace comprises the cell, a passage entry, a leave entryway, and a failing to remember entrance. The cell holds esteems for unusual timeframes, and the three entry oversees the progressions of information inside and outside the cell. LSTM networks are appropriate for characterizing, examining, and estimating dependent on time classification information because there might be deferrals of unsure length in a period classification between critical occasions. LSTMs have been created to address the breakdown and vanishing angle issues found in customary RNN readiness. Hole length obtuseness is an advantage of LSTM method over RNN, shrouded markov models, and the other classification techniques in numerous applications. The proposed architecture of CNN-LSTM method-based classification is shown in Fig. 1.
Fig. 1 CNN-LSTM method-based classification
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5 Proposed Work To meet the designing prerequisites of high precision and constant in welding in an Internet observing cycle, a CNN-LSTM calculation was proposed dependent on the traditional profound learning technique. The LSTM network measures the component map extricated by CNN rather than the first succession, as in writing. In this paper, the inspiration for utilizing LSTM was to cleverly combine the element data that CNN has separated instead of extricating the conditions between every person in the grouping. The half and half calculation was planned so that the lines of the component framework removed by CNN are considered as the essential units and put into the LSTM network for an element crossbreed. The calculation has high precision and a brief timeframe. Crossbreed network, CNN, comprises of convolution vector and max-pooling 1D layers as it were. The yield of the max-pooling vector layer is the ensuing long short wide memory layer. yi ¼ cnnðxi Þ
ð2Þ
xi is the underlying info vector to the CNN network with the class name. yi is the yield of the CNN network to be taken care of the following LSTM network xi and the element vector framed from the maximum pooling activity in CNN. It is taken care of to the LSTM to become familiar with the long-range fleeting conditions.
6 Experimental Results The accuracy of the proposed CNN-LSTM model is estimated by setting the convolution network size to 2. The convolution term is set with the pooling layer span by utilizing max-pooling. Utilizing the Adam enhancement calculation over the convolution framework, the pooling calculation plays out the sub-examining to limit the mistake work. We noticed enhancements in precision in the CNN by setting LSTM for the yield layer. Performance Analysis: Accuracy, specificity, and sensitivity are calculated to analyze the system’s performance described in Table 1. These parameters are estimated using true positive, true negative, false positive, and false negative values. The parameters are calculated by Accuracy ¼ ðTP þ TNÞ=ðTP + TN þ FP þ FNÞ
ð3Þ
Specificity ¼ TN=ðTN þ FPÞ
ð4Þ
Sensitivity ¼ TP=ðTP þ FNÞ
ð5Þ
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K. Anandan et al. Algorithm
Accuracy
Precision
Recall
Time period (s)
SVM CNN CNN_LSTM
92 95 97
89 94 96
91 93 95
60 50 40
Fig. 2 Performance analysis of CNN_LSTM method
Fig. 3 Time period comparison of the existing and proposed method
Figure 2 shows the parameter analysis comparison of the existing method (SVM and CNN) and proposed method hybrid of the CNN_LSTM method. The proposed method is very efficient to the existing method. Figure 3 shows the classification of existing SVM and CNN method, and proposed CNN_LSTM method performance analysis of time period is very efficient.
7 Conclusion This paper developed short-term memory based on a convolutional neural network in these approaches for the presented classification task. We evaluate the introduction of the CNN_LSTM approaches and differentiate them from another
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classifier. Information is an openly accessible LUCAS topsoil dataset. The proposed classification method shows highly efficient accuracy, and low time computation is compared to other existing methods. In future use, feature selection and extraction methods or optimization algorithms improved the classification accuracy.
References 1. Azab A, Alazab M, Aiash M (2016) Machine learning based botnet identification traffic. In: IEEE Trustcom/BigDataSE/ISPA, August 2016. IEEE, pp 1788–1794 2. Javaid A, Niyaz Q, Sun W, Alam M, (2016) A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI international conference on bio-inspired information and communications technologies (formerly BIONETICS), May 2016, pp 21–26 3. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484– 489 4. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. (Google Scholar Cross Ref) 5. Raman MG, Somu N, Kirthivasan K, Sriram VS (2017) A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems. Neural Netw 92:89–97 6. Venticinque S, Amato A (2018) Smart sensor and big data securityand resilience. In: Security and resilience in intelligent data-centric systems and communication networks. Academic Press, pp 123–141 7. Peddabachigari S, Abraham A, Grosan C, Thomas J (2007) Modeling intrusion detection system using hybrid intelligent systems. J Netw Comput Appl 30(1):114–132 8. Zhao B, Huang B, Zhong Y (2017) Transfer learning with fully pretrained deep convolution networks for land-use classification. IEEE Geosci Remote Sens Lett 14(9):1436–1440 9. Liu L, Ji M, Buchroithner M (2018) Transfer learning for soil spectroscopy based on convolutional neural networks and its application in soil clay content mapping using hyperspectral imagery. Sensors 18(9):3169 10. Panagos P, Borrelli P, Poesen J, Ballabio C, Lugato E, Meusburger K, Alewell C (2015) The new assessment of soil loss by water erosion in Europe. Environ Sci Policy 54:438–447 11. Lagacherie P, McBratney AB (2006) Spatial soil information systems and spatial soil inference systems: perspectives for digital soil mapping. Dev Soil Sci 31:3–22 12. Panagos P, Ballabio C, Yigini Y, Dunbar MB (2013) Estimating the soil organic carbon content for European NUTS2 regions based on LUCAS data collection. Sci Total Environ 442:235–246 13. Jones RJ, Hiederer R, Rusco E, Montanarella L (2005) Estimating organic carbon in the soils of Europe for policy support. Eur J Soil Sci 56(5):655–671 14. de Brogniez D, Ballabio C, van Wesemael B, Jones RJ, Stevens A, Montanarella L (2014) Topsoil organic carbon map of Europe. In: Soil carbon. Springer, Cham, pp 393–405 15. Mohamad Tahir H, Hasan W, Md Said A, Zakaria NH, Katuk N, Kabir NF, Yahya NI (2015) Hybrid machine learning technique for intrusion detection system
An Empirical Study on Selected Emerging Technologies: Strengths and Challenges Munir Kolapo Yahya-Imam and Murtadho M. Alao
Abstract Emerging technologies are characterized by curiosity, quick development, intelligence, noticeable effect, vulnerability, and vagueness. Therefore, it became necessary to investigate the strengths, potential weaknesses, and research gaps in those technologies. These will contribute to the body of knowledge and give researchers a sense of direction when embarking on a research journey to enhance any technologies. This paper was written in sections: Introduction; Trends of Emerging Technologies; Selected Emerging Technologies; Overview of the Selected Technologies; and Significance of the Selected Technologies in the Society. Lastly, a concise conclusion and a list of relevant references were presented.
Keywords Emerging technologies Smart eye Polymer memory Artificial passenger
3D searching Blockchain
1 Introduction Innovation advancement has gotten the way to severe separation. The speed of progress in innovation keeps on quickening as advancement advances are ceaselessly testing. Arising advancements are innovations whose improvement, logical applications, or both are still commonly covered up. The ultimate objective is that they are figuratively emerging into perceptible quality from establishing nonexistence or absence of definition. They are habitually observed as prepared for M. K. Yahya-Imam (&) Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] M. M. Alao Faculty of Business and Accounting, Lincoln College of Science, Management, and Technology, Abuja, Nigeria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_15
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changing the same old thing. They are portrayed by fanatic interest, for the most part, snappy turn of events, knowledge, noticeable impact, weakness, and vulnerability. In this way, they can be described as an introductory level novel and, for the most part, rapidly creating advancement. They are depicted by a particular degree of clarity proceeding as time goes on and the likelihood of applying a broad impact on the monetary spaces. They are viewed similarly to a few performers, establishments, and associations close by the connected data creation measures. The most obvious impact, in any case, lies later on along these lines; the advancement stage is still somewhat uncertain and sketchy. They fuse various headways, for instance, informational development, information advancement, nanotechnology, biotechnology, scholarly science, psycho technology, mechanical innovation, and human-made thinking. New inventive fields may result from the mechanical blend of different structures progressing toward similar goals. The mix brings late separated advances, such as voice, data, and video, to share resources and help each other, making new efficiencies. They are the specific progressions that address reformist enhancements inside a field for high ground. Hence, this paper presents some selected technologies that highlight their strengths, weaknesses, and improvement areas.
2 Trends of Emerging Technologies Cybersecurity, artificial intelligence, blockchain, cloud computing, and the Internet are the current dominating technologies. These technologies have already entered various spheres of the economy. Evidence of this is provided in Fig. 1.
2.1
Selected Emerging Technologies
For the year 2020, the leading ten arising advances, as indicated by the CompTIA Emerging Technology Community, are AI, 5G, IoT, serverless preparing, biometrics, AR/VR, blockchain, mechanical innovation, NLP, and quantum figuring. For relationship, the 2019 overview was according to the accompanying: IoT, AI, 5G, serverless handling, blockchain, progressed mechanics, biometrics, 3D printing, AR/VR, and robots. The best three AI, 5G, and IoT developments remained the same, just changed solicitation. New to the overview in the year 2020 were NLP and quantum enrolling, superseding 3D printing and robots in the best ten from the year 2019. Table 1 presents the list of selected technologies discussed in this paper and the research where they have been implemented.
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Fig. 1 Some unmistakable arising innovation drifts that make and empower new encounters, utilizing human-made reasoning (AI), and other developments to enable associations to exploit arising computerized environments [1]
Table 1 List of emerging technology Technology
Research work
Smart Eye Technology 3D Searching
[2] Hurst [3], Pearlman [4], Silva et al. [5], Tang et al. [6], Sedmidubsky and Zezula [7] Bodkhe et al. [8], Dai et al. [9] Miao et al. [10], Zhang et al. [11] [12–15]
Blockchain Polymer Memory Artificial Passenger
2.2
Overview of the Selected Technologies
The list presented in Table 1 was gathered based on ranking from CompTIA Emerging Technology Community, Gartner’s Hype Cycle for Emerging Technologies, and author’s findings through research papers.
2.2.1
Smart Eye Technology
Smart eye innovation ensures the protection and security of reports from the second it is open on a gadget screen. It provides continuous control and safety to all devices that the recipient opens. Artificial intelligence happens to be the underlying
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technology for smart eye devices. This technology can be used in medical research and neuroscience to diagnose Alzheimer’s and Parkinson’s diseases. Similarly, the aviation and defense industries can also follow suit by using it for research, development, and educational purposes. The technology utilizes a light source to enlighten the eye. This will cause an outstandingly recognizable reflection and a camera to grab an image of the attention through the reflection. The image got by the camera is then used to perceive the impression of the light source on the cornea and in the understudy. The vector framed by the cornea and understudy reflections joined with other mathematical highlights of the reflections is then used to figure the look course. Close infrared enlightenment is utilized to make the reflection designs on the cornea and understudy of the subject’s eye, and picture sensors are being used to catch pictures of the eyes and the reflection designs. Progressed picture handling calculations and a physiological 3D model of the eye are then used to gage the eye’s situation in space and the purpose of look with high precision. The technical challenges include security threats, instability of the user’s head, and user eyes shapes and sizes. More importantly, hackers may hack the face recognition password and watch the users from the blinking infrared light without their knowledge. There have been many cases filed due to this type of problem, and they remain unsolved in the past. The researcher can design improved engineering, develop and implement enhanced algorithms, and precisely solve these problems. The security system should be improved, as well.
2.2.2
3D Searching
3D searching is an innovation made to help web customers remove 3D models, for instance, complex planning traces and diverse graphical models. The web searcher takes commitment to requests like substance, 2D portrayals, 3D depictions, 3D models, and 2D models. The worker side handles the client's contribution, converts it into different images, and then stores it into the database, as shown in Fig. 2. Among the technical challenges that are facing this technology, they are 3D-shape-based similarity queries. To figure out a proficient method to list 3D-shaped descriptors is trying as 3D-shaped ordering has unwieldy models. 3D inquiry and recovery with multimodal uphold are another significant test in this domain. To make the 3D quest interface straightforward enough for fledgling, a multimodal recovery framework, which can take different sorts of info sources and give powerful inquiry results, is fundamental. In conclusion, analysts ought to consider coordinating shape-based coordination and recovery strategies into intelligent portraying apparatuses.
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Fig. 2 Illustration of how 3D searching technology works
2.2.3
Blockchain
Blockchain can be described as a worldwide appropriated record of monetary exchanges. It becomes an innovation to execute cryptographic money exchanges in the financial marketplace [16–18]. By definition, it alludes to a circulated framework that cryptographically catches and stores a reliable, permanent, and straight occasion log of exchanges between networked actors. Figure 3 illustrates how the technology works. Researchers have shown that it will significantly transform many activities and operations in the industries. These include health, food, finance, government, and supply chain industries [19]. It came with few challenges, such as a lack of technical know-how on the organization’s part and privacy-related issues. The literature’s current commitments do not give massive information about future enterprises’ suggestions [20, 21]. Additionally, many firms, especially supply chain players, are skeptical about the costs and risks of implementing it. Similarly, the adequacy and advantages in sectors other than finance are additionally doubtful [22]. Mthethwa [23] has reported that some enterprises do not comprehend the technology and are reluctant to utilize it concerning its moderate adoption. The innovation is progressive. It will make life simpler and more secure. Additionally, it will change how individual data is put away and how exchanges for products and enterprises are made. It makes a perpetual and unchanging record of each exchange. This impervious computerized record makes extortion, hacking, information burglary, and data misfortune practically inconceivable. The innovation will influence each industry on the planet, including fabricating, retail, transportation, medical services, and land. Organization goliaths, for the most part
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Fig. 3 Illustration of how blockchain technology works
attempting to turn out to be early adopters of the innovation. Almost $400 trillion across different enterprises is set to be changed by blockchain as reported in the research of [24–29].
2.2.4
Polymer Memory
Polymer memory suggests the new memory development that uses conductive polymer instead of a silicon-based product to store information. They are profoundly versatile, natural materials, including long chains of single atoms. They are basic electronic materials that can be prepared as fluids. With dainty film memory innovation, they are utilized in different mechanical standard cycles. A polymer known as PEDOT (polyethylene-dioxythiophene) is an uncommon plastic as it conducts power at low voltage, which makes it reasonable for hostile to static covering in different modern cycles. Afterward, it was found that the high beat of current changes it into a non-leading state. The PEDOT-based memory was used to store the electronic information as zeros and ones. Stacking layers of memory, a cubic centimeter contraption could hold as much as a gigabyte and be adequately humble to equal CDs and DVDs. Figure 4 presents the structure of this technology. The polymer-based memory’s primary guideline is a dipole second (DM) controlled by the polymer chains. The DM is set up when an electric field is applied to a string containing positive and negative charges. The positive charges get uprooted toward the negative end while negative charges get dislodged toward the field’s positive finish. Having assembled the consideration of a few scholastic and modern scientists in the course of recent years, the polymer market is relied upon to arise as a
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Fig. 4 Structure of polymer memory technology. Source Meena et al. [30]
conspicuous venture road for worldwide makers. The material holds the possibility to carry essential change to various high-income mechanical areas. They boost responsive materials that can be formed into other perpetual shapes without any difficulty. In any case, when presented to an outside upgrade, they are fit for returning to their unique shape. Attributable to this industry’s capacity to enter into a changed number of business areas, it is sure that the materials would observe raised requests from a few high-income application sections in the coming future. Global Market Insights, Inc. has anticipated that this technology’s yearly income would be surpassing $1.4 billion by 2025 (see Fig. 5).
2.2.5
Artificial Passenger
Artificial passenger (AP) is a device/technology/system installed in a motor vehicle that is designed to ensure that the driver stays alert and awake. It holds a conversation with the driver, analyzes the driver’s responses, and determines if they are fit to operate the vehicle. It is implemented in companion form and integrated within the dashboard of the vehicle itself. The leader in this endeavor is IBM.
Fig. 5 Polymer memory market size, by end user, 2014–2025
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Fig. 6 Components of artificial passenger technology. Source SlideShare.net
As shown in Fig. 6, the technology comprises a camera used to track the driver’s lip and eye movement. The microphone is mounted to record audio feedback from driver. Chipset to process the driver’s responses, determine if they can drive, and implement the measures have been preset to stimulate the driver. A storage device was also one of the components to store the reference profile of the driver. The most prevalent way of implementing AP is through integration with the vehicle itself within the dashboard. It uses a combination of a software program, camera, and microphone to determine if the driver is well enough to operate the vehicle. It does this by keeping a personalized profile of you in the system. It then uses this to carry a conversation with the driver using preprogrammed questions to see if the driver is still alert. The AP uses the microphone to pick up responses and then runs them through a voice analyzer to check if there are any signs of fatigue in your responses through checks for lethargic responses and/or lack of proper intonation. To ensure the accuracy of the system, the camera tracks your lip movements to make sure that the intonation and annunciation are precisely captured. The camera also tracks eye movement to see if the driver is still paying attention to the road or becoming attentive. Suppose the driver is found to be in a fatigued/inhibited state. In that case, the system shall immediately alert the driver, through various stimuli, that they are not fit to drive and must find the nearest place to stop the vehicle safely. However, it comes with security issues, which can be readily remedied but require a constant system update. This may become a bit too involved for some users as they may not enjoy treating their vehicles effectively as they treat their cell phone or laptop. Similarly, there is a possible privacy issue, as companies and governments may use this to track people covertly.
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Therefore, the technology can be improved by integrating it with preexisting smart assistants, like Alexa, Google Assistant, Siri, etc. This will help reduce the wariness people have toward AP as they are already familiar and accustomed to interacting with these assistants. An improvement toward the technological side would be creating a back-end system that sporadically checks on the AP to see if it is functioning to the standard and alert the developers and driver if there is any sign of issue with the AP. The checking system would not be monitored by any individuals directly, only checked on once in a while to see if it is still functioning to the standard. Only a selected few would have access to the checking system's hub to limit any potential damage that humans can do.
3 Significance of the Selected Technologies in the Society The fields where the selected technologies were significant and some of their major functions are presented in Table 2. Table 2 Significance of the selected technologies Technology
Fields of application
Functions
Smart Eye Technology
Medical, aviation, defense
3D Searching
Chemistry (pharmacophoric searching)
Blockchain
Health care, digital media, music, politics, real estate, gaming, identity security, Fintech, cryptocurrency, supply chain, cybersecurity, cloud, Internet of things, hardware, logistics, government, and software
Diagnose patients with Alzheimer’s and Parkinson’s diseases Research, development, and educational purposes in the aviation industry It is used to find molecules that have essential functional groups in the proper position to interact with a given receptor Secure sharing of clinical information, music sovereignties following, cross-outskirt installments, real-time IoT working frameworks, personal character security, anti-illegal tax avoidance global positioning framework, supply chain and coordinations checking, voting component, advertising experiences, original substance creation, cryptocurrency trade, and real bequest handling stage (continued)
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Table 2 (continued) Technology
Fields of application
Functions
Polymer Memory
Robotics, smart textile, photonics, medical, brand protection, and anti-counterfeiting
Artificial Passenger
Automobile
Savvy breathable articles of clothing that can control warmth and dampness Intravenous cannula, self-changing orthodontic wires, and specifically malleable instruments for limited scope surgeries where metal-based shape-memory composites, for example, Nitinol, are been utilized A rest avoidance-based vehicle framework that associates verbally with a driver to decrease the probability of them nodding off at the controls of a vehicle
4 Conclusion Prominent emerging technologies were investigated and presented in this paper. Their overviews, strengths, areas of application, and potential research gaps were uncovered and adequately explained. This will guide researchers who are keen on enhancing any of the discussed technologies as the emerging technologies’ research gaps have been identified and presented. Similarly, practical solutions were proffered to some of the identified and potential challenges.
References 1. Gartner (2019) Hype cycle for emerging technologies. Gartner Inc. https://www.informationage.com/5-emerging-technology-trends-gartner-123484932/ 2. Juhong A, Treebupachatsakul T, Pintavirooj C (2018) Smart eye-tracking system. In: 2018 International workshop on advanced image technology (IWAIT). IEEE, pp 1–4 3. Hurst T (1994) Flexible 3D searching: the directed tweak technique. J Chem Inform Comput Sci 34(1):190–196 4. Pearlman RS (1993) 3D molecular structures: generation and use in 3D searching. 3D QSAR in Drug Design 41–79 5. Silva M, Teixeira LML, Ferreira M, Oliveira-Silva P (2020) Searching for better 3D baseline stimuli. Affect, personality and the embodied brain (APE2020) 6. Tang H, Liu Z, Zhao S, Lin Y, Lin J, Wang H, Han S (2020) Searching efficient 3d architectures with sparse point-voxel convolution. In European Conference on Computer Vision (pp 685–702) Springer, Cham 7. Sedmidubsky J, Zezula P (2019) Similarity search in 3D human motion data. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval (pp 5–6) 8. Bodkhe U, Tanwar S, Parekh K, Khanpara P, Tyagi S, Kumar N, Alazab M (2020) Blockchain for industry 4.0: a comprehensive review. IEEE Access, 8, 79764–79800 9. Dai HN, Zheng Z, Zhang Y (2019) Blockchain for internet of things: a survey. IEEE Internet Thing J 6(5):8076–8094
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10. Miao W, Zou W, Luo Y, Zheng N, Zhao Q, Xie T (2020) Structural tuning of polycaprolactone based thermadapt shape memory polymer. Polymer Chem 11(7):1369–1374 11. Zhang Y, Huang L, Song H, Ni C, Wu J, Zhao Q, Xie T (2019) 4D printing of a digital shape memory polymer with tunable high performance. ACS Appl Mater Inter 11(35):32408–32413 12. Gallo M, De Luca G, D’Acierno L, Botte M (2019) Artificial neural networks for forecasting passenger flows on metro lines. Sensors 19(15):3424 13. Arora S, Rathore AS, Gautam S (2019) Passenger screening using deep learning and artificial neural networks. Int J Eng Manage Res (IJEMR) 9(3):40–42 14. Yuan Y, Shao C, Cao Z, Chen W, Yin A, Yue H, Xie B (2019) Urban rail transit passenger flow forecasting method based on the coupling of artificial fish swarm and improved particle swarm optimization algorithms. Sustainability 11(24):7230 15. Fontes T, Correia R, Ribeiro J, Borges JL (2020) A deep learning approach for predicting bus passenger demand based on weather conditions. Transport Telecommun J 21(4):255–264 16. Wamba SF, Queiroz MM (2019) Factors influencing blockchain diffusion in the supply chain: an empirical investigation. In: Industry 4.0 and hyper-customized smart manufacturing supply chains. IGI Global, pp 38–60 17. Biais B, Bisière C, Bouvard M, Casamatta C (2019) The blockchain folk theorem. Rev Financ Stud 32(5):1662–1715. https://doi.org/10.1093/rfs/hhy095 18. Cong LW, He Z (2019) Blockchain disruption and smart contracts. Rev Financ Stud 32 (5):1754–1797. https://doi.org/10.1093/rfs/hhz007 19. Queiroz MM, Wamba SF (2019) Blockchain adoption challenges in supply chain: an empirical investigation of the main drivers in India and the USA. Int J Inf Manage 46:70–82 20. Tian Y, Song Y, Yao H, Yu H, Tan H, Song N, ... Guan S (2019) Improving resistive switching characteristics of polyimide-based volatile memory devices by introducing triphenylamine branched structures. Dyes Pigment 163:190–196 21. Assunta DV, & Luisa V (2020) Blockchain technology in supply chain management for sustainable performance: evidence from the airport industry. Int J Inform Manage 52:102014 22. Sadouskaya K (2017) Adoption of blockchain technology in supply chain and logistics. Bachelor’s Thesis, Business Logistics, Kaakkois-Suomen Ammattikorkeakoulu Oy, Finland 23. Mthethwa RM (2016) Challenges in implementing monitoring and evaluation (M&E): the case of the Mfolozi Municipality 24. Amiri MJ, Agrawal D, El Abbadi A (2019) On sharding permissioned blockchains. In: IEEE international conference on blockchain (blockchain), July 2019. IEEE, pp 282–285 25. Miller A (2019) Permissioned and permissionless blockchains. In: Blockchain for distributed systems security. Wiley, Hoboken, pp 193–204 26. Van Flymen D (2020) Blockchains. In: Learn blockchain by building one. Apress, Berkeley, CA, pp 29–38 27. Liu Y, Zhang S (2020) Information security and storage of internet of things based on block chains. Futur Gener Comput Syst 106:296–303 28. Elasrag H (2019) Blockchains for islamic finance: obstacles & challenges. Munich Personal RePEc Archive, Munich University Library, University of Munich, Germany 29. Bertucci L (2020) Where do blockchains fit in the history of financial institutions? Available at SSRN 3580957 30. Meena JS, Sze SM, Chand U, Tseng TY (2014) Overview of emerging non-volatile memory technologies. Nanoscale Res Letter. 9:1–33. https://doi.org/10.1186/1556-276X-9-526
Energy Harvesting: A Panacea to the Epileptic Power Supply in Nigeria Munir Kolapo Yahya-Imam and Murtadho M. Alao
Abstract At present, just 40% of Nigeria’s populace is associated with the energy matrix, while power supply challenges are capable around 60% of the time. In the best-case scenario, the regular everyday power supply in Nigeria is assessed at four hours, albeit a few days can pass by with no force by any means. Neither force cuts nor reclamations are reported; prompting requires a heap shedding plan during the COVID-19 lockdowns to help reasonable conveyance and consistency. To address this issue, this paper proposes energy harvesting technologies to the epileptics power supply in Nigeria. The technology was chosen as proven efficient and reliable in many studies as an alternative and reliable power generation source. It is also largely maintenance-free and environmentally friendly, and powers hardware where there are no ordinary force sources, and ultimately, it wipes out the requirement for continuous battery substitutions and running wires to end applications. The paper was written in sections: Introduction; Power Supply in Nigeria; Current Situation in the Power Sector; Overview of Energy Harvesting Technology; Application Area of Energy Harvesting; and Solution to Epileptic Power Supply in Nigeria. Lastly, a concise conclusion and a list of relevant references were presented.
Keywords Energy harvesting Nigeria Power supply Thermoelectric elements Electromagnetic energy
Solar cells
M. K. Yahya-Imam (&) Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] M. M. Alao Faculty of Business and Accounting, Lincoln College of Science, Management, and Technology, Abuja, Nigeria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_16
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1 Introduction For example, the revelation of energy sources, light, movement, body warmth, and RF in our current circumstance has incited the musings of how to change those energies into usable electrical energy over to control apparatuses. More critically, development shows up where we can make valuable, supportive structures down to the milliwatts or even lower range. Likewise, at that range, it gets possible to accumulate some energy out of the atmosphere. Most importantly, tire pressure checking is a conspicuous spot where harvestable energy exists, substitution of a battery is expensive, and power lines are not practical. There have been conversations that supplanting the tire pressure sensors on a Lexus vehicle costs about $400. Envision the cost investment funds for this situation if the batteries in the Lexus tire pressure sensor should never have been traded for the vehicle’s life. Essentially, compact electronic gadgets are a key territory where expanding run time is wanted. These could be phones, interactive media players, handheld radios, retail location scanners, versatile clinical hardware, devices, electric razors, and so forth. In arising countries, changing wireless is risky since power sources are not promptly accessible. Here, energy gathering gets essential and is less expensive than introducing a whole electric matrix [1]. In choosing which energy reaping innovation to use, one must audit the different possible energy sources accessible, for example, light, warm, vibration, and RF. Out of the other advancements accessible for energy reaping, sun-based is most generally utilized. It has the highest assessed potential energy for collecting, from 100 mW for every centimeter squared outside to indoor lighting with an expected of 0.1 mW per centimeter squared. Warm point is the following mainly elevated potential energy age source and can gain from sources, such as friendly sources from our bodies, or warmth age by apparatuses or machines. Vibration energy is a possible source too, yet its potential energy age is not exactly light and warm sources. Even though we can recover energy from electrical signs communicated remotely, collected life appraisals are low and likely not a suitable energy gathering source [2–4]. Therefore, this paper proposes energy harvesting as a long-lasting solution to the epileptic power supply facing a country of over 200 million populations, Nigeria.
1.1
Power Supply in Nigeria
Nigeria is located in West Africa, with over two hundred million populations (censors.gov, 2020). It has a geographical area of 923,768 km2, and it is the 6th most populated nation on the planet. The evolution stages of Nigeria’s power sector have been reported by Akyuz et al. [5], Olowosejeje et al. [6], and Agbede et al. [7].
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Current Situation in the Power Sector
Nigeria is assessed to have a complete exploitable huge scope hydropower capability of more than 14,120 MW, which delivers 50,832 GW of power every year. Her potential for little hydropower is assessed at 3,500 MW of which just 60.58 MW (about 1.7%) has been created. The continuous energy supply emergency in Nigeria occurs because of the Nigerian force area’s disappointment to give homegrown families and modern makers’ adequate power supply. Regardless of a quickly developing economy, a portion of the world’s most significant coal, oil, and gas stores and the nation’s status as Africa’s biggest oil maker. Presently, just 40% of Nigeria’s populace is associated with the energy lattice, while power supply challenges are capable around 60% of the time. In the best-case scenario, the regular power supply is assessed at four hours, albeit a few days can pass by with no force by any means. Neither force cuts nor rebuilding efforts are declared; prompting requires a heap shedding plan during the COVID-19 lockdowns to help good dispersion and consistency [8–10]. The troubles experienced here have injured the agrarian, modern, and mining areas and block the continuous financial turn of events. The energy supply emergency is perplexing, originates from an assortment of issues, and has been progressing for quite a long time. Most Nigerian organizations and family units can bear to do such a vast number of diesel-filled generators to enhance the irregular stock. Since 2005, Nigerian force changes have zeroed in on privatizing the generator and circulation resources and empowering private interest in the force area. The public authority keeps on controlling transmission resources while gaining unassuming ground in establishing an organizational climate appealing to unfamiliar speculators. Minor expansions in standard power supply have been accounted for. The accessibility of power in Nigeria has deteriorated throughout the long term. The nation has been unable to fulfill a need based on its arrangements, guidelines, and tasks executives. Nigeria's lack of reliable force supply is a requirement for the nation's financial development. Business and mechanical areas in the country have gotten vigorously dependent on self-produced power, utilizing petroleum and diesel generators. This records for almost 50% of all power devoured (Fig. 1).
2 Overview of Energy Harvesting Technology Energy harvesting, also called power reaping or energy rummaging, is when energy is caught from a framework's current circumstance and changed over into usable electric force. It is helpful as it offers methods for driving gadgets where there are no conventional force sources. It also opens many new applications in numerous distant areas and submerged where batteries and traditional force are not pragmatic
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Fig. 1 Per capita power age has scarcely ascended in more than 30 years. Power characterizes advancement and industrialization, and this needs to represent the absolute disappointment of Nigeria. Oil abundance might have been spent on hydroelectric dams, and gas-terminated force stations and network advancement might have carried work and progress to rustic regions
Fig. 2 Architecture and components of a solar cell
to utilize [11–15]. Energy collectors give some measure of capacity to low-energy gadgets. Regular energy reapers are sunlight-based cells (Fig. 2), thermoelectric components, and electromagnetic energy.
2.1
Solar Cells
Sun-based cells are made out of a variety of P–N intersections and work on the photovoltaic impact. When light is on the corner, photons with energy more significant than the material’s energy band hole are invested in the material and produce electron-opening sets. These transporters are isolated by the electric field’s presence in the intersection and make a flow that corresponds to the rate of sun-based light. The cell can be shown as a current source relating to a diode: the
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shunting obstruction model's spillage and the arrangement opposition model's contact obstruction. The blue line on the plot shows the current versus voltage qualities. Expanding brightening builds the short out current and slightly affects the open-circuit voltage of the cell. From this data, we can likewise plot the force as a component of voltage. The most sun-powered cells’ most significant force purpose is ordinarily somewhere in the range of 70% and 80% of the open-circuit voltage. Furthermore, you can interface the sun-powered cells in one or the other arrangement or equal. In the arrangement, you will wind up with a higher voltage. Yet, the concealing of one cell will diminish the whole string's productivity, while associating them in equal will bring about a lower voltage, which will likely help control current hardware. However, concealing will influence one cell. Some normal sun based cell types incorporate nebulous silicon-based, glasslike based, and all the more as of late the color sharpened DSSC type, which are adaptable and can be formed into different shapes and sizes [16, 17].
2.2
Thermoelectric Elements
Thermoelectric components can be utilized to reap surrounding heat energy. When a temperature contrast is applied across the two finishes of a thermoelectric gadget, an electric voltage is produced through the Seebeck sway. The primary advancement unit of a warm finder is a thermocouple. This thermocouple is made out of an N-type material electrically in plan with a P-type material. When a temperature differentiation is applied across the material, the heat begins to move from the sizzling to the cooler side. All the while, the energy from the applied warmth allows the free electrons and openings to move and shape an electric potential and stream if there is a closed circuit [18–20]. Conventionally used warm gatherers for power age contain P- and N-doped bismuth telluride, inferable from its supervisor warm properties. One P–N leg of this material delivers around 0.2 millivolts per Kelvin distinction between the hot and cold sides [21, 22]. This is demonstrated in Fig. 3.
Fig. 3 Structure of a thermoelectric device
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Electromagnetic Energy
Electromagnetic energy reaping utilizes an attractive field to change mechanical energy over electrical power (Fig. 4). A curl connected to the wavering mass navigates through a beautiful area setup by a fixed magnet. The loop goes through a shifting measure of attractive transition, initiating a voltage as indicated by Faraday’s law. The instigated voltage is characteristically little and should like this be expanded to source fuel feasibly. Techniques to build the actuated voltage incorporate utilizing a transformer, developing the curl's number of turns, and expanding the lasting attractive field [23–25]. Nonetheless, each is restricted by the size imperatives of a CPU. Note that because of the electromagnetic vibration, the collector produces an AC kind of yield. To be utilized in an electric circuit, a full extension rectifier should be included as a request to correct the voltage coming out. In any case, using the rectifier will diminish the greatest force that can be accomplished from the vibration source. Lastly, the energy gathering market is vast and developing quickly. As indicated by investigators at IDTechEx, energy gathering was a $0.7 billion market in 2012 and is depended upon to outperform $5 billion by 2022; by then, 250 million sensors will be constrained by fuel gathering sources. The market for thermoelectric energy procuring alone will reach $865 million by 2023 [26–28].
2.4
Application Area of Energy Harvesting
Suppose a device’s energy need is sufficiently low and battery replacement would be problematic or excessive. In that case, it may be possible to renounce the battery without a doubt and rely exclusively upon get-together encompassing fuel hotspots for power. The mix of too low-impact MCUs and energy gathering have offered rise to a wealth of usages that in advance were unreasonable [29–31] (Fig. 5). There are lots of energy collecting headways in like way, with some innovative techniques directly into the extraordinary past. The most significant energy sources,
Fig. 4 Electromagnetic energy technology
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Fig. 5 An illustration of areas where energy harvesting can be applied
Table 1 Obtainable power from energy harvesting sources
Source
Source power
Light Indoor 0.1 mW/cm2 Outdoor 100 mW/cm2 Vibration/motion Human 0.5 m at 1 Hz 1 m/s2 at 50 Hz Machine 1 m at 5 Hz 10 m/s2 at 1 kHz Thermal Human 20 mW/cm2 Machine 100 mW/cm2 RF GSM BSS 0.3 µW/cm2
Harvested power 10 µW/cm2 10 mW/cm2
4 µW/cm2 100 µW/cm2 30 µW/cm2 1–10 mW/cm2 0.1 µW/cm2
as referred to before, are light, warmth, vibration, and RF. Short of roof sun-based sheets, none of them produces tons of energy (Table 1). However, at least one of them might be above and beyond to control low-control gadgets in a specific climate.
3 Solution to Epileptic Power Supply in Nigeria Nigeria’s lack of dependable force supply is a limitation on the nation’s economic development. The government needs to differentiate its economy past oil and gas incomes because of instability. Be that as it may, if the eagerness for private energy
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area put more in self-age to make this conceivable, contamination would rise. An expansion in self-age would build ozone-depleting substance outflows. In particular, her energy requirement is huge and developing, and it is anything but difficult to cause the government to notice the significant issues related to more generous energy makers. Yet, recuperating any of the energy that will overflow out of hands can do their part in energy execution while adding to natural advantages and new mechanical changes. Adopting and implementing energy harvesting in Nigeria will boost the sector and nation’s economy and expose the country to its various benefits [32–34]. The said technology is maintenance-free, mainly, and environmentally friendly, and powers gadgets where there are no regular force sources. Ultimately, it kills the requirement for successive battery substitutions and running wires to end applications.
4 Conclusion To comprehend why energy reaping is so significant and panacea to Nigeria's epileptic force supply, envision an extension where numerous sensors are set for structure observing. They should be vivaciously self-sufficient, little, light, and equipped for remote correspondence. These necessities are regular today as the problem related to wired and network for a sensor. Nobody needs to change the batteries because of the high support cost. Another situation is winded up in a huge and wild territory where no electrical cables are accessible, or as an architect, you need to embed a sensor inside a structure, for example, section made of concrete or under the black top, with the goal that you cannot separate it to change the battery. Consequently, investigations have indicated that the solitary efficient approach to provide food for these situations and force electronic frameworks for quite a while is to execute energy gatherer advances.
References 1. Zou HX, Zhao LC, Gao QH, Zuo L, Liu FR, Tan T, Zhang WM (2019) Mechanical modulations for enhancing energy harvesting: principles, methods and applications. Appl Energy 255:113871 2. Safaei M, Sodano HA, Anton SR (2019) A review of energy harvesting using piezoelectric materials: state-of-the-art a decade later (2008–2018). Smart Mater Struct 28(11):113001 3. Yun S, Zhang Y, Xu Q, Liu J, Qin Y (2019) Recent advance in new-generation integrated devices for energy harvesting and storage. Nano Energy 60:600–619 4. Liu H, Fu H, Sun L, Lee C, Yeatman EM (2020) Hybrid energy harvesting technology: from materials, structural design, system integration to applications. Ren Sustain Energy Rev 110473 5. Akyuz M, Zackariah I, Opusunju M (2020) Effect of power supply on the performance of Abuja electricity company of Nigeria. Int J Business Market Manage 5(8):09–16
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6. Olowosejeje S, Leahy P, Morrison A (2019) The economic cost of unreliable grid power in Nigeria. African J Sci Technol Innov Develop 11(2):149–159 7. Agbede MO, Onuoha FC, Uzoechina BI, Osunkwo FOC, Aja SU, Ihezukwu VA., ... Ogbonna UG (2020) Electricity consumption and capacity utilization in Nigeria. Int J Energy Econ Policy 10(6):483 8. Ayamolowo OJ, Buraimoh E, Salau AO, Dada JO (2019, August) Nigeria electricity power supply system: the past, present and the future. In 2019 IEEEPES/IAS Power Africa (pp 64–69) IEEE 9. Kareem OK, Adekitan AI, Awelewa A (2019) Power distribution system fault monitoring device for supply networks in Nigeria. Int J Electrical Comput Eng (2088–8708), 9 10. Nkalo UK, Agwu EO (2019) Review of the impact of electricity supply on economic growth: A Nigerian case study. IOSR J Electrical Electron Eng (IOSR-JEEE) 14(1):28–34 11. Sah DK, Amgoth T (2020) Renewable energy harvesting schemes in wireless sensor networks: a survey. Inform Fusion 63:223–247 12. Zhu J, Zhu M, Shi Q, Wen F, Liu L, Dong B, ... He T (2020) Progress in TENG technology— A journey from energy harvesting to nanoenergy and nanosystem. Eco Mat 13. Rodrigues C, Nunes D, Clemente D, Mathias N, Correia JM, Rosa-Santos P, ... Ventura J (2020) Emerging triboelectric nanogenerators for ocean wave energy harvesting: state of the art and future perspectives. Energy Environ Sci 13(9):2657–2683 14. Gholikhani M, Roshani H, Dessouky S, Papagiannakis AT (2020) A critical review of roadway energy harvesting technologies. Appl Energy 261:114388 15. Nozariasbmarz A, Collins H, Dsouza K, Polash MH, Hosseini M, Hyland M, ... Ramesh VP (2020) Review of wearable thermoelectric energy harvesting: from body temperature to electronic systems. Appl Energy 258:114069 16. Jeong M, Choi, IW, Go EM, Cho Y, Kim M, Lee B, ... Bae JH (2020) Stable perovskite solar cells with efficiency exceeding 24.8% and 0.3-V voltage loss. Sci 369(6511):1615–1620 17. Hu Z, Wang J, M, X, Gao J, Xu C, Yang K, ... Zhang F (2020) A critical review on semitransparent organic solar cells. Nano Energy 105376 18. Voronin AI, Novitskii AP, Ashim YZ, Inerbaev TM, Tabachkova NY, Bublik VT, Khovaylo VV (2019) Exploring the origin of contact destruction in tetradymite-like-based thermoelectric elements. J Electron Mater 48(4):1932–1938 19. Ponnusamy P, de Boor J, Müller E (2020) Using the constant properties model for accurate performance estimation of thermoelectric generator elements. Appl Energy 262:114587 20. Salgado EB, Llanderal DEL, Nair MTS, Nair PK (2020) Thin film thermoelectric elements of p–n tin chalcogenides from chemically deposited SnS–SnSe stacks of cubic crystalline structure. Semiconductor Sci Technol 35(4):045006 21. Liu W, Bai S (2019) Thermoelectric interface materials: A perspective to the challenge of thermoelectric power generation module. J Materiomics 5(3):321–336 22. Ma X, Shu G, Tian H, Xu, W, Chen T (2019) Performance assessment of engine exhaust-based segmented thermoelectric generators by length ratio optimization. Applied Energy 248:614–625 23. Yan B, Yu N, Zhang L, Ma H, Wu C, Wang K, Zhou S (2020) Scavenging vibrational energy with a novel bistable electromagnetic energy harvester. Smart Mater Struct 29(2):025022 24. He P, Cao MS, Shu JC, Cai YZ, Wang XX, Zhao QL, Yuan J (2019) Atomic layer tailoring titanium carbide MXene to tune transport and polarization for utilization of electromagnetic energy beyond solar and chemical energy. ACS Appl Mater Interfaces 11(13):12535–12543 25. Mirmoosa MS, Ptitcyn GA, Asadchy VS, Tretyakov SA (2019) Time-varying reactive elements for extreme accumulation of electromagnetic energy. Phys Rev Appl 11(1):014024 26. Lee H, Lee M, Suh SE, Roh JS (2019) Development of outdoor jacket design using energy harvesting system by arm swing motion during walking. Fashion Textile Res J 21(3):300–307 27. Pan Y, Lin T, Qian F, Liu C, Yu J, Zuo J, Zuo L (2019) Modeling and field-test of a compact electromagnetic energy harvester for railroad transportation. Appl Energy 247:309–321
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28. Zhang X, Zhang X, Wang D, Yuan H, Zhang S, Zhu C, ... Chen Y (2019) Three dimensional graphene-supported nitrogen-doped carbon nanotube architectures for attenuation of electromagnetic energy. J Mater Chem C 7(38):11868–11878 29. Zang X, Jian C, Zhu T, Fan Z, Wang W, Wei M, Lin L (2019) Laser-sculptured ultrathin transition metal carbide layers for energy storage and energy harvesting applications. Nt Commun 10(1):1–8 30. Zhang Y, Bowen CR, Ghosh SK, Mandal D, Khanbareh H, Arafa M, Wan C (2019) Ferroelectret materials and devices for energy harvesting applications. Nano Energy 57:118–140 31. Mahmoud A, Alhawari M, Mohammad B, Saleh H, Ismail M (2019) A gain-controlled, low-leakage Dickson charge pump for energy-harvesting applications. IEEE Trans Very Large Scale Integr (VLSI) Syst 27(5):1114–1123 32. Jimah KQ, Isah AW, Okundamiya MS (2019) Erratic and epileptic power supply in Nigeria: causes and solutions. Adv Electr Telecommun Eng (AETE) 2(1):47–53. ISSN: 2636–7416 33. Agbeboaye C, Akpojedje FO, Ogbe BI (2019) Effects of erratic and epileptic electric power supply in Nigerian telecommunication industry: causes and solutions. J Adv Sci Eng 2(2):29–35 34. Aremu JO (2019) Epileptic electric power generation and supply in Nigeria: causes, impact and solution. J Contemporary Res Soc Sci 1(3):73–81
Forecasting of Inflation Rate Contingent on Consumer Price Index: Machine Learning Approach Shampa Islam Momo, Md Riajuliislam, and Rubaiya Hafiz
Abstract Variations of inflation rate possess a diverse influence on the economic growth of any country. Inflation rate control can be accommodated to stabilize the financial aspect’s condition, including the political area. The way to restrain the inflation rate is the prediction of the inflation rate. This paper proposes forecasting the inflation rate by applying machine learning algorithms: support vector regression (SVR), random forest regressor (RFR), decision tree, AdaBoosting, gradient boosting, and XGBoost. These algorithms are employed since the predicting value is nonlinear and complex. Moreover, the regression and boosting algorithms confer good accuracy, as inflation is a frequent dynamic variable that depends on several factors. The models show decent accuracy using the elements consumer price index (CPI), food, non-food, clothing-footwear, and transportation. Among the models, AdaBoost retrospectives the most desirable outcome with the lowest MSE value of 0.041.
Keywords Inflation Consumer price index Machine learning regression Random forest regressor Micro economy policy
Support vector
1 Introduction Inflation is an influential component of finance in any developing nation. The increase in the prices of assets or daily services like food, clothing, housing, etc., can be inflation. It has a tremendous effect on the cost of living in the country. S. I. Momo (&) M. Riajuliislam R. Hafiz Computer Science and Engineering, Daffodil International University, 102, Sukrabad, Mirpur Road, Dhaka 1207 Bangladesh e-mail: [email protected] M. Riajuliislam e-mail: [email protected] R. Hafiz e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_17
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Inflation has a proportional relationship with the cost of living and an inverse relationship with economic growth. Hence, the prognosticating inflation rate is indispensable for the people and economy of the country. From 1990 to the start of 2000, Bangladesh’s economy had an average inflation rate of less than 4%. Nevertheless, it stabilized after 2011 with a rate between 5 and 7%. [1]. Recently, considerable attention has been raised concerning the increasing inflation rate and its negative influence on the nation’s people, particularly the poor. The critical conflicting impact of increased inflation exhibits the ambivalence in arriving at conclusions to invest, consume, and produce. It is a crucial obligation to determine the inflation of Bangladesh. In this country, inflation is considerably pricey for low-income people as their purchasing potential remains feeble. Also, ascertaining the inflation scale would help policymakers recognize what will be affected and what measurements have to be undertaken to regulate the sudden price rise. Prediction of inflation would serve to counterbalance the enhanced rate of inflation. Moreover, it may further assist in distinguishing the factors for the unforeseen raising of inflation. Our paper elects five key elements: consumer price index, food, non-food, clothing-footwear, and transportation to predict the inflation rate. Bangladesh is often driven by food inflation, though, after 2010, the value of non-food inflation intensified. Traditional statistical techniques have been applied to predict inflation. Since inflation is nonlinear and intricate, regression models of machine learning would be appropriate to predict the inflation rate accurately. This paper uses support vector regression (SVR), random forest regressor, decision tree, AdaBoosting, gradient boosting, and XGBoost. Regression models are most suitable when it comes to forecasting the continuous dependent variable. The principle purpose of this research paper is to forecast the inflation rate utilizing the data of CPI, food, non-food, clothing-footwear, and transportation with the guidance of machine learning. Firstly, the dataset is collected from the factors mentioned above. Secondly, SVR, random forest regressor, decision tree, AdaBoosting, gradient boosting, and XGBoost are applied for model designing. The result indicates that AdaBoosting shows good accuracy rather than other models. The following sections of the paper are designed as in Sect. 2 depict the descriptive analysis of the related work. Section 3 describes the methodology and how the models work on forecasting inflation. Furthermore, the detailed analysis of the task can be found in Sect. 4. Subsequently, in the last section, we concluded the paper with logical criticism.
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2 Literature Review Regression analysis of continuous variables has displayed an indispensable element for the growth of the financial sector. The number of work for practicing machine learning in forecasting in the economic sector is growing with time. Budiastuti et al. [2] predict daily consumer price index (CPI) using the support vector regression model (SVR). Also, compare the SVR model with linear regression and kernel ridge regression. CPI is one of the indicators used to measure the inflation rate. They compared them with their training time and meant absolute error MSE value. SVR model using kernel ‘rbf’ provides the value of MSE at 0.3454, which is less than other models, but the kernel ridge method has less training time than other methods. Sovia et al. [3] used back-propagation artificial neural network to predict the interest rate of bank Indonesia. They used as indicators for prediction which are dollar exchange rate, money supply, inflation rate, and JCI. They used MATLAB software to determine the weight and bias values, and with this software, it shows that it compares the network architectural patterns from several patterns used. And also find out which patterns give better accuracy. Cao et al. [4] predict the exchange rate using the support vector machine (SVM) model. They used the USD/GBP exchange rate to train the model. They found out that the MSE value of the model is 0.00300396. Tang et al. [5] propose an SVM-based approach for inflation forecasting. They apply several SVM-based kernel functions and models with BP neural networks. They compare their performance by the value of MSE. From fixed-SVM, PSO-SVM, GA-SVM, and BP model, PS0-SVM performs better with the value of MSE 0.006. Purnawansyaha et al. [6] predict an inflation rate based on the back-propagation neural network algorithm (BPNN). They used time-series data from (2012–2017) to train the model and predict inflation (2018). They measure the MSE value to measure accuracy and compare the actual value and expected value. Their BPNN method with architectural parameters is of 5-5-5-1. And their model produces an adequate level of prediction with a value of MSE 0.00000424. Oktanisa et al. [7] optimized the support vector regression (SVR) algorithm by genetic algorithm (GA) to predict the inflation rate. Usually, there is no rule to set the parameters of SVR. They proposed a classical GA to solve an optimization problem in SVR parameters. The parameters are used by the C (Complexity), ɛ (epsilon), and ɣ (gamma) for value prediction. They find out MSE values for both SVR and GA-SVR. GA-SVR provides the value of MSE 0.03767 and SVR 0.053158. Priliani et al. [8] predict inflation rate based on consumer price index (CPI) using support vector regression (SVR)-based weight attribute particle swarm optimization (WAPSO). They use WAPSO to find the optimal SVR parameters and increase the accuracy for forecasting. Their data period is 2010–2018 and divided for training and testing 50% for finding the best parameter. For SVR,
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C = 0.01, e = 0.000013, r = 0.911, c = 0.4, k = 0.9, and for SVR-WAPSO, C = 0.01, e = 0.00007, r = 0.7775, c = 0.1, k = 0.1. Then, they compare their predicted rate with the actual rate and measure the accuracy SVR 94.654% and SVR-WAPSO = 97.459%. Dharma et al. [9] predict the inflation rate using regression methods based on genetic algorithms. They used the genetic algorithm because it can handle various mathematical models with high accuracy value. They trained their model by consumer price index (CPI). Their model provides good results with an MSE value 0.1099. Yadav et al. [10] predict the inflation rate using machine learning. They used different types of regression algorithms such as linear regression, ridge regression, Lasso regression, boost regression, and random forest regression for prediction. They predict inflation by the consumer price index (CPI) and find the correlation between them. They expect several inflation rates like national, urban, and rural using regression algorithms. Also, compare them by the score of square and the values of MSE and MAE. XGBoost regression gives the highest 91.2363% accuracy among all algorithms. Zhang et al. [11] forecast inflation using support vector regression (SVR). They find optimal parameters in SVR using the grid search method. They compare SVR performance with back-propagation neural network and linear regression with RMSE and MAE values. SVR provides better results than the other two, with the value of RMSE 0.1 and MAE 0.2. From the above discussion, we can witness the difference and connections with our research. Prediction of the inflation rate is associated with our model. However, the independent variables or factors we choose to predict the inflation rate are our proposed model’s focal point.
3 Methodology In our proposed model, we obtained the data based on the Bangladesh scenario. Later, we composed machine learning models to get the most accurate prognostication of the inflation rate. The following segment explains the method to predict the inflation rate.
3.1
Dataset
Our research paper is gathered from a statistical data source named CEIC [12]. The utilized data is 107 records of 9 years from January 2012 to October 2020. The dataset was selected based on critical elements that are consumer price index, food, non-food, clothing-footwear, and transportation. The dataset is presented in Table 1.
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Table 1 Dataset of key factors of inflation Date
CPI
Food
Non-food
Clothing-footwear
Transportation
Jan-12 Feb-12 March-12 …. Aug-20 Sept-20 Oct-20
172.89 171.35 171.76 …. 282.11 288.12 290.91
186.97 183.14 183.08 …. 307.20 316.11 320.94
154.85 156.23 157.24 …. 249.95 252.24 252.40
161.93 163.51 164.73 …. 292.29 292.42 292.57
149.68 150.68 151.48 …. 257.59 263.02 263.41
Table 2 Description of key factors CPI Food Non-food Clothing-footwear Transport
Count
Mean
Std
Min
25%
50%
75%
Max
106.0 106.0 106.0 106.0 106.0
225.7007 243.4122 203.0280 233.6096 202.1323
33.4104 37.8894 27.8760 40.5590 33.1452
169.4 178.2 154.8 161.9 149.6
198.24 213.78 178.85 198.81 168.68
223.3 237.2 205.6 237.7 206.3
252.21 274.17 224.54 272.02 228.76
290.9 320.9 252.4 292.5 263.4
From Table 2, it is shown that the mean of each factor is in terms of the year. It also displays the minimum and maximum values of each element. Figure 1 illustrates the changing value of inflation and other key factors over the year. The key elements are increasing over the year though the inflation in terms of that decreases with time.
Fig. 1 Inflation and key factors position with year
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Table 3 Algorithm implementation Algorithms
Details
Support vector regression Random forest regression Decision tree regression AdaBoost regression Gradient boosting regression XGBoost boosting regression
Kernel=’rbf’, gamma = 0.001, epsilon = .001 n_estimators = 16 max_depth = 10 n_estimators = 100 Default objectibe = ‘reg:squarederror’, n_estimators = 100, seed = 120
3.2
Model Design
In Table 3, it is shown which parameters are used for each algorithm for implementation.
4 Result Analysis To analyze the execution of prophesying the inflation rate, the paper performs six models such as SVR, RFR, decision tree, AdaBoosting, gradient boosting, and XGBoost. The result is presented in Table 3. From Table 4, it is shown that among all the models, AdaBoosting has the lowest mean square error. It means that this model bestows promising approximating capability in contrast to other models. Moreover, the less the value of RMSE, the higher the performance of a model, and in our case that is also AdaBoosting. This model displays the lowest RMSE value than other models. Figure 2 illustrates that the three boosting regression algorithms dispense better efficiency than the other three general supervised regression algorithms. However, in terms of supervised regression algorithms, decision tree shows the highest accuracy. Among the boosting algorithms, the best accuracy achieved by AdaBoosting.
Table 4 Performance comparison of different models
Models
MAE
MSE
RMSE
SVR RFR Decision tree AdaBoosting Gradient boosting XGBoost
0.196 0.204 0.170 0.150 0.277 0.268
0.075 0.081 0.063 0.041 0.136 0.124
0.443 0.451 0.412 0.388 0.526 0.518
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Fig. 2 Performance of accuracy of different machine learning models
From Fig. 3, it can realize that the forecast is not quite specific. That is acquiring fluctuation a lot from the expected value. However, in Fig. 4 the fluctuation between the expected and predicted value is relatively low and precise when the AdaBoosting algorithm is implemented.
Fig. 3 Comparison of predicted and actual data in terms of the decision tree
Fig. 4 Comparison of predicted and actual data in terms of AdaBoosting
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5 Conclusion and Future Work Forecasting of inflation rate helps policymakers understand the economy’s variations and determine the policies based on them to suffer the least damage. Based on the experiment, it can be resolved that the lowest mean error was achieved using the AdaBoosting algorithm compared to other models like SVR, gradient boosting, and XGBoost. Employing AdaBoosting enables us to determine the inflation rate precisely. In the future, researchers may be able to implement neural network algorithms to gain the most accurate prediction of the Bangladesh inflation rate that might lead the country to acquire tremendous growth in the economic arena.
References 1. Hossain M (2020) Bangladesh’s Macroeconomic policy. Springer, Singapore 2. Budiastuti IA, Nugroho SMS, Hariadi M (2017, July) Predicting daily consumer price index using support vector regression method. In: 2017 15th international conference on quality in research (QiR): international symposium on electrical and computer engineering, pp 23–28. IEEE 3. Sovia R, Yanto M, Gema RL, Fernando R (2018, October) Bank indonesia interest rate prediction and forecast with backpropagation neural network. In: 2018 international conference on information technology systems and innovation (ICITSI), pp 429–435. IEEE 4. Cao DZ, Pang SL, Bai YH (2005, August) Forecasting exchange rate using support vector machines. In: 2005 international conference on machine learning and cybernetics, vol 6, pp 3448–3452. IEEE 5. Tang Y, Zhou J (2015, June) The performance of PSO-SVM in inflation forecasting. In: 2015 12th international conference on service systems and service management (ICSSSM), pp 1–4. IEEE 6. Purnawansyah P, Haviluddin H, Setyadi HJ, Wong K, Alfred R (2019) An inflation rate prediction based on backpropagation neural network algorithm. Int J Artif Intell Res 3(2) 7. Oktanisa I, Mahmudy WF, Maski G (2020) Inflation rate prediction in Indonesia using optimized support vector regression model. J Inf Technol Comput Sci 5(1):104–114 8. Priliani EM, Putra AT, Muslim MA (2018) Forecasting inflation rate using support vector regression (SVR) based weight attribute particle swarm optimization (WAPSO). Sci J Inf 5(2):118–127 9. Dharma F, Shabrina S, Noviana A, Tahir M, Hendrastuty N, Wahyono W (2020) Prediction of Indonesian inflation rate using regression model based on genetic algorithms. Jurnal Online Informatika 5(1):45–52 10. Yadav O, Gomes C, Kanojiya A, Yadav A (2019) Inflation prediction model using machine learning. Int J Inf Comput Sci 6(5):121–128 11. Zhang L, Li J (2012, December) Inflation forecasting using support vector regression. In: 2012 fourth international symposium on information science and engineering, pp 136–140. IEEE 12. CEICData. https://www.ceicdata.com/en?fbclid=IwAR1zQ6J0Lrh9NE7pqve9B9EDnI8U5I_ HZ04ta6sU7vM-f0JCnWf5EdpA4eU
Face Detection and Recognition System Tanjim Mahmud, Sajib Tripura, Umme Salma, Jannat Fardoush, Sultana Rokeya Naher, Juel Sikder, and Md Faisal Bin Abdul Aziz
Abstract Facial feature detection and recognition are widely used in current world scenarios and technologies. Almost every smartphone is embedding facial recognition & detection in security matters like phone unlocking. Another increasing use of this type of technology is being noticed in cameras and social media like snap chat. In this paper, we developed a system/platform in which facial features containing noise, eyes, mouth, and eyebrows can be detected from an image. The developed system can also recognize a human subject from two different input images. The proposed system works in two steps-in the first step, image segmentation has been employed on more than a few images to enhance the images by using various edge detection techniques and filtering out the noise. In the second part, features are extracted from an idea through histogram of gradient (HOG). The entire facial feature detection and recognition system was developed with a histogram of oriented gradients (HOG), support vector machine (SVM), and K nearest neighbor (KNN). The proposed system gives an accuracy of 96% in the detection and recognition of facial features and human subjects. The projected strategy of procedures offers improved truth once appeared otherwise in relevance the others mixture of ways in which. The assessment has been done in the Python platform on authentic images of the Indian face database.
T. Mahmud (&) S. Tripura J. Sikder (&) Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati, Bangladesh U. Salma Department of CSE, Bangladesh University, Dhaka, Bangladesh J. Fardoush Department of CSE, University of Chittagong, Chittagong, Bangladesh S. R. Naher Department of CSE, University of Information Technology and Sciences, Dhaka, Bangladesh M. F. B. A. Aziz Department of CSE, Comilla University, Comilla, Bangladesh © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_18
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Keywords Support vector machine (SVM) Histogram of gradient (HOG) K nearest neighbor (KNN) Face detection Face recognition Facial features
1 Introduction The searches identify human faces accurately from the existing image frame and the specific individuals they belong to were already well underway and have reached a new level in research and development in this modern era. In 2012, the famous Google X laboratory announced its facial recognition achievement, which has amazed the world [1]. They have managed to identify cats successfully on YouTube with their developed artificial intelligence (AI) model. It stands to be a great example of how far AI models and machine learning has reached where the system just studied just a few thousand YouTube thumbnails based on preset learning methods. This achievement is a reflection of decades of prior research and development on facial recognition technology. Just like other forms of biometric identifications like voice or fingerprint identification, facial recognition is given just as much priority in various modern-day technology, cameras, and security system [2]. In fingerprint identification, an available line of the fingerprint is used to compare fingerprints. Similarly, various aspects of a face can recognize/identify a human subject from images or video frames. In modern-day facial recognition, software can detect by a critical threshold of resemblance among sample image and example patterns, and direct detection is generally declared. These sorts of modeling have led to the detection of facial features such as eyes, mouth, and nose to find out quickly. However, when it comes to identifying a person’s entire face from multiple photographs, analysis is quite complicated. Many improvements have been made in face detection and recognition for security, recognizable proof, and participation reason, yet issues upset the advancement to reach or outperform human-level exactness. These issues are varieties in human facial appearance, such as fluctuating lighting conditions and clamor in face images, pose, expression, etc. In this paper, we propose an innovative combination of methods employing SVM [3] combined with HOG [4] and KNN [5] to address a portion of the issues obstructing face recognition accuracy to improve the widespread face detection and recognition framework. Accordingly, developed a system with an accuracy higher than 90% {or accuracy of 96%} in detecting facial features (nose, left eye, right eye, mouth, and ear) and identification/recognition of a human subject from multiple tests images. Consequently, these investigation results show that the proposed technique is exact, solid, and robust for face detection and recognition framework that can be essentially actualized. The proposed system was developed in open source
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software, which supports various advanced languages. Python was the preferred programming language. The investigation has been completed in the Python platform on authentic images of the Indian face database. The construction of the paper is according to the going with. Section 2 outlines related works, trailed by the proposed technique, exploratory consequences of assessment, correlation of the outcomes with results announced in related works, conclusion, and references.
2 Related Works Facial appearance can be brought to recognition using well-established methods. These can be started by looking behind the past two to three decades of research and development works. In developing automated systems to recognize facial appearance and detect facial features, its appropriateness depends on its application methods. However, most commonly, all approaches in creating these systems fall upon the face or geometrical calculation’s arrival. Using a geometric feature-based approach and maximum recognition using template matching as claimed [6], the findings obtained resulted in 90% accurate recognition. Haar-like features are evaluated using another image processing that produces an immense amount of features [7] and uses the AdaBoost boosting method [8] to minimize the enhanced heart degenerative tree and rapid obstruction classifiers, using only simple rectangular Haar-like features to have different results, such as a set of ad hoc area efficiency. A list of terribly enormous capabilities will be used to introduce a structure that used those features. From now on, the range of parts must be limited to a few essential elements obtained by the boosting technique, AdaBoost [8]. Pentland and Matthew Turk [9] applied principal component analysis (PCA). Reduction of dimensions of images can also be made with HOG. Facial features or landmarks are crucial in recognition and detection. The facial landmarks are obtained with HOG removing unnecessary noise [10]. This procedure ensures that the test/samples an image needs not to be used to its entire image form. Dalal and Triggs [4] stated in their analysis that histograms of directed gradient (HOG) descriptors significantly outperform current human detection feature sets. Small-scale gradients, proper orientation binning, comparatively coarse spatial binning, and high-quality local contrast normalization are all critical for successful results. Table 1 illustrates the taxonomy of most current face recognition methods in light of their technique and accuracy rate using AT&T datasets. Gabor [17] channels can misuse remarkable visual properties, for example, spatial localization, orientation selectivity, and spatial frequency qualities. Gabor [17] standards are heartless against modifications such as lighting, posture, and appearances, given
148 Table 1 Taxonomy of some most recent related works
T. Mahmud et al. Article
Accuracy rate (%)
Techniques
[11] [12] [13] [14] [15] [16]
95 85 86.95 92.87 92.60 95.10
PCA and MDC ICA and MDC Markov random fields Eigen face Statistical HOG and RVM
these enormous shortcomings and its excellent achievement in face recognition, and the fact that Gabor [17] alteration is not strictly meant for face recognition. The formula for change is predefined, rather than learned, in the practice of teaching. Additionally, PCA [11] and LDA classifiers think about global features, while LBP and Gabor classifiers think about local features [17].
3 Proposed Methodology Figure 1 Technique steps are shown to summarize the following primary portion: Firstly, has been applied histogram of oriented gradients (HOG), consequently apply the classifier: support vector machine (SVM), finally use the K nearest neighbor (KNN).
Fig. 1 Block diagram of the developed system
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Histogram of Oriented Gradients (HOG)
Basically, for features derived from an image, HOG is used. Feature extraction was started at the starting point, and derived functions were created. When an algorithm’s data is too huge to be handled and accused of being replicated, it must be modified into a reduced function structure. In the field of visual recognition and machine vision, HOG is an attribute identifier. The histogram of the directed gradient descriptor’s basic principle is that the dispersion of strength gradients or edge directions will reflect the nearby item’s appearance and form within a picture [4]. The image is isolated into tiny linked regions called cells, and a histogram of directed gradients is organized for the pixels within each cell. For these histograms, the reference is the descriptor. The nearby histograms can be normalized for better results by measuring a proportion of the amplitude over a wider area of the image, called a block [4]. To standardize all the cells, this power is then included within the square. This standardization combines variations in enlightenment and shadowing into a more extensive range. Over other descriptors, the HOG descriptor has a few favorable conditions. It operates on surrounding cells and is invariant to geometric and image metric changes except for the object’s location. It would simply turn up in broader regional regions. Analyzing fine alignment and good photographic metric standardization as coarse spatial areas frequently causes people to ignore the human body’s development as they expect a usually upstanding role. This way, in pictures, the HOG descriptor is better suited to human positions [10].
3.2
Classifier: Support Vector Machine (SVM)
Technologies for facial recognition are, unquestionably, still an area of study at work. The algorithm of a support vector machine was developed by Vapnik (1995). It has been associated with different accounts of difficulties with recognition. In this article, as a classifier, we use the assistance vector machine (SVM). A support vector machine (SVM) is formally embraced by an insulating hyperplane that is discriminating. When its allocated specified training results are spread, the equation generates an optimal hyperplane that organizes new models [3] called supervised learning. The initial SVM is a linear binary classifier, which helps two-class classification problems [16]. There are solid relations between the SVM and the neural network. Using the sigmoid kernel function, the SVM model is analogous to a two-layer perceptron neural network. Therefore, SVM modeling aims to identify the optimal hyperplane that isolates vector bunches, so that on one side of the plane are cases with one objective variable classification. On the other side of the plane are cases with the other category [3].
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K Nearest Neighbor (KNN)
Finally, a robust and straightforward classification algorithm as KNN [5] was introduced to smoothen the process of detection and recognition of facial features. It happens to be a vital classification algorithm of machine learning in this work. KNN is also a popular classification algorithm widely used in fingerprint detection and recognition. It is currently one of the most suitable algorithms for facial feature detection and recognition. Generally, with this classifier algorithm, training data goes through the algorithm and classified coordinates into clusters identified by an attribute.
4 Experimental Results At first, over 344 human subjects’ images were collected. Each subject contains five images. Subjects were of various age groups, from infant child to adult over the age of 50. We proceeded into the experiment by first select test sample images and sized them to a dimension of 969 pixels. Like the test, images were prepared, and then, it goes through the proposed developed system as shown in Fig. 2b–c. At first, the only detection was concerned, checked, and accuracy was calculated based on the output of detection; the proposed system gave output. At first, the test input images go through HOG for feature descriptor, noise cancellation. It eliminates an unnecessary portion of the image to increase higher accuracy of detection. It usually converts the images of size width and height, which are initially in vector and finally converted into length n. As the HOG stages are completed, the descriptive images go through a combination of SVM and KNN. Here, the dataset often decides the fate of the algorithm. At first, the KNN goes through the entire test dataset image and separates them into selective classification set as a parameter of detection according to individual facial features. The parameters are mainly the eyes, nose, mouth, and eye brow. Further analysis of SVM provides detection results of facial features. Facial features are identified using a red dotted line all around the particular facial part selected from the designed user interface as shown in Fig. 3. The developed system’s detection accuracy was always around 96% every time we experimented with the developed system. Now, recognizing a human subject from two images, image preparation was done just as done for detection. SVM and KNN analyzed the input images after it goes through HOG. A threshold value of 67,000 in dpi was set, which is the hyperplane in the SVM portion, as shown in Fig. 2a. Anything above that value found from the analysis of SVM and KNN results in that both input images are not of the same human subject. Suppose the
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Fig. 2 a Hyperplanes to divide into classes; b–c Test images with sized dimensions in pixel
Fig. 3 Detection- a Mouth; b Nose; c Right eye
Fig. 4 Result-a Face recognition; b Face recognition for the not same person; c Face recognition for the same person of the person being of different
results score below the preset threshold value in that case, the developed system results that both images’ recognition is a success and the inputted image is of the same human subject. Here, we design user interface results that are in plain text. For success in recognition, “Both of the same person.” For detection in not of the same person, “They are not same,” as shown in Fig. 4. For facial feature detection at first face model based on each individual, eyes were the primary concern. The coordinates for the center of individual features were landmarked. Essential elements are explained in Eq. (5)
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Hf ¼ Kf De
ð1Þ
he ¼ K e hf
ð2Þ
Here, De represents the distance between both eyes, which is needed to identify the left eye to the right eye. g¼
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi gx 2 þ gy 2
h ¼ arctan
gy gx
ð3Þ ð4Þ
Gx and Gy, respectively, are the horizontal and vertical components of the dpi transformation as shown in Fig. 5. For standard scenarios, the window size is prefixed here with a dimension between 128 144 and 456 550 depending on if the image dimension is not always pre-sized to the standard size. Here, descriptor values for each pixel over 8 8 blocks are quantized into 9 block sizes. Each block represents a directional angle of gradient and value in that block. Further, a histogram generates normalized over a 16 16-block size. SVM and KNN are introduced here to go through analysis here. Total Difference ¼
n X
jXi Yi j
ð5Þ
i¼1
Here, Xi is a pixel of the first input image with which the recognition is done, whereas Yi holds the other image’s pixels. These equation results in total difference which generates a value of dpi. The threshold value is then compared with the total difference. This recognition is done in this system. Tests are conducted on actual Indian face database images [18] and have a higher outcome for the test collection in terms of 96% accuracy score identification. The suggested methodology feature extraction technique compares and considers the accuracy with which the methodology feature extraction discussed provides greater precision, as seen in Tables 2 and 3 relative to the other feature extraction technique with the machine learning algorithm.
Fig. 5 Geometric face model focusing on eye
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Table 2 Results of face detection Total faces
Detection rate (%)
False detection rate (%)
Average processing time (s)
Methods
276 305 276 305 276 305
88.3 89.5 92.3 94.6 96.89 98.1
2.5 2.89 2.1 2.15 1.3 1.5
5.1 5.2 4.6 4.8 4.2 4.5
AdaBoost + LBP AdaBoost + Haar Proposed method
Table 3 Face recognition results Total faces
Recognition rate (%)
False acceptance rate (%)
Methods
344 344 344 344 344 344
72.6 75.7 81.3 83.2 92.1 96
4.1 3.9 2.6 2.4 1.7 1.5
PCA LDA PCA + LDA LBP Gabor Proposed method
5 Conclusions Face detection and recognition system have planned to solve the issues of existing manual frameworks. The framework performs acceptably in various poses and expressions. The system has also been developed to test the face recognition and recognition techniques used as a benchmark for current work. Local databases have been used for this purpose to compare a few uniformly executed procedures over various samples, thereby performing different methods arbitrarily depending on the normal execution of test outcomes. The results of facial recognition and identification strategies on the season are as given in Tables 2 and 3, respectively. Current framework is working moderately, yet it has a lot of false identifications than the developed system. Facial recognition based on HOG feature extraction works very well in developed system. The developed system gives a 96% accuracy rate on facial recognition. As compared to the other techniques, our methodology provides
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better accuracy. The developed method is also memory efficient because SVM uses a subset of training points in the decision function. On the other hand, if the dataset has more noise like target classes are overlapping, then the SVM classifier will not work accurately. In future, real-time facial recognition can be done by having help from this system.
References 1. https://www.wired.com/2012/06/google-x-neural-network 2. Rashid RA, Mahalin NH, Sarijari MA, Aziz AAA (2008, May) Security system using biometric technology: design and implementation of Voice Recognition System (VRS). In: 2008 international conference on computer and communication engineering, pp 898–902. IEEE 3. Joachims T (1999, June) Transductive inference for text classification using support vector machines. In: Icml, vol 99, pp 200–209 4. Dalal N, Triggs B (2005, June) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 886–893. Ieee 5. Imandoust SB, Bolandraftar M (2013) Application of k-nearest neighbor (knn) approach for predicting economic events: theoretical background. Int J Eng Res Appl 3(5):605–610 6. Wolf L (2009) Face recognition, geometric vs. appearance-based. Encycl Biometrics 2 7. Mita T, Kaneko T, Hori O (2005, October) Joint haar-like features for face detection. In: Tenth IEEE international conference on computer vision (ICCV’05) volume 1, vol 2, pp 1619–1626. IEEE 8. Solanki DV, Kothari AM (2015) Comparative survey of face recognition techniques. Proc ETCEE 63 9. Turk MA, Pentland AP (1991, January) Face recognition using eigenfaces. In: Proceedings 1991 IEEE computer society conference on computer vision and pattern recognition, pp 586– 587. IEEE computer society 10. Ghinea G, Kannan R, Kannaiyan S (2014) Gradient-orientation-based PCA subspace for novel face recognition. IEEE Access 2:914–920 11. Mondal S, Bag S (2017) Face recognition using pca and minimum distance classifier. In Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications, pp 397–405. Springer, Singapore 12. Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137 13. Huang R, Pavlovic V, Metaxas DN (2004, August) A hybrid face recognition method using markov random fields. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 3, pp 157–160. IEEE 14. Ma Y, Li S (2006) The modified eigenface method using two thresholds. Int J Signal Process 2(4):236–239 15. Rabbani MA, Chellappan C (2007) A different approach to appearance–based statistical method for face recognition using median. Int J Comput Sci Netw Secur 7(4):262–267 16. Karthik HS, Manikandan J (2017, October) Evaluation of relevance vector machine classifier for a real-time face recognition system. In: 2017 IEEE international conference on consumer electronics-Asia (ICCE-Asia, pp 26–30. IEEE
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17. Wiskott L, Krüger N, Kuiger N, Von Der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19(7):775–779 18. https://ieee-dataport.org/keywords/indian-face-database
Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period S. Sirajudeen, Balaganesh Duraisamy, Haleema, and V. Ajantha Devi
Abstract The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/ learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student’s fraternity as it is during this COVID-19 pandemic period. Keywords Sentiment analysis
Online learning Covid-19 pandemic period
The original version of this chapter was revised: The author’s affiliation “PhD Scholar, Lincoln University College, Malaysia” of author “S. Sirajudeen” has been updated. The correction to this chapter is available at https://doi.org/10.1007/978-981-16-3153-5_61 S. Sirajudeen (&) PhD Scholar, Lincoln University College, Petaling Jaya, Malaysia B. Duraisamy Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] Haleema Adjunct Faculty, University of Stirling, RAK Campus, Stirling, UAE e-mail: [email protected] V. Ajantha Devi AP3 Solutions, Chennai, TN, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021, corrected publication 2022 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_19
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1 Introduction The use of the Internet by various participants in different fields is expected. The Internet provides access to vast pieces of information to many communities. Many online learning platforms are available on the Internet, where additional resources are used to engage participants in individual and group learning. The development of online courses helps educational institutions and organizations disseminate knowledge to the students’ community worldwide irrespective of their geographical location. The notion of online learning was generated by the uniqueness of using the Internet as a learning tool. Online learning is a popular learning process, as it is versatile and facilitates learning on-demand at any time from anywhere. It is essential to develop user-friendly e-learning systems to allow the users to seamlessly access the e-learning content online, collaborate with their peers/cohort learners, and communicate with the service providers. E-learning management essential role is to address the needs and expectations of its customers effectively. Systematic surveying of consumer perceptions and views is one of the key tasks of e-learning management to fulfill their online learning requirements and expectations as effectively as possible. This method of periodically receiving feedback from the learners about the course content, trainer/mentor, course relevant technical support facilities, and other services helps the e-learning service providers assess the quality of their services and take effective corrective measures to resolve the issues related to the content delivery. Effective design of the learning management system (LMS) and providing a quick and reliable response to the learners’ concerns is essential to keep them engaged in the uninterrupted learning process [1]. Sentiment analysis is the technique for characterizing and classifying assessments computationally communicated in a bit of text, especially to choose whether the author’s disposition toward a particular subject, item, and so on is positive, negative, or impartial. Sentiment analysis has been applied in various fields to anticipate the clients’ assessment and use the got input to improve the business needs. For instance, sentiment analysis in business can help organizations assess client discernments to upgrade their items, give better client support, and even discover potential business openings. Sentiment analysis [2] in legislative issues will conjecture changes in popular assessment on a contender for decisions. Individuals can become better educated in regular day-to-day existence about the choice of electronic items, books to peruse, or films to watch, consequently settling on better buying choices. This paper has been organized into five different sections. Section 2 describes the literature review about the research topic. Section 3 elaborates on the proposed research methodology for COVID-19 pandemic situation. Section 4 demonstrates the case study of assessing the students’ perception of online learning adoption by performing sentiment analysis on a dataset collected during the pre-COVID-19 period. The key results and conclusions are explained in Sect. 5.
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2 Literature Review E-learning gives a virtual classroom environment to the students with the adaptability to get to the substance of interest using any keen gadget from anyplace. Hence, e-learning [3] can be depicted as a strategy for training in which the understudies can connect with instructors (guides) and different understudies (students) through email, electronic gatherings, video conferencing, talk rooms, announcement sheets, and different methods for correspondence regardless of their actual topographical area. To be successful in any e-learning framework, specific necessities and particular highlights should be met, including flexibility, which empowers the framework to adjust to the capacities and targets of every client [4]; intelligence [5]; convenience and strong assets to oblige the framework and give speedy and quick framework access for members. Criticism is gathered through inquiries of target type, online overviews, and text depiction as expressed by Sangeetha et al. [6]. Literary info is the best to determine all the perspectives on their learning progress, as distinguished by Nasim et al. [7]. Different procedures confine their conclusions by methods for questions. Vietnamese understudy criticism is utilized in their work, in which students give their feelings through a short printed rundown. It incorporates positive, negative, and unbiased remarks of Van Nguyen et al. [8]. Clarizia et al. [9] and Wang et al. [10] proposed a methodology to measure students’ moods in a physical classroom concerning various topics and applied it in an e-learning setting to improve the students learning experience. Salloum et al. [1] analyzed that perceived ease of use and perceived usefulness are the main powerful predictors for usage intention. Hence, the developers should focus on building a system that is user-friendly and easy to use. The e-learning system should be aided with multiple content delivery modes such as visual, audio, animations, and videos, along with content quality. The process of learning is made fun and easy for the students. It is always in the designer and developer phase to design the system for easy use, making the user adopt and accept e-learning. Sultana et al. [11] stated that student feedback is essential to determine e-learning technologies’ efficacy. Their analysis results indicate that SVM and MLP-deep learning systems have generally achieved good performance in classification precision, RMSE, sensitivity and specificity, and ROC curve area compared to the other classifiers. Ali et al. [12], in their work, proved that the output of multinomial Naive Bayes and MLP classifier in the process of e-learning sentiment analysis is outclassed over all three classifiers, namely random forest, stochastic gradient descent, and support vector machine. The updated numeric data file is taken, and classification techniques are applied to predict the students’ results. The RBFNN-based classification is better for predicting the grades of the students among the implemented models J48, random tree,
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multilayer perceptron neural network (MLPNN), radial basis neural network (RBFNN) than other classification techniques as proved by Arumugam et al. [13]. The assessment findings of Ganpat Singh Chauhan et al. [14] show that the quality of the teaching/learning process can be enhanced while analyzing the students’ feedback based on different aspects. Machine learning models employing aspect-based methods can also be considered in the future to find the elements and their syntactic structure polarities. Research work by Jegatha Deborah et al. [15] proposes a clear dialogue of mutual authentication to enable the students and the server to complete the mutual authentication. The online assessment system safely distributes the question papers and answers to the teachers and the students respectively and gathers them. The work of Tiwari and Nanhay et al. [16] introduces the Twitter sentiment analysis ensemble process. Two ensemble methods, AdaBoost and Extra Tree, are used to increase the accuracy of these algorithms. Finally, it has been concluded that the Extra Tree classifier of this study outperforms all the other algorithms applied. Imani et al. [17] reviewed distinct theories of emotion. Different methods of identifying emotions were seen, and their advantages and drawbacks were addressed for use in e-learning systems. According to the results of this analysis, the multimodal emotion recognition systems through knowledge fusion, such as facial expressions, body movements, and user messages, provide better efficiency than single-modal ones. Qomariyah et al. [18] have implemented a recommendation framework model to help students find the right content during their research and keep them inspired. By creating more acceptable material content, the students would find it beneficial and the teachers as they want to understand their students’ learning style. An architecture of service design that uses the REST API to integrate the application between entities has also been suggested.
3 Proposed Method The advancements in Internet technologies have brought revolutionary changes in almost all domains from agriculture to banking, finance, marketing, education, etc. E-learning is one of the popular applications of the Internet that enables students to upskill themselves in any field of interest without attending the classes in the physical classroom environment. E-learning provides a comfortable and convenient platform to learn about anything from any trainer/mentor of the learner’s choice. The lockdown, work from home option for the employees, and the job loss due to the COVID-19 pandemic crisis enabled many individuals to utilize their free time constructively to upskill themselves. There is exponential growth in the number of registrants for popular e-learning service providers’ online courses during the COVID-19 pandemic period. In this period, more and more individuals voluntarily opt for online learning, and it has been enforced on the entire student fraternity by
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all the educational institutions worldwide. This paper focuses on analyzing the students’ sentiments who have undertaken online courses from various e-learning platforms during the pre-COVID-19 period to determine how well the students’ community adopt online learning during the COVID-19 pandemic period. During this challenging situation, many people have shown great interest in learning new skills by enrolling in Massive Open Online Courses (MOOCs) provided by prominent e-learning service providers such as edX, YouTube, Udemy, FutureLearn, Lynda, and Coursera to kill the boredom during the COVID-19 lockdown and to increase one’s professional value in the job market. In addition to strengthening one’s career during this economic turmoil, having a new talent will give people a sense of security that will help deal with the epidemic’s anxiety. For example, since the start of the coronavirus outbreak, Coursera has seen an eightfold increase in social science, personal growth, arts and humanities courses, and many more. Businesses employ periodic feedback collection mechanisms to ensure the continuous quality improvement of any products/services. Sentiment analysis refers to extracting the sentiments hidden in the text reviews [19] provided by the customers/clients about a product/service. It employs natural language processing techniques to classify customer opinion [20] as positive/negative/ neutral. The process of extraction and classification implements sentiment analysis with high accuracy machine learning algorithms.
Fig. 1 Flowgraph of the proposed methodology
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The dataset used in this study is collected prior to covid - 19 to assess the percep-tion of the students about e-learning. The respondents were school and college stu-dents, working professionals and other individuals who had the exposure to learning courses online.During covid - 19, this dataset is used to determine how well the stu-dents can adopt to online learning by applying sentiment analysis on the feedback given by the respondents. Jupyter Notebook IDE for Python was used to implement the sentiment analysis on the collected dataset. The implementation steps are summa-rized as given below: 1) Import the dataset 2) Data Preprocessing – Exploratory Data Analysis 3) Text Preprocessing a) Accented Character Removal b) Tokenization c) Stop Words Removal d) Special Characters Removal e) Lowercase Conversion f) Stemming / Lemmatization 4) Apply Vectorization to get DTM (Document Term Matrix) – Converting Un-structured Data into Structured (Feature Extraction) 5) Apply VADER Sentiment Analysis The below figure depicts the proposed methodology’s flowgraph to perform sentiment analysis on the students’ text reviews about online learning during the pre-COVID-19 pandemic period.
4 Case Study In this paper, we utilize the datasets from Kaggle, which were slithered from the web and named positive/negative. The information gave emojis (emoticon), usernames, and hashtags that needed to be handled (to be meaningful) and changed over into a standard structure. We utilize different machine learning algorithms dependent on NLP to lead conclusion investigation using the extricated highlights. At last, we report our exploratory outcomes also and discoveries toward the end. • The number of respondents that had the exposure to online learning via vari-ous modes of learning such as recorded content, online live classrooms, Face-book/ YouTube Live, and access to text materials (Type of Class System). • The number of respondents that had faced internet speed issues while in the process of online learning (Internet Speed from Data). • The number of respondents that are interested in taking up offline assessments (projects) or online assessments (quiz) or both.
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Fig. 2 Type of class system
Finally, Sentiment Analysis (VADER sentiment) has been performed to assess the willingness of the respondents to adopt online learning. The paper confers most of the case study conducted on comments/reviews/feedback/opinions of online learning participants during the Pre-Covid19 situation. Likewise, comment on class system type has been analyzed based on recorded classes, online platform (Zoom), Facebook live, and uploaded lecture notes (PDF, PPT) with the highest 41.9%, 41.3% to recorded level uploaded lecture notes. Whereas utilization of online platform was 13.1%, and Facebook live was very low with 3.8%. When the usage of data becomes higher, the speed of the Internet goes critically very low. Whereas according to the analysis, 63.8% faced very poor speed of internet compared to 4.3% of the high speed of Internet. The case study on class test analysis proves that the higher percentage of 73 is calculated after resuming offline classes and the most minor 4 to online platforms. Based on the above-said case studies, it is understood that the comments provided for diverse categories are not equally commented.
Fig. 3 Internet speed from data
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Fig. 4 Class test analysis
Fig. 5 Sentiment analysis of online class
Comments on online classes have been segregated as positive, negative, and neutral. The highest percentage of 40% was calculated for neutral comments, a moderately high rate of 36% for positive comments, and a low rate of 24% to negative comments.
5 Conclusion and Future Work Due to the COVID-19 pandemic, the learning process worldwide has been disrupted. In education and all other industries, online learning is becoming much more necessary and is very important. In particular, educational institutions face the specific challenges of smoothly sustaining the learning process during COVID-19 while ensuring that it is still beneficial. These institutions must also consider what pushes teachers and learners into the online learning framework. This study’s main idea was to understand online learning’s real usage during the pre-COVID-19 situation and COVID-19 pandemic situation. As per EDA analysis, it is observed that online learning usage before the COVID-19 situation was normal. This lockdown period is a shocker for computerized administrations, such as the use of apps,
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the development of virtual study halls, fake online testing, online video testing, live talks, considerations, and report sharing. It generally uncovers fundamental e-learning in schooling, especially during this disengagement.
References 1. Salloum SA, Alhamad AQM, Al-Emran M, Monem AA, Shaalan K (2019) Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access 7:128445–128462 2. Chen MH, Chen WF, Ku LW (2018) Application of sentiment analysis to language learning. IEEE Access 6:24433–24442 3. Pozgaj Z, Knezevic B (2007, June) E-Learning: survey on students’ opinions. In: 2007 29th international conference on information technology interfaces, pp 381–386. IEEE 4. Seng LC, Hok TT (2003, December) Humanizing E-learning. In: Proceedings 2003 international conference on cyberworlds, pp 418–422. IEEE 5. Giroire H, Le Calvez F, Tisseau G (2006, July) Benefits of knowledge-based interactive learning environments: a case in combinatorics. In: Sixth IEEE international conference on advanced learning technologies (ICALT’06), pp 285–289. IEEE 6. Sangeetha K, Prabha D (2020) Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM. J Ambient Intell Humanized Comput 1–10 7. Nasim Z, Rajput Q, Haider S (2017, July) Sentiment analysis of student feedback using machine learning and lexicon based approaches. In: 2017 international conference on research and innovation in information systems (ICRIIS), pp 1–6. IEEE 8. Van Nguyen K, Nguyen VD, Nguyen PX, Truong TT, Nguyen NLT (2018, November) UIT-VSFC: Vietnamese students’ feedback corpus for sentiment analysis. In: 2018 10th international conference on knowledge and systems engineering (KSE), pp 19–24. IEEE 9. Clarizia F, Colace F, De Santo M, Lombardi M, Pascale F, Pietrosanto A (2018, January) E-learning and sentiment analysis: a case study. In: Proceedings of the 6th international conference on information and education technology, pp 111–118 10. Wang K, Zhang Y (2020) Topic sentiment analysis in online learning community from college students. J Data Inf Sci 5(2):33–61 11. Sultana J, Sultana N, Yadav K, AlFayez F (2018, April) Prediction of sentiment analysis on educational data based on deep learning approach. In: 2018 21st Saudi computer society national computer conference (NCC), pp 1–5. IEEE 12. Kandhro IA, Chhajro MA, Kumar K, Lashari HN, Khan U (2019) Student feedback sentiment analysis model using various machine learning schemes: a review. Indian J Sci Technol 12(14) 13. Arumugam S, Kovalan A, Narayanan AE (2019, November) A learning performance assessment model using neural network classification methods of e-Learning activity log data. In: 2019 international conference on smart systems and inventive technology (ICSSIT), pp 441–448. IEEE 14. Chauhan GS, Agrawal P, Meena YK (2019) Aspect-based sentiment analysis of students’ feedback to improve teaching–learning process. In: Information and communication technology for intelligent systems, pp 259–266. Springer, Singapore 15. Karthika R, Vijayakumar P, Rawal BS, Wang Y (2019, May) Secure online examination system for e-learning. In: 2019 IEEE Canadian conference of electrical and computer engineering (CCECE), pp 1–4. IEEE 16. Tiwari D, Singh N (2019) Ensemble approach for twitter sentiment analysis. IJ Inf Technol Comput Sci no. August 20–26
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17. Imani M, Montazer GA (2019) A survey of emotion recognition methods with emphasis on E-Learning environments. J Netw Comput Appl 147: 18. Qomariyah NN, Fajar AN (2019, December) Recommender system for e-learning based on personal learning style. In: 2019 international seminar on research of information technology and intelligent systems (ISRITI), pp 563–567. IEEE 19. Santosh DT, Vardhan BV, Ramesh D (2016, February) Extracting product features from reviews using Feature Ontology Tree applied on LDA topic clusters. In: 2016 IEEE 6th international conference on advanced computing (IACC), pp 163–168. IEEE 20. Soong HC, Jalil NBA, Ayyasamy RK, Akbar R (2019, April) The essential of sentiment analysis and opinion mining in social media: introduction and survey of the recent approaches and techniques. In: 2019 IEEE 9th symposium on computer applications & industrial electronics (ISCAIE), pp 272–277. IEEE
Encoding and Refuge Shelter by Embracing Steganography with Hybrid Methods in Image Reduction Processing U. Reethika and S. Srinivasan
Abstract The assessment chooses to propose a mutt technique to move the data from the source to the objective securely. Security is something imperative on the Web. Numerous programmers will assault the information during transmission. In some cases, the data does not hold for what it is worth at the spot of the objective. In a current framework, the information is installed with the picture. Thus, this technique has a disadvantage with a few restrictions, and commotion is distinguished with this strategy. Pixel abandonment is a significant clash in the current framework. It was shut to find the intruder which getting to the steganography system, and it serves as the ciphertext to the different sides of the beginning to end structure. It guaranteed and produced the security as vital for the sensitive information has been ensured to that. At the same time, the encoded information pack is itself proof of the proximity of critical data. Steganography works out in the right way past and makes the ciphertext vague to unapproved clients. In the proposed framework, the disadvantage defeats by the mixture strategy. The half and half methodology contains MD5, DES, and 3DES. Here, the pictures are changed over into ciphertext by applying the steganography imaging framework (SIS). These strategies installed the content inside the image and sent it without the deficiency of pixels.
Keywords DES—Data encryption standards 3DES—Thrice data encryption standards MD5—Message digest 5 SIS—Steganography imaging system
1 Introduction Steganography is a procedure wherein a secret message is changed over into a fake message. Steganography implies spread structure. Steganography is the arrangement to thwart secret information by making uncertainty. Steganography is less U. Reethika (&) S. Srinivasan Vivekanandha College of Arts and Science for Women(Autonomous), Tiruchengode, Namakkal, Tamilnadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_20
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unmistakable than cryptography. In steganography, the design of data cannot be altered. With the improvement of development, likely results for data stowing ceaselessly have extended. For example, the microdot development is used in basically all bleeding-edge printers. It grants to check all made printouts figuratively speaking, that is indistinguishable for customers. As of now, an astoundingly standard kind of steganography is disguising information in mechanized pictures. There is some redundancy in taking care of images. All pixels in modernized images are coded using a predefined proportion of pieces, and usually, it is hard to see the changing of the most un-critical details. The most un-critical components can be used for taking care of secret information. A near situation happens when taking care of cutting edge sound. The examination is foreseen at building up an application to safely pass the message between the general population’s source and goal mindful of anticipating the delicate data individually. It has to stay away from helplessness and avoid classified information to the administrator and workers. In particular, whenever utilized, the cryptography used the triple-DES (3 DES) calculation to encode three times which aides symmetric key and square figure at each season of encryption yet in the every datum square all the while. Single encryption is having been done to establish the content precisely to satirizing the helplessness effectively. It considers the 16 and 32-bit keys used to encode the content record greatly to create a diverse figure message in the given block. So it is made a perplexity at outsider individuals to influence uproar of information alone. Next, it moved to add the sound record to the offered content to convey the goal [1].
2 Related Works Weiqi Luo et al. proposed and recognized the quantization table from the propelled pictures. JPEG weight botch examinations are balanced in the dimensional scale JPEG [2]. A novel framework enhances the steganography security via post-processing on the embedding units (i.e., pixel values and DCT coefficients) of stego directly. Canister Li et al. proposed MBNS steganography. Their data is inserting into the pictures in a notational structure. The pixels of a picture are segmented by the objective premise [3]. Provided a survey on steganalysis and steganography for images. The container image was transmitted in a lossless environment. Xinpeng, Wang et al. proposed a stego-coding strategy for spreading the nuts consecutive. One yet can be installed with the other puzzle [4]. Tomáš Denmark et al. portrayed the spread parts are balanced together to control the embedded pictures size. Denmark and Fridrich [5] presented the steganography model for hiding sensitive information in multiple JPEG images. The model had used the discrete cosine transform (DCT) for the embedding process and, hence, was suitable for real-time applications.
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Linjie Guo et al. proposed a discrete cosine change (DCT) coefficient confined from the uniform inserting’s turning (UED). Jessica Fridrich et al. proposed a decision honest portrayal instead of a co-event grid. Diverse sort of residuals are used in the projection spatial rich model (PSRM). The reliable steganalysis techniques were used to detect and estimate the secret message are PoV, WS steganalyser, and MLSB-WS. Joao Rafael Carneiro Tavares et al. proposed an LSB-word chase used to diminish the expected number of modifications per pixel (EMPP) [6]. K. Satish et al. proposed a spread range picture steganography (CSSIS) method used to actualize the circuit plans, working at low control in the equipment field. Rui Du et al. proposed an essential east piece (LSB). The message length is assessed. An upper bound of 0.005 bits per pixel is at a sheltered stage. The secret message length is derived by inspecting the lossless capacity in the LSB and shifted LSB plane. R. Acosta-Buenaño et al. proposed a propelled TV system; this technique’s objective is to transmit the two pictures. One uses the lower information move limit. Donghui Hu et al. proposed steganography without embeddings (SWE) which insert the information into the image with no misfortune. Tomáš Denmark et al. depicted a pre-spread picture. These photographs chose JPEG adjustments with the costs of individual DCT coefficients. S. Trivedi, R et al. proposed a sign to-uproar extent (SNR) to introducing the counts for encoded the messages consecutive which features the host signal [6] and method for detecting correct stego-key in sequential steganography. Chung-Ming Wang et al. portrayed a strategy for resamples the one surface picture with neighborhood appearance with the self-assurance size [7]. A data hiding criteria for color images using the binary space partitioning (BSP tree) results in a high capacity steganography system with low visual distortion. Maria Gkizel et al. proposed a spread-go (SS) message embeddings the boots sign furthermore into the uproar extent (SINR) [8]. Jan Kodovsky et al. proposed three steganography strategies called HUGO, edge-adaptable count by Luo, and preferably coded ternary ±1 embedding [9]. Ran-Zan et al. proposed a reversible differentiation mapping (RCM) watermarking. The LSB histogram changes between stego picture and the spread picture and proposed a method tended for ensuring pictures that include the scattering of the secret picture into numerous shadow pictures. Farid et al. [10] depicted that the objective of steganography is finding the covered messages. Steganalysis recognizes the message embedded inside the propelled pictures. They demonstrated a possibility to detect image modifications caused by steganography and watermarking in images that were initially stored in the JPEG format.
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3 Methodology Steganography portrays concealing the mixed-media information inside to identify the objective. Finding the closest is inconvincible; the given key thought sidestep to communicated to the specific friend framework, which helps the calculation. It decides to recognize that the distinction has not been wanted to be the media recipients should not for the situation, which conjectures the current information. In cryptography, a figure message, for instance, may actuate uncertainty concerning the recipient while an impalpable message made with steganographic techniques will not. Regardless, steganography can be useful when the use of cryptography is illegal. Where cryptography and strong encryption are expelled, steganography can keep an essential separation from such ways to deal with pass the message clandestinely. At whatever point, utilizing the steganography strategies cannot create the ideal answer forever. So we need security to send the record (information) without weakness. So this is a significant commendation to defeat these downsides and helps execute the adages and wordings separately. It portrays to get to similar information in different sorts of the calculation to deliver high proficiency and the presentation range.
3.1
Data Encryption Standards (DES)
Data encryption standard (DES) has been found vulnerable against very stunning attacks, and as such, the noticeable quality of DES has been discovered to some degree on the rot. It passes on the 64 pieces similar to the two sides. Also, what is more, it encoded similarly as unscrambled the data should be secure transmission to the DES structure. The critical thing is the DES pass on the 64 pieces in the given communications. However, the key piece has been taken to convey for the 56 pieces just re-imaging eight keys are adding clamor with fracture information separately. It is utilized to help for giving security between the transmissions of data (Fig. 1). (a) Steps • A 64-digit block handles the underlying stage of the given framework. • The plain content has performed IP. • It has been created to the two parts that help to get to one side plain content just as the right side. It is called for LPT and RPT. • Both these different sides have performed to 16 rounds of encryption handling from the given information. • Hence, both two sections consolidate to the last stage well to create the ciphertext. • Getting the outcome.
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Retrieving
Data
Transformation
Data base
Enhanced by DES, 3DES and MD5
Return
Output
Compress by Secured
Fig. 1 Overall framework
(b) Permutation Processing Key IP Spreading out the permutation Consequently, the planning has been settled for the various sides. One is left a plain message (LPT), and right plain message (RPT), which passed on 48 pieces and squares to the 40 pieces, remaining four pieces has been portrayed for the impact data transmission, and the last four pieces take a gander at the change of joins those things. Additionally, the RPT has settled the method updates for the XOR control, underpinning the key change frameworks. It extends the utilization methodology that has better execution to the multimedia transmission structure.
3.2
Formulas
1. Fundamental counting Generally, we take the two variables for a, b and outcomes to be c. It manipulates to access the variable for, NM
or
nm
where the variable of a, b is an independent variable. Choosing and arrange:
ð1Þ
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nPk ¼ n!=ðN kÞ!
ð2Þ
nCk ¼ nPk=k! ¼ n!=k!ðn kÞ!
ð3Þ
2. Combination
Whatever the formulas manipulated before that find the probability of given data then move on the permutation processing. Here, 10!/10−3! = 10 * 9 * 8 = 720. 3. Permutation & Combination
Pðn; rÞ ¼ n!=ðn rÞ!
ð4Þ
Cðn; rÞ ¼ n!=ðn rÞ!r!
ð5Þ
where • p depicts the permutation of the data and C is combination. • n represents a number of data from the database. • r determine accessing the limitation of data.
3.3
3DES
A similar preparation has been done at this point for the DES. In any case, the cycle goes to the higher defensively in the given information by getting to the threefold season of DES separately. So it is controlled to discover the exactness as higher contrasted with existing while at the same time utilizing the steganography procedures. Thinking about the DES and 3DES has been created as a shield in the steganography cycle sidestep to communicating the start to finish association. The troublesome test has been delivered to the weaknesses.
3.4
MD5—Message-Digest 5
An MD5 hash is made by taking a line of any length and encoding it into a 128-piece remarkable engraving. Encoding a practically identical string utilizing the MD5 tally will dependably accomplish the equivalent 128-piece hash yield. MD5 hashes are ordinarily used with smaller lines while making sure about passwords,
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Mastercard numbers or other delicate information in information bases, for example, the standard MySQL. This gadget gives a practical and essential way to deal with encoding an MD5 hash from a direct line of up to 256 characters in length. MD5 hashes are besides used to guarantee the information goodness of records. Since the MD5 hash computation reliably conveys a comparable yield for comparative given data, customers can break down a hash of the source record with an as of late made hash of the objective archive to watch that it is flawless and unmodified. An MD5 hash is not encryption. It is an extraordinary finger impression of the given data. In any case, it is a solitary heading trade, and likewise, it is nearly hard to sort out an MD5 hash to recuperate the primary string. For example: Welcome goes to encrypt MD5 which is: 83218ac34c1834c26781fe4bde918ee4. This steganography has been sending the document for text to change the MD5 calculation over to create the best answer for the steganography while utilizing the content record. Since the programmer has does not have the foggiest idea about the delicate data at any expense. So we are getting to make all the more testing in the nerd cycle of the MD5 calculation in the steganography techniques. (a) Steps 1. 2. 3. 4. 5.
To fastening the cushioning (extending) bits. To decide the bits length. To introduce the MD cradle The cycle has been controlling by the 16 bit block preparing Return the outcomes.
This cycle has been set for encryption preparation. Thus, the unscramble, the information just as done to the decoding as standard, worn out for the procedure steps. So clearly, how about we locate the most security to the steganography while utilizing the half and half techniques produce the insurance for without criminals. It helps execute the precision, execution, and assurance to the information transmission in the media strategies.
4 Results and Discussion 4.1
Experimental Results
This way, the transmission of the system’s encryption and translation has been changed to convey the higher outcome. It has various kinds of transmission has done adequately and improved the changing over system’s profitability in the given issue. Additionally, the keys used to change over similarly as the two sides and completed as the critical change to the security and open key, which gets to the symmetric key overhaul. By then, it empties the defects and improves better
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100
Accuracy (%)
80 DES
60
3DES
40
MD5
20 0 secret key
Aack
outcome
performance
security
Acvity
Fig. 2 Accuracy chart
Table 1 Accuracy report
S. no.
Techniques
Accuracy
Performance evaluation
1 2 3 4
AES DES 3DES MD5
80 88.9 91 98.9
Optimal Optimal Optimal Optimal
execution while taking a gander at the current system. Every part changes over added to the different arrangements of the key transmission (Fig. 2; Table 1).
5 Conclusion and Future Enhancement The steganography procedure has been executed as excellent and it delivered an ideal arrangement that the multimedia control gives because of believers the transmission methods. At that point, the presented technique has been actualized by half and half strategies, for example, DES, 3DES, and MD5, in a different situation. This strategy implements the security level of information as more vital and being implanted. The hybrid algorithms made encryption with steganography which further improves the security level. It was expanded to create the exhibition, precision, and valid data that has been recognized and the delivered reliable information separately. Further, research will be actualized for the propelled systems to locate the best arrangement that maintains a strategic distance from the gate-crashes. To facilitate the process of encryption and decryption using video files, the video and image-oriented encryption method should be enhanced to use this software online. And the application will provide many more benefits compared to the presented system.
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References 1. Brandao AS, Jorge DC (2016) Artificial neural networks applied to image steganography. IEEE Latin America Trans 14(3):1361–1366 2. Luo W, Wang Y, Huang J (2010) Security analysis on spatial $\pm $1 steganography for JPEG decompressed images. IEEE Signal Process Lett 18(1):39–42 3. Li B, Fang Y, Huang J (2008) Steganalysis of multiple-base notational system steganography. IEEE Signal Process Lett 15:493–496 4. Zhang X, Wang S (2006) Dynamical running coding in digital steganography. IEEE Signal Process Lett 13(3):165–168 5. Denemark T, Fridrich J (2017) Steganography with multiple JPEG images of the same scene. IEEE Trans Inf Forensics Secur 12(10):2308–2319 6. Trivedi S, Chandramouli R (2005) Secret key estimation in sequential steganography. IEEE Trans Signal Process 53(2):746–757 7. Wu KC, Wang CM (2014) Steganography using reversible texture synthesis. IEEE Trans Image Process 24(1):130–139 8. Abbas C, Joan C, Kevin C, PaulMc K (2010) Digital image steganography. Survey and analysis of current methods. Signal Process 90(3):727–752 9. Hasan YMY, Karam LJ (2000) Morphological text extraction from images. IEEE Trans Image Process 9(11):1978–1983 10. Fridrich J, Goljan M, Du R (2001) Detecting LSB steganography in color, and gray-scale images. IEEE Multimedia 8(4):22–28
Bigdata Analysis Using Machine Learning Algorithm in Predicting the Cardiovascular Disease D. R. Krithika and K. Rohini
Abstract Group of unconditional of heart and blood vessel cerebrovascular disease, coronary heart disease, and rheumatic heart disease are (CVD) cardiovascular disease. 31% of global cardiovascular deaths 85% are due to strokes and heart attacks. The heart itself is a muscle, and it needs oxygen. The arteries getting blocked or clogged are coronary arteries. The formation of plaque is obstructing the artery. We call that coronary artery disease as heart failure. The ruptured plaque highly thrombogenic material happens, when blood clot obstructs the blood vessel. Part of muscle tissue dies is a heart attack. Cardiac arrest is the actual death of the heart. Machine learning algorithms in bagging, boosting, and stacking of ensemble techniques. This paper proposed to predict heart disease in classification techniques using a machine learning algorithm. Keywords CVD—Cardiovascular disease
Heart disease Machine learning
1 Introduction Nowadays, cardiovascular disease is a high cause of death. The primary reason for this disease is cholesterol, pulse, blood pressure The basis of ensemble learning, weak and robust learners, bagging, boosting, and stacking is also discussed in this paper. Continuous and discrete problems have good results in machine learning. It depends upon many variables of the issues, and it depends upon distribution hypothesis, the dimensionality of space, and quantity of data. We can call ensemble learning theory as a weak learner. Bias is the difference between the correct value and the average predicted value. The spread of our data is variance. Low bias and low variance are a good balance. Bagging is avoiding overfitting and reduces variance. Boosting reduces bias and variance in the machine learning algorithm. XG Boost is an open-source and supervised learning algorithm. Ada boosting is an D. R. Krithika (&) K. Rohini Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_21
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Fig. 1 Ensemble model
adaptive boosting algorithm. We were stacking combined different learning algorithms (heterogeneous weak learners). But homogeneous weak learners are present in bagging and boosting. The ensemble technique is combining multiple models. Trained particular dataset; finally, we get output. Bagging is also called bootstrap aggregation. Bootstrap is row sampling with replacement giving different rows or records to the model that trained, and the model gives an output of majority voting. The combining of the majority of votes is aggregation. Random forest is in bagging technique (Fig. 1).
2 Literature Review The paper proposed that patients take control of their health status. Wearable technology was used to monitor vital body signs (VBS), and my heart package concept of the application was the management of heart failure [1]. The paper discusses the 116 heart sound signals diagnosed using regression trees and classification. Preprocessing two-step filtering is the first one, and then, segmentation takes place. K-means clustering algorithm is applied in this paper [2]. This paper discussed machine learning algorithms (SVM, NB, KNN) to identify heart disease [3]. This paper discusses using Hadoop technology in the healthcare industry. I analyzed and organized the data using Hadoop [4]. This paper discussed heart disease prediction and reduced the number of tests, using a classification algorithm. Various datasets compared the accuracy [5]. This paper discovered deep neural networks and attention mechanisms in deep risk based. Ordered medical data and integrated heterogeneous efficiently to predict CVD (cardiovascular disease)
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patients [6]. The paper discussed heart disease and evaluated with classification techniques, KNN, decision tree, SVM, ANN, and Naïve Bayesian neural network. SVM and Apriori algorithm are proposed [6]. This paper discussed heart disease diagnosis which is developed as an automated diagnostic system using machine learning algorithms [7]. The paper submitted about the ECG signal collected and inspected to determine condition of heart attack [8]. This paper discussed heart disease prediction using a random forest algorithm [9]. This paper analyzed classification and heart disease prediction using deep learning and machine learning algorithms [10]. This paper proposed data collected from the medical field; fourteen attributes are applied to predicting heart disease in data mining techniques using in MAFIA algorithm, genetic algorithm, decision tree, and K-means algorithm [11]. The paper discussed heart disease using machine learning algorithms to enhance hybrid random forest linear model (HRFLM) applied [12]. This paper proposed data mining techniques using association rules to predict heart disease [13]. The paper discussed machine learning and data mining techniques to predict and diagnose heart disease [14]. In this paper, 14 eigenvalues analyzed heart rate variability disease classification which is improved in KNN algorithm [15]. This paper discussed predicting each person’s risk level using various classification techniques [16].
3 Data and Description Print(data file) shows the number of rows and columns with data. There are 7000 rows and 12 columns in this dataset. So the shape of the dataset is Dataset Shape (7000, 13) (Figs. 2, 3, 4 and 5; Table 1).
4 Work Results Cardiovascular disease data is used to compare with machine learning algorithms and data implemented with an ensemble methods like bagging, boosting, and stacking. Random forest algorithm in bagging method [17]. The recall is out of total actual values of how many correct predicted positive values. It is also called true positive rate (TPR) and sensitivity. Precision is total positive predicted result values to how many actual positives. Logistic regression is the output values between 0 and 1. Support vector machine distinctly classified data point. Naïve Bayes is probabilistic machine learning task [18]. Random forest consists of many decision trees and bagging techniques used. K-neighbor classifier finds the distance between several examples. Gradient boosting is minimizing error and boosting accuracy [19]. Stacking is combining various learning algorithms and predicting the best accuracy results [20] (Figs. 6, 7, 8, and 9).
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Fig. 2 Dataset shape
Fig. 3 Cholesterol cardio relation
Confusion Matrix - Before Tunning Random forest algorithm with Example for sample calculation of precision Recall and accuracy [[5078 1378] [2256 4398]] Precision = 5078/5078 + 1378 = 0.786555 Recall = 5078/5078 + 2256 = 0.69 Accuracy = 5078 + 4398/5078 + 1378 + 2256 + 4398 = 0.7228 = 72.28%
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Fig. 4 Gender distribution
Fig. 5 Dataset heat map
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Table 1 Data description Data
Type of data
Patient age Patient height Patient weight Patient gender Patient systolic blood pressure Patient diastolic blood pressure Patient cholesterol level Patient glucose Patient smoking Patient alcohol intake Physical activity CVD disease presence
Int type data in days Int type data in cm Float type in kg Categorical data Int type data Int type data Normal, above, and well above normal Normal, above, and well above normal Binary data Binary data Binary data Binary format
Fig. 6 Characteristics curve Fig. 7 Accuracy of logistic regression
Bigdata Analysis Using Machine Learning … Fig. 8 Accuracy of Naïve Bayes regression
Fig. 9 Accuracy of random forest
Fig. 10 Accuracy of extreme gradient boost
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Fig. 12 Accuracy of KNN classifier
Fig. 13 Accuracy of stacking classifier
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Fig. 14 Decision tree classifier
Fig. 15 Changes made in random forest - Accuracy after tunning
Fig. 16 Tableau tool explores cardio data
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These machine learning algorithms are applied in Python. Two or more predicting algorithms combined in the form of a stacking classifier. These results are implemented using Python jupyter notebook.
5 Conclusion In this paper, cardiovascular disease 70,000 data is implemented in machine learning algorithms. Algorithms applied in this paper are logistic regression, Naïve Bayes, random sorest, extreme gradient boost, K-nearest neighbor, decision tree, and support vector machine. Stacking classifier xgb, knn, svc, rf algorithms are taken, and accuracy increased after tunning the random forest algorithm; it gives results of higher accuracy. My work extends to get algorithms more accurate using a large amount of cardiovascular data in the future.
References 1. Habetha J (2006, August) The MyHeart project-fighting cardiovascular diseases by prevention and early diagnosis. In: 2006 international conference of the IEEE engineering in medicine and biology society, pp 6746–6749. IEEE 2. Amiri AM, Armano G (2013, August) Early diagnosis of heart disease using classification and regression trees. In: The 2013 international joint conference on neural networks (IJCNN), pp 1–4. IEEE 3. Louridi N, Amar M, El Ouahidi B (2019, October) Identification of cardiovascular diseases using machine learning. In: 2019 7th mediterranean congress of telecommunications (CMT), pp 1–6. IEEE 4. Thakur S, Ramzan M (2016, January) A systematic review on cardiovascular diseases using big-data by Hadoop. In: 2016 6th international conference-cloud system and big data engineering (Confluence), pp 351–355. IEEE 5. Chandra P, Deekshatulu BL (2012, November) Prediction of risk score for heart disease using associative classification and hybrid feature subset selection. In: 2012 12th international conference on intelligent systems design and applications (ISDA), pp 628–634. IEEE 6. An Y, Huang N, Chen X, Wu F, Wang J (2019) High-risk prediction of cardiovascular diseases via attention-based deep neural networks. IEEE/ACM transactions on computational biology and bioinformatics 7. Ali L, Rahman A, Khan A, Zhou M, Javeed A, Khan JA (2019) An automated diagnostic system for heart disease prediction based on ${\chi^{2}} $ statistical model and optimally configured deep neural network. IEEE Access 7:34938–34945 8. Goyal A, Mittal S, Sawant R, Gidhwani A, Bagate J (2017, July) Portable heart attack detector. In: 2017 8th international conference on computing, communication and networking technologies (ICCCNT), pp 1–6. IEEE 9. Buettner R, Schunter M (2019, October) Efficient machine learning based detection of heart disease. In: 2019 IEEE international conference on E-health networking, application & services (HealthCom), pp 1–6. IEEE 10. Rajamhoana SP, Devi CA, Umamaheswari K, Kiruba R, Karunya K, Deepika R (2018, July) Analysis of neural networks based heart disease prediction system. In: 2018 11th international conference on human system interaction (HSI), pp 233–239. IEEE
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11. Venkatalakshmi B, Shivsankar MV (2014) Heart disease diagnosis using predictive data mining. Int J Innov Res Sci Eng Technol 3(3):1873–1877 12. Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554 13. Khare S, Gupta D (2016, August) Association rule analysis in cardiovascular disease. In: 2016 second international conference on cognitive computing and information processing (CCIP), pp 1–6. IEEE 14. Rajmohan K, Paramasivam I, SathyaNarayan S (2014, February) Prediction and diagnosis of cardio vascular disease—a critical survey. In: 2014 World Congress on computing and communication technologies, pp 246–251. IEEE 15. Feng Y, Zhang Y, Cui X, Wang XA (2018, March) A universal implementation of cardiovascular disease surveillance based on HRV. In: 2018 China semiconductor technology international conference (CSTIC), pp 1–. IEEE 16. Thomas J, Princy RT (2016, March) Human heart disease prediction system using data mining techniques. In: 2016 international conference on circuit, power and computing technologies (ICCPCT), pp 1–5. IEEE 17. Krithika DR, Rohini K (2020) Blockchain with bigdata analytics. In: Intelligent computing and innovation on data science. Springer, Singapore, pp 403–409 18. Krithika DR, Rohini KA (2020) Survey on challenging capabilities of big data analytics in healthcare 19. Rohini K, Suseendran G (2016) Aggregated K means clustering and decision tree algorithm for spirometry data. Indian J Sci Technol 9(44):1–6 20. Thiyagaraj M, Suseendran G (2020) Enhanced prediction of heart disease using particle swarm optimization and rough sets with transductive support vector machines classifier. In: Data management, analytics and innovation. Springer, Singapore, pp 141–152
Anomaly Detection in Business Process Event Using KNN Algorithm B. Sowmia and G. Suseendran
Abstract Process mining deals with data analysis of a specific kind, namely data extracted from the execution of business processes. The investigation of such data may be influenced by outliers suggesting rare behavior or “noise.” in the method of process discovery. This results in occasional journey directions, which clutch the process model in-process exploration, where a process model is automatically derived from the data. It presents this essay with an intuitive approach for extracting unusual activity from case records. The proposed method is tested in-depth. Its implementation dramatically increases the discovered process model efficiency combined with some current process discovery algorithms and scales well to large data sets. Keywords Event log learning
Feature extraction Classifications Unsupervised
1 Introduction Nowadays, more and more businesses are selling and selling. Their goods are produced through e-commerce sites. The product of this situation in the availability of an extensive range of commodity options for users makes it impossible for them to determine which things they like to buy [1]. With the development of electronic commerce online, the company could conquer space and time barriers and are now ready to serve consumers electronically and intelligently [2, 3]. The dynamic essence of today’s market pushes. Businesses are continually developing their operations. Company: Business method modeling and interpretaB. Sowmia Department of Computer Science, Vels Institute of Science Technology and Advanced Studies, Chennai, India G. Suseendran (&) Department of Information Technology, Vels Institute of Science Technology and Advanced Studies, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_22
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tion is one of the most critical methods. The industry process analysis and research are already ongoing in the past and indirectly—manual and organizational intervention. Mining has evolved to reduce the labor needed for the simulation and study of market processes. The mining methodology attempts to derive business process modeling and to analyze its features using existing information technology know-how. One of the critical tasks in process mining is to decide how tasks relate in a graphical model to each other and reflect them. But most of the models have not been found. It reflects all the information on splits and joins processes [4]. This paper discusses finding the mechanism of good quality models in the case of noise in case records, contributing to an automatic method of routinely filtering out of those logs’ peculiar behavior. In our day-to-day life, it is customary to make our choices based on the thoughts and recommendations of those whose preferences are the same as ours [5]. Our filtering technique first constructs an abstraction of the actions of the mechanism documented in the log as automation (a graph directed to it). The direct dependence of the case marks in the log, guy, is caught by this automation. This automation ultimately reduces infrequent transfers. This diminished log automation would then replay the original log to detect not fitting occasions anymore. Incidents are not included in the registry. The method seeks to delete the full number of irregular automation transformations, which would reduce the number of incidents that have not been logged out. This leads to an automation-friendly filtered log. K-nearest neighbor (KNN) algorithm has been used in recent years. It is commonly used in CF [6]. By looking for similar or similar users issues in the KNN algorithm, we can figure out any potential characteristics between users or objects and then forecast the items used. Classification algorithms may be used for intelligence purposes filtering by splitting the sample to the group achieve the function of filter samples. KNNs simple theory algorithm is the following: According to the K sample groups in the training sample array, the (most similar) sample is closed since KNN is a conventional aid for learning the comparison-based algorithm, because each group must have an analog. A minimum number of representative training samples have to be taken to ensure the accuracy of the classification.
2 Related Works Raffaele Conforti et al. [7]—One of the critical problems in the age of big data is the processing of vast volumes of data generated in a meaningful and scalable format. Forms of life. The field of process mining is the analysis of data of a particular sort, that is to say, data arising from the implementation of corporate systems. The existence of outliers reflecting abnormal behavior or “noise” behavior will adversely affect the interpretation of such results. In discovering, the process, where a process model is to be removed automatically, could lead to seldom-travelled paths that overpower the process model.
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Mohsen Mohammadi et al. [8] In process mining, it is widely known as a contemporary philosophy that focuses on knowledge extraction to create event records from Please document details in a process-conscious system of records. One of the most complicated mining practises in the process is the mining system’s intent to extract data on business operations and process exploration. Furthermore, the complete process model is based on the records of incidents. Dipto Barua et al. [9] Phase mining techniques offer an incentive for finding knowledge on in organizations method models, or Web-based structures, are based on data execution in the log scenario. Our study is limited to the generated process models’ data in the absence of logical inference and measurable abstraction—none except the mechanism’s basic bone structure and its relations. Ontology is the most intuitive of all but a versatile approach that can interact with conventional data mining. Improve study of techniques and process mining techniques that are special or common. Prerna Juneja et al. [6] are spaghetti-like and are created from case logs with a wide number of edges, interconnections, and process templates. Then, there is the nodes. These models are confusing and difficult to learn. Comprise the process researcher. By integrating structurally identical traces to break an event log into homogenous sub-sets, we optimize the goodness (fitness and structural complexity) process models. The cluster algorithm K-medoid is updated with two distance metrics: longest common subsequence (LCS) and dynamic time warping (DTW). Oswaldo et al. [10], the computer system creates an information system in a company generic execution log (GEL) containing daily details activities normally. This GEL is too big, and its contents are not quality. A tool based on process mining is used in the present work proposed to produce a quality event log (QEL), and the content of the event log allows the user to explore the tactics (user’s intentions) that the future work will be formalized and modeled by every form of mining purpose. In comparison, a report has discussed the case of the distribution market operation.
3 Proposed System This paper tackles the difficulty of finding high-quality method models in the presence of noise in incident logs by implementing a structural tool for the systemic filtering from these logs of rare events. First, our filtering approach creates an abstraction of the process operation described in the log (a directed graph). This auto detects immediately following dependencies between event labels in the log. This automaton is essentially omitted from frequent transformations and complete system architecture, as shown in Fig. 1. The original log is then replayed in this reduced automaton to classify incidents that are no longer the same [11].
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ARTIFICIAL LOG
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Fig. 1 System architecture
3.1 3.1.1
Modules Description Business Process Management
This results in seldom-travelled paths that overrun the process model in this exploration module, where the goal is to eliminate a process model from the data automatically. This trial introduces an intuitive approach for extracting unusual activity from case records. The proposed method is tested in-depth. Its implementation significantly increases the discovered process models’ efficiency combined with some current process discovery algorithms and scales well to large data sets [12].
3.1.2
Process Mining
Service mining aims to gain actionable process information from IT machine case records widely accessible in modern organizations. Method one area of focus in the broader process mining sector is exploration, which concerns deriving process models from case documents. To address this problem, multiple algorithms have been proposed over time.
Infrequent Behavior Process case logs in this module are typically preprocessed, so that the adverse results are minimized where they are cleaned from noise manually. However, this is
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a complicated and lengthy task without guaranteeing the result’s success, particularly in large logs that demonstrate complex process action [13]. Algorithm 1. Infrequent Behavior Algorithm 1. 2. 3. 4. 5. 6. 7.
Input: L is Event log Initial t-trace name = Auto Locate(L) is Process S-Sorted E-Expectation SE(ei,ej) ← Add to Compute Expectations (RAM) Filtering Threshold T ← Approved Interquartile SE Infrequent approved event IFE (ej) ← SE(ei,ej) < T Aligned(L’) ← Subtract Infrequent Event (ej,L) Initial trace T’ ← Auto Find Process(L’)
Frequent Behavior This module proposes the first helpful technique for filtering out noise from process event logs. The technique’s advancement relies on the option of modeling the unusual dilemma, the automated log filtering. This technique enables the detection of rare process activity at the thin grain level and helps to eliminate. Figure 2 shows the flow diagram of the proposed method at; first, it takes the incident state data set and categorizes the inputted data and processes the result in different categories into the required field for detecting infrequent data and applying Fig. 2 Incident flow diagram Incident_state dataset
Categorizing data (number,incident_s tate, acve, reassignment state
Selecng required field for detecng infrequent data
Applying Knn algorithm on the processed dataset
Plong graph on the data acve, incident_state in dataset
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it KNN algorithm in the processed data. The resulted output is shown in the graphs for easy understanding. Individual events from the log rather than entire traces (i.e., sequences of events). Algorithm 2. Filter Log Algorithm 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
Input: Event L, finite task set T, finite job set Tr , where Key1 is located. The test data set’s value is the value of the index, and value1 is the string S and Mark C. 3. Process map (Key, Value, Key1, Value1) 4. { 5. Do value for each line the data in the line is broken down into . 6. The similarity of computing S(x, y); 7. x is the appraisal vector and y is the instruction vector.
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8. The issue emit (Key1, Value1); 9. } Algorithm 4. Reduce Function 1. Input: the mapping function output 2. Output: , where the value of the Key2 group is the product of the Key1 group and Value2 group. 3. Minimize Method Process (Key1, Value1, Key2, Value2) 4. { 5. New Semi Collection = ArrayList();; 6. Set Description = new ArrayList();; 7. Do this for each v in Value1 8. Create a pair of key values 9. There, S is the name of the division and C is the name of the category; 10. Plus S to the semi set, and C to the Classify collection, respectively; 11. Arrange the semi kit weights, compare the closest K Neighbor sets, and get the associated groups 12. Set up the data for the SEMI concurrently. 13. Assign Key1 to Key2 value; 14. Released (Key02, Value02) 15. } To parallelize, the KNN algorithm was designed to process the text classification into a Map-Reduce Program. The key to the map function is the test data set’s line number, and the sum is the value. Education sets the data that fits the lines. The data collection contains the associated attribute fields and specifies their names. The Key1 performance of the map stage represents the number of lines of the test data set and the similitude S and C values. Key2 is the reduce stage test data set, and the measured classification result is Value2 [15].
4 Experimentation In this segment, we present the results of the testing. Test the efficacy of our filtration technique. We used our own Web site’s approach to explain these functions better to run these experiments [16]. Figure 3 shows the activity diagram for the proposed experimentation method. The personal user login or register data are collected in the artificial logs we created, and we conclude the infrequent behavior. Figure 4 shows our sequence diagram for the proposed method. The number of users is either login or register new, and the data got from the register or login section is taken in the noise or filter logs to find the infrequent behavior [17].
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Fig. 3 Activity diagram
APPLICATI ON
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Fig. 4 Sequence diagram
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Fig. 5 Class diagram
Figure 5 shows the class diagram for the proposed method. The data information from the register and login, and data acquired from the filter logs is processed for feature extraction. Figure 6 shows the event logs that are processed in our method and graph result from the event log filtering. The Web pages’ data is taken in as event logs and sent as input in our proposed algorithms.
5 Conclusion In this study, we implemented the automatic technique of removal of irregular procedural log behavior. The primary hypothesis is the use of rare direct dependencies between events as a surrogate for abnormal actions. Dependencies were identified and omitted from the built-in case automation register and then corrected the original log correctly using alignment-based replay to erase personal events. We showed the suggested methodology’s viability and efficacy using a mixture of artificial and real-life logs on our preferred algorithms. No negative impact on generalization is a substantial improvement in fitness, precision, and complexity. Filtering the case logs of the process reveals that our methodology provides a statistically significant increase in quality and accuracy. These methods, while
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Fig. 6 Resulted graph for the event log filtering
contributing to comparable versions, scale, Oh. Amount. Such developments are a by-product of a noise-free record. Fewer events and direct-follow-dependencies are contained in a noise-free log. In the output and the accuracy of the discovery algorithm, these two variables play a major role. The development of the algorithm of the number of events in the case of discovery is addictive. Uh, guy, the log. As a result, less log events indicate less time taken to find out about a model. Furthermore, less direct-follow-dependencies allow less (uncommon) steps to be taken into account by the model, thus increasing precision and model complexity (fitness and precision).
References 1. Abdullah N, Xu Y, Geva S, Chen J (2010, December) Infrequent purchased product recommendation making based on user behaviour and opinions in E-commerce sites. In: 2010 IEEE international conference on data mining workshops, pp 1084–1091. IEEE
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2. Wang C, Li Y (2008, October) Mining changes of E-shopper purchase Behavior in B2c. In: 2008 fifth international conference on fuzzy systems and knowledge discovery, vol 2, pp 240– 244. IEEE 3. Chong W, Yi-Jun L, Qiang Y (2006, October) Research on changes of E-shopper behavior in the internet environment. In: 2006 international conference on management science and engineering, pp 68–73. IEEE 4. Savickas T, Vasilecas O (2015, April) Business process event log use for activity sequence analysis. In: 2015 open conference of electrical, electronic and information sciences (eStream), pp 1–4. IEEE 5. Luna JM, Ramírez A, Romero JR, Ventura S (2010) An intruder detection approach based on infrequent rating pattern mining. In: 2010 10th international conference on intelligent systems design and applications, pp 682–688. IEEE 6. Juneja P, Kundra D, Sureka A (2016, June) Anvaya: An algorithm and case-study on improving the goodness of software process models generated by mining event-log data in issue tracking systems. In: 2016 IEEE 40th annual computer software and applications conference (COMPSAC), vol 1, pp 53–62. IEEE 7. Conforti R, La Rosa M, ter Hofstede AH (2016) Filtering out infrequent behavior from business process event logs. IEEE Trans Knowl Data Eng 29(2):300–314 8. Mohammadi M (2019, September) Discovering business process map of frequent running case in event log. In: 2019 international conference on information technologies (InfoTech), pp 1–4. IEEE 9. Barua D, Rumpa NT, Hossen S, Ali MM (2018, December) Ontology based log analysis of web servers using process mining techniques. In: 2018 10th international conference on electrical and computer engineering (ICECE), pp 341–344. IEEE 10. Díaz-Rodriguez OE, Hernández MGP (2020, March) Quality event log to intention mining: a study case. In: 2020 international conference on computer science, engineering and applications (ICCSEA), pp 1–6. IEEE 11. Díaz-Rodríguez OE, Pérez M (2018, April) Log design for storing seismic event characteristics using process, text, and opinion mining techniques. In: 2018 international conference on eDemocracy & eGovernment (ICEDEG), pp 281–285. IEEE 12. Huang Y, Wang Y, Huang Y (2018, November) Filtering Out infrequent events by expectation from business process event logs. In: 2018 14th international conference on computational intelligence and security (CIS), pp 374–377. IEEE 13. Prodel M, Augusto V, Jouaneton B, Lamarsalle L, Xie X (2018) Optimal process mining for large and complex event logs. IEEE Trans Autom Sci Eng 15(3):1309–1325 14. Lu Y, Seidl T (2018, October) Towards efficient closed infrequent itemset mining using bi-directional traversing. In: 2018 IEEE 5th international conference on data science and advanced analytics (DSAA), pp 140–149. IEEE 15. Zakarija I, Škopljanac-Mačina F, Blašković B (2015, September) Discovering process model from incomplete log using process mining. In: 2015 57th international symposium ELMAR (ELMAR), pp 117–120. IEEE 16. Suseendran G, Chandrasekaran E, Akila D, Kumar AS (2020) Banking and FinTech (Financial Technology) embraced with IoT device. In: Data management, analytics and innovation, pp 197–211. Springer, Singapore 17. Rajesh P, Suseendran G (2020, June) Prediction of N-gram language models using sentiment analysis on E-learning reviews. In: 2020 international conference on intelligent engineering and management (ICIEM), pp 510–514. IEEE
Comparison of Multidimensional Hyperspectral Image with SIFT Image Mosaic Methods for Mosaic Better Accuracy G. Suseendran, E. Chandrasekaran, Souvik Pal, V. R. Elangovan, and T. Nagarathinam Abstract Hyperspectral images can offer a lot of clarity by blending both spectral and spatial data. Details are for the researcher. A multidimensional paper in this paper, the hyperspectral image mosaic solution, was suggested to properly assemble hyperspectral images. This approach is a synthesis of texture details of the single gray picture, the hyperspectral spatial details image, and location details obtained during the purchase phase. This method is used in the world of medicine. Image and experimental findings hyperspectral suggest that this technique is useful compared to other image mosaic approaches based on the line segment function of scale-invariant feature transform (SIFT). Keywords Hyperspectral imaging
SIFT
G. Suseendran (&) Department of Information Technology, Jeppiaar Engineering College, Chennai, India E. Chandrasekaran Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India S. Pal Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishnanagar, India V. R. Elangovan Department of Computer Applications, Agurchand Manmull Jain College, Meenambakkam, Chennai, India T. Nagarathinam PG and Research Department of Computer Science, MASS College of Arts and Science, Kumbakonam, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_23
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1 Introduction Classification of hyperspectral images solves the issue of identifying land-cover groups and thematic production of maps with broad applications in precision agriculture, mining discovery, environmental monitoring, etc. It usually consists of several main phases, sampling, preprocessing of data, extraction of features, and model building. Among those measures, extraction of features is of great importance, and goals are to find the most lightweight and detailed set of functions to boost the classification tasks’ accuracy and reliability. The thematic map also suffers from salt and pepper noise due to noise in hyperspectral data [1]. The two-dimensional photographs can be applied to spectral as a further dimension of information. In the sector, the resulting spectral imager was commonly used for monitoring climate and modern agriculture [2]. The categorization of hyperspectral images was formulated from the point of view of segmentation and classification tasks [3]. Spectral image feature classification is an active field of visual image analysis study. The feature is due to the dataset’s high dimensionality. Classification is a low-precision and complex task—the use of traditional methods. A variety of research projects have shown their participation. Spatial details in the extraction process of features may be required to increase the description of hyperspectral image accuracy [4] significantly. Details on the three-dimensional image has only points on the morphological structure. It has only points on the morphological structure. At the same time, the hyperspectral image has details on tissue components. It is commonly used in the field of remote sensing and also in the medical area fields. Some used a hyperspectral representation of the pig’s blood vessel to make a successful differentiation between arteries and veins classification approach for the support vector machine [5]. (SIFT) David G. Lowe suggested that it has been proven to be the most durable among the other descriptors of local invariant features concerning various geometrical changes. What are the functions invariant for rebalancing, localization and respective user, and partly invariant lighting shifts and Affine or 3D visualization. For a particular benefit, SIFT functionality can be used in image-matching applications Performed in many areas, such as anti-spam [6]. SIFT is an algorithm that translates image data to local the function of the vector. These features have geometric robustness transformations such as localization, scaling, and rotation [7]. To achieve this, we suggested a hybrid process. Take advantage of the two approaches described above, scale-invariant function transform (SIFT) and multdimensional hyperspectral image for mosaic method. This approach synthetically uses texture knowledge, hyperspectral information, and location information obtained at the technique, and when comparing the two above methods, we can get which one produces better results.
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2 Related Works Zhu and Ren [8] suggested a modern picture mosaic technology based on the scale-invariant aspect transform (SIFT) line segment feature to overcome event scaling, rotation, lighting state changes, etc, the technique of mosaics. The Harris corner detector operator uses this method first to detect key points. Secondly, it creates directed line segments, recognizes them with the SIFT function, and meets those guided segments in the rough-point matching acquisition. Datta and Chakravorty [9]—The goal of hyperspectral image segmentation is to minimize the challenge of evaluating hyperspectral data by dividing the image into categories dependent on correlations within homogenous or spectral areas. The segmentation method is one of the first steps in the distance sensing analysis of the image; the image is separated into regions that reflect the objects in the image better. Hu and Ai [10]—SIFT based on image attribute extraction and primary point matching approach is suggested. A wide-angle spinning triangle shift images are measured from unmanned aerial vehicles (UAV). It has a far wider use than conventional aerial photogrammetry as a particular low-altitude UAV photography process based on the concept of photogrammetry. Mendlovic and Raz [11] said that common imaging systems based on silicone benefit in-depth and spectral data by significant resolution penalties. However, compact detection methods eliminate the liability for computer cameras and involve upgraded optical equipment and preliminary information reliance. This paper lays forth a new idea for a positive imaging system: color and hyperspectral imagery multiplex and compressed images.
3 Methodology 3.1
Multidimensional Hyperspectral
In Fig. 1, the workflow for our algorithm is shown. First of all, for each hyperspectral band, we pick the comparison band as one band. Imagine and calculate the processing matrix of the two hyper-speed images. Depending on the phase, Up the Robust Attribute (SURF) function reference band then the pair. Depending on the auxiliary bands and their change, the bands are selected. The matrix is calculated in the same manner and stored for further predictions [12]. We use additional location information to filter off matrices to delete the second level’s error matrix. To use this fusion matrix, we calculate the merger filtered matrix in the fourth step. Stitch the images hyperspectrally.
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Fig. 1 Workflow of algorithm
Single Band Image Calculaon
Mul-Band Image Calculaon
Posion correcon
Mul-band fusion calculaon
Hyperspectral Image Stch
3.1.1
Definition of the Framework
The following method, seen in Fig. 2, captures the hyperspectral image that we used in this article. This sample machine hyperspectral picture is referred to below and above. Electronic dot, AOTF, gray science CMOS(sCMOS) camera, color charging coupled device (CCD), light splitter, and modern computing devices are the traditional optical microscope methods. Typical optical microscope systems [13]. The image of the slide is visible immediately in the optical microscope, like all microscopes. Also, it can be relocated to capture devices like SCMOS and CCD. This is the light splitter that is used to pick a picture system through a button. The stage can be controlled by hand, or power orders can be accessed from industrial computers. Oh, machine. Oh, machine. The acoustic-optical tunable filter will filter the target light frequency using a self-adjustment. The computer obtains computer and electronic level conditions sCMOS/CCD image.
3.1.2
Registration Picture
Image registration is one of the most effective procedures in the mosaic of images. By recognition and matching features, many images have been transformed into the
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Opcal Microscope Devices
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Three-Axis Translaon Electronic Stage
Light Splier
AOTF Color CCD
Industrial Compung System
Gray sCMOS
Fig. 2 Hyperspectral image sample system
same coordinate system. You may define the algorithms used as field-based and practical methods in this method. The feature-based solution has been proposed in the past few years by various people [14]. Thanks to the opportunity to handle the transition of a few pictures’ scales and rotation for ten years and perform a far more critical role in the picture registry than field-based approaches.
3.1.3
Selection of the Reference Image
For both of us, we pick each picture hyperspectral pictures {H1, H2} on the band Bref as the reference image(Iref) that is valid, which is seen in Fig. 3. We are denoting that these two pictures are in H1, H2as IBref1, IBref2. In practice Bref no, the following formula is derived:
Fig. 3 Hyperspectral image illustration
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Bref ¼ ðBstart þ Bend Þ=2
3.1.4
ð1Þ
Function Identification and Connection
In this document, we are applying the SURF function-based approach for estimating the matrix of two hyperspectral pictures. Function points for two photographs SURF, which is resilient against the SURF, are detected by IBref1 and IBref2, scaling and rotation. Each function point includes 64 descriptors carrying knowledge about the surrounding environment, so that it is possible to, as a vector, see any point. Each picture has dimensions of NBref*64, and the Iref number is NBref. Concerning K, the characteristics show the nearest relative. Algorithm to find a relationship between two features of the matrix {ABref1, ABref2}. There will be some points during this process unmatched owing to the image’s self-correlation [15]. RANSAC is used to solve this discrepancy condition and to achieve a robust estimate of the MBref transition matrix. MBref is the matrix of transformation between IBref1 and IBref2.
3.1.5
Matrix Fusion Transform
Calculations for multi-band: Relevant spectrum descriptions are given by each pixel in the hyperspectral system imaging—for frames properties of a hyperspectral image and the object under either wavelength illumination. This adds to the circumstance in which each image has information different from other artists, with its contrast and texture. Therefore, only one pair of gray images active in the computation process can lead to the mosaic’s uncertain result [16]. Correction of place: Poor contrast or lack of texture information on specific pictures can turn out to be non-normal. Transformation matrixes interact with the final fusion process matrix, guy [17]. We are importing additional location information to sort these non-normal matrices. Place details shall be gathered throughout. The method of image capture contains a three-axis coordinate details for each graphic. The course of scanning hyperspectral representations is shown in Fig. 4. Only one path may contain two adjacent images. As seen in Fig. 4, change the xor y-axis. For the matrix to be corrected, only the x and y coordinates are used, while the z-axis is used to correct the matrix. The focal length of this phase we do not care about. There are also two x-y offsets used in the transformation matrix. Photographs are parallel. We map the physical distance between the two of us by using the following formula to point to the distance of the pixel in the image:
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Fig. 4 Direction of scanning
a¼
Lpixel Lphysical
ð2Þ
Lpixel it can be determined as follows: 1. Single image capture, three-axis electronic phase transition via Lphysical value. The value of another picture is being arrayed and filmed. 2. For two images, calculate the transformation matrix. 3. In the matrix, the x/y offset value is Lpixel. Algorithm 1. For finding Mpos 1. Input; dxBl1,dyBl1. Assume the (dxBl1,dyBl1) as the x-y offset in the transformation matrix MBl1. 2. If dyBl1 = 0 3. Then a* Lphysical * 0.9 * < dxBl1 < a* Lphysical * 1.1 4. If dxBl1 = 0 5. Then a* Lphysical * 0.9 * < dyBl1 < a* Lphysical * 1.1 6. Find Mpos Mpos ¼
7:
m X
bi MBli
ð3Þ
i¼1
and m X i¼1
bi ¼ 1
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If m = n, Then bi ¼ binit If m 6¼ n Then bi ¼ binit þ bstep ði ¼ 1Þ; 1 i u Result Mpos is calculated as {MBl1, MBl2,…MBlm}
The fusion matrix. A relative error to produce a fusion matrix which specifies between Mpso, Mref is as follows: Merr ¼ Mpos Mref =Mref
ð5Þ
Mfusion ¼ x Mref þ ð1 xÞ Mpos
ð6Þ
where x = 1/2, if o 5-007, of which 5-007 corresponds to the proper positive number; 2. pick the point for each column corresponding to the principal elements of each row. Step, G(i,j) > ala, in which ala is a successful pass. Number if the elements in row I and J are the same, as in G(i,j). The number is the same for all elements in row I and ; (3) If there is a Factor limit in row, I and the element cap are not the same in column j. Choose one randomly; choose a limit, for example. For each Othering, elements in rows I G(i, q), ai, and bq fit; sets all elements in row I and column q to be zero,
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Fig. 8 Accuracy of multidimensional hyperspectral images and SIFT
Images
Hyperspectral
SIFT
a b c d e f
90.5 94.7 91.6 96.1 97.2 91.2
88.2 90.2 87.3 91.5 92.3 86.4
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Table 2 Accuracy of multidimensional hyperspectral images and SIFT
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100 98 96 94 92 90 88 86 84 82 80
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From Table 2 and Fig. 8, we can conclude that the accuracy of the input images that we experimented on multidimensional hyperspectral images and SIFT for mosaic method shows that the multidimensional hyperspectral accuracy images are more significant than the SIFT mosaic method.
4 Conclusion This paper proposes enhancing the precision of the mosaic image in a solid multidimensional mosaic image solution. The issue of misalignment in the photographic field is eliminated by photography and additional details on the positioning. Redundancy of hyperspectral spatial data is the experiment presentations showing that the suggested solution is successful in multiple image mosaic problems. This technique can also be found in certain spatial information redundancy implementations for image mosaics for some of you. We experimented on multidimensional hyperspectral images, and SIFT for mosaic method shows that the accuracy of the multidimensional hyperspectral images is greater than the SIFT mosaic method.
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References 1. Liang J, Zhou J, Gao Y (2016, September) Tensor morphological profile for hyperspectral image classification. In: 2016 IEEE international conference on image processing (ICIP), pp 2197–2201. IEEE 2. Luo J, Cai F, Yao X, Li J, Huang Q, He S (2020) Experimental demonstration of an anti-shake hyperspectral imager of high spatial resolution and low cost. IEEE Sens J 3. Priego B, Duroy R (2018, September) Amigo: a tool for the generation of synthetic hyperspectral images. In: 2018 9th workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS), pp 1–5. IEEE 4. Wang Y, Cui S (2014, July) Hyperspectral image feature classification using stationary wavelet transform. In: 2014 international conference on wavelet analysis and pattern recognition, pp 104–108. IEEE 5. Huang Y, Zhou M, Li Q, Liu H, Guo F (2016, October) A multi-dimensional microscopic imaging system and reconstruction methods. In: 2016 9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 576–580. IEEE 6. Chen J, Zhang L, Lu Y (2008, December) Application of scale invariant feature transform to image spam filter. In: 2008 second international conference on future generation communication and networking symposia, vol 3, pp 55–58. IEEE 7. Baykal E, Ustubioglu B, Ulutas G (2016, June) Image forgery detection based on SIFT and k-means++. In: 2016 39th international conference on telecommunications and signal processing (TSP), pp 474–477. IEEE 8. Zhu J, Ren M (2014) Image mosaic method based on SIFT features of line segment. Comput Math Methods Med 9. Datta A, Chakravorty A (2018, October) Hyperspectral image segmentation using multi-dimensional histogram over principal component images. In: 2018 international conference on advances in computing, communication control and networking (ICACCCN), pp 857–862. IEEE 10. Hu Q, Ai M (2011, June) A scale invariant feature transform based matching approach to unmanned aerial vehicles image geo-reference with large rotation angle. In: Proceedings 2011 IEEE international conference on spatial data mining and geographical knowledge services, pp 393–396. IEEE 11. Mendlovic D, Raz A (2015, August) Multi-dimensional hyperspectral imaging system. In: 2015 International conference on optical MEMS and nanophotonics (OMN), pp 1–2. IEEE 12. Cheung W, Hamarneh G (2007, April) N-sift: N-dimensional scale invariant feature transform for matching medical images. In: 2007 4th IEEE international symposium on biomedical imaging: from nano to macro, pp 720–723. IEEE 13. Bedruz RA, Sybingco E, Quiros AR, Uy AC, Vicerra RR, Dadios E (2016, November) Fuzzy logic based vehicular plate character recognition system using image segmentation and scale-invariant feature transform. In: 2016 IEEE region 10 conference (TENCON), pp 676– 681. IEEE 14. Chen Q, Kotani K, Lee F, Ohmi T (2008, December) Scale-invariant feature extraction by VQ-based local image descriptor. In: 2008 international conference on computational intelligence for modelling control & automation, pp 1217–1222. IEEE 15. Setiawan W, Wahyudin A, Widianto GR (2017, October) The use of scale invariant feature transform (SIFT) algorithms to identification garbage images based on product label. In: 2017 3rd international conference on science in information technology (ICSITech), pp 336–341. IEEE 16. Pal S, Bhattacharyya S, Doss S, Akila D, Suseendran G (2019) Hyperspectral and multispectral image fusion using NSCT and FDCT methods. J Crit Rev 7(5):2020 17. Qinglong H, Zhang Y, Hongbo L, Tianjiao F (2017, May) A novel target tracking method based on scale-invariant feature transform in imagery. In: 2017 international workshop on remote sensing with intelligent processing (RSIP), pp 1–5. IEEE
The Role of Innovativeness in Mediating the Relationship Between Overall Quality and User Satisfaction Among the Financial Information Systems in Yemen Dhoha Younis, Divya Midhunchakkaravarthy, Ali Ameen, Balaganesh Duraisamy, and Midhunchakkaravarthy Janarthanan
Abstract The government utilizes the development of ICT in providing public facilities through a financial information system. Therefore, all public services become integrated, thus generating profits such as cost and time efficiency. This research focuses on the uneven application of financial information systems (FIS) in various regions and identifies factors that support such services’ optimization. This research explains the model of financial information systems adoption by expanding the technology acceptance model with self-efficacy and system quality. If people perceive that financial information systems are accessible and believe that they can operate the system, they will use it properly. Besides, the quality of the FIS is also the determinant of smart-government implementation. The more quality of the system provided, the more the number of people who use the system. Keywords Financial information system
Self-efficacy System quality TAM
1 Introduction Personal innovations can play a vital role in the information technology transformation at the organization level and state level. Personal innovations are the state of mind attributed to the recognition and active pursuit of opportunities for innovation. Innovation is what allows leaders to benefit from the influence of associations, create space for innovation, and apply innovation regularly [1–4]. Moreover, D. Younis D. Midhunchakkaravarthy A. Ameen (&) M. Janarthanan Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] B. Duraisamy Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_24
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information technology introduces a new arena for the global competition of knowledge, connectedness, and agility by facilitating the deployment and dissemination of knowledge and information in different activities. Further, it has placed ICT and innovation at the core of future smart development more than ever before. The continuous change in the business environment has led organizations of all types to viciously pursue new technologies to utilize their benefits and continue to be relevant in their respective industries. Technology advancement has provided organizations with countless benefits and has changed their standard operations by making them networked, flatter, and flexible. In the twenty-first century, organizations are allocating huge sums of investments to be in their technology infrastructure, which will be central to achieving competitive advantage, and such investments can be in communication networks, hardware, databases, software, and training of its staff [1, 4–7]. One of the key goals of public management or government services is to improve public satisfaction with the government and the growing information needs by different stakeholders, increasing the urban population to a greater extent, the lack of effective and efficient communication channels between the government and citizens. This research investigates the effect of technology characteristics on user satisfaction of financial information systems services users and the moderating role of innovativeness on the said relationship among employees in the Ministry of Finance in Yemen.
2 Literature Review 2.1
Overall Quality (QUL)
Overall quality encompasses quality of service, systems, and information. System quality is defined as the suitability and reliability of the system, as well as the software and hardware stability needed to support the required data [8]. The system also has advantages in terms of usability, functionality, flexibility, and understanding. In their upgraded information system success mode, in the context of information system research.
2.2
User Satisfaction (SAT)
User satisfaction is defined as the degree to which users think that a particular system or application satisfies their business needs, and it defines the user’s attitude toward the specific computer applications with which he communicates directly. User satisfaction is a very important and critical aspect of systems and technology. Hence, it has been used in a wide range of fields in research into the use of
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technological applications. Furthermore, user satisfaction is the belief that the system is useful and that the user wants to use it again.
2.3
Innovativeness (INN)
Innovativeness is referred to as the judgment of one’s ability to use a computer and technology. Innovativeness is also defined as the belief that an individual has of his/ her ability to fulfill goals and control factors that may affect his/her life in a successful way [9]. Earlier researches on innovativeness have confirmed the critical role that innovativeness plays in understanding individual responses to IT. In this research, innovativeness is defined as the extent to which the individual is believed to interact with financial information systems services successfully. Based on the social cognitive theory, innovativeness is the ability to perform a specific behavior. Based on the above arguments, the research hypothesis will be formulated as the following: H1: Overall quality has a positive effect on user satisfaction. H2: Innovativeness strengthens the effect of overall quality on user satisfaction.
3 Research Method Figure 1 displays the construct containing factor. These relationships are derived from [8] and are studied among employees in Ministry of Finance in Yemen.
4 Result of Analysising Data 4.1
Measurement Model Assessment
The findings show that key parameters such as (AVE), (CR), and Cronbach’s alpha and factor loadings were both higher than the values suggested by the measuring indicators in previous studies [10, 11] as described in Table 1.
Fig. 1 Conceptual framework
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Table 1 Measurement model assessment Constructs
Item
Loading (>0.7)
M
SD
a (>0.7)
CR (>0.7)
AVE (>0.5)
System Quality (SYSQ) Information Quality (INFQ) Service Quality (SERQ) Innovativeness (INN)
SYSQ1 0.940 3.946 0.931 0.924 0.952 0.869 SYSQ2 0.939 SYSQ3 0.917 INFQ1 0.939 3.730 0.955 0.936 0.959 0.887 INFQ2 0.938 INFQ3 0.949 SERQ1 0.889 3.640 0.918 0.857 0.913 0.777 SERQ2 0.853 SERQ3 0.901 INN1 0.954 3.877 0.934 0.962 0.972 0.897 INN2 0.950 INN3 0.952 INN4 0.931 User satisfaction SAT1 0.911 3.136 1.075 0.919 0.948 0.860 (SAT) SAT2 0.935 SAT3 0.935 Note M = Mean; SD = Standard Deviation, a = Cronbach’s alpha; CR = Composite Reliability, AVE = Average Variance Extracted Key SYSQ: system quality, INFQ: information quality, SERQ: service quality, SAT: user satisfaction, INN: innovativeness
Table 2 Fornell–Larcker criterion
INFQ SAT INN SERQ SYSQ
INFQ
SAT
INN
SERQ
SYSQ
0.942 0.511 0.313 0.738 0.724
0.927 0.309 0.521 0.504
0.947 0.256 0.363
0.881 0.681
0.932
The discriminant validity has been presented in Table 2. The exogenous constructs showed a correlation of n. After getting any k of these n packets from the sender, the receiver can renovate the entire set of packets using the RS decoder. Let dx,y bean element in the x row and y column of the Vandermode matrix. The nth packet encoded can be recovered using the below equations: Zn ¼ d2;n d2;n ¼ ) Zn ¼
pffiffiffiffiffiffiffi dx;n ; where x [ 1
x1
pffiffiffiffiffiffiffi dx;n ; where x [ 1
x1
ð1Þ ð2Þ ð3Þ
The sender directs the block of packets to the Vandermonde matrix encoder. The packets are uncovered from their headers and trailers before they are changed into blocks signifying the Vandermonde matrix’s rows. The mathematical equation demonstrating how packets are transformed to Vandermonde matrix is as given below: Let Z1, Z2, … Zn be the packets generated. Let Q1, Q2, … Qn be the generated blocks Q1 ¼ Z1 þ Z2 þ þ Zn Q2 ¼ Z12 þ Z22 þ þ Zn2 : : Qn1 ¼ Z1n1 þ Z2n1 þ þ Znn1
ð4Þ
Cost-Effective Anomaly Detection for Blockchain Transactions …
0 B B B B B B B B B B B @
1
0 1 C Q1 C B Z C B 1 Q2 C Z2 C B C B 1 :C ¼ B C B B: :C C B C @: :A Z1n1 Q0
Qn1
1 Z2 Z22 : : : Z2n1
1 Zn Zn2
Znn1
449
10 CB CB CB CB CB CB CB CB AB @
1 1 1 :
1
C C C C C C :C C C :A 1
ð5Þ
Note: (1) The Vandermonde Matrix is made virtuously from the packet payload. (2) The first row of the Vandermode Matrix is always occupied with ones as such, and this row is at no time transferred. (3) The encrypted data packets are linearly independent. Therefore the receiver can recuperate the packets.
3.4
Block Propagation Using FEC
Let X be a block and H(X) and He(X) be the hash value and header of X, respectively. Let X contains n transactions with IDs {TxIDj}, j = 1, 2 … n. Let m be the number of missing transactions among the n transactions. If a CH wants to send X to a node N, the following steps will be executed: Algorithm 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
CH sends the message ADVcontaining the H(X) to N. N replies with the message “REQDATA” to CH to get the unknown block. CH sends He(X)and the message {TxID j} to N. N checks for any missing transactions. If there is no missing transactions, N reconstructs X Transmission is completed. Else, N sends the m to CH. CH encodes the n transactions of X with (n+m; n) RS codes CH sends the parity packets {P i} to N, where i = 1 to m N decodes the missing transactions by performing(n+ m; n) and {Pi}. N reconstructs the new block X End if
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Feature Extraction Technique
The transaction history is grouped based on the address. Each address consists of previous dealings (from which the succeeding data is utilized), the transaction’s timestamp and its present currency value. The history contains information that is stored in a sequence along with the timeline. The features from these time series data have to be extracted by applying the rolling window aggregation to detect anomalous transactions. The time series data is examined in order, from the beginning in size v, which can be denoted as the no. of measured values in the order or timeframe. The rolling window size is described as q, which is the number of extents in one window. Every time window forms a fresh order that is then utilized to analyze the consecutive data’s accumulations. The step size v is denoted as one transaction. As any blockchain address may consist of diverse forms of transactions, numerous dissimilar time frames as windows sizes. Accordingly, as the size of transactions varies, the size of the window becomes unstable. Accumulating the transactions permits the alteration of the time sequence data to table data and anomaly recognition procedures. The groupings of time frame q and accumulation functions f are signified as the grouping qi fj, for each i2[1, v] and j2[1, k]. V is the no. of different time frames utilized for window sizes, and k is the no. of distinct accumulation functions used on the group of transactions. The extracted features for each transaction form the blend of qi, fs of the time frames w, and the functions f.
3.6
Anomaly Detection Model
The unsupervised learning method K-means clustering approach is utilized to make the anomaly detection model. This model is utilized to assess every transaction, to be either standard or irregular. It involves the following steps: 1. 2. 3. 4.
Pass the whole dataset and estimate the points with the maximum degrees. Associate these elements based on k number of clusters. Choose the maximum k no. of vertices. Go through the dataset to verify the resemblance and distance functions from every centroid in the groups to its fundamental elements. 5. Every processed element consists of one of the following options: (i) to be located in the same group as the centroid, thus reducing it, and the procedure will not ever choose it up another time (ii) to be retained for later lookup for a probable centroid as the distance function is representing that it should be in an additional group
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(iii) to substitute the present centroid and will succeed both the resemblance and distance vector 6. The procedure will continue to repeat and keep considering the 4(ii) part of the process of every k group. 7. If the execution does not alter the centroids, we announce that it has effectively joined and exhibited the centroids array. 8. Iterate once via centroids array and counterpart their color with the element colors to demonstrate the whole group to exhibit every group.
4 Experimental Results 4.1
Experimental Setup
The proposed CEAD has been simulated in NS3, and the blockchain module has been implemented in Python with the Anaconda platform. The proposed CEAD technique is compared with the anomalous transaction detection system (ATDS) [8], and performance is analyzed based on the parameters: detection delay, resilience against node capture, bandwidth utilization ratio (BUR), and residual energy. Attack Model: The present study emphasizes the advanced attacks centered on the heresies of safety strategies. DDoS attacks, sleep deficit, data falsifying, and node capture are the other attacks taken into account.
4.2
Results
Fig. 1 Detection accuracy for traffic size
Detection Accuracy (%)
In this experiment, traffic size has been varied from 50 to 250 KB per block. Figure 1 shows the detection accuracy for varying the traffic size. The figure shows that the detection accuracy of CEAD ranges from 98.2 to 96.3% and the detection accuracy of ATDS from 97.4 to 94.4%. Ultimately, the detection accuracy of CEAD is 1.5% higher when compared to the ATDS technique.
99 98 97 96 95 94 93 92
CEAD ATDS 50
100 150 200 250 Traffic size (KB/Block)
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Figure 2 shows the resilience against node capture results for varying the traffic size. The figure shows that the strength of CEAD ranges from 98.1 to 99.1% and resilience of ATDS ranges from 96.4 to 97.7%. Ultimately, the strength of CEAD is 1.4% higher when compared to the ATDS technique. Figure 3 shows the bandwidth utilization ratio for varying the traffic size. The figure shows that the BUR of CEAD ranges from 0.96 to 0.98 and BUR of ATDS ranges from 0.93 to 0.94. Ultimately, the BUR of CEAD is 3.8% higher when compared to the ATDS technique. Figure 4 shows the average residual energy of the BN for varying traffic sizes. The figure shows that the residual power of CEAD ranges from 11.5 to 12.3 J and the residual energy of ATDS ranges from 11.2 to 11.8 J. Ultimately, the residual life of CEAD is 3% high when compared to the ATDS technique. Fig. 2 Resilience for traffic size
100
Resillience
99 98
CEAD
97
ATDS
96 95
Fig. 4 Residual energy for traffic size
Residual Energy (Joules)
Fig. 3 BUR for traffic size
100 150 200 250 Traffic size (KB/Block)
Bandwidth utilization Ratio
50
1 0.98 0.96
CEAD
0.94
ATDS
0.92 0.9
50 100 150 200 250 Traffic size (KB/Block)
12.5 12 11.5
CEAD
11 10.5
ATDS 50
100
150
200
250
Traffic Size (KB/Block)
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5 Conclusion In this article, CEAD for blockchain transactions is designed using unsupervised learning techniques. If any node of a cluster has missed transactions, it can be reconstructed using the erasure coding technique. Then, the K-means clustering approach is used for anomaly detection. The proposed CEAD has been simulated in NS3, and the blockchain module has been implemented in Python. Performance results show that CEAD has improved detection accuracy, resilience against node capture, bandwidth utilization ratio, and residual energy than the ATDS technique.
References 1. Phan L, Li S, Mentzer K (2019) Blockchain technology and the current discussion on fraud 2. Saldamli G, Reddy V, Bojja KS, Gururaja MK, Doddaveerappa Y, Tawalbeh L (2020) Health care insurance fraud detection using blockchain. In: 2020 seventh international conference on software defined systems (SDS). IEEE, pp 145–152 3. Cai Y, Zhu D (2016) Fraud detections for online businesses: a perspective from blockchain technology. Financial Innovation 2(1):1–10 4. Jin M, Chen X, Lin SJ (2019) Reducing the bandwidth of block propagation in bitcoin network with erasure coding. IEEE Access 7:175606–175613 5. Dorri A, Kanhere SS, Jurdak R (2017) Towards an optimized blockchain for IoT. In: 2017 IEEE/ACM second international conference on internet-of-things design and implementation (IoTDI). IEEE, pp 173–178 6. Joshi P, Kumar S, Kumar D, Singh AK (2019) A blockchain based framework for fraud detection. In: 2019 conference on next generation computing applications (NextComp). IEEE, pp 1–5 7. Tucker S (2018) TrustCom/BigDataSE 8. Podgorelec B, Turkanović M, Karakatič S (2020) A machine learning-based method for automated blockchain transaction signing including personalized anomaly detection. Sensors 20(1):147 9. Huang H, Chen X, Wang J (2020) Blockchain-based multiple groups data sharing with anonymity and traceability. SCIENCE CHINA Inf Sci 63(3):1–13 10. Hammi MT, Hammi B, Bellot P, Serhrouchni A (2018) Bubbles of Trust: A decentralized blockchain-based authentication system for IoT. Comput Secur 78:126–142
Understanding of Data Preprocessing for Dimensionality Reduction Using Feature Selection Techniques in Text Classification Varun Dogra, Aman Singh, Sahil Verma, Kavita, N. Z. Jhanjhi, and M. N. Talib Abstract The volume of textual data in digital form is growing with each day. For arranging these textual data, text classification has been used. To achieve efficient text classification, data preprocessing is an important phase. It prepares information for machine learning models. Text classification, however, has the issue of the high dimensionality of space for features. Feature selection is a technique for data preprocessing widely used on high-dimensional data. By feature selection techniques, this high dimensionality of feature space is solved and increases text classification efficiency. Feature selection explores how a list of features used to create text classification models may be chosen. Its goals include reducing dimensionality, deleting uninformative features, reducing the amount of data available to classifiers for learning, and enhancing classifiers’ predictive performance. The different methods of feature selection are presented in this paper. This paper also presents the advantages and limitations of feature selection methods. Keywords Text classification Dimensionality reduction
Data preprocessing Feature selection
V. Dogra A. Singh School of Computer Science and Engineering, Lovely Professional University, Phagwara, India S. Verma (&) Kavita Department of Computer Science and Engineering, Chandigarh University, Mohali, India e-mail: [email protected] Kavita e-mail: [email protected] N. Z. Jhanjhi School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] M. N. Talib Papua New Guinea University of Technology, Lae, Papua New Guinea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_48
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1 Introduction Text classification is the process by which text documents are categorized into pre-defined tags or classes. Using natural language processing (NLP) models, the classifiers can interpret text documents and then assign a pre-defined tag or category to each document based on the content and its context. Text classification has one limitation. Owing to the vast number of features leads to the high dimensionality of feature space. This would increase the difficulty of machine learning techniques used for text classification and decrease the feature space’s performance due to redundant or insignificant words. To solve this issue of high data dimensionality, data preprocessing techniques are used, called feature selection. This review seeks to provide such a thorough understanding of feature selection concepts and techniques to researchers and practitioners, especially the state-of-the-art feature selection techniques designed for text classification.
2 Data Preprocessing: Data Cleaning and Dimensionality Reduction To perform the text classification task, it is always required to process the raw corpus data. There are several steps involved in data preprocessing; generally, data cleaning, i.e., organizing the data as per the structure and removal of unneeded sub-texts, tokenization, i.e., breaking up text into words, normalization, i.e., converting all texts into the same case, removing punctuation (stemming leaves out root forms of the verb and lemmatization), and substitution, i.e., identifying candidate words for translation, performing word sense disambiguation [1]. In one of the studies, the researchers have also focused on how machine learning techniques [2] needed to design to recognize similar texts when text data downloaded from multiple and heterogeneous resources. In the labeling task, the text documents are labeled with two commonly used approaches; one is to label each part of the text individually, and the second is to label the group of texts. The first approach includes different supervised learning methods, and the second is called multi-instance learning [3]. In data preprocessing, dimensionality reduction is the essential technique for preparing data for text classification. Due to the higher volume of data, the dimensions of data are also rising. It becomes necessary to lower the dimensions of the data to map high dimensions to space with low dimensions. Such issues are handled in other applications to reduce the computation and storage overheads [4]. Many researchers have observed that the quantity of features in samples is much higher than the number of samples in some circumstances. This leads to the issue called overfitting [5]. Thus, to avoid overfitting, dimensionality reduction becomes a necessity. The major sub-task in reducing dimensions is feature selection. Feature selection is the technique of providing the part of the original features crucial for the task [6].
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3 Feature Selection Methods The objectives of the feature selection method are: (1) To enhance the prediction power of the classifiers (2) To generate a cost-effective classifier and making the process fast (3) To enrich the clarity of the technique that generated the data The features selection is the method of identifying a part of the original features important for the training set, as shown in Fig. 1. It is used to design an efficient classifier keeping necessary vocabulary features and eliminating the noisy features that tend to decrease accuracy. The feature selection objective is to reduce the feature space by selecting the subset of important features and minimizing overfitting without sacrificing text classification performance [6]. For instance, a feature set X ¼ fX1 ; X2 ; . . .; XN g N is a collection of feature sets. There are 2N feature subsets. Ed each feature subset is mentioned as the vector of dimension N. The algorithms find a feature subset with size K, K\N without compromising the accuracy with the actual feature set. Its been the research area, and the authors have presented the different methods so far. These methods fall into three categories; Filter, Wrapper, and Embedded. The filter method selects top-N features based on the scores in several statistical tests to find a correlation with the target label or determine which features are more predictive of the target label, and the process is independent of any learning algorithms. This method does not consider feature dependencies, so it acts computationally fast. The wrapper method assesses a subset of features based on their usefulness to a given class, and it utilizes a learning model to rank the subset of features according to their
Subset of features
Evaluating features subset Candidate set
Original set
Temporary best selection
Final selected subset
Stopping Criterion No
Fig. 1 Criteria for feature selection for dimensionality reduction
Yes
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predictive power. Still, they are computationally more expensive due to repeated learning processes and cross-validation. Embedded methods similar to the wrapper method, but it embeds feature selection into the training phase.
4 Related Work The following sub-sections present the standard feature selection methods that have been applied by researchers to address the issue of dimensionality reduction in text classification.
4.1
Filter Methods
Univariate Feature Selection: Filters work without the consideration of a classifier. They make classifiers work computationally efficiently. The two commonly used filter methods are univariate and multivariate. Univariate feature selection considers each feature individually to find the relationship of the features with the target variable. The following univariate feature selection methods are linear because they refer to linear classifiers created with single variables’ help. The filter method selects features without keeping the primary objective of optimization of the performance of any classifier. It uses essential attributes of data to score the features. If d features are given represented as S, the objective of the filter-based method is to select the part of m\d features, represented by T, which maximizes some function F. It ends up choosing the topm ranked features with high scores. s ¼ arg max F ðsÞ s.t. jsj ¼ m sYS
ð1Þ
In-text classification, commonly preferred linear univariate filter methods are: The Information Gain method chooses the features based on the item’s frequency about the class/label prediction. Researchers have proved that the method can reduce the text’s vector-dimensionality by excluding unneeded features without changing the features and improving classification results [7]. It is defined as how much knowledge it produces in the term’s availability or non-availability to decide about any classifier. It has a maximum value when text maps to the particular label or class, and a term is also available in that document. Equation 2 can be formed as:
Understanding of Data Preprocessing …
IGðtÞ ¼
m X
459
PðCi Þ log pðCi Þ þ pðtÞ þ
i¼1
m X Ci Ci P log p þ pðtÞ t t i¼1
m X Ci Ci P log p t t i¼1
ð2Þ
IG stands for information gain, Ci is the ith class, PðCi Þ is the probability of ith class, and m is the number of target classes. PðtÞ is the probability the feature t appears in the documents and probability PðtÞ for feature, t does not appear in the document. PðCi jtÞ is the conditional probability of the feature t appears in ith class. PðCi jtÞ is the conditional probability of the feature t does not appear in ith class. Chi-Square Test a statistical method applied to a group of categorical features to assess their association between them using their frequency distribution and finding how far results are from the expected output [8]. This can be calculated for events A and B where A and B are said independent if: pðABÞ ¼ pð AÞpðBÞ
ð3Þ
Chi-square can be calculated from the following Eq. 4: P CHI ðt; CÞ ¼ 2
tf0;1g
P C2f0;1g
Nt;C Et;C
EtC
2 ð4Þ
Here, t represents the feature, C represents a specific class, Nt;C is the frequency of feature t and class C occurred together, and Et;C is the frequency of feature t occurs without class C. The chi-square is calculated between every feature and class and then selects the elements with the best chi score. Fisher Score determines the information score that how much information one variable possesses about the unknown parameter on which the variable depends by calculating the variance of the expected value from the observed value, and when variance results minimum, then information score becomes maximum. Researchers have used Fisher’s linear discriminant for support vector-based feature ranking model [9]. For instance, let lkj and rkj are the mean and standard deviation of kth class, concerning the jth feature. Let l j and r j represent the mean and standard deviation of the entire training data concerning jth feature. The Fisher equation for calculating the score of the jth the feature is stated as: F xj ¼
Pc k¼1
2 nk lkj l j r2j
ð5Þ
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Here, r2j is computed as higher fisher scores.
Pc k¼1
nk ðrkj Þ2 . The algorithms pick top-m features with
Pearson’s Correlation Coefficient for quantifying linear dependence within two continuous variables by taking the covariance of these variables and dividing by the product of their standard deviation and its value falls between −1 and +1. With −1 value represents negative correlation, +1 represents positive correlation and 0 represents no linear correlation in two variables. Using vectors, the Pearson’s coefficient r can be computed as: r¼
ðx1 x1 LÞðx2 x2 LÞ ðjx1 jjx2 jÞ
ð6Þ
Here, x1 is the mean of vector x1 and similarly for x2. L is the vector of 1s. j xj is the magnitude of vector x. Variance threshold is the method of removing all low-variance features to reduce the vector-dimensionality. Features with a training-set variance lower as compared to the threshold will be removed [10]. s¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X ðxi xÞ2 =ðn 1Þ
ð7Þ
The equation helps to identify the features with the variation below a certain threshold. It is believed that the part has less predictive power when it does not vary much within itself. Multivariate Filter Methods: In the multivariate filter selection method, during the evaluation, the interdependencies of features are also considered to select relevant features. mRMR (Minimal Redundancy Maximum Relevance) based on mutual information, maximum dependency finds the feature set’s features with the highest dependence with the target label. But it is not suitable for application when the objective is to gain large accuracy with fewer features. The alternative is to use max relevance, which finds features with a dependency with the average of all mutual information values between all features xi and target label c. S is the set of features and I represents mutual information; in Eq. 8, it is calculated between feature i and class c. max DðS; cÞ; D ¼
1 X I xi; c jSj x 2S
ð8Þ
i
But it leads to high redundancy, i.e., the higher dependency between features. Therefore, minimal redundancy may be applied to find mutually exclusive features [11].
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min RðSÞ; R ¼
1 X I xi ; xj 2 jSj xi; xj 2S
ð9Þ
I xi ; xj is the mutual information between feature i and j. Multivariate Relative Discriminative Criterion, the author proposes a multivariate selection method that considers both relevance and redundancy of features in the selection technique. RDC is considered to evaluate relevance, and Pearson’s correlation is considered to evaluate redundancy between features [12].
RDC wi ; tcj ðwi Þ ¼
dfpos ðwi Þ dfneg ðwi Þ min dfpos ðwi Þ; dfneg ðwi Þ tcj ðwi Þ
ð10Þ
Here, dfpos ðwi Þ; dfneg ðwi Þ is the collection of positive and negative text documents in which term wi is occurred. The word may be repeated several times in specific documents and represented by tcj ðwi Þ. Researchers have been searching for new methods that could improve the accuracy and reduce processing time for classification. The author has presented a filter-based method named distinguish feature selector, which has selected distinctive features that possess term characteristics to eliminate uninformative ones. It offered performance by decreasing processing time and increasing classification accuracy.
4.2
Wrapper Methods
Wrappers methods are directly bound to a particular classifier; the methods select the subset of features based on their impact on the classifier by evaluating the predicting performance of all possible features subset in a given space. It means that the features subset will be evaluated by interacting with the classifier that can enhance the accuracy of the classification technique. Due to this approach, as the feature space grows, the computational efficiency decreases. Wrappers are used as filters to select features for other models. The process can be achieved with three different approaches; the first methodology uses the best-first search approach. Second, it may use a stochastic approach such as random selection. It may use heuristics approach like forward and backward passes to include and exclude features. Multivariate Feature Selection: Univariate feature selection methods are computationally efficient, but these methods discard features due to lack of interaction between components; those might have provided useful information about the classification when they were taken together. While measuring the performance of
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features, multivariate considers the dependencies between the features. “Linear multivariate” uses linear classifiers built with a subset of features by measuring feature subsets’ scores according to classification performance. However, “nonlinear multivariate” uses nonlinear classifiers to perform the task. In text classification, commonly preferred linear multivariate wrapper methods are: Recursive Feature Elimination is a recursive method, and it ranks features based on some important metric. During every iteration, features’ importance is evaluated and less relevant features are eliminated. The inverse method in which features are discarded is used to design ranking. This process finds top-N features from this ranking [13]. This is the greedy optimization to find the best performing feature subset. Forward/Backward Stepwise Selection is an iterative process that starts with the evaluation of each feature and selects that which results in the best performing selected model, using some defined evaluation criteria (like prediction accuracy). Next, all possible combinations of that selected feature and a subsequent feature are evaluated, and if the model improves, then the second feature is selected. In each iteration, the model keeps on appending the list of features that best enhances the model’s performance until the subset of the required features is selected. The backward feature selection starts with the entire set of features and discards the least appropriate feature in every iteration that further increases the method’s performance. This process repeats until no improvement is evaluated on removing features, and it ends up with the optimal subset of the features. The researcher has developed a fast-forward selection technique for selecting the best subset of a feature that demanded less computational efforts than other methods [14]. A Genetic Algorithm operates on a feature set to produce a better subset of features without noise. At each process, a new subset is created by selecting individual features according to the order and combining them using operations based on natural genetics. The output is cross-validated variance by the percentage of correct prediction. The result might also undergo mutation. This process helps create a feature set of individual features that are better suited to the model than the original feature set. The chaos genetic algorithm was proposed to simplify the feature selection method and obtained higher accuracy of the classification technique [15]. In text classification, commonly preferred non-linear multivariate wrapper methods are: Nonlinear Kernel Multiplicative Updates include training a classifier iteratively and rescaling the feature set by multiplying them by a scaling factor that reduces the importance of the less influenced feature. The subset of features selected by nonlinear approaches can outperform linear systems [16]. Relief based on instance-based learning. The algorithm assigns scores ranging from −1 to +1 to each feature for its relevance corresponds to the target label. The scope of the algorithm is feasible for binary classification [17].
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“Embedded methods” perform better than wrapper computationally. Still, they serve selection features as a sub-part of the learning technique, mostly specific to the learning model and might not work with any other classifier. In text classification, commonly preferred embedded methods are: LASSO method is commonly used in social sciences. To reduce the dimensionality problem, it puts in the unfavorable condition the features with large coefficients by including a penalty while maximizing the log-likelihood. LASSO assigns zero to some coefficients by selecting a proper weight and reduce the dimensionality. It causes a problem when the correlation between some features is observed high [18]. Ridge Regression reduces the complexity of the model by coefficient shrinkage but retaining all features. Ridge regression has elements; the issue remains complex if the feature set is large [19]. Elastic Net assigns penalty as a compromise between lasso and ridge penalties. The elastic net penalty may be handled quickly to give more strength to either lasso or ridge penalties. It exhibits a grouping effect; the features having a strong correlation tend to be in or out from the feature subset. It combines both L1 and L2 regularization (Lasso and Ridge). It helps in implementing both techniques by tuning the parameters [20].
5 Conclusion We have provided a thorough review of the state-of-the-art feature selection techniques for text classification in this paper. While there exist an incredibly vast number of methods for choosing features set, a comparatively limited number of them are designed for text classification. We have discussed the three broad categories of feature selection techniques or methods; filter method, wrapper method, and embedded methods. The purpose of the feature selection approach is to reduce the issue of high dimensionality of feature space due to the availability of many redundant or irrelevant features during text classification for dealing with various natural language processing challenges. However, some of the feature selection techniques mentioned primarily concentrate on generic data, but it is unclear whether they will be applied to text data. We can see a pattern with the emergence of text applications where these feature selection methods will be extended to text classification.
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References 1. Magnini B, Pezzulo G, Gliozzo A (2002) The role of domain information in word sense disambiguation. Natural Language Engineering 2. Ramisetty S, Verma S (2019) The amalgamative sharp wireless sensor networks routing and with enhanced machine learning. J Comput Theor Nanosci 16(9):3766–3769 3. Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048 4. Batra I, Verma S, Malik A, Ghosh U, Rodrigues JJ, Nguyen GN, Mariappan V (2020) Hybrid logical security framework for privacy preservation in the green internet of things. Sustainability 12(14):5542 5. Armanfard N, Reilly JP, Komeili M (2015) Local feature selection for data classification. IEEE Trans Pattern Anal Mach Intell 38(6):1217–1227 6. Pölsterl S, Conjeti S, Navab N, Katouzian A (2016) Survival analysis for high-dimensional, heterogeneous medical data: exploring feature extraction as an alternative to feature selection. Artif Intell Med 72:1–11 7. Lei S (2012) A feature selection method based on information gain and genetic algorithm. In: 2012 international conference on computer science and electronics engineering, vol 2. IEEE, pp 355–358 8. Jin X, Xu A, Bie R, Guo P (2006) Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles. In: International workshop on data mining for biomedical applications. Springer, Berlin, Heidelberg, pp 106–115 9. Youn E, Koenig L, Jeong MK, Baek SH (2010) Support vector-based feature selection using Fisher’s linear discriminant and support vector machine. Expert Syst Appl 37(9):6148–6156 10. Wang Y, Wang XJ (2005) A new approach to feature selection in text classification. In: 2005 international conference on machine learning and cybernetics, vol 6. IEEE, pp 3814–3819 11. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238 12. Labani M, Moradi P, Ahmadizar F, Jalili M (2018) A novel multivariate filter method for feature selection in text classification problems. Eng Appl Artif Intell 70:25–37 13. Granitto PM, Furlanello C, Biasioli F, Gasperi F (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemom Intell Lab Syst 83(2):83–90 14. Rani P, Verma S, Nguyen GN (2020) Mitigation of black hole and gray hole attack using swarm inspired algorithm with artificial neural network. IEEE Access 8:121755–121764 15. Chen H, Jiang W, Li C, Li R (2013) A heuristic feature selection approach for text categorization by using chaos optimization and genetic algorithm. Mathematical Problems in Engineering 16. Guyon I, Bitter HM, Ahmed Z, Brown M, Heller J (2003) Multivariate non-linear feature selection with kernel multiplicative updates and gram-schmidt relief. In: BISC flint-CIBI 2003 workshop, Berkeley, pp 1–11 17. Urbanowicz RJ, Olson RS, Schmitt P, Meeker M, Moore JH (2018) Benchmarking relief-based feature selection methods for bioinformatics data mining. J Biomed Inform 85:168–188 18. Vickers NJ (2017) Animal communication: when i’m calling you, will you answer too? Curr Biol 27(14):R713–R715 19. Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67 20. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J Royal Stat Soc Ser B (Stat Methodol) 67(2):301–320
A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques Gopal Ghosh, Divya Anand, Kavita, Sahil Verma, N. Z. Jhanjhi, and M. N. Talib
Abstract In the new era, multimedia technology is used extensively. Multimedia data transfers over the Internet are not adequately stable. Data protection is needed for digital images transfer to avoid unauthorized entities. Many encryption strategies have been established for multimedia data over a medium that is not stable. This paper aims to compare the techniques used in encoding multimedia content. This paper explains the process for assessment for chaos image encryption algorithm. The chaos-based image encryption algorithm is used mainly because of its high protection and efficiency. Parameters such as MSC, encryption quality and avalanche effect check the image’s quality. This paper presents a comparative review of non-chaotic and chaotic image encryption techniques. Keywords Image encryption
Chaotic Non-chaotic CBES CFES
G. Ghosh D. Anand School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Kavita (&) S. Verma Department of Computer Science and Engineering, Chandigarh University, Mohali, India e-mail: [email protected] S. Verma e-mail: [email protected] N. Z. Jhanjhi School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] M. N. Talib Papua New Guinea University of Technology, Lae, Papua New Guinea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_49
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1 Introduction Community is closely associated with the area of science. Knowledge is distributed electronically or over a network. Electronic devices share details. Digital data like audio, video, satellite, medical and military data was granted too much independence on the Internet. Information in all areas of work was being utilized. Security of records, storage and transmission is of significant importance. However, wireless and wired networks are quickly impacted. For successful multimedia facilities, a viable security mechanism is required. The main challenge in optimizing the protection algorithm is to preclude attackers from decoding the initial data. Many encryption algorithms have been developed and discovered over time. Only the government and military were made secure contact restricted in the early 1960s. Key digital multimedia technologies have been addressed. In [1] presented interoperability, storage of data in IoT-based systems [1–4]. Encryption and watermarking encryption has been seen as a method to deter unauthorized access to knowledge. In this scenario, the cypher text is an authenticated message, and plain text is the uuencoded message. The technique of decrypting is to find message’s original information. There are two algorithms for encryption: symmetric key and asymmetric key. Symmetric keys can be used as encryption or decryption keys. Cryptography and decryption keys are different in asymmetric algorithms. A public key is used to encrypt the plaintext, and the secret key owners can decrypt the cypher text. A cryptosystem consists of two components: cypher blocks and cypher streams. This stream encrypts a string of text one bit at a time. A block cypher is a cylindrical encryption system that generates block cypher in the shape of a cube. Bit shifts can cause the cryptosystem to be defunct in traditional cyber techniques [5]. Cryptography could be used by text-based communications services where messages have to be recovered. However, these regulations are not required for digital interactive technology. Under which the current does not explicitly define the potential. And Chaos arises from being susceptible to the initial condition, pseudo-randomness, procedural complexity and control parameters. In 1967, Edward Lorenz introduced the butterfly effect, and there would be a substantial shift in performance according to the tiny changes in the initial condition. Noise and uncertainty suggest irregular behaviours in a system. The disorder is now the standard for chaotic structures. The chaotic-based image encryption scheme is a mixture of entity permutation and phase diffusion. Pixel location varies in the permutation phase and pixel grey values shift in the broadcasting process. Distribution involves the recessing of the pixels to improve the signal such that there is no contrast image anymore. Permutation and diffusion matrix can be implemented to solve a generalized Bernoulli change map. The Bernoulli shift function is used to obtain random numbers ordered in the permutation. Encryption can be achieved bit by bit in chaotic maps because of the block-by-block localization of the chaotic maps. There are several approaches proposed for data security in an ineffective medium [6]. This paper aims to estimate an algorithm for encryption using different parameters to be best at its efficiency.
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In this paper, MATLAB is used utilized for two encryption schemes (CBES and CFES). Also, cryptography research has been performed to improve software protection through data confidentiality technology [7].
2 CBES and CFES Image Encryption Schemes In, the author has proposed a scheme focussed on permutation and distribution [8]. Figure 1 shows the schematic of the CBES. The encryption method comprises two stages of permutation and propagation. For the first stage, imagine taking into consideration the permutation. Then, go through Bernoulli’s travelling diagram and locate the truncated ellipse of P. Sorting is implemented to produce the index series. Then a single mixed picture is made to transform the old informative image. Measure additional variables regularly such that diffusion can proceed smoothly. Build an 8-bit random grey value using the “quantization” formula and then render a transformation of the two-point distribution. Describe the picture using a generalized Bernoulli Shifter equation. You should carry out the approach for the entire picture. It is necessary to unscramble this digital picture that has been encrypted. Any researchers have shown a compression-compatible encryption scheme [1] (CFES). The arrangement of CFES is seen in Fig. 2. To obtain a frequency domain representation of an image, the simple DCT images are first generated and multiplied by an orthogonal matrix. This is then applied, and recursive DCT is used for spatial domain description. Thus, scaling is carried out, and eventually, the cyphers are collected. A simple text picture is selected and is reverse to delete. In presented Spatio Temporal Features for smart cities [9]. Encryption and decryption of chaotic schemes are shown in Fig. 1.
Fig. 1 Encryption and decryption of chaotic scheme
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Fig. 2 Encryption and decryption of non-chaotic scheme
3 Quality Measures for Image Encryption Schemes With Figs. 3 and 4, it could be shown that all messy secured files have even encryption. In the camera shot, there is no single picture detail in the chip-text image. Besides, linearity is another image consistency parameter. The estimation of the encryption efficiency, maximal (maximum) and uniform (uniform) variance is used. The outcomes of unstable parameter strategies can be contained in Tables 3, 4 and 5. An example of a disorderly structure is a picture of the text due to a lack of consistent boundaries. It may have conflicting consequences in a setting in which there Chaos. Any encryption scheme with low value is successful. This means that bruising methods are more applicable because they are less invasive. This means that the most probable intensity is lower if the sum is fewer. The requirement for a non-chaotic solution contributes to divergence from the optimal curve even further. The importance of a disorderly system is much less than a well-organized one.
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The standardized histogram is more different from the cypher chart. However, a chaotic system’s encryption norm is comparatively better than a non-chaotic system. In reviewed security surveillance systems [10–14]. The avalanche effect is calculated using the following equation MSE ¼
N 1 M 1 X 1 X ½C1 ði; jÞ C2 ði; jÞ2 M N i¼0 i¼0
ð1Þ
UACI is calculated as follows UACI ¼
1 X ½C1 ði; jÞ C2 ði; jÞ 100 M N i;j 255
ð2Þ
NPCR is calculated as follows P NPCR ¼
i; j Dði; jÞ 100 MN
ð3Þ
The comparison of chaotic and non-chaotic quality measures is given in Table 1. The above comparison table chaotic scheme shows the best performance, i.e. MSC is 39.7, Encryption Quality is 4.86, avalanche effect is 99.7 (NPCR), 27.9 (UACI).
Fig. 3 Encryption and decryption of image using chaotic scheme
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Fig. 4 Encryption and decryption of image using non-chaotic scheme
Table 1 Comparison analysis
Quality measure
Chaotic scheme
Non-chaotic scheme
MSC Encryption quality Avalanche effect
39.7 4.86
35.23 4.12
99.7 (NPCR) 27.9 (UACI)
80.7 (NPCR) 14.99 (UACI)
4 Conclusion This paper presented a comparative analysis of chaotic (CBES) and non-chaotic (CFES) schemes. If there is being dysfunctional, it tends to be particularly resistant to the original state. I outlined a variety of assessment requirements for these image encryption algorithms. Multiple comparisons were conducted to determine the efficiency of the PAC program. For example, correlation coefficient, data entropy tests, compressiveness, maximum variance, abnormal deviation, standardized histogram deviation, avalanche influence, NPCRs, UACIs, and the primary spatial analysis are presented.
References 1. Yang G, Jan MA, Rehman AU, Babar M, Aimal MM, Verma S (2020) Interoperability and data storage in internet of multimedia things: investigating current trends, research challenges and future directions. IEEE Access 8:124382–124401
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2. Rani DNS, Juliet DANM, Devi KR (2019) An image encryption & decryption and comparison with text-AES algorithm. Int J Sci Technol Res 8(7) 3. Pisarchik AN, Zanin M (2008) Image encryption with chaotically coupled chaotic maps. Physica D 237(20):2638–2648 4. Millérioux G, Amigó JM, Daafouz J (2008) A connection between chaotic and conventional cryptography. IEEE Trans Circuits Syst I Regul Pap 55(6):1695–1703 5. Schneier B (1996) Applied cryptography, second edition: protocols, algorthms, and source code in C (cloth). John Wiley & Sons Inc., USA 6. Kumar S, Shanker R, Verma S (2018) Context aware dynamic permission model: a retrospect of privacy and security in android system. In: 2018 international conference on intelligent circuits and systems (ICICS). IEEE, pp 324–329 7. Christopher MB (2013) Mathematics of planet earth, AMS Blog 8. Ahmad M, Zaman N, Jung LT, Ilyas M, Rohaya DA (2014) An integrated approach for medical image enhancement using wavelet transforms and image filtering. Life Sci J 11 (6):445–449 9. Vijayalakshmi B, Ramar K, Jhanjhi NZ, Verma S, Kaliappan M, Vijayalakshmi K, Ghosh U et al (2021) An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city. Int J Commun Syst 34(3):e4609 10. Ghosh G, Verma S, Jhanjhi NZ, Talib MN (2020) Secure surveillance system using chaotic image encryption technique. In: IOP conference series: materials science and engineering, vol 993, No. 1. IOP Publishing, p 012062 11. Ahmed F, Siyal MY, Abbas VU (2010) A perceptually scalable and jpeg compression tolerant image encryption scheme. In: 2010 fourth pacific-rim symposium on image and video technology. IEEE, pp 232–238 12. Ghosh G, Sood M, Verma S (2020) Internet of things based video surveillance systems for security applications. J Comput Theor Nanosci 17(6):2582–2588 13. Gleick J, Berry M (1987) Chaos-making a new science. Nature 330:293 14. Stallings W (2006) Cryptography and network security, 4/E. Pearson Education India
A Review on Chaotic Scheme-Based Image Encryption Techniques Gopal Ghosh, Divya Anand, Kavita, Sahil Verma, N. Z. Jhanjhi, and M. N. Talib
Abstract In the face of an adversary, cryptography is a matter of coordination. It addresses a variety of topics such as confidentiality, authentication, and several vital distributions. Modern cryptography offers the framework for knowing precisely what these concerns are, how to test protocols intended to address them, and how to create protocols that you can trust in their protection. Advanced computer technology can access multimedia easily. Multimedia technologies have recently become popular in operation, and multimedia data protection has become the key concern. In this correspondence, the fundamental problems related to the problem of cryptography were addressed, and surveys of imaging strategies focused on disorderly schemes were also discussed. The chaotic cryptography of images can be accomplished with chaos properties, including deterministic dynamics, unpredictable action, and nonlinear transformation. This definition contributes to approaches that can simultaneously provide protection functionality and an overall visual check that might be acceptable for such applications. In different implementations, including military, legal, and medical processes, digital photographs are commonly used. These applications must monitor the access to images and include ways of checking the accuracy of images. In this paper, a detailed review of chaotic Scheme-based image encryption techniques is presented. G. Ghosh D. Anand School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Kavita (&) S. Verma Department of Computer Science and Engineering, Chandigarh University, Mohali, India e-mail: [email protected] S. Verma e-mail: [email protected] N. Z. Jhanjhi School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] M. N. Talib Papua New Guinea University of Technology, Lae, Papua New Guinea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_50
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Chaotic Cryptography
1 Introduction In modern telecommunications, cryptography is a fundamental problem. An individual sends hidden messages via media at the behest of another group. Innovations also culminated in the dissemination of rich digital material across the Internet. According to the common law, it has become simpler for copyright piracy and unregulated interactive material sharing through modern technologies. This results in a large number of other consequences. The protection of information against multimedia content is another major problem in the multimedia environment. The two main image security technologies are used, (a) to distribute digital content and (b) to protect the user’s image rights, copyright and authentication by watermarking. This paper addresses studies on the usage of disorderly schemes in picture encryption techniques.
2 Image Security and Its Issues Multimedia security is offered in a way or in a combination of methods. These copyright defense approaches are strongly dependent on cryptography. Digital images and textual documents can be defended and guaranteed using standardized, public-key cryptography. You can treat these kinds of communication channels as binary sequences and you can encrypt the entire messages with AES or DES [1]. Multimedia data may be saved as standard text or another file type. It is not easy to decide on what level of safety is necessary. These costs should be measured correctly to prevent spending on overprotective steps. There are several encryption algorithms, including Arnold map [2], Tangram, Baker [3], Magic cube, and Affine [4], etc. It would be challenging to distinguish key from the algorithm in specific algorithms. This method of cryptography is not reliable enough to use in real society. There have been changes in picture encryption since 2014.
3 Recent Studies in Image Encryption Techniques Recent image encryption methodologies provide new complex coding systems. This paper discusses existing (at the time) encryption schemes that rely on the following domains shown in Fig. 1.
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Fig. 1 Flowchart of image encryption methodologies
4 Spatial Domain There are essential tools for encrypting large amounts of digital evidence. In [3] presented interoperability, storage of data in IoT-based systems. A random sequence of a symbol is created from a chaotic system. The algorithm is easier, extremely stable, and has no distortion. The author introduced an algorithm for image encryption/decryption and its design. Each pixel’s grey level is XOR to either the I or “o” icon, centered on a chaotic binary chain. Sobhy used the Lorenz equation for digital authentication, generating tamper-resistant databases for this article. In this article, the chaotic algorithm is used for text and image encryption. Algorithms have also been attacked in [4]. A. Sinha proposed a novel method to encrypt images for the safe transfer of these images. The current image’s digital signature is appended to the initial image. Bose-Chaudhuri Hochquenghem code is an error control code that is used to encrypt images. The digital signature was used in this case to guarantee the authenticity of the images. Multi-level image encryption using binary step exclusive OR and picture division approach was established by a team led by “Chang-Mok Shin, Dong-HoanSeo, Kyu-Bo Chol, Ha-Wm Lee, and S Kim.” The degree of grey has shades of various brown color. They converted binary images to binary phase encoding and then, using binary phase XOR, they encrypted the binary encoded images. The grey picture was generated by combining binary grey pictures. Fethi Belkhouche and Uvais Qidwai used a one-dimensional chaotic map in 2003 [5]. The method is useful in the sense that it is modular and implementation may be extended to analog pictures. The initial state knowledge plays a key role in extremely secure cryptography. Zhang first permutes pixels, accompanied by nonlinear morphological processing. Failure of encryption is due to self-likeness of image and visual attributes. MAR Chen, Zhang, Shao, and Kai-Chi Yi used a Tmatrix. This was confirmed. The T-matrix is twice as long as the Arnold matrix, which explicitly conforms to the prototype. In image processing, we may use watermark algorithms. A messy neural system may learn a complex cat chart. Neural networks were used to disrupt the normal methodologies. To use an efficient image data encryption algorithm, G. Gu merged permutation and substitution into one algorithm. To enhance the pseudorandom behaviors of chaotic sequences, optimal care and cross-discarding have been applied.
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A chaotic sequence is generated utilizing a framework of the chaotic system. In this way, a threshold function converted the previous sequence into two streams [6]. An alternate approach was introduced to measure the permutation matrix. Instead of manipulating the binary stream, the pixel values were dynamically handled. Secondly, the modified image was encoded with a permutation matrix again.
5 Frequency Domain Proposed some suitable algorithms to encrypt images compressed by quadtree and wavelet [6]. Droogenbroeck and Benedett offered selective encryption methodologies in 2002 for uncompressed images and JPEG images. If there are less significant bitplanes being encrypted, then the level of visual deterioration is significant. For uncompressed (raster) files, Uhl suggested an algorithm that was ultimately the opposite of the method taken by Droogenbroeck and Benedett. In this way, only the prime bit planes of the raster chart are encrypted in the algorithm created by Schmidt and Uhl. According to the algorithm of the proposed underlying cryptosystem is the AES. Every conventional encryption scheme may be selected instead. The chaotic image encryption flowchart is shown in Fig. 2.
Fig. 2 Flowchart of chaotic image encryption
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Table 1 Recent studies on image encryption schemes Authors
Year
Remarks
Sumit Kumar et al. [8] Vijayalakshmi et al. [9]
2018
The author presented “context-aware dynamic permission model: a retrospect of privacy and security in android system” Author proposed “an attention-based deep learning model for traffic flow prediction using spatio temporal features toward sustainable smart city” The author studied “colour image encryption technique using differential evolution in the non-subsampled contourlet transform domain” Author proposed “JPEG image encryption with improved format compatibility and file size preservation” Author proposed “internet of things-based video surveillance systems for security applications”
2020
Kaur and Kumar [10]
2018
He et al. [11]
2018
Gopal Ghosh [12]
2020
Fig. 3 Encryption and decryption model for chaotic scheme
Recent studies on image encryption schemes is given in Table 1. The general encryption and decryption model for the chaotic scheme is shown in Fig. 3 (Figs. 4 and 5).
6 Other Contributions, Results, and Discussions In this section, reviews are conducted to extract existing techniques applied to image encryption by various researchers. The observations have been depicted in Table 2, showing the performance evaluations and how they contributed to the security while sharing image data. Later, several encryption techniques are compared with different proposed algorithms and their results in Table 3.
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Fig. 4 Image to be encrypted
Fig. 5 Encrypted image using chaotic scheme
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Table 2 Contributions of other researches related to the present review Reference
Domain
Contribution
Outcomes
[4]
Encryption systems
Chaotic encryption algorithm
[10]
Image encryption approach Encryption methods
Memetic differential evolution
Every system is split into several computer times. Developed countermeasures also for better security Secret key generation to shuffle and encrypt the RGB color channel
[11]
[13]
Spatial
[14]
Spatial
[15]
Spatial
[16]
Spatial
Extended criteria on the compatibility of file formats Mirror like encryption algorithm VLSI architecture and image encryption/ decryption algorithm Image encryption with digital signature Multi-level encryption technique with image diving
Preserving file size using reduction feature to JPEG image encryption format Scrambled image with minimal complexity, no distortion and high security A gray level of each image pixel with two predetermined keys An encrypted image with a digital signature used to verify the image authenticity Grayscale encrypted image is obtained with an encrypted binary image
Table 3 Comparison of various image encryption algorithms with results Reference
Proposed technique
Parameters
Results
[20]
Image encryption algorithm
Applied confusion strategy and achieved good security
[17]
Crypto and chaotic-based encryption technique Block cipher framework
Binary bit plane decomposition and diffusion XOR operation on pixels
Quantified measurement data
Achieved confusion, diffusion, sensitivity, comprehensibility, and encryption speed Achieved superior security and high performance
[18]
[19]
Linear and nonlinear encryption technique
Bitplanes pixels
Obtained more security by generating many confusions and permutations
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7 Conclusion The current vast number of encryption techniques have been addressed in this paper. The initial survey’s emphasis was on the already developed image encryption algorithms; however, the best way to secure multimedia data such as images and video is with the naive algorithm, encrypting the entire multimedia bit series using a standard cipher method. Many previous and current works aim at cryptographic operations that encrypt only a carefully selected portion of the picture bitstream to ensure a high-security standard. Many of the systems evaluated were only able to attain a moderate to low degree of safety, where systems under which partial failure is likely may be established. However, such techniques provide only superficial security in certain media implementations. There have been numerous proposed measures aimed at chaotic networks. The problem was slightly illustrated in section three of the survey report. Particular emphasis should be made on a cryptosystem that is well studied, quick, and secure.
References 1. Stinson DR, Paterson M (2018) Cryptography: theory and practice. CRC Press 2. Fridrich J (1997) Image encryption based on chaotic maps. In: 1997 IEEE international conference on systems, man, and cybernetics. Computational cybernetics and simulation, vol 2. IEEE, pp 1105–1110 3. Yang G, Jan MA, Rehman AU, Babar M, Aimal MM, Verma S (2020) Interoperability and data storage in internet of multimedia things: investigating current trends, research challenges and future directions. IEEE Access 8:124382–124401 4. Sobhy MI, Shehata AE (2001) Methods of attacking chaotic encryption and countermeasures. In: 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. No. 01CH37221), vol. 2. IEEE, pp 1001–1004 5. Belkhouche F, Qidwai U (2003) Binary image encoding using 1D chaotic maps. In: Annual technical conference IEEE region 5. IEEE, pp 39–43 6. Ghosh G, Verma S, Jhanjhi NZ, Talib MN (2020) Secure surveillance system using chaotic image encryption technique. In: IOP conference series: materials science and engineering, vol 993, no 1. IOP Publishing, p 012062 7. Ahmad M, Zaman N, Jung LT, Ilyas M, Rohaya DA (2014) An integrated approach for medical image enhancement using wavelet transforms and image filtering. Life Sci J 11 (6):445–449 8. Kumar S, Shanker R, Verma S (2018) Context aware dynamic permission model: a retrospect of privacy and security in android system. In: 2018 international conference on intelligent circuits and systems (ICICS). IEEE, pp 324–329 9. Vijayalakshmi B, Ramar K, Jhanjhi NZ, Verma S, Kaliappan M, Vijayalakshmi K, Ghosh U (2021) An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city. Int J Commun Syst 34(3):e4609 10. Kaur M, Kumar V (2018) Colour image encryption technique using differential evolution in non-subsampled contourlet transform domain. IET Image Proc 12(7):1273–1283 11. He J, Huang S, Tang S, Huang J (2018) JPEG image encryption with improved format compatibility and file size preservation. IEEE Trans Multimedia 20(10):2645–2658
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12. Ghosh G, Sood M, Verma S (2020) Internet of things based video surveillance systems for security applications. J Comput Theor Nanosci 17(6):2582–2588 13. Yen JC, Guo JI (2000) A new chaotic mirror-like image encryption algorithm and its VLSI architecture. Pattern Recogn Image Anal (Adv Math Theory Appl) 10(2):236–247 14. Guo JI (2000) A new chaotic key-based design for image encryption and decryption. In: 2000 IEEE international symposium on circuits and systems (ISCAS), vol 4. IEEE, pp 49–52 15. Sinha A, Singh K (2003) A technique for image encryption using digital signature. Optics Commun 218(4–6):229–234 16. Shin CM, Seo DH, Cho KB, Lee HW, Kim SJ (2003) Multilevel image encryption by binary phase XOR operations. In: CLEO/Pacific Rim 2003. The 5th pacific rim conference on lasers and electro-optics (IEEE Cat. No. 03TH8671), vol 2. IEEE, p 426 17. Wang XY, Zhang YQ, Bao XM (2015) A novel chaotic image encryption scheme using DNA sequence operations. Opt Lasers Eng 73:53–61 18. Huang R, Rhee KH, Uchida S (2014) A parallel image encryption method based on compressive sensing. Multimedia Tools Appl 72(1):71–93 19. Zhang YQ, Wang XY (2014) A symmetric image encryption algorithm based on mixed linear–non-linear coupled map lattice. Inf Sci 273:329–351 20. Xu L, Li Z, Li J, Hua W (2016) A novel bit-level image encryption algorithm based on chaotic maps. Opt Lasers Eng 78:17–25
FANET: Efficient Routing in Flying Ad Hoc Networks (FANETs) Using Firefly Algorithm Manjit Kaur, Aman Singh, Sahil Verma, Kavita, N. Z. Jhanjhi, and M. N. Talib
Abstract In recent years, the use of emerging technologies and the role of flying ad hoc networks (FANETs) have rapidly changed. Flying ad hoc networks are generally used in different areas such as media, agriculture, business, entertainment, security services, and various emergency services. Flying ad hoc network provides highly dynamic environments. The unmanned aerial vehicles (UAV) depend on nodes (packets) where nodes are moving very fast and thus packets loss during transmission. In this paper, define an approach that is based on the firefly algorithm (FA). The proposed algorithm applied the firefly algorithm’s idea on flying ad hoc networks where geographical position mobility-oriented routing protocol (GPMOR) objectives to reduce the number of hops based on Gauss Markov (GM) mobility model. It improves the performance of routing by efficient packets. Keywords Unmanned aerial vehicle (UAV) Routing protocol Firefly algorithm
Flying ad hoc network (FANET)
M. Kaur A. Singh School of Computer Science and Engineering, Lovely Professional University, Phagwara, India e-mail: [email protected] S. Verma (&) Kavita Department of Computer Science and Engineering, Chandigarh University, Mohali, India e-mail: [email protected] Kavita e-mail: [email protected] N. Z. Jhanjhi School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] M. N. Talib Papua New Guinea University of Technology, Lae, Papua New Guinea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_51
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1 Introduction 1.1
Flying Ad Hoc Network (FANET)
Flying ad hoc network (FANET) is a part of the mobile ad hoc network. To perform different kinds of operations, FANET is using driverless aircraft, which are also known as unmanned aerial vehicles [1, 2]. These can be easily installed in non-deterministic areas. The usage of flying ad hoc networks is in various areas, such as mining, missile launching, bomb-dropping, videography, photography, water, crop monitoring, soil monitoring, logistic, traffic monitoring, surveillance, and many more [3, 4]. There are various characteristics of wireless networks, such as processing power, storage, data volume, communication standards, and routing protocols. There are multiple characteristics of flying ad hoc networks such as mobility model, node mobility, topology change, energy consumption, computational power, node density, localization, radio propagation model.
1.2
Firefly Algorithm
Firefly algorithm explains many optimization problems that exploit the flashing performance of fireflies in nature [5]. There are two sections of the firefly algorithm working: One is the flashing behavior of the firefly, and the other is the step-by-step procedure, which defines the set of rules or instructions to be executed in a specific order to get the best-desired output. There are several pros of the firefly algorithm, as mentioned below: (a) High speed of convergence to find an optimized solution. (b) Flexible with other optimized techniques. (c) Efficient with the nonlinear model and multi-model optimization. There are different taxonomies of the firefly algorithm as explained below: (a) Optimization techniques a. b. c. d. e.
Combinational optimization Continuous optimization Constraint optimization Multi-objective optimization Dynamic and noisy optimization
(b) Classification algorithm (c) Engineering applications a. Used for antenna design b. Used in robotics and semantic web
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c. d. e. f. g.
1.3
Used Used Used Used Used
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in image processing for wireless networks for industrial optimization in civil engineering in business applications.
Routing Protocols
Numerous routing protocols are used for flying ad hoc networks, such as static protocols, proactive protocols, reactive protocols, hybrid protocols, geographical protocols, and hierarchical routing protocols. A static routing protocol is specially designed for small structured networks that users and administrators can easily maintain—for example a mail server. Proactive routing protocols are used to store or maintain the data in the table format. If the data has been kept in table form, it is again easy to select the best route. A reactive routing protocol is used when there is no need to find the route between two nodes [6]. A hybrid routing protocol is a mixture of the proactive protocol and reactive routing protocol and is dynamic in nature. A geographical routing protocol is used to search for the best feasible location between multiple nodes. It is also called position routing protocol. It is very complex due to its memory size. A hierarchical routing protocol is used at the lower level. The structure of the nodes is maintained through the nodes hierarchy [7].
2 Related Work The protocol [8] was proposed to resolve the issues based on topology and routing between nodes. The results showed the network’s significance using overheads, throughput, channel utilization, end-to-end delay, and packet delivery ratio parameters. Authors [9] surveyed position-based routing protocols and discussed detailed descriptions of the pros and cons of these routing protocols used in FANET. Author [10] described the firefly algorithm, which has defined different parameters such as attractiveness function and calculating distance. Mainly, they are bees mating, bats echolocation, bacterial foraging, bees foraging, etc. In the paper [11], the authors discussed the dynamic nature of traffic information which provides better accuracy. Author [12] intended to analyze the efficiency of swarm intelligence, firefly algorithm, levy flights, cuckoo search algorithms. Authors [13] discussed security approaches on the execution time, and different algorithms have been compared based on energy consumption. In the paper [14], the author explained the classical firefly algorithm using search heuristic. This technique was realistic to a specific mathematical concept that is graph 3-coloring.
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3 Proposed Methodology Geographical position mobility-oriented routing (GPMOR) protocol is used to find the best available next-hop network [15]. It is used for the highly dynamic structure. The main question is how to find the best available next-hop in a particular network. The answer to this question is the use of the Gauss Markov mobility model [16]. Yang X. S. offered the fFirefly algorithm (FA) to solve various problems in various applications [12]. The firefly algorithm can be stated as: Methodology: (firefly algorithm). This algorithm is used to search the shortest path in a network with the help of two properties. (i) The firefly’s intensity and (ii) the couples (two) of firefly are inverse to the difference between them. Step 1: Start [Initialization of objective function.] Step 2: Generation of the initial population of fireflies (nodes). Step 3: Expression of light intensity and state absorption coefficient. Step 4: Repeat Steps 5–8 while iteration < maximum_generation_value (maximum iteration). Step 5: Repeat For I = 1 to N where N indicates all ‘N’ fireflies. Step 6: Repeat For J = 1 to I: Step 7: If light intensity of J > I (light intensity of ‘I’), then set: vary mate selection and prey attraction with a distance. Step 8: Move the fireflies based on the attraction I toward J and evaluate new solutions. [End of If structure]. [End of inner for structure]. [End of outer for structure]. Step 9: If the result is not found, then go to Step 4. Otherwise, go to Step 10. Step 10: Display the best-desired result. Optimization is the concept to find the best path (route) in FANETs (Fig. 1). The term optimization is the act of making decisions and provides the best solution from the set of all feasible and possible solutions using the 3D view (Fig. 2). There are three main parameters of optimization technique: (a) write a function to optimize, (b) to select a value using the possible solution, and (c) the rule of optimization. This algorithm is used to discover out the feasible (optimal) solution for a specific shortest path. The methodology is a system of efficient routing methods in FANET with the firefly algorithm’s help. In FANET, there are two approaches: finding the neighbor node and second defining the destination’s position. After obtaining the sorted list by applying the firefly algorithm’s objective function, every packet’s broadcasting has been done. Selection of the highest value from the network is the next step and then checks the middle node. If the middle node is the destination node, so reach back to the source and update the objective function for
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Fig. 1 Using firefly algorithm
Fig. 2 3D view of firefly algorithm
further implementation and get the desired result. Firefly algorithm is a developing swarm intelligent algorithm. The use of this algorithm is to discover the shortest path in a network. There are two sections of the firefly algorithm working: One is the firefly’s flashing behavior and the step-by-step procedure, which defines the set of rules or instructions to be executed in a specific order to get the best-desired output.
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4 Simulation and Results There are three parameters to measure the overall results based on the efficient routing protocol as mentioned below. Figure 3 shows the packet delivery ratio (PDR) of flying nodes. This figure shows the relationship between the total number of packets from the source to the destination and the total number of packages established. The packet delivery ratio for GPMOR retains at a high level. The result shows that GPMOR can provide a better packet delivery ratio than other protocols under a highly dynamic environment. GPMOR can give much more effective and accurate routing for the highly active airborne network in a high mobility environment. In this, there is a comparison of different routing protocols of flying ad hoc networks. There is a comparison of AODV, DSR, DSDV, OLSR, and GPMOR protocols, where GPMOR is best compared to other protocols. This is based on three parameters such as packet delivery ratio, end-to-end delay, and the number of hops in the flying ad hoc network. The packet delivery ratio (PDR) is 99.87% in GPMOR, but 96.56, 98.77, 98.65, and 96.22 shows in AODV, DSR, DSDV and OLSR protocols, respectively. Figure 4 shows the end-to-end delay (E-to-E Delay) of flying nodes. This figure shows the difference between sending time of every node at the source and receiving the node’s time at the destination. This shows 84.27, 60.50, 57.22, and 67.25 in AODV, DSR, DSDV, and OLSR protocols, respectively. GPMOR shows 100% that is indicating the improvement in end-to-end delay. For DSR and DSDV, the performances are similar in all the parameters. Figure 5 shows the number of hops of flying nodes. This figure shows a hop arises when a packet is passed from one network slice to the next part. Data packets pass through routers as they travel between a source node and a destination node.
Fig. 3 Packet delivery ratio of flying
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Fig. 4 End-to-end delay of flying nodes
Fig. 5 Number of hops of flying nodes
This shows that of 0.14, 0.67, 0.27, and 0.27 in AODV, DSR, DSDV, and OLSR protocols. GPMOR shows 0.97 in several hops in the flying ad hoc network. For DSDV and OLSR, the performances are similar in this parameter. The overall performance of the GPMOR protocol is excellent compared with the other four routing protocols of the flying ad hoc network.
5 Conclusions and Future Scope Geographical position mobility oriented routing (GPMOR) protocol is used to handle the challenge faced in the highly dynamic environment of FANET. We used a geographical position-based routing protocol for the high-speed environment, which is further based on the Gauss Markov (GM) mobility model. The proposed
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work is based on FA’s steps to UAV parameters such as packet delivery ratio (PDR), end-to-end (E2E) delay, speed, and performance. This work shows the best route of the UAV nodes in terms of scalability and less execution time. The major concept is load balancing in the ad hoc network. The load balancing needs to be taken as a research part in the future. The same method would consider for the traffic of UAVs in a dynamic environment. It is interesting to analyze and investigate more about the different altitude scenarios.
References 1. Oubbati OS, Atiquzzaman M, Lorenz P, Tareque MH, Hossain MS (2019) Routing in flying ad hoc networks: survey, constraints, and future challenge perspectives. IEEE Access 7:81057–81105 2. Datta D, Dhull K, Verma S (2020) UAV Environment in FANET: an overview. Applications of cloud computing: approaches and practices, p 153 3. Kaur M, Verma S (2020) Flying ad-hoc network (FANET): challenges and routing protocols. J Comput Theor Nanosci 17(6):2575–2581 4. Sun Z, Wang P, Vuran MC, Al- MA, Al- AM, Akyildiz IF (2011) BorderSense: border patrol through advanced wireless sensor networks. Ad Hoc Netw 9(3):468–477 5. Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley 6. Radwan AAA, Mahmoud TM, Houssein EH (2011) Evaluation comparison of some ad hoc networks routing protocols. Egypt Inf J 12(2):95–106 7. Wu J, Zou L, Zhao L, Al-Dubai A, Mackenzie L, Min G (2019) A multi-UAV clustering strategy for reducing insecure communication range. Comput Netw 158:132–142 8. Sharma V, Kumar R, Kumar N (2018) DPTR: distributed priority tree-based routing protocol for FANETs. Comput Commun 122:129–151 9. Oubbati OS, Lakas A, Zhou F, Güneş M, Yagoubi MB (2017) A survey on position-based routing protocols for flying ad hoc networks (FANETs). Veh Commun 10:29–56 10. Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press 11. Vijayalakshmi B, Ramar K, Jhanjhi NZ, Verma S, Kaliappan M, Vijayalakshmi K, Ghosh U (2021) An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city. Int J Commun Syst 34(3):e4609 12. Sennan S, Somula R, Luhach AK, Deverajan GG, Alnumay W, Jhanjhi NZ, Sharma P (2020) Energy efficient optimal parent selection based routing protocol for internet of things using firefly optimization algorithm. Trans Emerg Telecommun Technol e4171 13. Batra I, Verma S, Alazab M (2020) A lightweight IoT‐based security framework for inventory automation using wireless sensor network. Int J Commun Syst 33(4):e4228 14. Fister Jr I, Yang XS, Fister I, Brest J (2012) Memetic firefly algorithm for combinatorial optimization. arXiv preprint arXiv:1204.5165 15. Lin L, Sun Q, Li J, Yang F (2012) A novel geographic position mobility oriented routing strategy for UAVs. J Comput Inf Syst 8(2):709–716 16. Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wirel Commun Mob Comput 2(5):483–502
Energy-Efficient Model for Recovery from Multiple Cluster Nodes Failure Using Moth Flame Optimization in Wireless Sensor Networks Sowjanya Ramisetty, Divya Anand, Kavita, Sahil Verma, N. Z. Jhanjhi, and Mamoona Humayun Abstract For transmission and collection of sensed data, it is essential that the connectivity among deployed sensor nodes in WSNs. The maintenance of network connectivity is a challenging task in harsh environmental conditions when participating nodes’ failures lead to the network’s disjoint partitions. To improve the connectivity and coverage with energy efficiency for the partitioned network, optimal positioning of sensor nodes has been performed based on the moth flame optimization algorithm (OPS-MFO). In the anchor node, the relay nodes have exploited in the proposed model—two phases involved in the proposed model, such as the inter-partition phase and intra-partition phase. For intra-partitioning and inter-partitioning, all sensor nodes and relay nodes’ positions have been estimated using the moth flame optimization algorithm for better connectivity. The proposed
S. Ramisetty Department of Computer Science and Engineering, KG Reddy College of Engineering and Technology, Hyderabad, India S. Ramisetty Lovely Professional University, Phagwara, India D. Anand School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Kavita (&) S. Verma Department of Computer Science and Engineering, Chandigarh University, Mohali, India e-mail: [email protected] S. Verma e-mail: [email protected] N. Z. Jhanjhi School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] M. Humayun Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka, Kingdom of Saudi Arabia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_52
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model is outperformed based on the experimental analysis and evaluation by comparing them with the existing algorithms.
Keywords Network lifetime Energy efficiency Coverage Wireless sensor network
Cluster failure Connectivity
1 Introduction Wireless sensor networks (WSNs) include various autonomous sensor nodes with small sizes that utilize monitoring the sensing of occurrences of events and areas of interest. For advanced processing, the communication of sensed data has been made to a BS or a sink from the sensor nodes cooperatively. A mobile or static sensor node’s deployment can be made either in a predetermined way or randomly over the deployment area [1, 2]. The reduction of human intervention is the main objective of WSN per harsh environmental constraints such as battlefield, deep forest, disaster-prone area, and border security area. It is crucial to ensure the connectivity among sensor nodes and the SINK that allows the monitored area’s network events reporting [3]. Due to factors like physical damage, hardware fault, energy exhaust, low deployment of nodes, etc., the nodes are prone to fail in extreme environmental conditions. The communication path or network connectivity can be affected by the node’s failure adversely [4]. Due to the single node failure, serious consequences result in the network connectivity if a node is considered a gateway or an articulation node that partitions or joins different regions. The failed node can replace with the active node for restoring the partitions connectivity from the proximity. The designing of a fault-tolerance mechanism is a challenging one for simultaneous node failures. The failure of multiple nodes results in cases where the network’s different regions could get damaged. The proposed model designs a coverage-aware and energy-efficient protocol where both mobile relay nodes and mobile sensor nodes are in the network. The data collected using relay nodes aggregate it and communicate the SINK data from the underlying sensor nodes. The number of relay nodes estimated using a proposed heuristic technique by keeping the high as the sensor nodes’ density over network areas in the proposed model: (1) intra-partition and (2) inter-partition. All sensor nodes’ position computes for better connectivity in the intra-partition phase, whereas all sensor nodes’ position estimate for relay nodes is in the inter-partition phase.
2 Literature Survey In [5], a recovery process is proposed which helps to move the relay nodes toward the network’s center. Between the relay nodes, a communication link builds as they come in a range of each other, and it can be utilized for connecting the disjoint partitions further.
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The author utilizes a security mechanism to implement ANN as a machine learning method and ABC as a swarm-based approach for improving MANET performance in malicious nodes, specifically GHA and BHA nodes [6]. This paper presents the model of attention-based CNN-LSTM multistep prediction designs for extracting the temporal and spatial features. The designed model outperforms short-term traffic prediction during both peak and non-peak hours [7]. The author proposes a security framework for IA that provides validation, data confidentiality, and authentication. The proposed approach shows better results in terms of high-security impact, less energy consumption, more throughput, fewer memory requirements, less execution time, and less number of CPU cycles than the other existing approaches [8]. The author [9] proposed the fault-tolerant disjoint multipath distance vector routing algorithm to minimize the routing overheads significantly. For active path selection, the highest residual energy contains in each node. The proposed method shows better results regarding the reduced end-to-end delay, packet delivery ratio, and routing overhead than the existing protocols such as ZD-AOMDV and AOMDV. Thus, the reduction in energy utilization is achieved. The author [10] presents the energy-efficient and fault-tolerant distributed algorithm for data aggregation in WSNs (EFTA). Based on the density of SNs, some SNs are chosen as CHs and report the MA to collect the data from the chosen CHs. Compared to the previous algorithms, the proposed algorithm EFTA performs better results in improved network lifetime and reduced execution time. In this paper, the hybrid logical security framework (HLSF) has been proposed to provide a lightweight security solution for next-generation IoT. HLSF performs well in energy and computational overhead than the previous approaches like OSCAR and CoAP [11].
3 Proposed Framework To increase the connectivity and coverage, sensor nodes’ optimal position based on moth flame optimization algorithm (OPS-MFO) proposes which uses to compute all sensor nodes’ position for intra-partitioning and relay nodes’ position for inter partitioning to achieve better connectivity. The best value of Bi estimates at each node which is utilized to update the moth position is shown in Fig. 1.
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Moth Flame Optimization Overview
In this paper, moths are considered small species with two wings similar to a grasshopper which can fly out. One hundred sixty thousand species are contained in the moth [12]. To improve the moth flame optimization algorithm, Seyedali Mirjalili utilizes the navigation method of moths called transverse orientation. In a
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Fig. 1 Sharing of best moth positions among the sensor nodes
straight line, moths can fly at night based on the moon’s constant angle because of the long distance between earth and moon. Artificial light like a lamp, bulb, etc., can trap during their flying path. However, moth flame optimization is considered a population-based algorithm that initializes the first population of moths and flames. Equation (1) shows the initialization process. 2
m11 6 m21 6 M¼6 . 4 .. ms1
m12 m22 .. . ms2
m1d m2d .. .. . . msd
3 7 7 7 5
ð1Þ
where d represents the number of dimensions and s indicates the number of moths. In Eq. (2), the flame position can be initialized: 2
f11 6 f21 6 F¼6 . 4 .. fs1
f12 f22 .. . fs2
3 f1d f2d 7 7 .. 7 .. . . 5 fsd
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Where s represents the number of flames. A similar number of flames and moths are assumed initially. Moths are considered as real search agents, and the best values of moths are the flames. In Eq. (3), the moths’ position updates toward the flames, and it is given as follows: S Mi ; Fj ¼ d^i egt cosð2ptÞ þ Fj
ð3Þ
S indicates the spiral function that describes moths’ flying path around the flame in a logarithmic form. The shape of the logarithmic path is given by a constant g which is taken as 1. By using (7), the value of t generates within the range of [−1, 1]. The absolute distance of ith moth concerning the flame demonstrates by the di, which is given as follows: d^i ¼ Fj Mi
ð4Þ
where Fj is the jth flame position and Mi is the ith moth. The number of flames (Nf) reduces by the increment of iteration, which is given by using Eq. (5): ðs 1 Þ Nf ¼ round s l: T
ð5Þ
T indicates the maximum number of iterations, l shows the current iteration, and S represents the maximum number of flames. The exploitation and exploration could maintain by the reduction of flame number. In the initial phase, better prospecting and good exploitation in the later phase are possible with the term t by using the following equations:
1 a ¼ 1 þ l: T
t ¼ ða 1Þ:rand þ 1
ð6Þ ð7Þ
The spiral path reduction is made continuously with iteration using Eqs. (6) and (7). This leads to better exploitation in a later phase and good exploration in the initial stage.
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Intra-Partition Phase
The network’s coverage improves with the enhancement of each partition in this phase. The proposed algorithms process locally by choosing each partition dynamically. Within each partition, the redundant nodes detect and move toward
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the partition boundary later. If the redundant nodes are displaced maximally, a partition’s coverage increases. All redundant sensor nodes are considered in the proposed algorithm for improving the network coverage area. The maximum coverage ensures by repositioning the redundant nodes in each relay node’s representative area. Without any loss between the redundant node and relay node connectivity, each partition’s full possible coverage achieves.
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Inter-Partition Phase
During this process, communication between partitions is restored. The free relay node is not a member of any relay node partition. The detection of all free relay nodes and the regions’ movement between different partitions have been made in the algorithm’s first phase. From the available information, each partition’s boundary calculates in the phase of intra-partition. In the region between partitions, the free relay node places to overlaps the coverage.
4 Simulation Results In both inter-partition and intra-partition phase, the proposed algorithms’ performances have been evaluated based on the simulation results. Here, the network area terrain considers as obstruction-free. The proposed model OPS-MFO validates with EFTA [12] and FD-AOMDV [11] using the Network Simulator 2. The parameters of a simulation are demonstrated in Table 1. SNs distribute over the area of 1500 1500 m of square monitoring area uniformly in the simulation setup where the number of nodes varies from 10 to 100 SNs. Based on the simulation time, the obtained delay and mobility impacts are computed. By using different protocols, the evaluation was made with the delay
Table1 Simulation table
Parameter
Value
Node number Monitoring field size Pause time Routing protocol Traffic model Packet size Initial energy Simulation type
100 1500 1500 0, 20, 40, 60, 80, 100 (s) AODV CBR 512 bytes 100 J Network simulator 2.35
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measurement. The obtained delay in a network with other existing protocols is shown in Fig. 2. By comparing with the EFTA and FD-AOMDV, the OPS-MFO results in 20% of low delay in several nodes’ presence. The routing overhead is described in Fig. 3, and it compares the proposed protocol with various existing protocols. Twenty-seven percentage of low routing overhead results with OPS-MFO considerably if the nodes’ number increases with the limited mobility speed. The overall energy consumption of all SNs is shown in Fig. 4 for proposed OPS-MFO and existing protocols like FD-AOMDV and EFTA. As the number of SNs increases, the consumed energy is increased using each algorithm. The lower energy consumption results with the proposed OPS-MFO than all other protocols such as EFTA and FD-AOMDV. The proposed protocol of OPS-MFO’s packet delivery ratio (PDR) vs. the increasing number of SNs is shown in Fig. 5. As the network size increases, the ratio of OPS-MFO improves. The PDR of EFTA results in 98% when the network size is 200 SN, but it increases to 98.8% with the network size of 500 SN.
Delay (ms)
Fig. 2 End-to-end delay
0.12 0.1 0.08 0.06 0.04 0.02 0 25
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Fig. 3 Routing overhead
Overhead (%)
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Fig. 4 Energy consumption
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Fig. 5 Packet delivery ratio
1 0.95 0.9 0.85 0.8 0.75 25
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The proposed algorithm outperforms all other existing protocols based on the assessment of simulation results. It consumes less energy, and less execution requires as the proposed protocol has the shortest itinerary length. The alternative CH will continue traveling among cluster nodes by the fault tolerance strategy-based alternative path in node failure.
5 Conclusion In contrast to other existing strategies, a model for improving coverage and connectivity in partitioned networks is provided. The proposed model restricts both coverage loss and connectivity for partitioned wireless sensor networks with energy efficiency. For a given set of SN locations, the necessary minimum number of relay nodes is detected in order to achieve the optimal fault tolerance and a fully connected network. The heuristic approach is ideal for network recovery since it obtains a minimum number of relay nodes. For better connectivity, the position of all sensor nodes is estimated in the phase of intra-partition. For relay nodes, all sensor nodes’ position is computed in the phase of inter-partition. The proposed solution becomes energy efficient when average and residual energies are considered energy-centric. In the case of a dense network, one typical relay node between two partitions is assumed. In the case of a dense network, one typical relay node between two partitions is assumed.
References 1. Rawat P, Singh KD, Chaouchi H, Bonnin JM (2014) Wireless sensor networks: a survey on recent developments and potential synergies. J Supercomput 68(1):1–48 2. Mishra R, Jha V, Tripathi RK, Sharma AK (2018) Design of probability density function targeting energy efficient network for coalition based WSNS. Wireless Pers Commun 99 (2):651–680 3. Al-Karaki JN, Gawanmeh A (2017) The optimal deployment, coverage, and connectivity problems in wireless sensor networks: revisited. IEEE Access 5:18051–18065
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4. Younis M, Senturk IF, Akkaya K, Lee S, Senel F (2014) Topology management techniques for tolerating node failures in wireless sensor networks: a survey. Comput Netw 58:254–283 5. Jha V, Prakash N, Mohapatra AK (2019) Energy efficient model for recovery from multiple nodes failure in wireless sensor networks. Wirel Pers Commun 108(3):1459–1479 6. Rani P, Verma S, Nguyen GN (2020) Mitigation of black hole and gray hole attack using swarm inspired algorithm with artificial neural network. IEEE Access 8:121755–121764 7. Vijayalakshmi B, Ramar K, Jhanjhi NZ, Verma S, Kaliappan M, Vijayalakshmi K, Ghosh U et al (2021) An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city. Int J Commun Syst 34(3):e4609 8. Batra I, Verma S, Alazab M (2020) A lightweight IoT-based security framework for inventory automation using wireless sensor network. Int J Commun Syst 33(4):e4228 9. Robinson YH, Julie EG, Saravanan K, Kumar R (2019) FD-AOMDV: fault-tolerant disjoint ad-hoc on-demand multipath distance vector routing algorithm in mobile ad-hoc networks. J Ambient Intell Humaniz Comput 10(11):4455–4472 10. El Fissaoui M, Beni-Hssane A, Saadi M (2019) Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks. J Ambient Intell Humaniz Comput 10(2):569–578 11. Sennan S, Somula R, Luhach AK, Deverajan GG, Alnumay W, Jhanjhi NZ, Sharma P et al (2020) Energy efficient optimal parent selection based routing protocol for internet of things using firefly optimization algorithm. Tran Emerg Telecommun Technol e4171 12. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249
Analyzing DistilBERT for Sentiment Classification of Banking Financial News Varun Dogra, Aman Singh, Sahil Verma, Kavita, N. Z. Jhanjhi, and M. N. Talib
Abstract In this paper, the sentiment classification approaches are introduced in Indian banking, governmental and global news. The study assesses state-of-art deep contextual language representation, DistilBERT, and traditional contextindependent system, TF-IDF, on multiclass (positive, negative, and neutral) sentiment classification news-events. The DistilBERT model is fine-tuned and fed into four supervised machine learning classifiers Random Forest, Decision Tree, Logistic Regression, and Linear SVC, and similarly with baseline TF-IDF. The findings indicate that DistilBERT can transfer basic semantic understanding to further domains and lead to greater accuracy than the baseline TF-IDF. The results also suggest that Random Forest with DistilBERT leads to higher accuracy than other ML classifiers. The Random Forest with DistilBERT achieves 78% accuracy, which is 7% more than with TF-IDF. Keywords Sentiment classification classifiers and Transformers
DistilBERT TF-IDF Machine Learning
V. Dogra A. Singh School of Computer Science and Engineering, Lovely Professional University, Phagwara, India S. Verma (&) Kavita Department of Computer Science and Engineering, Chandigarh University, Mohali, India e-mail: [email protected] Kavita e-mail: [email protected] N. Z. Jhanjhi School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] M. N. Talib Papua New Guinea University of Technology, Lae, Papua New Guinea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_53
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1 Introduction 1.1
Text Sentiment Classification
Classification of text sentiment is an increasingly growing concept of natural language processing (NLP). It is currently commonly applied in several business areas, such as social media, customer experience, algorithmic trading, and human resources management. The study uses text analysis methods to analyze the Central Bank of Nigeria’s communication policy [1]. Their findings suggest that the issues influencing the communications linguistic content were affected by the policy goals of the Bank and the significance of economic shocks per period. A literature study shows that much of the former works centered on dealing with articles on business news-covering stock market-related events and corporate organizations. Economists accept that financial markets can exhibit strange behavior due to the sentiments of socio-political events, acts of terrorism or natural disasters, etc. [2]. With the exponential development of newsgroups, views and sentiments in the news domain can be examined. In similar studies, authors use sentiment classification methods in political news from columns on numerous Turkish news websites [3]. The authors suggest the inclusion and strength of emotion words as features to characterize news reports sentiment on the stock market[4]. While authors feel that stock or financial markets can react to financial news sentiment, there has still been no systematic attempt to control and use, especially banking events, to predict sentiments in an automated manner. There is no systematic attempt to study the sentiments of banking events published in various online news wires to the best of our information. This may because the collection of all potential banking-related events that are expected to influence stock markets cannot be established. Our previous study has classified the banking news articles or events from the collection of financial news of 2018-2020. And further various news-events on Fraud, Global, Governmental, Merger or Acquisition, RBI Policies, Ratings Agencies or Experts View, and Results have extracted from these banking and other related financial news. In this paper, we are interested to automate the process of classifying the sentiments of the mentioned news-events.
1.2
Why DistilBERT?
The selection of text representation is typically more important than the selection of classifier in text classification tasks such as sentiment classification (in this paper, we concentrate on multiclass sentiment classification of banking financial and other related news, i.e. deciding if each news item is positive, negative or neutral). The idea of text representation is to transform variable lengths into vectors of fixed length as user inputs to a classifier. The text embedding models accomplish this by
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transforming each text article into a dense real-valued vector. The scope for learning language representations on broad unlabeled text data through pre-training models began from word embeddings such as Word2Vec [5] and GloVe [6]. We had embeddings now that could detect semantic associations between words. The growth in Transfer Learning methods in NLPthroughvast pre-trained language models has become a fundamental method in several NLP tasks in the last two years. To claim that BERT has substantially transformed the NLP paradigm is not a misconception. To obtain state-of-the-art outcomes on different NLP challenges, using a single model that is a pre-trained unlabeled dataset is commendable and with little fine-tuning [7]. The researchers demonstrated that on several downstream tasks, with using smaller language models pre-trained with information distillation, it is possible to obtain comparable outcomes, resulting in models that have been smaller and quicker at inference time, while still needing a lower budget for computational training [8]. They showed that a 40% smaller Transformer pre-trained via distillation, called DistilBERT, can achieve comparable efficiency on a variety of downstream tasks by supervision of a larger Transformer language model while being 60% faster at inference time. This paper aims to utilize the benefits of transfer learning from DistilBERT for sentiment classification with fine-tuning on Indian Banking financial and other correlated news on seven events. This is the first study, to the best of our experience, attempting to extract sentiments from the events on banking financial news, the study covers news events for sentiment classification on Fraud, Global, Governmental, Merger or Acquisition, RBI Policies, Ratings Agencies, or Expert’s View, and Results. The motivation behind this is to use the context or semantic association of the words for understanding sentiments behind the news articles because most studies use context-independent approaches, i.e., only words and their polarities, to perform classification [9]. Section 2 presents the old and state-of-art studies in text, news, or sentiment classification.
2 Related Work Text classification is a problem formulated as a learning process where a classifier is used to differentiate between predefined classes based on the features derived from the collection of text documents. In real-world scenarios, text classification has been studied and applied by many researchers, such as sentiment classification of stock market news and its impact [10], domain adaptation for sentiment classification [11], and many more. The most challenging area of machine learning for understanding texts written in any natural language is text classification. However, since almost all algorithms take input in numbers as feature vectors with a predefined size instead of textual data of variable length, the texts cannot be given input to the machine learning models. The following sub-section covers the different text representations from the early days to the newest state-of-the-art NLP models.
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Text Representation
The most commonly used technique of text transformation is Bag-Of-Words. While the words’ order and their context are overlooked, the model considers any word in the text document as a feature. Documents are then translated as feature vectors where each feature with its frequency number is represented. Another word weighing scheme was introduced called TF-IDF (Term Frequency- Inverse document frequency). It believes that very high word frequency will not offer much information gain; uncommon words add more to the model. The researcher has demonstrated enhanced features subset using this method to incorporate the word frequency and document frequency characteristics [12]. Conversion of the word into a vector in vector space is called word embedding. To predict the word in the middle, the distributed representations of context (or surrounding words) are combined in the CBOW model. Word embedding has been reshaped by authors who recommended CBOW [5]. The authors proposed creating a word embedding GLOVE model for distributed word representation [6]. For texts, like word2vec, we have embedding, which turns a word into an n-dimensional vector. We will go through an approach to generating sequence embedding that takes a sequence into a Euclidean space to map the words into a Euclidean space. RNN-based designs interpret the text as a collection of words and are designed for text classification to extract word dependencies and text structures [13]. LSTM fixes issues with gradient vanishing or exploding. The RNNs and the authors also incorporate a Bidirectional-LSTM (Bi-LSTM) model with twodimensional max-pooling collect text features [14]. Researchers have repeatedly shown the usefulness of a neural network model’s transfer learning pre-training on a current issue in various NLP challenges. ELMo [15] is a deep context-dependent representation acquired by the two LSTM layers’ combined training in both directions, learned from the deep bidirectional language model’s internal states. The hidden layers of ELMO are weighted averaged and afterward fed into the layers of the classifier. The authors propose a new form of language representation called BERT [16], unlike the deep contextualized language representation that considers the backward model as in the ELMO bidirectional LSTM. BERT utilizes a transformer, an attention method that in a document understands contextual relations amongst words. By applying information distillation to BERT, specifically the bert-base-uncased model, DistilBERT [8] is created and developers omitted the token-type embeddings and the pooler the design to make a smaller version of BERT. In this paper, the embeddings from the DistilBERT are fed into the classifiers for the task of text classification.
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Classifiers
The efficiency of learning models is determined by the selection of suitable features in the feature space. Machines conveniently comprehend numbers as input rather than texts. Texts as tokens are therefore needed for most learning algorithms to be transformed into numbers (vectorization). Naïve Bayes, a linear classifier, is used to vectorize text documents as per probability distribution with two widely used models: Multivariate Bernoulli Event and Multinomial Event. The possible features are chosen to decrease the dimensionality. Using the model SVM, which recognizes the best decision-boundary among feature vectors of the document with their categories, can overcome the possibility of significant feature reduction caused by classifiers’ use. Many researchers also used the Decision Tree-based algorithm (decision support tool) that represents a decision-making graph based on a tree structure. The authors mentioned that Random Forest has almost every benefit of decision trees. Due to the use of sample bagging, random subsets of features, and voting structures, it produces superior outcomes much of the time [17]. Using different computing layers to learn hierarchical representations of data, neural networks, or deep learning techniques. They have achieved state-of-the-art outcomes in many domains, including IoT-based applications [18] and automation [19]. For text classification challenges in NLP, logistic regression is the basic supervised machine learning algorithm and it has a very close association with neural networks. A neural network may be seen as a collection of logistic regression classifiers arranged on top of each other. To assign a sample into one of two classes (such as ‘positive’ and ‘negative’ polarity), or into one of several classes (such as ‘positive sentiment’, ‘negative sentiment’, and ‘neutral sentiment’), regression can be used. In classification problems, the feed-forward neural network, generally known as multilayer perceptron (MLP) is the most favored strategy. The information passes from the input layer to the hidden layers in one direction in the feed-forward neural network and is then followed by the output layer. They have no memory of the recently obtained input, so they do not anticipate what is coming next. To resolve this RNN (Recurrent Neural Network) is used where data passes through the loop. The RNN networks undergo the gradient vanishing problem that creates difficulty in recognizing and changing the network’s previous layers’ factors. It is used with LSTM [20], which has long-term memory and expands the standard RNN memory. One of the system inefficiencies faced by RNNs is the sequential processing of text. Transformers removes this limitation by implementing self-attention to calculate an ‘attention score’ in parallel with each word in a phrase or text to model the power each word has on another. The Transformer design is particularly ideal for massive text corpus pre-training, contributing to substantial improvements in accuracy in downstream tasks, including text classification. Unlike other transformers that forecast words based on previous predictions, another model is used, i.e., BERT [16], intended to pre-train deep bidirectional representations from the
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unlabeled text by conditioning the left and right context together in all layers. Deep learning offers a way to tackle large amounts of processing and data with almost no manual engineering.
3 Experimental Set up To demonstrate the text sentiment classification task, the transferability of DistilBERT is analyzed, where news articles on banking financial news classified as positive, negative, and neutral classes.
3.1
Data
To construct optimal models through task-specific fine-tuning on very little data, contextual language representations are considered to have the ability to decrease the needed training data volume significantly. The 10000 instances of news articles published from 2018 to 2020 on Fraud, Global, Governmental, Merger or Acquisition, RBI Policies, Ratings Agencies or Expert’s View, and Results were labeled as positive, negative, and neutral. It has been shown that even for domain professionals, the classification of financial news events might be ambiguous. Much of the uncertainty comes from financial news complexity. Understanding a banking financial event will rely heavily on announcements of expected quarterly results, the use of terminology by analysts during ratings, the perception of RBI policies, etc. The labeling or annotation of the news-events into the respective polarity needs the expertise of the annotator. Data splits align with the 75% and 25% proportion for training and test sets, respectively, for the task.
3.2
Embeddings and Classifiers
In this study, the DistilBERT embeddings are fed into five machine learning models like Decision Tree, Random Forest, Logistic Regression, and Linear SVC. This is implemented in Python Programming using libraries transformers and learn. We implemented this with traditional TF-IDF also and the same classifiers. The standard models use basic word representations that do not maintain information about word order and context. We aim to understand whether the overhead of deep contextual models is helpful in this task by comparing traditional ML algorithms to large pre-trained large contextual networks.
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4 Results and Discussion DistilBERT and TF-IDF are compared in this section using different classifiers on the sentiment classification of banking news-events.
4.1
DistilBERT Fine-Tuned on Banking News-Events Sentiments for Classification with Machine Learning Classifiers
Table 1 lists the result of DistilBERT fine-tuned with different machine learning classifiers on banking news-events sentiments regarding precision, recall, and F-1 score. Table 2 lists the result of the accuracy of the models. From the different classifiers Logistic Regression, Random Forest, Decision Tree, and Linear SVC, the Random Forest performed best with F-1 score, as shown in Table 2. Except for the ‘Negative’ class, the Random Forest has good precision and recall for ‘Neutral’ and ‘Positive’ classes. The F-1 score has resulted in a higher value and even equally good for the ‘Negative’ class. Also, from the different classifiers Logistic Regression, Random Forest, Decision Tree, and Linear SVC, the Random Forest performed best with an accuracy of 78%, as shown in Table 3. Comparing all the mentioned classifiers for different classes on Banking News-Events sentiments is shown in Table 2 in Precision, Recall, and F-1 score. And the accuracy of the classifiers varies from 73% to 78%.
Table 1 Results of the DistilBERT fine-tuned on banking news-events sentiments with machine learning classifiers Classifier
Logistic Regression P R
Negative Neutral Positive
0.73 0.85 0.68
0.59 0.92 0.77
Random Forest
Decision Tree
F1
P
R
F1
P
R
F1
P
R
F1
0.65 0.88 0.72
0.57 0.88 0.88
0.72 0.88 0.74
0.63 0.88 0.81
0.52 0.78 0.88
0.62 0.84 0.74
0.56 0.81 0.81
0.68 0.82 0.70
0.65 0.69 0.82
0.67 0.75 0.75
Table 2 Accuracy of the classifiers with DistilBERT
Linear SVC
Classifier
Accuracy
Logistic Regression Random Forest Decision Tree Linear SVC
0.76 0.78 0.74 0.73
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Table 3 Results of the machine learning classifiers with TF-IDF Classifier
Logistic Regression
Random Forest
Decision Tree
Linear SVC
P
R
F1
P
R
F1
P
R
F1
P
R
F1
Negative
0.45
0.38
0.42
0.48
0.58
0.53
0.52
0.65
0.58
0.43
0.38
0.41
Neutral
0.76
0.67
0.71
0.76
0.76
0.76
0.77
0.70
0.73
0.82
0.70
0.75
Positive
0.70
0.84
0.76
0.85
0.74
0.79
0.79
0.71
0.75
0.65
0.79
0.71
4.2
Results of Banking News-Events Sentiments Classification Using Machine Learning Classifiers with TF-IDF
Table 3 lists the result of different machine learning classifiers with TFIDF language representation of banking news-events sentiments for classification in terms of precision, recall, and F-1 score. Table 4 lists the result of the accuracy of the models. From the different classifiers Logistic Regression, Random Forest, Decision Tree, and Linear SVC, the Random Forest performed best with F-1 score as shown in Table 3. Though the precision and recall are on a higher side with the decision Tree on ‘Negative’ class. However, the overall F-1 score is on the higher side, with Random Forest on both positive and neutral classes. Also, from the different classifiers Logistic Regression, Random Forest, Decision Tree, and Linear SVC, the Random Forest performed best with an accuracy of 70%, as shown in Table 4. Comparing all the mentioned classifiers for different classes on Banking News-Events sentiments is shown in Table 4 in terms of Precision, Recall, and F-1 score. And the accuracy of the classifiers varies from 65% to 70%.
Table 4 Accuracy of the classifiers with TF-IDF
Classifier
Accuracy
Logistic Regression Random Forest Decision Tree Linear SVC
0.66 0.70 0.69 0.65
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5 Conclusion and Future direction This paper goals to classify the sentiments of banking news-events amongst three classes positive, negative and neutral. DistilBERT with fine-tuning on this sentiment classification task is compared with traditional TF-IDF. The key objective was to observe how much this task can be benefitted from the deep contextual pre-trained language representation. It is found that DistilBERT has performed better than TF-IDF with all four machine learning classifiers. The DistilBERT with Random Forest has achieved 78% accuracy, 7% better than Random Forest with TF-IDF. This is also found that Random Forest with DistilBERT has performed better than other classifiers like Logistic Regression, Decision Tree, and Linear SVC. The precision and recall for all classes were also higher with DistilBERT as compared to TFIDF. In conclusion, despite much longer training times and memory requirements, when a model’s transfer capacity is a priority, it is worth choosing contextual neural models over traditional approaches. More training data and test data may be obtained for future work to generalize sentiment classification findings in the banking news domain. To compare the effects more conveniently, different tests may be performed. Moreover, dictionary-based rules may be created for each class, positive, negative, and neutral, to support the classification with DistilBERT or other pre-trained transformer-based models.
References 1. Omotosho BS, Tumala MM (2019) A text mining analysis of Central Bank Monetary Policy Communication in Nigeria 2. Verma I, Dey L, Meisheri H (2017) Detecting, quantifying and accessing impact of news events on Indian stock indices. In: Proceedings of the international conference on web intelligence, pp 550–557 3. Kaya M, Fidan G, Toroslu IH (2012) Sentiment analysis of turkish political news. In: 2012 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology, vol 1. IEEE, pp 174–180 4. Yu L, Wu J, Chang P, Chu H (2013) Knowledge-based systems using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowl Based Syst 41:89–97 5. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 6. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543 7. Azar PD (2009) Sentiment analysis in financial news. Doctoral dissertation, Harvard University 8. Sanh V, Debut L, Chaumond J, Wolf T (2019) DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 9. Nguyen TH, Shirai K, Velcin J (2015) Sentiment analysis on social media for stock movement prediction. Expert Syst Appl 42(24):9603–9611
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10. Schumaker RP, Chen H (2009) A quantitative stock prediction system based on financial news. Inf Process Manag 45(5):571–583 11. Xia R, Zong C, Hu X, Cambria E (2013) Feature ensemble plus sample selection: domain adaptation for sentiment classification. IEEE Intell Syst 28(3):10–18 12. Jing LP, Huang HK, Shi HB (2002) Improved feature selection approach TFIDF in text mining. In: Proceedings of the international conference on machine learning and cybernetics, vol 2. IEEE, pp 944–946 13. Mikolov T, Karafiát M, Burget L, Černocký J, Khudanpur S (2010) Recurrent neural network based language model. In: Eleventh annual conference of the international speech communication association 14. Zhou P, Qi Z, Zheng S, Xu J, Bao H, Xu B (2016) Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv preprint arXiv:1611.06639. 15. Sujatha R, Chatterjee JM, Jhanjhi NZ, Brohi SN (2021) Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst 80:103615 16. Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 17. Elagamy MN, Stanier C, Sharp B (2018) Stock market random forest-text mining system mining critical indicators of stock market movements. In: 2018 2nd international conference on natural language and speech processing (ICNLSP). IEEE, pp 1–8 18. Batra I, Verma S, Malik A, Ghosh U, Rodrigues JJ, Nguyen GN, Mariappan V (2020) Hybrid logical security framework for privacy preservation in the green internet of things. Sustainability 12(14):5542 19. Batra I, Verma S, Alazab M (2020) A lightweight IoT-based security framework for inventory automation using wireless sensor network. Int J Commun Syst 33(4):e4228 20. Hochreiter S (1997) JA1 4 rgen Schmidhuber, Long short-term memory. Neural Comput 9(8)
An Enhanced Cos-Neuro Bio-Inspired Approach for Document Clustering Vaishali Madaan, Kundan Munjal, Sahil Verma, N. Z. Jhanjhi, and Aman Singh
Abstract Data mining is a dynamic and attractive research domain that has become known to discover information from the vast amount of constantly created data. Clustering is an unsupervised approach to data mining in which a group of similar items is assembled in one cluster. The quality of documents retrieved within a lesser amount of time has always been a fundamental problem in web document clustering. The authors introduce similarity technique-based K-means clustering using bee swarm optimization and artificial neural networks in this work. The artificial neural network helps classify the best centroid location based on the similarity index of the document and according to the trained structure of ANN to organize the best cluster number to test queries. The quality of papers returned is improved significantly with lesser execution time and improved efficiency through the projected method. Keywords Cosine similarity Artificial neural network
K-means clustering Bee swarm optimization
V. Madaan Maharishi Markandeshwar University, Mullana, India K. Munjal Apex Institute of Technology, Chandigarh University, Mohali, India K. Munjal University College of Engineering, Punjabi University, Patiala, India S. Verma (&) Department of Computer Science and Engineering, Chandigarh University, Mohali, India e-mail: [email protected] N. Z. Jhanjhi Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] A. Singh School of Computer Science and Engineering, Lovely Professional University, Phagwara, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_54
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1 Introduction Finding valuable and unseen knowledge from the raw data is termed data mining. In the case of small dataset, the traditional approaches have performed well, but as the use of the Internet increases, data has also increased enormously; thus, handling and extract useful information from extensive raw data became a difficult task for the traditional approach of data mining [1, 2]. The data mining phases face several challenges during data mining when the data is raw, incomplete, and many times uncertain. Clustering is considered one of the most valuable schemes to group similar data types to solve this problem. It is an unsupervised approach that is used to identify unseen patterns in the raw data. Here (Fig. 1), we take three types of data represented by three different colors (Red, Blue, and Green) and three dissimilar shapes. When clustering is done on the unlabeled data, then the data having similar features come under one category.
1.1
Document Representation
Clustering of documents is not just a single method but a series of processes. The vector space model (VSM) is an accepted model used to represent text documents in algebraic format. All the documents are represented in Euclidean space as multidimensional vectors of relevant words (keywords). All the keywords of the documents are assigned weights based on their relevance in the document [3]. Hence, documents in the VSM are characterized as a series of importance given to every keyword. Di ¼ fW1 ; W2 ; W3 ; . . .; Wn g
Fig. 1 Clustering process
ð1Þ
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The Tf-Idf model is a generally used mechanism to assign a composite score to each document term. Here Tf is how many times a particular keyword appeared in the document. Idf is the number of times a particular word is appearing in the whole corpus of documents, Wmn ¼ tf nm idf nm
ð2Þ
where Wmn is the value assigned to the mth term of nth document, tfnm is the number of mth term occurrences in the nth document and idfmn = log 2 (i/tfnm) where tf is the term frequency of the nth term in i documents.
2 Related Study Giving labels to data is a method of separating it so that all the labels have some regular features among them and are unusual from other labels. This grouping of information is done either through clustering or classification of data in Hearst [4]. Mac Queen [5] firstly proposed this technique for clustering. Nazeer and Sebastian [6] discussed the drawback of K-means that it does not provide the most optimal solution as it is dependent on the initial choice of the centroid. Kapil and Chawla [7] discussed k-means on different databases, e.g., Ionosphere, Iris, wine, vowel, oil, etc., and concluded that performance depends on the similarity measure the dataset used. In Shafeeq and Hareesha [8], an optimal number of cluster selections is a big problem in k-means, due to which results of k-means are always dependent on prior choice. They suggested both the approaches that previous knowledge of several clusters and the cluster number’s varying user selection. Many researchers have proved that the categories of text data are distorted and uneven. Tan [9] proposed an adapted K-nearest neighbor to develop uneven document set usefulness to overcome the setback. Zheng et al. [10] and Del Castillo and Serrano [11] improved the accuracy of the model. They optimized the feature selection method and focused on the features of small categories. Particle swarm optimization was used for the clustering process. Because the optimal local solution’s problem is there in k-means clustering, PSO searches the entire document space globally in Cui et al. [12]. Bharathi and Deepan kumar [13] had provided a brief survey on the different algorithms used for classification purposes in data mining. Bee swarm optimization (BSO) is used in multiple domains. Like numerical function optimization (NFO) Akbari et al. (2009), dynamic economic dispatch (DED) Niknam and Golestaneh (2013), association rule withdrawal (ARW), Djenouri et al. (2013), text categorization Belkebir and Guessoum (2013). Djenouri et al. [14] projected an approach where BSO and frequent pattern mining are used. Valuable information is first retrieved using data mining techniques; then bee swarms used that information to travel around the entire space of documents wisely.
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Classification mainly utilizes mathematical and statistical approaches to classify the data items into sets of pre-defined groups or classes such as k-nearest neighbor classifiers, Naïve Bayes. Bio-inspired algorithms are viable algorithms and can effectively be applied to train feed-forward neural networks on categorization problems Karaboga and Ozturk [15]. A classification system was created, and its training was done using the multi-output perceptron learning (MOPL) algorithm and ANN in Li and Park (2007). Lenc and Král [16] used a neural network for the multi-label document classification. Zheng et al. [17] used neural networks in the document classification application. In the proposed work, BSO is used to train the feed-forward neural network for the classification process. This paper aims to carefully explore the effectiveness of BSO in preparing the feed-forward neural network and further use it for the classification process.
3 Problem Definition Text document clustering is unsupervised and aims to find out the usual grouping among text documents based on maximum similarity elements belonging to the same cluster. During clustering, the researchers have faced several types of issues like best centroid localization, less uniqueness of clustered data, clustering time is more, etc. Most of the prior research focuses on similarity techniques for clustering to minimize these problems, but the clustering results are not adequate [18]. We introduce a similarity technique in the proposed work and further used bee swarm optimization and artificial neural network. The artificial neural network helps classify the best centroid location, and according to the centroid, we find out the cluster number to test documents.
4 Proposed Approach: Cos-Neuro Bio-Inspired Document Clustering (CNBDC) Previous work has utilized cosine similarity and BSO for similarity index and cluster optimization. CNBC enhances the previously done job by the utilization of ANN combined with the prior work. In the proposed system, there are several steps used for clustering query text data. The methodology of the proposed work is given below. The flowchart (Fig. 2) is the proposed work used to validate web document clustering’s efficiency using the K-means and cosine similarity concept along with the bee swarm algorithm and the artificial neural network as a classifier.
An Enhanced Cos-Neuro Bio-Inspired Approach …
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Start
Training of System
Testing of System
Upload Documents Database
Enter Text Data to Test
Pre-processing of Data
Pre-processing of Test Data
Find out Cosine Similarities
Cosine Similarities w.r.t Training Data
K-means on Similarities Classification Apply BSO to improve clusters using Fitness Function
Training using ANN
Matching No
Yes
Classify cluster according to the Error Rate
Trained ANN Structure
Optimize Fitness
Calculate Performance Parameters
End
Fig. 2 Flowchart of CNBDC approach
4.1
Similarity Measure: Cosine Similarity
The cosine similarity measure determines the normalized dot product of the two elements. By calculating the cosine similarity, we will effectively try to locate the cosine of the angle between two parts. Data 1I X Data 2J Cosine SimðI; J Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Data 12I X Data 22I
ð3Þ
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The Clustering Approach: K-Means
The K-means is a partitioned clustering method that is applied to divide the total number of data items into an initially fixed number of clusters, where a data item fits in a single group with the nearest average Algorithm 1: K-mean Algorithm Input: Cosine Similarities List Output: Clustered Data Define centroids (K) of Cosine Similarities List randomly Repeat { a. Assign each K to the cluster to which the K is most alike using the average value b. Update cluster means according to similarities } Until Mean updating = Fixed Return; Clustered Data = Updated Clusters End
4.3
Optimization Approach: Bee Swarm Optimization (BSO)
We apply BSO as an optimization algorithm to find out the text file’s optimal similarity value. Here algorithm will readjust the elements of clusters. In the proposed work, the BSO algorithm is initialized with three types of bees, such as onlooker, forger, and scout bees, to determine the better clusters. These bees are flying in the n-dimensional search area to find the best cluster according to their centroid. Input: Similarity Measure Output: Optimal Clusters Load Similarity Measure Create the initial population and define – Employ Bee – Onlooker Bee – Scout Bee Initialize BSO algorithm for I = 1 to all Cluster Element for J = 2 to all Next Cluster Employed Bee = Cluster Element (I) Employed Bee food = Find (Similarity Measure = Employed Bee) Average Employed Food = Average of Employed Bee Food
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Onlooker Bee = Summation (Find (Similarity Measure = Next Cluster (J))) Call fitness function. Bee Food Check = Bee fitness (Average Employed Food, Onlooker Bee) IF Bee Food Check = True Consider bee as a fit bee Else Adjust Cluster End IF End For 2 End For 1 Return Optimal clusters Fitness function (Bee fitness) Consider; F = True Updated Employ Bee = Employ Bee X Behaviour Change Diff=(Onlooker Bee-Updates P Bee)/Total elements of Onlooker Bee Updated onlooker Bee = Onlooker Bee X Total Onlooker Bee If Diff > Updated Onlooker Bee X 80 F = false End If Return Fit bee End The above-defined fitness function is used in the proposed work to calculate the similarity index of documents to determine the better cluster number of copies. In the previous work, the fitness function of BSO is defined by using the concept of Jaccard similarity, which is based on the set theory.
4.4
Artificial Neural Network (ANN)
Artificial neural network is a classification technique used for classifying complex data. ANN consists mainly of the input layer, a hidden layer, and an output layer. The algorithm of ANN used in this research work is written below. Input: Optimized Cluster Data as a Training Data (T), Target (G), and Neurons (N) Output: Trained Network Structure Initialize Artificial Neural Network with Following parameters – Epochs (E) – Neurons (N) – List of Performance parameters: MSE, Gradient, Mutation, and Validation Points
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– Training Techniques: Levenberg Marquardt (Train lm) – Data Division: Random For each set of T Group = Categories of Training data in terms of Clusters End Newff(T, G,N) According To the requirements set, the Training data If validation of T = True Net = Train (Net, Training data, group) Else Reject that feature set and consider it as an error End Define Test Data = Ttest Classified Best Clusters = simulate (Net, Ttest) Calculate Error Rate and performance parameters Return Classified Best Clusters
5 Simulations and Results The quality of service (QoS) parameters are measured to know the proposed research work’s efficiency. The performance parameters that are measured are defined below:
5.1
The CACM (Collection of ACM) Dataset
In the proposed work, we use the CACM database to validate the simulation, and the CACM dataset is taken from the Classic3 and Classic4 datasets. The concept of term frequency, TF-IDF, and normalization of TF-IDF weighting technique is used in the dataset. Terms are particular words; that is, there are no n-grams present in the dataset. The minimum term length used in the dataset is three. One term appears at least in three documents, and a term can appear at most 90% of the documents. In the dataset, Porter’s stemming approach is applied while preprocessing the documents. We are providing a total of 3204 documents, out of which 50, 100, 500, 1000 documents are tested during the simulation of the proposed work. Table 1 represents the values of precision, recall, and F-measure obtained from the testing operation performed on 50,100, 500, and 1000 documents, respectively.
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Table 1 Performance parameter Number of documents
Precision
Recall
F-measure
50 100 500 1000
0.84 0.835 0.83 0.82
0.83 0.82 0.81 0.79
0.834 0.827 0.819 0.814
Fig. 3 Comparison of performance parameter
The graph (Fig. 3) compares performance parameters named precision, recall, and F-measure. Here, the blue bar represents the precision values, the red bar displays the recall values, and the green bar line depicts the F-measure values observed for 50, 100, 500, and 1000 test documents. The average value of the proposed work’s precision rate is 0.831, recall is 0.812, and F-measure is 0.814. Figure 4 shows the classification error for a different number of documents (50, 100, 500, and 1000). The average value of classification error measured for the proposed work is 0.032. The classification error is used to determine the mismatched test data feature during the matching from the training data feature. Here, in the proposed work, classification error is small, which indicates that this system’s training performance is the best. If the system’s training is best, then the system’s accuracy is also comparatively high. The graph (Fig. 5) is plotted for average F-measure values observed for both proposed and previous work [14]. The blue line defines the average F-measure value of the proposed work, whereas red line represents F-measure’s value for the current work. The average value measured for the proposed and previous work is 0.814 and 0.75, respectively. Thus, the F-measure value obtained for the proposed work exceeds the last work by 6.4%. The comparison of the proposed model and the existing model in terms of run time values is shown below:
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Fig. 4 Classification error
Fig. 5 Comparison of proposed F-measure with existing F-measure
The graph (Fig. 6) plotted for average runtime values observed for both proposed and previous work [14]. The blue line defines the proposed work’s runtime value, whereas the red line represents the existing work value. The average runtime value measured for the CNBDC approach is 0.244 s which is very more minor and remained constant through iterations. Further, if the number of documents increases, there could be a slight increase in the runtime but still lesser than existing approaches.
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Fig. 6 Comparison of the runtime of proposed work with the existing approach
Fig. 7 Comparison of accuracy of proposed work with the existing approach
In the Graph (Fig. 7) plotted for the accuracy of the existing model and proposed model, the average accuracy calculated for the existing model was 93.45%. In contrast, the accuracy of CNBDC came out to 94.77%. In terms of F-measure, as well as accuracy proposed model outperforms the existing model.
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6 Conclusion and Future Work There are many document classification applications in the industrial world already presented by researchers. Still, they faced several problems like best centroid localization, less uniqueness of clustered data; clustering time is more, etc. The proposed research work enhances the clustering mechanism and validates the clustering using artificial neural network (ANN). To solve the problem mentioned above, cosine similarity and BSO and ANN are used to determine the text documents’ resemblance. The neural network is a computation algorithm used to classify the problem iteratively. From the experiment, it has been concluded that the average value of precision, recall, average classification error, and F-measure for 1000 number of the document are 0.82, 0.79, 3.2%, and 0.814, respectively. The value of the proposed F-measure is high compared to the existing F-measure value, where the F-measure value of the proposed work is increased by 6.4% from the current work. This indicates that the classification accuracy of the ANN structure is high. The average run time taken for 100 iterations was 0.244 s which remained constant. Finally, the comparison is made where our approach outperforms the existing system with an accuracy of 94.77%. Proposed work is proved effective in improving the quality of returned documents as the F-measure parameter value is increased efficiency. The future investigation includes evaluating the whole process using other evolutionary approaches, comparing the work done with different state-of-art methods. Work can be enhanced to reduce the run time by trying to use other classification approaches.
References 1. Vijayalakshmi B et al (2020) An attention based deep learning model for traffic flow prediction using spatio temporal features towards sustainable smart city. IJCS, Wiley, Hoboken, pp 1–14 2. Batra I et al (2020) Hybrid logical security framework for privacy preservation in the green internet of things. MDPI-Sustainability 12(14):1–15 3. Batra I et al (2019) A lightweight IoT based security framework for inventory automation using wireless sensor network. IJCS, Wiley, Hoboken, pp 1–16 4. Hearst MA (1999, June) Untangling text data mining. In: Proceedings of the 37th annual meeting of the association for computational linguistics, pp 3–10 5. MacQueen J (1967, June) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, no 14, pp 281–297 6. Nazeer KA, Sebastian MP (2009, July) Improving the accuracy and efficiency of the k-means clustering algorithm. In: Proceedings of the world congress on engineering, vol 1. Association of Engineers, London, pp 1–3 7. Kapil S, Chawla M (2016, July) Performance evaluation of K-means clustering algorithm with various distance metrics. In: 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES), pp 1–4, IEEE
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8. Shafeeq A, Hareesha KS (2012) Dynamic clustering of data with modified k-means algorithm. In: Proceedings of the 2012 conference on information and computer networks, pp 221–225 9. Tan S (2005) Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Syst Appl 28(4):667–671 10. Zheng Z, Wu X, Srihari R (2004) Feature selection for text categorization on imbalanced data. ACM SIGKDD Explor Newsl 6(1):80–89 11. Del Castillo MD, Serrano JI (2004) A multistrategy approach for digital text categorization from imbalanced documents. ACM SIGKDD Explor Newsl 6(1):70–79 12. Cui X, Potok TE, Palathingal P (2005, June) Document clustering using particle swarm optimization. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS 2005, pp 185–191, IEEE 13. Bharathi A, Deepan kumar E (2014) Survey on classification techniques in data mining. Int J Recent Innov Trends Comput Commun 2(7):1983–1986 14. Djenouri Y, Belhadi A, Belkebir R (2018) Bees swarm optimization guided by data mining techniques for document information retrieval. Expert Syst Appl 94:126–136 15. Karaboga D, Ozturk C (2011) A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657 16. Lenc L, Král P (2016, April) Deep neural networks for Czech multi-label document classification. In: International conference on intelligent text processing and computational linguistics. Springer, Cham, pp 460–471 17. Zheng J, Guo Y, Feng C, Chen H (2018) A hierarchical neural-network-based document representation approach for text classification. Math Probl Eng 18. Datta D et al (2020) UAV environment in FANET: an overview. Applications of cloud computing: approaches and practices. CRC Press, Taylor & Francis Group, Boca Raton, pp 1–16
Improved Decision Tree Method in E-Learning System for Predictive Student Performance System During COVID 19 T. Varun and G. Suseendran
Abstract E-Learning courses are top-rated in recent years. While COVID 19 primarily affects public health, spillover effects can already be observed in education, stemming mainly from extended educational institution closures. This is undoubtedly the critical time for the education sector because, during this period, many universities’ admission exams and competitive examinations are conducted. For them, we should forget about tests, admissions, etc. The need to study student success and forecast their success, along with that is increasing. With the increasing number of it was tested for instructional technology’s popularity, various data mining algorithms perfect for predicting student performance. The right algorithm depends on the algorithm’s nature. A guess has to be made by the faculty. If the number of students, the need to correct data complexity raises data relationship and their processing is an issue for the student to be found at the expense of failure. The decision tree approach to the statistical analysis of academic findings in this paper concerns and the big data implication. Keywords Decision tree method
E-Learning Covid-19
1 Introduction The e-learning scheme has been a modern one in recent years—a system to train without time barriers and learners’ distance barriers. Literacy is correlated with the influence of semantic web technology. It is the experience of simplified data and energy for transportable devices. Ontology is a description of partnership theories and principles that promote shared knowledge and the reuse of it [1]. In e-learning,
T. Varun Department of Management Studies, Anna University, Chennai, India G. Suseendran (&) Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_55
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students must learn on an initiative basis. They make their development across the machine and the Internet. This is to say; students also manage the course material from the other side of the screen by themselves [2]. During COVID 19 period, the students’ impacts are as follows: Worldwide Higher Education Institutions were closed, which affect the students. Students’ degrees are postponed due to lack of examination Increase in flexibility on education: online classes and examinations preference on distance education. For current vacancies, some institutions can delay faculty recruiting plans that in turn impact quality and excellence. The teaching and learning system involve methodologies for instruction and examination and will be influenced by the closing. In the lockdown era, electronics can play an important role, such as studying from home and working from home. The e-learning advantages in this COVID 19 period as follows: It provides students with an opportunity to build their knowledge base in a scalable environment by utilizing local resources and money. Due to improvements in architecture, graphics, simple navigation, and quality content, online platforms are steadily gaining popularity. Many studies have found that e-learning can further develop the knowledge base and include bite-sized, shared, and immersive information to make it easier to grasp concepts. Motivation is essential for learning and success; in e-learning contexts, learners have to participate and be self-directed with their work to read [3]. Apprenticeship or online learning programs may be given. They should involve consumers in the learning process by including needed information quickly and efficiently [3]. Yet consumers of e-learning in personality Characteristics, cultural history, personal skills, and learning Preferences have marked variations, such that the E-learning growth is facing a considerable challenge [4]. Researchers have acknowledged the advantages of learning technologies and professionals of school [5]. An increase in various fields’ technologies made the need for an increased rate of information, so many data are needed for a person, so the big data is used in e-learning [6]. Also, leading to learning instruments helps educate the younger, incredibly portable devices [7]. E-Learning Infrastructure links a lot to schooling institutes for students to address superficial shortcomings research place and period. These devices serve people to get the subject’s awareness from a distant location rather than traditional schools. It’s been possible for an instructor to be related to college students, by way of those systems. Various algorithms have been used to develop educational data mining (EDM) technology and implemented in the e-learning programs review. EDM aims to prepare statistical models with high accuracy, stability, and ease of analysis. The two most popular mining data strategies for finding students at risk of failure are regression & regression classification. The numerous algorithms investigated are discussed in this article. Scientists predicted the student’s success. Complexities of incorporating big data into the e-learning methodology The data mining approach is often faced with the need to handle the student’s rising data and data.
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In this paper, we use the C4.5 decision tree in data compile from college. Here, the target is to get there. The student history and academic information to be presented understand the primary element influencing the student’s product. C4.5 is a representative decision tree algorithm that is an extension use the ID3 algorithm and the rate of information gain to locate the attribute in the tree nodes with a more significant amount of knowledge [8]. It is used to make the predictive analysis of student progress more successful. It will help us find students at risk of failing or abandoning the course of action during this COIVD 19.
2 Related Works Xiang and Zhang [9]—The inductive inference method used is a simple inductive inference method. The decision tree algorithm is the most widely used. Various examples may be grouped into representative categories, such as classifiers and regression constructs, to represent knowledge. This essay explores the classifier’s basic principles, classifier, decision tree theory, and ID3 algorithm, analyses Algorithm C4.5. It offers more research to improve it, and testing reveals that correct performance and high performance are possible for the modified algorithm. Lin et al. [10] Dependent on the vast volume of data collected in method of school management that lacks intelligent data Research software, student decision tree research model The accomplishment was achieved by the use of decision tree algorithms in data mining, and the model was planned for use after pruning. The algorithm and classification rules were developed, which defined the basis for decision-making on education reform. Experiments also showed that the C4.5 algorithm was designed with high precision for the decision tree classification. Lim et al. [11] In terms of classification accuracy, training time and (in the case of trees) number of leaves, twenty-two decision tree, nine statistical, and two neural network algorithms are related to thirty-two datasets. The classification precision is determined by the mean rate of error and the mean rate of error. Both conditions are a spline-based mathematical algorithm called POLYCLASS at the top, but it is not wildly different statistically from POLYCLASS at the top. A further twenty algorithms. Second to the two precision parameters is another statistical algorithm, logistic regression. The linear split search, which ranks fourth and sixth, is the most precise decision tree algorithm. While spline-based statistical algorithms seem to have reasonable precision, they require relatively good accuracy, respectively—a lengthy training time. In terms of median training time, for example, POLYCLASS is the third most recent. Compared to seconds for other algorithms, it also requires training hours. QUEST and logistic algorithms for regression are much faster than that. C4.5, IND-CART, and QUEST are among the decision tree algorithms with univariate splits available. The perfect balance of rate and speed of error. Yet C4.5 keeps growing trees with twice as many leaves as they do. The IND-CART as well as the QUEST.
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Das et al. [12] the rule generation in web-based software has been discussed with C4.5 decision tree algorithm in their paper. The program provides a function for imputing missing data values. The program is used in data processing, agriculture, and other areas where scientists, researchers, and students generate large amounts of data. Mazid et al. [13] Their paper incorporates a balanced coefficient for change The reality of the C4.5 algorithm. The decision-maker can set it. According to the intellectual sphere and the academic domain, its harmonized information gain-ratio of each attribute in a particular environment artificially. Classification is more so veracious and logical by the tree of judgment taken by the enhanced algorithm. And compared to the improved algorithm C4.5 algorithm, by evaluating cases, to show the algorithm’s usefulness. It’s an updated algorithm. Zhen et al. [14] In this article, we’re classifying shots at the form level in A link to several kinds of images. Extract descriptions of motion, hue, and pixel changes as attributes for the experiment when comparing the differences in these types of shots’ low-level characteristics. The bridge is meant to bridge the distinction between low-level functionality and high-level semantics by planning the decision tree of C4.5, and fair shot classification efficiency is achieved.
3 Proposed Method Training has been shut down all over the world as a result of COVID-19. About 1.2 billion kids are out of school worldwide, as seen in Fig. 1. Education has changed significantly with the phenomenal growth of e-learning, which enzures that teaching is delivered digitally and on digital networks. Data show that it has been found that online learning improves knowledge acquisition and takes less time.
Fig. 1 Staggering impact on global education
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Figure 2 shows the e-learning tools or apps used for teaching and study purposes during COVID 19 period. Predicting student success by using E-learning programs can classify student performance students at risk of disappointment. There are 3 in predictive mining elementary categories-(1) Classification. Regression. (2) Regression. (3) Calculating the density. As far as forecasting student’s production of the implementation of the classification methodology and regression technique is more common. Students at risk bad output can be detected by application classification. Then, a performance could be granted to them the basic meaning after regression is used. The most suitable algorithms for predictive student analytics are the E-Neural networks learning method and decision tree. CGPA, internal evaluation, external assessment, are some of the typical attributes used in predictive mining. Assessment, psychometric, and socioeconomic considerations. The model tree decision outperforms the classical regression approach when forecasting the student’s results. Even if we need a model to be developed simple to present individuals a model of the decision tree. A linear model is also going to be better than that. Versions of the tree of the decision can be represented even differently than linear regression. The decision tree can treat both the numerical and classic data. Often, there could be vast volumes of data analyzed using standard computational capabilities in less time implementation of large data in diverse fields. Domains have also suggested the need to combine E-learning programs with big data due to rising data. Significant dates platform would boost the performance
Fig. 2 E-learning apps used for teaching
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of the predictive algorithm mining. Big data tool sponsorship can help efficient mathematical modeling of student results. Tree-based learning algorithms also tend to be one of the most effective algorithms for computational mining. Tree-based approaches inspire learners with precision, stability, and ease of understanding with high predictive models. Students’ features are very useful compared to linear models that will be able to map non-linear interactions. The decision tree is one of the easiest variables to define and the association between the most relevant variables. Two or three variables exist. The growing number of cloud environment collaboration big data decision tree algorithms is also limited. It would take a lot of time to build a decision tree. The student dataset size is enormous. The new predictive methodology can also be used for the classification of such datasets. In this case, we suggest using the decision tree C4.5 algorithm (successor of ID3) technique in the Hadoop scheme and improved experimentation compared to the usual Decision tree algorithm. Predict a pupil’s performance. Our rationale for the C4.5 algorithm being proposed is that it does: (1) Accommodate all algorithms. Discrete attributes and continual attributes; (2) It is possible to use discrete features and continuous details. Process partially complete training data sets with values not included present; (3) As the trees are being installed; pruning may be performed; to prevent over-fitting issues.
3.1
C4.5 Algorithm
The algorithm C4.5 is an extension of the algorithm ID3. The instructional system included the learning method attribute list. C4.5 is a decision tree algorithm descriptive of the algorithm and is an ID3 algorithm extension [15]. The algorithm is based on the premise that attributes are directly related to the decision tree’s complexity and the amount of information given. C4.5 extends the number of classifications to the digital attribute. A metric norm of the Entropy of two groups. Many algorithms are based on the Entropy of information. The so-called Entropy, containing at least the nodal points of the decision tree created, is representative of the degree of systematological disease of the artifacts. It is clear to grasp that the minor disorder is the greater Entropy [9]. The C4.5 algorithm produces the initial training sample decision tree [15]. The other term is more reliable, the more linear in the collection of data. This is also the reason we’re searching for. S is a teaching sample as follows, the entropy formula: Please enzure that you have one if you have more than one surname. In the author tab, the volume editor knows how to mention you. Tab. Index.
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info S ¼
531
0
1
l B X B @
C FreqðCi; SÞ C FreqðCi;SÞ A S log 2 S
i¼1
ð1Þ
The number of Freq(Ci, S) samples belonging to the catalog Ci(one of the possibly k) Catalog is representative, including Freq(Ci, S). I S I is the number of samples taken from Set S. The preceding formula includes an estimate for the subset’s Entropy, and then, we need a weighted measure of the subgroup. The Entropy of the subsets [16]. The formula is the following: lnfo xT ¼
n X i¼1
Ti T info Ti
ð2Þ
That T is the zoning collection for the attribute X. To directly measure the scale of the Entropy of the different subsets, determine the Entropy before zoning and the entropy difference after zoning (we call this disparity again). Nodal point has more than we need to be able to win. The formula is the same as the one below: GainðX; T Þ ¼ info T info xT
ð3Þ
The improvement in knowledge is possible to generate a more excellent feature price. It will lead to further divisions of the decision tree, and predictable results would not be ideal. The benefit details ratio considers the number of sub-nodal-points separated by each day, each sub-nodal point’s scale, and the size of each sub-nodal point. The goal ought to be viewed sequentially. Benefit details attribute ratio is broken down as follows Gain rateðx; tÞ ¼
gainðx; tÞ slit lnfoðx; tÞ
ð4Þ
The tree’s root-nodal-point has the highest attribute. In all samples, quantities of information, the tree’s middle-nodal point have a root sub-tree function. The tree type is the cumulative sum of details of the nodal point and the trees’ leaf-nodal point—the specimens’ consistency. Algorithm 1: C4.5 algorithm 1. 2. 3. 4. 5. 6.
Start Input: Training data set If I < N Then for i = i+1 Decision Tree I and pruning are created
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7. Decision Tree I and Precision of Storage 8. Else 9. End
3.2
Fuzzy Decision Tree
For the development of a clear judgment, the ID3 is a useful heuristic. In 1986, Quinlan previously came forth as an ID3 version focused on minimal data entropy to pick extended attributes. As the increased sophistication is implemented in the information knowledge structure, it has been shown that the crisp ID3 is not sufficient to reflect Imprecise outcomes—version of Blurred ID3 based on minimal. Fuzzy Entropy. The product of FuZZy-ID3 heuristic learning is a fluffy decision tree that can be used. It is turned into several boring rules [17]. if ðs1; s2; . . .snÞ ¼
n X
pi logðpiÞ
ð5Þ
i¼1
Attributes A ¼ ðs1; s2; . . .; snÞ is a function set, a fuzzy character set. Algorithm 2: Decision Tree 1. Preparation of results. If the data is numerical, it must be confused into a categorical or numerical type in linguistic words. Step Gain(A) = I(SL)s2;-sm)E(A) (3). 2. Induction of a fuzzy tree of judgment. Select the Gain attribute with all attributes as the root node for most details when the degree of truth is greater than the given threshold of a particular class in all nodes. Render a leaf, threshold B. After all this, the attributes for creating a plate of a node are used. If not, repeat Step 2. 3. Convert the tree of judgments to several rules. 4. I am applying fuzzy laws on sorting.
3.3
Experimentation
The verification is split into the presentation results, and the performance verification algorithm is based on the following consideration of I. The algorithm enhancement does not affect Iteration’s outcome, so the development of the final is improved the algorithm should be compatible with or comparable to the previous one. Results are essential to increase the measurement’s efficiency based on the guarantee of the outcome. Otherwise, there is no need for change.
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Table 1 The data speed of C4.5 decision tree and fuzzy decision tree D1 D2 D3 D4
C4.5 decision tree in seconds
Fuzzy decision tree in seconds
0.7 0.86 0.5 0.6
0.89 1.03 0.72 0.85
The Data Speed of C4.5 Decision Tree and fuzzy Decision Tree
Fig. 3 The data speed of C4.5 decision tree and fuzzy decision tree
1.2
C4.5
Seconds
1 Fuzzy
0.8 0.6 0.4 0.2 0 D1
D2
D3 DATA
D4
The C4.5 Decision Tree and fuzzy Decision Tree algorithms’ operation speed is seen in Table 1 and Fig. 3. This is because the C4.5 Decision Tree algorithm outperforms the processing speed of the fuzzy Decision Tree algorithm. Table 2 and Fig. 4 show the C4.5 Decision Tree percentage value’s accuracy and the decision tree’s fuzzy algorithms. Therefore, the algorithm C4.5 Decision Tree exceeds the fluid algorithm Decision Tree in terms of the evaluated results’ accuracy. The COVID-19 pandemic for all school programs is proving to be a creative disturbance of challenging obstacles. Around the same time, there is a good possibility for us to implement modern strategies that are more fitting for the present generation’s learners. In some states in India, the lockout is ongoing. At the same time, some other states are beginning their operations with a slow and prioritized reconstruction. The academy is expected to stick with e-learning sites for at least a few more months with social distance requirements in place and UGC’s recommendations. Using the C4.5 Decision Tree algorithm, we can conclude that calculating the students’ performance is the best. Table 2 The accuracy of C4.5 decision tree and fuzzy decision tree Data set
C4.5 decision tree in percentage
Fuzzy decision tree
D1 D2 D3 D4
93.3 92.1 94.1 90.2
78.2 77.7 79.4 75.6
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The Accuracy of C4.5 Decision Tree and
Fig. 4 The accuracy of C4.5 decision tree and fuzzy decision tree
100
Percentage
80 60
C4.5
40
Fuzy
20 0 D1
D2
D3
D4
DATA
4 Conclusion The COVID-19 pandemic for all school programs is proving to be a creative disturbance of challenging obstacles. Around the same time, there is a good possibility for us to implement modern strategies that are more fitting for the present generation’s learners. In this article, we evaluated the various mining data Algorithms used to forecast student success in the method in E-Learning. Decision tree algorithm is better than forecasting student success ways of data processing, input values, and consistency approaches were deemed better for all of them. C4.5 Algorithm of the decision tree for analysis of big students data has been presented. It would then lead to a more effective predictive study of student success. It’s going to help us recognize learners at risk of failure or dropping out of the course, man. As more colleges collaborate, the student’s progress will be investigated. And we can conclude that the C4.5 decision tree algorithm is superior to a fuzzy decision tree algorithm when calculating student outcomes as an experiment between the C4.5 decision tree and a fuzzy decision tree algorithm. Therefore, we proposed C4.5 decision tree implementation of the approach in big data map-reduce E-learning systems to predict students’ achievement better.
References 1. Wang S, Gu M (2012, May) Searching for dependencies among concepts in a e-learning system with decision tree. In: 2012 international conference on systems and informatics (ICSAI2012), pp 1012–1016, IEEE 2. Lin JC, Wu KC (2007, July) Finding a fitting learning path in e-learning for juvenile. In: Seventh IEEE international conference on advanced learning technologies (ICALT 2007), pp 449–453, IEEE
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3. Utomo AY, Santoso HB (2015, April) Development of gamification-enriched pedagogical agent for e-learning system based on community of inquiry. In: Proceedings of the international HCI and UX conference in Indonesia, pp 1–9 4. Zhou TF, Pan YQ, Huang LR (2017, December) Research on personalized e-learning based on decision tree and RETE algorithm. In: 2017 International conference on computer systems, electronics and control (ICCSEC), pp 1392–1396, IEEE 5. Duo S, Ying ZC (2012) Personalized e-learning system based on intelligent agent. Phys Procedia 24:1899–1902 6. Bakhshayeshian Z, Khalili M (2015, November) An suggested method to university courses presentation based on cart algorithm to pun universities. In: 2015 2nd international conference on knowledge-based engineering and innovation (KBEI), pp 1170–1173, IEEE 7. Kamaludeen ZS, Devi VU Determine the user satisfaction on e-learning using the decision tree algorithm with Kano analysis 8. Lee S, Park I (2013) Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines. J Environ Manage 127:166–176 9. Xiang Z, Zhang L (2012, October) Research on an optimized C4. 5 algorithms based on rough set theory. In: 2012 International conference on management of e-commerce and e-government, pp 272–274, IEEE 10. Lin CF, Yeh YC, Hung YH, Chang RI (2013) Data mining for providing a personalized learning path in creativity: an application of decision trees. Comput Educ 68:199–210 11. Lim TS, Loh WY, Shih YS (1999) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Department of Statistics, University of Wisconsin 12. Das S, Dahiya S, Bharadwaj A (2014, March) An online software for decision tree classification and visualization using c4. 5 algorithm (ODTC). In: 2014 international conference on computing for sustainable global development (INDIACom, pp 962–965, IEEE 13. Mazid MM, Ali S, Tickle KS (2010, February) Improved C4. 5 algorithm for rule based classification. In: Proceedings of the 9th WSEAS international conference on artificial intelligence, knowledge engineering and data bases. World Scientific and Engineering Academy and Society (WSEAS), pp 296–301 14. Zhen Y, Yong Q, Dan H, Jing L (2011, August) The application of shot classification based on C4. 5 decision tree in video retrieval. In: 2011 6th IEEE joint international information technology and artificial intelligence conference, vol 1, pp 426–429, IEEE 15. Dongming L, Yan L, Chao Y, Chaoran L, Huan L, Lijuan Z (2016, October) The application of decision tree C4. 5 algorithm to soil quality grade forecasting model. In: 2016 First IEEE international conference on computer communication and the internet (ICCCI), pp 552–555, IEEE 16. Nie B, Luo J, Du J, Peng L, Wang Z, Chen A (2017, November) Improved Yahaya BZ, Muhammad LJ, Abdulganiyyu N, Ishaq FS, Atomsa Y (2018) An improved C4. 5 algorithm using L’Hospital rule for large dataset. Indian J Sci Technol 11(47):1–8 17. Dong AT, Wang JH (2004, August) A comparison between decision trees and extension matrixes. In: Proceedings of 2004 international conference on machine learning and cybernetics (IEEE Cat. No. 04EX826), vol 6, pp 3798–3801, IEEE
Improving Content Delivery on User Behavior Using Data Analytics T. Thirumalaikumari and C. Shanthi
Abstract Internet traffic has increased, mainly as video viewers have increased. Business estimates indicate that video accounts for 90% of Internet traffic by 2020. Due to the rising traffic, content distribution is underlined by the growing volumes of video traffic. The content delivery model must be planned, delivered and managed adequately to meet these rising requirements. We identified many user access trends with large-scale analysis of user behavior data which have significant consequences for the design in our unique dataset, including 100 video selections from the two largest Internet video providers spanning about two months. These involve a partial emphasis on material, regional interests, transitional times and trends. By conducting a comprehensive measurement analysis, we examined the effect of our results on the designs. There is considerable synchronous viewing behavior for video content, which may increase video federation availability considerably by as much as 95%. Keywords Content delivery Web
Internet Traffic Video content World Wide
1 Introduction Nowadays, online users are not only consumers of content but also publishers of content. Also, users can at any time and anywhere access content through a mobile device. The online video industry has been tremendously influenced by this kind of content created by users in the mobile Internet age. The content delivery networks, a traditional application of software-defined networks/networks, have played a vital role in delivering rapidly and efficiently communicated services by transmitting content to cache or edge servers close to users. The sophistication of the content distribution over the Internet, ranging from simple transmission of files among two T. Thirumalaikumari (&) C. Shanthi Department of computer science, Vels Institute of Science and Technology & Advanced Studies, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_56
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computers tied directly through a cable, involves many complex apps such as interactive video streaming, peer-to-peer file sharing, huge online gaming community, cloud services and the cloud computing. Over the years, many developments have been made supporting content supply development, in both protocols for content delivery and incapacity to ensure and develop new content delivery applications (Nallaperumal et al. (2010)). Video providers also rely upon content distribution networks for overlay applications such as Akamai, a significant source of knowledge to exploit their presence in various geographic regions. However, the mobile Internet environment’s transition has introduced new opportunities in transmitting content videos produced by the user for content delivery networks. All the industry participants face difficulties with Web equipment’s ubiquitousness and low latency online services. Users demand instant, high-end connectivity from any device to their content. Leaders in the ecosystem for content distribution know that better output is closely associated with higher sales (Pathan and Buyya [5]). Online users with various attitudes, viewpoints and tastes are unique. They use the Web and mobile apps in many locations on multiple platforms, from different browsers. We use custom content and experience to reach and communicate with them through a range of tools and techniques that add complexity to applications. This complexity brings incoherent variability through devices, sites and network connections that lead to slowdowns and errors and the challenges of availability or business continuity. Over the previous couple of decades, traffic and the Internet have grown tremendously, with projections by 2020. Video accounted for 51% of Web traffic in 2019. Market estimates indicate that by 2025, over 90% of Web traffic will be video. The low costs of accessing video over the Internet are significant to this growth. We have reached a point where online video replaces conventional TV viewers (Panatula et al. (2016)). A further secondary benefit of the lower costs of accessing content, particularly video, over the Web is the increase of the interest of different content providers. The consumer standards of content quality have gradually risen with numerous entrants on the market. Content providers will want to optimize consumer participation to make better profits from business models based on ads or subscriptions. As one of the critical aims of content delivery today, this has contributed to improved user experience (Elkotob and Andersson [3]; Anjum et al. [1]). To balance rich and sensitive online experiences, understand user behavior according to location, networks and access to devices and know-how; they influence how the application is used. In an ongoing digital performance management strategy, user interactions to optimize online sales are tracked, optimized and validated. In the last few years, mobile traffic growth is another recent expansion of the content distribution scenario. Today, mobile traffic accounts for 28% of total Web traffic and one visit to sites from a smartphone is one in four. Mobile Web use is projected to rise 11 times by 2020, with approximately 50 billion smartphones connecting to the Internet between 2015 and 2019. However, phone systems are lower than wireline networks, especially as the Web has not been developed for mobile architecture. Today’s content distribution landscape consists of many parties like content suppliers, network operators, analytics from third parties, content
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optimization services, etc. Content distribution for consumers is getting much more challenging because of many parties’ rising complexity and participation. Whether a process characterized or fixed to a framework or mechanism has a meaningful effect on users or sales would still be more challenging to assess (Tso et al. [6]). A highly dynamic content delivery system is confronted with the ever more significant consumer base, increased user demands and the variety of access technologies, content providers and mobile carriers. The present study offers a new way to help address these problems by evaluating the data obtained from both the service provider and the service provider on a broad scale. In general, in the advancement of big data analytics techniques, substantial progress was made in the last decade in computer sciences. This work aims to enhance the use of large-scale data analysis to handle information and turn it into an actionable perspective. In particular, we show that large-scale research observations will contribute to better resource provision for increased content distribution and increased traffic [2]. With this introduction, Sect. 2 reviews the background study, and Sect. 3 depicts the system model with results and discussion followed by a conclusion in Sect. 4.
2 Background Study Today, the Internet is mainly a network powered by content. The sophistication of Internet content distribution has taken a long way from a simple transferring of information between two directly wired computers to include many complicated functions, including video adaptation, peer-to-peer file sharing, huge online gaming, cloud services and cloud-based computing. Big data analytics tools can be used to inform system design choices and construct automatic models that can be used explicitly in decision-making processes to enhance content delivery. Content distribution is an efficient way to reduce network and server congestion to enhance end user responses. It optimizes the distribution of content by reproducing the objects on the Web’s replacement servers. Besides rising Web traffic, flash crowds are a new phenomenon of congestion on the Internet. Flash crowds, rather than Internet congestion, unexpectedly contribute to hard labour on some Web sites. Therefore in this volatile situation, it becomes vital to maintain Web efficiency. Also, streaming media items are rapidly being distributed on the Internet. These artifacts require more excellent reliability and bandwidth. This paper analyzes the current content duplication techniques, demand routing, flash crowd mitigations and digital media streaming to create and design effective content distribution networks (Gupta and Garg [4]). Smartphone, PC and Internet consumers have used content providers throughout their lifetime and will use platforms. Due to its rising use, content delivery skill is beneficial. This article focuses on customers, services, functionality, processes and content delivery architecture. This article begins with inspiration and structure and then explores content delivery approaches and model delivery of hierarchical content. Finally, the demand for content distribution and major service providers is
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presented, accompanied by studies on content delivery and its different topics (Desai et al. (2015)). The work focuses on explaining the advantages that caches can pool their systems to accommodate their user requests. It is demonstrated that resource pooling benefits rely on the popularity profile of the VoD service content. In reality, if there is no substantial difference in popularity among contents, the concurrency results in an orderly reduction in the transfer rate on the central server as the device size increases. On the other side, the central server transfer rate is in the same direction with and without cloud providers if content’s prominence is distorted (Kota et al. (2019)). Content distribution networks have recently become increasingly popular. Academic research in this field is ahead of technology itself. Academia has not adequately explored many aspects of technology. These include management outlines, protection and standardization. The first step to help academia close the gap with industry or even take a step forward in a significant sector in the right direction is to find out and emphasize aspects of this technology that could or were not covered by academic research. This indicates a detailed investigation into research in this area. None of the surveys currently underway explain the dark facets of innovation, not subject to scientific study. As the current research indicates, the life cycle of content delivery is first derived from this survey. Each stage of a life cycle is then analyzed for past empirical works to explain the research’s role and course at present. It is also demonstrated how content delivery technology is more advanced in academic study and convergent to new paradigms such as cloud computing, advanced computing and machine learning. This helps to assess the best path for future research by exposing the shortcomings of current work.
3 System Model In computer technology, such as recommendation systems, search, retrieval of information, computer vision and image processing, large-scale data analysis is now commonly used and is entering reality in terms of business intelligence, health and supply chain analysis. In areas such as network defense, it is also often used within the domain of networks. To analyze large quantities of data, many technological advances were significant in the past decade. We use a simplified data analytical model framework for content distribution to examine the federation’s possible benefits. There are multiple country-represented geographic regions, each region can include several ISPs, and each ISP has a specified number of users to serve. In this setup, the content delivery issue can be formulated as a problem for resource allocation. First, because of the existing capacity presented by different ISPs in various regions, we want to serve as many clients as possible. Secondly, the activities’ network footprint should be reduced, and requests should be met locally as practicable.
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Let X ¼ \X0 ; X1; X3; Xi [ denote the number of accesses’ time series vector. where Xi is the number of accesses at time index i E (Xi) represents the expected value and rXi is standard deviation. For a given time lag k, the cross-correlation coefficient between two time series vectors X ¼ \X0 ; X1; X3; . . .; Xi [ and Y ¼ \Y0 ; Y1; Y3; . . .; Yj [ can be defined as E ðXiÞEðYi þ K Þ þ k ð 1Þ aðkÞ ¼ E ðXiYi þ k Þ rXirYi The cross-correlation coefficient lies in the range [−1,1] where a(k) = 1 is a perfect correlation at lag k and a(k) = 0 means no correlation at lag k.
3.1
Regional Interest
In general, we observe a broad population-induced load difference through various regions (Fig. 1). However, we find markedly skewed reference intake from impoverished areas of the classic scenario for live events with geographic biases such as a local squad.
Fig. 1 Content delivery—region-wise
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Temporal Shift
Significant diurnal effects are observed in network traffic, and transient changes among regions are confirmed by cross-correlation analysis for VDOs. Differences in timescales trigger a temporal change in the access pattern. The start time for each area is approximately 8 am.
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Patterns
While we expect to see live video in synchronous form, we experience a synchronous display of video artifacts unexpectedly. This particularly applies to famous shows during the highest demand era. We develop easy modeling to capture the implementation of the federated telco content supply and evaluate the organization’s potential advantage to maximize accessibility and reduce the collection of video workloads.
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Behavior Analysis
When an user first observes a video on the player, the player is given a unique id that will be placed on a Flash cookie for future views. Information of other user interaction activities like pause and stop are detected at the start time and length of the sitting. In general, the name and actual length of the content of the video (which we use to divide the videos into genres).
4 Results and Discussion The data used for that study revealed in real time by conviva.com through a client-side video player arrangement library that gathers data on a session. When the user views video from the partner service providers of conviva.com, this library is enabled. The library is also listening to player events (e.g., seek and pause). The data is then collected and analyzed with Hadoop. We can recognize many video visual trends relevant to these two designs from a data collection of around 100 videos and live sessions gathered by viewers across the Web for over 2 weeks. In Fig. 2, our dataset identified an unforeseen increase in demand for some items from areas that can be served on servers in other areas with a spare capacity if material supplies federated. Similarly, when different regions reach peak loads in the video dataset, we have noticed significant temporal changes, opening new
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Fig. 2 Aggregate access pattern
opportunities to handle peak loads using federation. Finally, we present distribution statistics and their relative results, which also have massive consequences (Figs. 3 and 4). In the first week, we use the user access actions to establish a basis for resource provisioning on each ISP in each area. We use the user access behavior in the first week and evaluate a resource base for each configuration of ISP regions for the maximum load at each ISP in each region. Every region of ISPs has to over-supply with 1:6 times the peak loads experienced for the first week, even though peak loads are consistent for video content so that they can achieve 100% availability without federation. In regional cooperation and supply with 1:2 times the measurable first week peak load, supplying 1:4 times the peak load will suffice to maintain the workload with a national federation for the next three weeks. This highlights that while the behavior is constant, maximum loads across the various ISPs in a region are linearly polarized, allowing users to increase availability through other ISP’s resources in the same region. Federation improves overall system availability with less overhead supply. The advantages of greater collaboration are higher in all resources (Tables 1 and 2).
5 Conclusion The video distribution infrastructure must be planned and provided for high-quality content for the most significant consumer populations as Internet-based video consumption becomes common. However, recent technologies suggest that rising video traffic stresses the content distribution infrastructure. Our analysis of more than 100 videos has uncovered many fascinating access designs, like regional and
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Fig. 3 Correlation coefficient between views and population
Fig. 4 Federation for videos
Table 1 Correlation coefficient between views and population
Pearson correlation coefficient
Regional
Non-regional
1 2 3 4 5
0.2 0.3 0.4 0.5 0.6
0.5 0.6 0.7 0.8 0.9
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Provisioned capacity
Regional
No federation
1 2 3 4 5
92 93 96 97 98
85 87 91 94 96
daytime activities, synchro views, predictability of demand and a partial content preference. However, we find that the federation can substantially reduce resource provision costs by using regional wises and equally increasing its productive capability. This study shows that simple consumer behavioral data analysis can also be used to enhance content provision.
References 1. Anjum N, Karamshuk D, Shikh-Bahaei M, Sastry N (2017) Survey on peer-assisted content delivery networks. Comput Netw 116:79–95 2. Braiki K, Youssef H (2019, June) Resource management in cloud data centers: a survey. In: 2019 15th international wireless communications and mobile computing conference (IWCMC). IEEE, pp 1007–1012 3. Elkotob M, Andersson K (2012, December) Challenges and opportunities in content distribution networks: a case study. In: 2012 IEEE globe.com workshops, pp 1021–1026, IEEE 4. Gupta M, Garg A (2014) Content delivery network approach to improve web performance: a review. Int J Adv Res Comput Sci Manag Stud 2(12):374–385 5. Pathan AMK, Buyya R (2007) A taxonomy and survey of content delivery networks. In: Grid computing and distributed systems laboratory, University of Melbourne, Technical Report, vol 4, p 70 6. Tso FP, Jouet S, Pezaros DP (2016) Network and server resource management strategies for data centre infrastructures: a survey. Comput Netw 106:209–225
Improved Nature-Inspired Algorithms in Cloud Computing for Load Balancing D. Akila, Amiya Bhaumik, Balaganesh Duraisamy, G. Suseendran, and Souvik Pal
Abstract A core area of research is cloud computing. The crucial part of nature-inspired algorithms (NIAs) is now done in the cloud. NIAs are algorithms whose name means that nature influences them. NIAs can further be categorized as swarm intelligence (SI) algorithms, biological phenomena, physics and chemistry processes, or based on any other topic. Si-based algorithms are known as smart since they are known to understand and improve their productivity by evaluating the previous production and the movements that they made. For several real-world optimization challenges that are categorized as NP hard issues, NIAs provide an efficient solution. The NIAs have a long number of implementations, and most of them tend to be more powerful and, therefore, more effective than other algorithms. A great deal of time was spent to make it possible in parallel with other algorithms. Boost output and thus offer a stronger QoS. The paper aims to study different NIAs, and various NIAs are defined to improve for better load balancing in a cloud environment. The principle behind each algorithm is to encourage more study. The paper also mentions numerous NIAs.
D. Akila (&) G. Suseendran Department of Information Technology, Vels Institute of Science Technology and Advanced Studies, Chennai, India A. Bhaumik Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] B. Duraisamy Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] S. Pal Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishnanagar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_57
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Keywords Cloud computing
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Load balancing NIAs
1 Introduction Cloud networking can provide pooled computational tools and info, too. Because of a host program service supplier, this may take effect so that power and cooling electricity or a computer is not a consumer’s responsibility. Moreover, increasing the number of cloud users in computing contributes to a rapid improvement in environmental efficiency. However, it is difficult for these suppliers to balance their loads to schedule the projects. [1]. We have seen tremendous progress in using the Internet in the last few years. Network bandwidth needs to be strengthened to provide decent network reliability to deal with excessive network traffic volume. They can successfully exploit the tools of the network. Traditional methods cannot be fully applied to fulfill this condition because gathering network statistics from each network machine is inefficient. Challenging conventional networks, network management, network troubleshooting, cloud networks, and changing traffic trends also contributed to a spike in new network architecture [2]. For example, cloud computing has various research interests in energy efficiency [3]. The value of contact and the Internet is growing, and the systems are getting more complex built up. With the increase in complexity, a need emerges to resolve the numerous degrading network efficiency issues [4]. Cloud computing is a fast-growing distributed scale and array computing technology for large companies. Cloud data centers are designed to offer comfortable and reliable networking using virtualization and to provide access to a public pool of configurable consumer computing resources with minimal involvement [5]. Cloud storage is used as a back-end layer to store data and data processing since the cloud will have a large amount of data storage and processing capacities not otherwise used for IoT users [6]. Load balancing is one of the largest cloud computing challenges, which needs a big emphasis. It is a technique to distribute the excess load to make them similarly loaded by under using nodes. It also aims to minimize the scale of energy use and carbon dioxide levels and minimize management activities. Also, it ensures the equal and efficient distribution of cloud storage resources [5]. There are many brilliant solutions and metastasized algorithms in existence, and we only have to delete them and use them to fix our problems. The bear algorithms are known as the root of nature’s inspiration. Nature algorithms powered (NIAs), Metaheuristics approaches inspired by evolution are among the most potent optimization [8]. Nature-inspired optimization algorithms such as optimization of an ant colony, particle swarm optimization, artificial bee colony, lion algorithm optimization, and learning-based optimization can be beneficial in addition to the facets of machine learning. Decision research focused on the healthcare environment [7].
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The recent past has witnessed the widespread implementation of nature-inspired algorithms (NIAs) for various optimization issues. This algorithm is based on randomization: definition and draw inspiration from natural phenomena. Any of the NIAs that have been suggested so far have proven to be quite successful. It is effective. The key characterizing characteristics of a good-natured algorithm are higher convergence rates and lower convergence rates. Generally speaking, the output of nature-inspired algorithms is evaluated for the benchmark features. This is a benchmark function with various characteristics such as modality, separability, scalability, and differentiability. The efficiency of the NIA also depends on the amount of the NIA. Plan variables for the problem. This paper presents a summary and comparative study of seven well-known nature-inspired algorithms [9]. A cloud computing operation applies to load balancing. An environment is in which workloads are allocated and reserves are measured. Load in cloud storage balancing is also essential for controlling overload and handling the device’s overload—digital load state (VM) [10]. Few represents the virtual machine load balancing system. It assigns several roles to VMs, which can be concurrently carried out so that both remain in balance, OK, VMs, and well. Load balance in the cloud world has a direct target; it attempts to handle the host’s workload in an environment [1].
2 Related Works Sathyanarayana et al. [2] proposed a planned work is now being conducted in joint ACS routing and complex algorithm for the LB server as an LB module for the SDN. The algorithm uses server loading and network statistics obtained by the controller to find both the best server and the best network flow path. The suggested algorithm is added as a package to the OpenDayLight controller to confirm this principle, and Mininet, an emulator for OpenDayFlow, carries out experiments. The experimental one findings indicate that compared to the other two benchmark algorithms, the proposed algorithm performs considerably better, retaining both higher and lower network throughput delays. This thesis will serve as an overview of the SDN device’s application and the management of network power and a basic example of collaborative approaches inspired by design. Mr. Pankaj et al. [11]—For a decade, swarm intelligence, an artificial intelligence discipline, has been dealing with creating autonomous multi-agent networks by drawing influence from social insects’ collaborative actions other animal communities. Swarm intelligence is a useful model for a dynamic problem algorithm. This paper reflects on the most popular optimization approaches inspired by swarm intelligence. This paper also includes a comparative study of PSO and ACO for data clustering. Shobana et al. [10]—Scheduling assignments in cloud computing allocates tasks to a single machine. An integral function of cloud job scheduling is load balance on the virtual machine preventive autonomous functions (VM). Load balancing aims to
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optimize resource performance, reduce reaction time, increase production, and prevent overload, whatever the resources are. If load balance is not properly handled, it can lead to a situation in which specific virtual machines can become idle or under loaded. The job is not finished on time due to the imbalanced load, and, as a consequence, customer loyalty is not attained. By preemptive work preparation, the suggested algorithm almost removes makespan matching the foraging actions of honeybees. The goals of the assignments and their estimated time are considered by this algorithm, with the target of optimizing throughput and minimizing latency. Our solution increases user reaction time by allowing fair use of limited resources. Kashyap et al. [12]—Cloud computing is emerging across the network as a new model for the manipulation, configuration, and access to massively distributed storage networks. Load balance is one of the significant cloud computing challenges needed to diffuse workload evenly among all nodes. The load is an operating system measurement of the workload categorized as CPU load, network load, memory power, and storage capacity. It ensures that customer loyalty and resource utilization are high by having both compute resources optimal and equal. Proper load management aids the execution of faults, allows for scalability, oversupply, minimizes energy consumption, avoids bottlenecks, etc. This paper gives an overview of cloud computing algorithms for load balancing and their respective benefits, drawbacks, and efficiency metrics. Shabnam Sharma et al. [13]—Swarm knowledge is useful in solving many issues, including knapsack problems, minimal tree spanning, troubles with planning, routing, load balancing, and much more. The analysis here focuses on the bat algorithm. In recent years, the bat algorithm has drawn the investigator’s attention thanks to echolocation’s fantastic feature. It applies to various issues, such as vehicle routing optimization, rail optimization problem scheduling, and cloud computing load balancing. The key aim of this research is to propose a load balancing technique in a cloud computing environment between virtual machines.
3 Proposed Method 3.1
Load Balancing
Load balancing in clouds is a strategy that distributes cloud balancing excessive dynamic local burden uniformly between all nodes. It is used to ensure the improved provision of services and assets consumption ratio, thereby improving the device’s overall output. The incoming tasks coming from the different position are received by the load balancer and then distributed to the data center to ensure adequate load distribution [12].
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Load Balancing Goals
The goal of load balancing is as follows: • • • • • • • • •
Group to improve availability of resources. Group to improve customer loyalty. PARY optimizing capital efficiency. POLY minimizing the time of execution and the waiting time of the job. It comes from a different place. Group to enhance efficiency. Ensure reliability of the system. Affirmative to build fault tolerance system. Failed to consider potential modification.
3.1.2
Improved Load Balancing Algorithms
There is an extreme need for load balancing in the complex and the large networks, distributed. The load balancer is deciding. For load balancing, pass the job to the remote server. The load balancer can operate in two ways: One is cooperative, and the other is non-cooperative. In a non-cooperative manner, the response time is improved by running the functions individually. Most of the improved NIA algorithms are for load balance being studied in this article. The NIA algorithms are defined as nature-inspired algorithm such as ant colony optimization, honeybee algorithm, etc., some of the improved NIA algorithms are used for better performance.
3.2
Improved Ant Colony Optimization
A probabilistic strategy to overcome problems is used by ant colony optimization (ACO) algorithms. The ants’ proper behavior inspires the algorithms. Randomly, the ants roam and return by laying down the pheromone roads after finding food. Other ants pursue these pheromone tracks instead of running blindly. This technique can be used if modified to address computer network problems such as detecting the right paths in a network graph. Multiple methods, including AntNet, AntHocNet, HopNet, etc., are available. Stigmetry to solve issues is by routing the data network [2]. Algorithm for optimization of ant colony (ACO) M— In 1992, the basic ACO algorithm, ant method, was proposed by Dorigo (AS). In 1997, the ant colony system (ACS) was proposed by Mr. Dorigo as an AS improvement. They have the general property to find their food path, according to the writers of ants. Ants tend to deposit a compound known as a pheromone, which often evaporates with time in this food location phenomenon. In essence, this pheromone activates new axes to discover the fastest way to feed. The shortest route is the road with the most
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significant number of pheromones. Thus, this method takes the fresh ants to their position in the shortest possible period. Ant Colony System: To improve AS efficiency and decrease its complexity, the ACS algorithm was developed. The critical distinction is that not all ages are permitted to deposit pheromones, and only the ants with the best results can deposit pheromones. Also, the volume of pheromone accumulated is more significant than those in an ideal solution. The ACS algorithm’s simple steps are as follows: (a) Initialization stage: m ants first roam around n nodes randomly. For VMS, all functionality is initially assigned automatically to all VMS. (b) Local Pheromone Deposition and Updating Phase: Each ant makes its initial visit, and initial pheromone deposition is carried out by the repeated implementation of the state transformation rule. Using the local pheromone rule, ants often upgrade pheromones to the visited edges during their tour. Algorithm 1: ACO 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
Require: flow 1: for each(flow) { // Dynamic server LB chooses the least-loaded server dst = getLeastLoadedServer(flow); // ACS dynamic routing selects the best path path = Apply state transition rule using Eq. (1) for each(switch in path) { FlowEntry(switch) installation; } Execute a local update using Eq. (2) Then Complete a global update using Eq. (3) after any of the iterations }
When a flow to be redirected is received by the load balancing device, it calculates the less-loaded server on which the submission can be created. Please submit them. Please. The servers record their current CPU regularly. Load it onto the controller. The dispatcher uses this knowledge to determine the least populated server automatically. The best way to classify the algorithm is to pick the server to use the ACS algorithm. Formulas involved in the ACS algorithm are discussed below. Law of State Transition: The path k is chosen as follows from the several possible routes from source x to destination y: ( k¼
i arg max i 2 paths½ðTiÞa : niÞb ; S;
if q q0 otherwise
ð1Þ
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If q is the random integer equally divided in [0,1], then the relative number in [0,1] is calculated. Past data is relevant versus new road knowledge quest, and S is a random variable specified by the route. The distribution of probability in Eq. (2). Pk ¼ P
ðTk Þa :ðnk Þb i 2 allowed pathsðTk Þa :ðnkÞb
ð2Þ
Ti is the amount of pheromone stored for route i. 5-007 is the parameter used to monitor the effect of D 0 and can/will affect the desirability of the path I from x to y to pick. The available bandwidth of the course will be incorporated in our building i. The effect of b is regulated by the parameter b. The value of all pheromones and the bandwidth that is available together affect the way desirability. Local Update Rule: Where the ant (i.e., the request) goes, the local update is performed. The pheromone value of the path crossed by the order reduces slightly. Percentage is to maximize the interpretation of the maps. Updating the equation by the local group as shown in Eq. (3) Tk ¼ ð1 eÞ:Tk þ eTo
ð3Þ
The pheromone is deposited from source x to destination y on path k, where the local evaporation of pheromones is coefficient and where the initial pheromone value is.
3.3
Normal Honeybee
The machine carries out load balancing with a preventive schedule. Load balancing system helps to spread the load between devices to stop an unbalanced load and idleness. Scout bees and bees are found in beehives. Scouting bees are searching for a supply of food, collecting information about the nectar food source, and checking whether or not nectar sources are saturated. Scouting bees return to beehives to publicize this with the dance waggle/tremble/dance with vibration. This dance gives us the impression of food quality and quantity, and even distance from beehive food, man. The spectator bees are following. The bees scout to the position of the bees. Sources of food are to commence the picking. They go back to the beehive and go to a party in the hive with the other bees. The understanding of how much food in the food chain persists [10]. Bee Colony Optimize (BCO) C algorithms. Zhang reports that honeybees are the most closely tracked social species. Recently, honeybees have been the most closely investigated social insects. D. In the honeybee swarm system, Karaboga notes the three fundamental elements [14]. These are as follows: Approximately to the nest, the energy quality and concentration, the taste of nectar, easiness, and difficulties in producing energy are considerations of the food supplies used. All of this is represented easily by a single number known as the ‘profitability’ of the food supply.
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Foragers Employed: They are attached to a particular source of food that they use or operate. They provide knowledge about this specific food source, its size and location from the nest, and the source’s survival and will presumably share it. Unemployed Foragers: They are always searching for a supply of food to manipulate. Approximately to the nest, the energy quality and concentration, the taste of nectar, easiness, and difficulties in producing energy are considerations of the food supplies used [15]. All of this is represented easily by a single number known as the ‘profitability’ of the food supply. Foragers Employed: They are attached to a particular source of food that they use or operate. They provide knowledge on this specific food source, its size and location from the nest, and the source’s survival and will presumably share it. After seeing the waggle dance of a working forager, they begin looking for a new food supply. There are scout bees and bees in beehives. Scouting bees are looking for a food supply and gathering knowledge about the food source nectar and verify whether or not the nectar supply is saturated. Scouting with a waggle/tremble/dance with vibration, bees return to the beehives to advertise this [16]. This dance gives us the impression of food quality and quantity, and even distance from beehive food, man. The spectator bees are following scout’s bees at the spot of the bees. Sources of food are to commence the picking. They go back to the beehive and go to a party in the hive with the other bees. The misconception of how much food in the food chain is left behind [17]. Algorithm 2: Bee Colony Optimization 1. Scout bees are looking for food sources and collecting information on the load measurement supply and the potential of all nectar supplies. 2. Check whether or not the nectar is balanced at its source. 3. If (source of nectar = zk þ 1 ¼ aðzk wk þ bk Þ > ; z1 ¼ aðFWin þ bin Þ
ð3Þ
ð4Þ
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Fig. 3 Architecture of DNN model
Where F denotes the forest matrix with m samples and n estimations, y is the sorting output vector, £ signifies the DNN model’s factors, Zout and Zk are concealed neurons with equivalent weight matrices Wout and Wk and bias vector bout and bk. a() is the sigmoid initiation function, and b() is the softmax function that changes the output vector’s values into a possibility forecast. The sizes of Z and W vary based on the no. of concealed neurons hin and hk, k = 1, 2, … For DNN model, the activation function is a rectified linear unit with the following form bReLU (p) = max (p, 0) The classifying act of the DNN model is related to factors of both the forest and DNN models. Forest factors are given by • Number of trees constructed in the forest • Tree associated factors: tree depth, least dividing sample size.
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DNN factors are given by • Design associated factors: Total no. of layers and hidden neurons present in each layer • Regularization associated factors: dropout ratio and the disadvantage scale • Training associated factors: learning rate and the group size. The processes in this technique are as follows: • In this technique, forest is a collection of devices
DðiÞ ¼ fDn ; On g; n ¼ 1; . . .; N
ð5Þ
Where N = the total number of devices. O = dividing variables and dividing values. • During the feature recognition phase, D is estimated by training data a, b. Where a = input data matrix B = outcome vector • From the observation, we get the prediction from every device in D: d ðai ; OÞ ¼ ðT1 ðai ; OÞ; . . .; Tn ðai ; OÞÞT
ð6Þ
Where TN (ai, O) = binary prediction of observation The DNN with v concealed layers has a regular design. PðbjD; ;Þ ¼ gðXout Yout þ BVout Þ 9 Xout ¼ dðXv Yv þ BV Þ > = Xk þ 1 ¼ dðXk Yk þ BVk Þ > ; X1 ¼ dðDXin þ BVin Þ
ð7Þ
ð8Þ
Here, D is the forest matrix with n samples, B is the output vector, $ denotes all factors in the DNN model, and Xout are the hidden neurons with associated weight matrices Yout
4 Experimental Setup The disease prediction and diagnosis model is implemented using IFogSim [6]. It can assimilate several resource management methods, which will be additionally modified based on the investigation part. It is a high-performance stimulant that is related to CloudSim. The performance of the RF-DNN classifier is compared with the traditional ANN technique. The classification results are evaluated in terms of different parameters such as sensitivity, specificity, and accuracy.
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Fig. 4 Accuracy results for both techniques
Accuracy (%)
Figure 4 shows the results of accuracy for the classification techniques RF-DNN and ANN. It can be seen that RF-DNN achieves higher accuracy in the case of all the diseases. Moreover, heart attack and stroke have the highest accuracy when compared to other conditions. Figure 5 shows the results of sensitivity for the classification techniques RF-DNN and ANN. It can be seen that RF-DNN achieves higher sensitivity in the case of all the diseases. Moreover, heart attack and stroke have the most heightened sensitivity when compared to other conditions. Figure 6 shows the results of Specificity for the classification techniques RF-DNN and ANN. It can be seen that RF-DNN achieves higher specificity in the case of all the diseases. Moreover, heart attack and stroke have the highest specificity when compared to other conditions.
100 98 96 94 92
RF-DNN ANN
Fig. 5 Sensitivity results for both techniques
SensiƟvity (%)
Disease Type
85 80 75 70 65 60
RF-DNN ANN
Fig. 6 Specificity results for both techniques
Specificity (%)
Disease Type
95 90 85 80 75
RF-DNN ANN Disease Type
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5 Conclusion This paper has developed a disease prediction and diagnosis model for IoT– cloud-based critical healthcare systems. For each patient, a medical information database (MDB) is constructed from the EHR. RF-DNN classifier model is used to predict the diseases based on these databases. The proposed model is implemented using IFogSim, and its performance is compared with the traditional ANN technique. Experimental results have confirmed that the RF-DNN classifier achieves the highest accuracy in all the diseases than ANN.
References 1. Suneetha KC, Shalini RS, Vadladi VK, Mounica M (2020) Disease prediction and diagnosis system in cloud based IoT: a review on deep learning techniques. In: Materials today: proceedings 2. Verma P, Sood SK (2018) Cloud-centric IoT based disease diagnosis healthcare framework. J Parallel Distrib Comput 116:27–38 3. Kumar PM, Lokesh S, Varatharajan R, Babu GC, Parthasarathy P (2018) Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Gener Comput Syst 86:527–534 4. Jayaram R, Prabakaran S (2020) Onboard disease prediction and rehabilitation monitoring on secure edge-cloud integrated privacy preserving healthcare system. Egypt Inf J 5. Alqahtani A, Crowder R, Wills G (2017) Barriers to the adoption of EHR systems in the Kingdom of Saudi Arabia: an exploratory study using a systematic literature review. J Health Inf Developing Countries 11(2) 6. Tavares J, Oliveira T (2017) Electronic health record portal adoption: a cross country analysis. BMC Med Inform Decis Mak 17(1):1–17 7. Abdali-Mohammadi F, Meqdad MN, Kadry S (2020) Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms. Int J ArtifIntell ISSN 2252(8938):8938 8. Wang J, Zhang G, Wang W, Zhang K, Sheng Y (2021) Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT. J Cloud Comput 10 (1):1–12 9. Maini E, Venkateswarlu B, Gupta A (2018, August) Applying machine learning algorithms to develop a universal cardiovascular disease prediction system. In: International conference on intelligent data communication technologies and internet of things. Springer, Cham, pp 627– 632 10. Feng C, Adnan M, Ahmad A, Ullah A, Khan HU (2020) Towards energy-efficient framework for IoT big data healthcare solutions. Sci Prog 11. Kong Y, Yu T (2018) A deep neural network model using random forest to extract feature representation for gene expression data classification. Sci Rep 8(1):1–9 12. Farnaaz N, Jabbar MA (2016) Random forest modeling for network intrusion detection system. Procedia Comput Sci 89:213–217 13. Akila D, Balaganesh D (2021) Semantic web-based critical Healthcare system using Bayesian networks. In: Materials today: proceedings
Predication of Dairy Milk Production Using Machine Learning Techniques G. Suseendran and Balaganesh Duraisamy
Abstract This paper proposes an automated model based on the machine learning (ML) technique to predict cows’ dairy milk production. For predicting milk production, the factors which are considered are the health condition (HC) of cows, feed intake capacity (FIC), and expected relative milk yield (ERMY). Based on the deviations between the observed and the average values, the cow’s health condition is determined. The Artificial Butterfly Optimization (ABO) algorithm is used to estimate the Woods parameters. The objective function of ABO minimizes the root mean squared error (RMSE) of the average daily milk yield for each farm. Finally, artificial neural network (ANN) model was applied based on the variables: HC, FIC, and ERMY and the other parameters like age at calving, the month of calving, the days in milk after calving, and the lactation number. The experimental results show that the proposed ANN-ABO algorithm attains the highest accuracy than the ANN-GA, ANN, and SVM algorithms.
Keywords Diary milk production Prediction Expected relative milk yield Artificial Butterfly Optimization (ABO) Artificial neural network (ANN)
1 Introduction Being nature’s only complete food, milk has enormous significance in every individual and family’s routine life. It offers all vital nutrients that are essential for the growth and progress of the body. It is the principal source of protein. Global protein ingestion using dairy products is 10.3% of overall protein consumption. It is
G. Suseendran (&) Department of Computer Science, Lincoln University College, Petaling Jaya, Malaysia B. Duraisamy Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_60
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the most manufactured and used up product in India. Indian dairy industry containing milk and milk products is of Rs. 3.6 lakh crores. Furthermore, it is unceasingly increasing at the rate of 10% yearly. Milk production from dairy cows is liable to disparity because of the seasonality of pasture manufacture, grazing environments, disease, nutritional interferences, and other conflicts. The skill to predict herd milk yield previously offers advantages for the organization at the processor and farm level. Daily milk manufacturing sturdily impacts energy ingestion, plant utilization, and farm revenue. A milk yield prediction system’s expediency relies upon how precisely it can forecast every day milking designs and its skill to modify to the aspects distressing the source [1]. In the medical field, machine learning (ML) methods have enhanced diagnostics in numerous diseases. ML models can hold actual data and are impervious to missing values. ML procedures show efficacy in creating interpretations about particular fitness threats and expecting fitness actions [2]. Moreover, they can assess massive datasets, which frequently are tough to evaluate with conventional statistical models. This necessitates the need for ML methods to provide decision-making for dairy farming. Setting large integrated datasets may permit farmers with improved decision-making systems and support them to intensify their animals’ comfort and competence [3]. With the help of ML, the cost of supporting EHR systems has been reduced by enhancing and regulating the mode those schemes are intended [4, 5]. For grouping and expectation of milk manufacture levels in dairy cattle, specific approaches are accessible like various data mining procedures, genetic algorithms, discriminant analysis (DA), decision trees, regression methods, and artificial neural network (ANN) models [6, 7].
2 Related Works Jensen et al. [8] have established a dynamic linear model (DLM) to predict distinct cows’ milk yield. It implements Wood’s function to represent the estimated overall daily milk yield (DMY). It also executes a polynomial function to represent the time interludes’ outcome amid milkings on the ratio of the predicted overall daily milk yield. The DLM can unceasingly predict the quantity of milk manufactured in a given milking by relating these two functions. The DLM can continuously predict the amount of milk production in a given milking. Hosseinia et al. [9] have established a malleable technique for health, fertility, lifespan, and other cost-effective equalities in the dairy industry. This work’s main objective is to predict the second parity milk yield and fat.% of cows from first parity milk data, using the ANN model. They have also proposed examining the dependability and effectiveness of recently made neural networks associated with the standard BLUP technique. Grzesiak et al. [10] have concentrated on the ability of ANN to estimate milk yield for both complete and homogeneous lactations. Using the actual data and the milk recording test days, numerous NNs were intended, and Wood’s model
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constraints were assessed. To examine the models’ predictive factors, they have arbitrarily chosen a subset of cows from the calculated populace. They used a dataset of daily milk yields of dairy cows maintained in a cattle farm. Radwan et al. [6] have associated the outcomes of DA and ANN. The results specify that DA is an insufficient model for data classification in association with ANN, which offered an improved variety and forecast of milk production levels across several calving periods. Moreover, first calving age, breedings per start, and calving outset recess were the finest analysts to assess and forecast the level of milk production in Holstein Friesian cattle. Fuentes et al. [11] have exposed actual AI applications using detailed data from a robotic dairy farm to enhance affordability in a challenging universal market. It proposes an AI system model for any dairy farm to monitor and lessen heat stress by implementing XML models automatically. The milk productivity parameters were obtained from various milk stations.
3 Proposed Solution 3.1
Overview
This paper proposes an automated model based on the machine learning (ML) technique to predict dairy milk production. For predicting milk production, the factors are considered: • Health condition of cows • Food intake capacity • Expected relative milk yield. The ANN model was constructed based on these factors and other variables.
3.2
Estimation of Health Condition of Cows
Though many works have been done to monitor cattle’s health conditions, they have considered a limited set of attributes and failed to predict the illness in advance accurately. An innovative cattle monitoring system using IoT sensors is designed [12] to accurately predict cattle’s disease in advance and take the necessary actions. Health sensors are deployed over the body of the cattle for measuring various health factors. The measured data is examined at MRC and compared against the reference table’s range of values. The average value of the health parameters stored in the reference table and its deviation with aggregated values are listed below:
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Havg(tj) and D(H) Tavg(tj) and D(T) Pavg(tj) and D(P) RUavg(tj) and D(RU) REavg(tj) and D(RE)
average heartbeat and its deviation body temperature and its deviation pulse rate and its deviation rumination value and its deviation respiration value and its deviation 9 DðH Þ ¼ abs NH Havg tj > > > > DðT Þ ¼ abs NT Tavg tj > > = DðPÞ ¼ abs NP Pavg tj > > DðRUÞ ¼ abs NRU RUavg tj > > > > ; DðREÞ ¼ abs NRE REavg tj
ð1Þ
Where NH, NT, NP, NRU, and NRE are the normal range of values corresponding to the health parameters. The health condition (HC) of the cow will be considered as ABNORMAL if the deviations (1) to (5) become more significant than a threshold value Dth, NORMAL, otherwise.
3.3
Estimation of Feed Intake Capacity (FIC)
The main factors positively associated with the cow’s growth and affect the FIC are age, parity, lactation, and gestation stage. Hence, the FIC model is derived based on these factors, as given in the following equation: FICðp; d Þ ¼ a0 þ ða1 a2 d Þ ð1 epaage Þ
ð2Þ
Where the age of cow is given by
d age ¼ ðp 1Þ þ 365
ð3Þ
Here, FIC(p, d) is the base FIC, p is parity no, d is days of lactation, a0 is the initial level of FIC, a1 is a maximum increase of FIC, a2 is the factor of interaction between p and d, pa is the rate of rising of FIC.
3.4
Estimation of Expected Relative Milk Yield (ERMY)
Hogeveen et al. the fraction of the expected total daily milk yield at observation time t was expected from a given milking based on the interval of time since the last
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milking of the same cow. Thus, the three parameters w0, w1, and w2 were estimated for each of the five farms separately. MYrelative ðIntervalt Þ ¼ x0 þ x1 Intervalt þ x2 Intervalt
ð4Þ
The three parameters’ estimation was achieved with linear regression, using the built-in R function lm with the model specification poly()—the number of degrees in the polynomial set to 2. Only observations with intervals of at least one hand less than 24 h were used. All words with intervals outside this range (n = 36) were considered unreliable.
3.5
Estimation of Wood’s Parameters
The Wood’s function parameters (Wood 1967) were estimated separately for primiparous and multiparous cows for each of the included farms based on the learning set. The Wood’s function is given by y ¼ atb expðctÞ
ð5Þ
Where y is the average daily milk yield during time t. The parameters a, b, and c are related to the peak lactation, ascending part of curve between calving and peak lactation, descending part of the curve after peak lactation.
3.6
Artificial Butterfly Optimization (ABO) Algorithm
In this phase, the ABO algorithm is used to estimate the Woods parameters. The objective function of this algorithm is given by oðF Þ ¼ Minimize RMSEEMRY
ð6Þ
The objective function minimizes the root mean squared error (RMSE) of each farm’s average daily milk yield. All registered milkings, regardless of the corresponding interval of time since the last milking, were used when aggregating the milk yields to total daily results. The ABO algorithm considers the initial vector with three values, corresponding to the three parameters in Wood’s function (i.e., a, b, and c). From this initial vector, a “population” with an aggregate of 1000 vectors was produced by arbitrarily enhancing or reducing each component in the vector. By repeatedly removing the 80% of the population with the highest RMSE and reestablishing a population of 1000 vectors based on the remaining 20%, the best RMSE was
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lowered with each iteration. This procedure was repeated until no further reduction in RMSE was observed for ten consecutive iterations. At this point, the best parameter vector (i.e., the one producing the lowest RMSE for a given farm) was saved from being used with the DLM for that specific farm. The initial parameter vectors were taken from Cole et al., who estimated Wood’s parameters for six different dairy cow breeds. The parameters for each of the species were tested as initial parameters once per farm. The ABO has two types of butterflies with their corresponding flight modes: sunspot butterfly (SB) with sunspot flight (SF) mode and canopy butterfly (CB) with canopy flight (CF) mode. Apart from these two modes, there is a free flight (FF) mode. ABO Algorithm 1. Initialize the population P containing n butterflies bi (i = 1, 2, …, n) 2. The ability of every butterfly bi is determined depending on the strength. The backdrop of the objective function determines the impetus strength of a butterfly. 3. All butterflies are arranged by their ability 4. Choose the number of butterflies with improved ability to form SB and the remaining to form CB 5. For each SB, • Fly to one novel position based on SF mode • Assess the ability of the novel sunspot and use the greedy selection on the actual position and the improved one 6. For each CB, • Fly to one randomly selected SB according to CF mode • Compute the fitness function 7. If (fitness is better than the previous one) Apply greedy selection on the original and new locations. Else Fly to a new location according to FF mode xit þ 1 ¼ xti þ q2 xc xti fi
Where = position of ith butterfly at time t xc = current best position fi = fragrance of the ith butterfly q = random number in the range of [0,1] 8. Find three the parameters based on the search Eq. (7) 9. The algorithm stops when all the nodes are localized.
ð7Þ
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E. ANN Model for Prediction The ANN model was constructed based on the variables listed below: • • • • • • •
x1—HCvalue, x2—FIC, x3—ERMY, x4—Age at calving x5—Month of calving, x6—Lactation number. Y—Actual milk yield per day
The choice of these variables is made according to the dataset, which is similar to the one used in regression models. Our model consists of two hidden layers with several neurons on the first and second layers as 10 and 6, respectively. This model exhibits an optimum SD ratio, which is defined as the ratio of std. deviation of prediction error to the std. variation of actual data. It also attains an excellent linear correlation coefficient among the input and the output layers.
4 Experimental Results The ANN classifier’s results with the ABO algorithm (ANN-ABO) are compared with ANN-GA, traditional ANN, and SVM algorithms. The classification results are evaluated in terms of different parameters, which are defined by the following formulae Sensitivity ¼
No of TP No of TP þ No of FN
ð8Þ
Specificity ¼
No of TP No of TP þ No of FN
ð9Þ
ðTP þ TNÞ TP þ TN þ FP þ FN
ð10Þ
Accuracy ¼
PPV ¼
TP ðTP þ FPÞ
ð11Þ
NPV ¼
TN ðTN þ FNÞ
ð12Þ
TP TN FP FN MCC ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðTP þ FPÞðTP þ FNÞðTN þ FPÞðTN þ FNÞ
ð13Þ
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precision recall F1 score ¼ 2 precision þ recall
ð14Þ
Where, Precision ¼ Recall ¼
TP ðTP þ FPÞ
ð15Þ
TP ðTP þ FPÞ
ð16Þ
Where TP—true positive, TN—true negative, FP—false positive, FN—false negative. Table 1 shows that ANN-ABO achieves higher accuracy and performance in comparison to other algorithms. The accuracy of ANN-ABO is 97%, whereas the accuracy of ANN-GA is 92%, the accuracy of ANN is 81%, and accuracy SVM is 62%. Figures 2 and 3 show the prediction results of ANN-ABO classification accuracy with ANN-GA, ANN, and SVM. Figure 2 states that ANN-ABO attains the highest sensitivity, around 70%, ANN-GA attains around 64%, ANN achieves around 62%, and SVM earns approximately 45%. The specificity of ANN-ABO is about 90%, the specificity of ANN-GA is back to 86%, the specificity of ANN is about 81%, and the specificity of SVM is about 50%. The PPV of ANN-ABO is around 75%, the PPV of ANN-GA is approximately 65%, the PPV of ANN is around 62%, and the PPV of SVM is about 46%. The NPV of ANN-ABO is around 80%, NPV of ANN-GA is approximately 77%, NPV of ANN is around 72%, and NPV of SVM is about 62%. It can be seen from Fig. 3 that ANN-ABO attains the highest MCC score around 70%, whereas the MCC of ANN-GA is about 53%, MCC of ANN is about 45%, and MCC of SVM is around 39%. Similarly, ANN-ABO attains the highest F1-score around 72%, the F1-score of ANN-GA is about 61%, the F1-score of ANN is about 56%, and the F1-score of SVM is around 44%.
Table 1 Comparison of ANN-ABO prediction results with other techniques Performance measure
ANN-ABO
ANN-GA
ANN
SVM
Sensitivity Specificity Accuracy PPV NPV MCC F1-score
0.7015 0.9042 0.9747 0.7514 0.8041 0.7085 0.7214
0.6421 0.8625 0.9218 0.6516 0.7725 0.5332 0.6156
0.6237 0.8125 0.8143 0.6217 0.7295 0.4513 0.5627
0.4534 0.5011 0.6273 0.4634 0.6211 0.3946 0.4456
Values
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1.2 1 0.8 0.6 0.4 0.2 0
587
ANN-ABO ANN-GA ANN SVM
Performance metrics
Values
Fig. 2 Comparison of prediction results for various classification algorithms (sensitivity, specificity, accuracy, PPV, and NPV)
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
ANN-ABO ANN-GA ANN SVM MCC
F1-Score
Performance metrics Fig. 3 Comparison of prediction results for various classification algorithms (MCC and F1-score)
5 Conclusion This paper proposes an automated model based on a machine learning technique to predict dairy milk production. For predicting milk production, the factors which are considered are the health condition (HC) of cows, feed intake capacity (FIC), expected relative milk yield (ERMY). ABO algorithm is used to estimate the Woods parameters. The objective function of ABO minimizes the RMSE of the average daily milk yield for each farm. Finally, the ANN model was used based on certain variables. The prediction results of the proposed ANN-ABO algorithm are compared with ANN-GA, ANN, and SVM algorithms. In all the results, it has been shown that the proposed approach attains the highest accuracy of 97%, sensitivity of 70%, specificity of 90%, PPV of 75%, NPV of 80%, MCC score of 70%, and F1-score of 72%.
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References 1. Deshmukh SS, Paramasivam R (2016) Forecasting of milk production in India with ARIMA and VAR time series models. Asian J Dairy Food Res 35(1):17–22 2. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, …, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2(4) 3. Al-Ayyoub M, Husari G, Darwish O, Alabed-alaziz A (2012, April) Machine learning approach for brain tumor detection. In: Proceedings of the 3rd international conference on information and communication systems, pp 1–4 4. Alqahtani A, Crowder R, Wills G (2017) Barriers to the adoption of EHR systems in the Kingdom of Saudi Arabia: an exploratory study using a systematic literature review. J Health Inf Developing Countries 11(2) 5. Tavares J, Oliveira T (2017) Electronic health record portal adoption: a cross country analysis. BMC Med Inform Decis Mak 17(1):1–17 6. Radwan H, El Qaliouby H, Elfadl EA (2020) Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models. J Adv Vet Anim Res 7(3):429 7. Ha N, Xu K, Ren G, Mitchell A, Ou JZ (2020) Machine learning-enabled smart sensor systems. Adv Intell Syst 2(9):2000063 8. Jensen DB, van der Voort M, Hogeveen H (2018) Dynamic forecasting of individual cow milk yield in automatic milking systems. J Dairy Sci 101(11):10428–10439 9. Hosseinia P, Edrisi M, Edriss MA, Nilforooshan MA (2007) Prediction of second parity milk yield and fat percentage of dairy cows based on first parity information using neural network system. J Appl Sci 7(21):3274–3279 10. Grzesiak W, Błaszczyk P, Lacroix R (2006) Methods of predicting milk yield in dairy cows— predictive capabilities of Wood’s lactation curve and artificial neural networks (ANNs). Comput Electron Agric 54(2):69–83 11. Fuentes S, Gonzalez Viejo C, Cullen B, Tongson E, Chauhan SS, Dunshea FR (2020) Artificial intelligence applied to a robotic dairy farm to model milk productivity and quality based on cow data and daily environmental parameters. Sensors 20(10):2975 12. Suseendran G, Balaganesh D (2021) Smart cattle health monitoring system using IoT sensors. In: Materials today: proceedings
Correction to: Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period S. Sirajudeen, Balaganesh Duraisamy, Haleema, and V. Ajantha Devi
Correction to: Chapter “Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period” in: S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_19 In the original version of the book, the following belated corrections have been incorporated: In chapter “Sentiment Analysis to Assess Students’ Perception on the Adoption of Online Learning During Pre-COVID-19 Pandemic Period”, the affiliation “Hajee Karutha Rowther Howdia College, Uthamapalayam, TN, India” of author “S. Sirajudeen” has been changed to S. Sirajudeen, PhD Scholar, Lincoln University College, Malaysia The correction chapter and book has been updated with the changes.
The updated original version of this chapter can be found at https://doi.org/10.1007/978-981-16-3153-5_19 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5_61
C1
Author Index
A Addaim, Adnane, 85 Ajantha Devi, V., 157 Akila, D., 445, 547, 569 Alagirisamy, Mukil, 329 Alao, Murtadho M., 115, 127 Alhazmi, Omar H., 319 Alluhybi, Wiam I., 319 Almusaylim, Zahrah A., 413 Alrajawy, Ibrahim, 221, 257 Ameen, Ali, 213, 221, 257, 351 Anandan, K., 107 Anandan, R., 247 Anand, Divya, 465, 473, 491 Anita, M., 63 Antony Athithan, A., 329, 361 Aziz, Md Faisal Bin Abdul, 145 B Balaji, L., 73 Basha, Murtaza Saadique, 393 Bhaumik, Amiya, 547 Bindu, G., 247 Brohi, Sarfraz Nawaz, 413 C Chandrasekaran, E., 201 D Deepa, M., 445 Devi, K. Aruna, 41 Dhanalakshmi, R.S., 41 Dhivya, A. Josephin Arockia, 229 Dhivya, R.S., 339
Dogra, Varun, 455, 501 Dorathi Jayaseeli, J.D., 279 Duraisamy, B., 157, 213, 383, 547, 559, 569, 579 Duraisamy, S., 107 E Elangovan, V.R., 11, 201 El Mokhi, Chakib, 85 F Fardoush, Jannat, 145 G Gayathri, S., 31 Ghosh, Gopal, 465, 473 Gunasundari, R., 299 Gunawan, Imam, 351 H Hafiz, Rubaiya, 137 Haleema, 157 Haran, Riya, 309 Hemalatha, R.J., 229 Humayun, Mamoona, 425, 435, 491 I Isaac, Osama, 221, 257 J Jabeen, T. Nusrat, 31 Janarthanan, Midhunchakkaravarthy, 213 Jarret, Michelle Maria, 309 Jayaraj, Greeta Kavitha, 279
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S.-L. Peng et al. (eds.), Intelligent Computing and Innovation on Data Science, Lecture Notes in Networks and Systems 248, https://doi.org/10.1007/978-981-16-3153-5
589
590 Jeyakumar, S., 329 Jhanjhi, N.Z., 51, 413, 425, 435, 455, 465, 473, 483, 491, 501, 511 Johannesson, Paul, 289 John, Jacob, 51 K Kalaiarasi, G., 73 Kalaiarasi, I., 403 Kanakarajan, Mehaa, 279 Kaur, Manjit, 483 Kavita, 455, 465, 473, 483, 491, 501 Kavitha, P.M., 371 Khalil, Muhammad Ibrahim, 425 Khan, Navid Ali, 413 Kiruba Raji, I., 97 Krithika, D.R., 177 Kumar, Ambeshwar, 21 Kumaresh, Sakthi, 309 M Madaan, Vaishali, 511 Magesh, S., 21 Mahmud, Tanjim, 145 Malathi, D., 279 Manikandan, R., 21 Midhunchakkaravarthy, 383 Midhunchakkaravarthy, Divya, 213 Momo, Shampa Islam, 137 Moyeenudin, H.M., 247 Mukil, A., 361 Munjal, Kundan, 511 Muruganantham, B., 371 N Nagarathinam, T., 11, 201 Naher, Sultana Rokeya, 145 Nambiar, Prabhakaran, 51 Napoleon, D., 403 Nirmala, G., 97 Nirmala, R., 31 Niveditha, V.R., 21 P Pal, Souvik, 201, 547 Prakash, Sneha, 299 Punitha, A., 41 Purushothaman, Rajeswari, 237 R Rajakumar, P.S., 21 Rajesh, P., 265
Author Index Ramisetty, Sowjanya, 491 Reethika, U., 167 Riajuliislam, Md, 137 Rohini, K., 177 Rose Varuna, W., 1 Rubi, Jaya, 229 S Sadique, Kazi Masum, 289 Said, Faridah Mohd, 351 Salma, Umme, 145 Sarli, Desi, 351 Sasirekha, D., 41 Sekar, Kalaiarasan, 329, 361 Shah, Mudassar Hussain, 435 Shah, Shahani Aman, 361 Shakila, S., 63 Shankar, R., 107 Shanthi, C., 537 Sharifonnasabi, Fatemeh, 51 Sikder, Juel, 145 Singh, Aman, 455, 483, 501, 511 Sirajudeen, S., 157 Sowmia, B., 189 Srinivasan, S., 167 Sujatha, P., 339 Sulistyowati, Wiwit Apit, 221, 257 Suseendran, G., 31, 41, 189, 201, 237, 265, 435, 525, 547, 579 T Tabbakh, Thamer A., 425 Talib, M.N., 425, 435, 455, 465, 473, 483, 501 Thamizhvani, T.R., 229 Thirumalaikumari, T., 537 Thyagharajan, K.K., 73, 97 Tripura, Sajib, 145 V Vadivel, R., 1 Varun, T., 525 Verma, Sahil, 455, 465, 473, 483, 491, 501, 511 Vignesh, T., 73, 97 Vijaykumar, Hannah, 31 Vivekanandam, B., 383 Y Yahya-Imam, Munir Kolapo, 115, 127 Younis, Dhoha, 213 Yulianto, Agung, 257