Cognitive Informatics and Soft Computing: Proceeding of CISC 2020 981161055X, 9789811610554
This book presents best selected research papers presented at the 3rd International Conference on Cognitive Informatics
300 56 42MB
English Pages 680 [960] Year 2021
Editorial Board Members
Preface
Contents
About the Editors
A Hybrid Face Recognition Scheme in a Heterogenous and Cluttered Environment
1 Introduction
2 Related Work
3 Proposed Model
4 Dataset Descriptions
5 Result Analysis
6 Conclusion and Future Works
References
Smart Critical Patient Care System with Doctor and Bystander Support with Wireless Sensor Network Using IoT and Intelligent Recommender Algorithm
1 Introduction
1.1 Related Work
2 Problem Identification
3 Methodology
4 Result and Discussion
5 Conclusion and Future Work
References
IoT-Enabled Toxic Gas Detection and Alarming System Using Wireless Sensor Network with TAGDS Smart Algorithm
1 Introduction
1.1 Related Work
2 Problem Identification
3 Proposed Methodology
4 Results and Discussion
5 Conclusion
References
A Deep Neural Network Model for Effective Diagnosis of Melanoma Disorder
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 Results and Discussion
5 Conclusion and Future Scope
References
Sentiment Analysis and Evaluation of Movie Reviews Using Classifiers
1 Introduction
2 Literature Review
3 Proposed Model
4 Result Analysis
5 Conclusion
References
Risk Factors Analysis for Real Estate Price Prediction Using Regression Approach
1 Introduction
2 Related Work
3 Proposed Work
4 Result and Analysis
4.1 Implementation of Multiple Regression
4.2 Implementation of Stepwise Regression
4.3 Implementation of Support Vector Regression
5 Parameter for Performance Analysis
6 Conclusion
References
A Support Vector Machine Approach for Effective Bicycle Sharing in Urban Zones
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Data Pre-processing
4 Proposed Prediction Model
5 Conclusion and Future Work
References
Biosensor for Stress Detection Using Machine Learning
1 Introduction
1.1 Brief About Biosensors
1.2 Objectives of the Study
1.3 Applications of Biosensors and Types of Biosensors
1.4 Advantages and Disadvantages of Biosensors
2 Stress Detection Using Biosensors and Other Applications: Literature Review
2.1 Stress Detection: Aspects and Its Use
2.2 Related Work and Case Studies About Stress Detection Using Biosensors
3 Machine Learning Model Used in Biosensor Design
3.1 Data Pre-processing
3.2 Feature Extraction and Dimension Reduction
3.3 Feature Subset Selection
4 Various Challenges of Using ML in Biosensor Design
5 Conclusion and Future Work
References
An Accurate Automatic Traffic Signal Detector Using CNN Model
1 Introduction
2 Literature Survey
3 Dataset Description
4 Proposed Work/model with Description
4.1 Preparing the Training Dataset
4.2 Proposed Machine Learning Model
5 Result and Analysis
6 Conclusion
References
Classification of Arrhythmia Through Heart Rate Variability Using Logistic Regression
1 Introduction
2 Materials and Methods
2.1 ECG Database
2.2 Plotting
2.3 Preprocessing
2.4 Thresholding
2.5 Peak Points Detection in Ecg Signal
2.6 Feature Extraction
2.7 HRV Analysis
3 Classification
4 Conclusion
References
U-INS: An Android-Based Navigation System
1 Introduction
2 Related Works
3 Proposed System
4 Result and Discussion
5 Conclusion and Future Work
References
LSTM-Based Cardiovascular Disease Detection Using ECG Signal
1 Introduction
2 Research Gap, Motivation and Objectives
2.1 Research Gap
2.2 Motivation
2.3 Objective
2.4 Organization
3 Source and Details of the Data
4 Development of the Proposed LSTM Detection Method
5 Simulation-Based Experimental Results and Discussion
6 Conclusion
References
Network Intrusion Detection Using Genetic Algorithm and Predictive Rule Mining
1 Introduction
2 Related Work
3 Intrusion Detection System
3.1 Drawbacks of the Existing System
4 Proposed Work: IDS Using Genetic Algorithm and Predictive Rule Mining
4.1 NSL-KDD Dataset
4.2 Feature Selection and Predictive Rule Mining
4.3 Data Representation
4.4 Evaluation Function
4.5 Genetic Operators
5 Implementation and Testing
6 Results and Discussion
6.1 Comparative Analysis
7 Conclusion and Future Work
References
A Comparison of Different Methodologies for Predicting Forest Fires
1 Introduction
2 Related Work
3 Methodology
4 Comparison of Results
5 Conclusion and Future Scope
References
Isolated Converters as LED Drivers
1 Introduction
2 Literature Survey
3 Specifications of LED Down Light
4 Mathematical Modeling of Flyback Converter
4.1 Calculation of Inductance and Capacitance
5 Closed Loop Operation
6 Mathematical Modeling of Forward Converter
6.1 Calculation of Inductance and Capacitance
7 Closed Loop Operation of Forward Converter
8 Results and Conclusion
9 Recommendation for Further Research
References
Fractional Order Elliptic Filter Implemented Using Optimization Technique
1 Introduction
1.1 Literature Review
1.2 The Objective of the Proposed Work
2 Optimization of Proposed Filter
3 Sensitivity and Stability Analysis
4 Simulink Simulations
5 Comparative Analysis
6 Analog Realization of Proposed Filter
7 Conclusion
References
Energy-Efficient MPLS-MANET Using Ant Colony Optimization and Harmony Search Algorithm
1 Introduction
2 Literature Review
3 Multi-Protocol Local Switching
4 ACO–HSA Methodology
4.1 Deployment of Routers
4.2 Optimal Paths Selection Using Ant Colony Optimization
4.3 Path Optimization Using Harmony Search Algorithm
4.4 Derivation of Fitness Function
5 Results and Discussion
5.1 Performance Analysis
5.2 Comparative Analysis
6 Conclusion
References
Performance Evaluation of Novel Feature Selection Method for Classification of Diabetic Drugs Based on Twitter Data Using SVM Algorithm
1 Introduction
2 Related Works
3 Feature Selection: An Overview
3.1 Genetic Algorithm (GA)
3.2 Particle Swarm Optimization (PSO)
3.3 Glowworm Swarm Optimization (GSO) Algorithm
4 Classification: SVM
5 Results and Discussions
5.1 Dataset
5.2 Experiments and Results
5.3 Evaluation Accuracy
5.4 Time Analysis
5.5 Summary of Related Articles with Social Media, Feature Selection and Classification Algorithm
6 Conclusion
References
Enhancing Periodic Storage Performance in IoT-Based Waste Management
1 Introduction
2 Related Works
3 Methodology and Materials
3.1 Block Diagram
3.2 Hardware Components
3.3 Software Tools
3.4 Interface
3.5 K-Means
3.6 Optimizing Periodic Clean-Ups and Dustbin Placement
4 Results and Analysis
4.1 Standard Deviation
4.2 Range
4.3 Levels of Result
5 Conclusion
References
Oil Spill Detection and Confrontation Using Instance Segmentation and Swarm Intelligence
1 Introduction
1.1 Preface
1.2 Problem Definition
2 Literature Survey
3 Methodology
3.1 Design
3.2 Implementation
3.3 Specifications
3.4 Working
4 Results
5 Conclusion
References
Impact of a Parameter Selection Using eARIMA and Ensemble by SKMC in Time Series Data Analysis
1 Introduction
2 Literature Review
3 Time Series Analysis
4 Methodology
4.1 eARIMA Weightage Index
4.2 Segment K-Means Cluster Algorithm
5 Result and Discussion
6 Conclusion
References
Secure Transfer of Images Using Pixel-Level and Bit-Level Permutation Based on Knight Tour Path Scan Pattern and Henon Map
1 Introduction
2 Related Work
2.1 Knight Tour Path
2.2 Mutation Rule
2.3 Henon Map
2.4 Tent Map
2.5 Knight Travel Path Based Pixel Permutation
3 Proposed Method
4 Performance Analysis of Proposed Scheme
5 Discussion
6 Conclusion
References
Performance Analysis of Irregular Shaped MEMS Switch with Gold and Aluminum as Composite Cantilever Beam Material
1 Introduction
2 Switch Design and Description
3 Simulation Results
3.1 Effect of Varying Irregular Shaped Cantilever Thickness on the Pull-in Voltage
3.2 Effect of Varying Cantilever Length Between the Upper Cantilever and Bottom Electrode on the Pull-in Voltage
3.3 Effect of Varying Airgap on the Pull-in Voltage
3.4 Effect of Perforations on Pull-in Voltage
4 Conclusion
References
Gesture-Controlled System Using Convolutional Neural Network
1 Introduction
2 Literature Survey
3 System Model
3.1 Methodology
3.2 Flowchart
3.3 Proposed Work
4 Applications
5 Results and Discussion
6 Conclusion
References
A Survey on Security and Safety in Vehicular Ad hoc Networks (VANETs) Cloud
1 Introduction
2 VANET Cloud Architecture
3 Application of VANET Cloud
4 Issues in VANET Cloud
4.1 Architecture Creation of VANET Cloud
4.2 Issues in Privacy and Security of VANET Cloud
4.3 Issues in Policy and Operational Management
5 Security Challenges in VANET Cloud
6 Safety Challenges in VANET Cloud
7 Discussion
8 Conclusion
References
Overview and Analysis of RPL Protocol Objective Functions
1 Introduction
2 Related Works
3 An Overview of RPL
3.1 Control Messages
3.2 Objective Functions
4 Experimental Analysis
5 Conclusion
References
Implementation of Low-Power High-Speed Clock and Data Recovery
1 Introduction
1.1 Theory of CDR
1.2 Code Mapping of 8b/10b
1.3 Details of Running Disparity
1.4 Transmit Serial Driver and Data
1.5 Clock and Data Recovery Unit
1.6 Receiver Bit Analyzer and Parallel Data Conversion Unit
1.7 Control and Timing Unit
1.8 Phase-Locked Loop (PLL)
2 Design Implementation
2.1 Programming FPGA Device and Tools Used Details
2.2 Features f Low-Power High-Speed Clock and Data Recovery
2.3 SERDES Links
3 Test Result and Discussion Details
4 Conclusion and Future Scope
References
Unit Testing of Speech Recognition over Mobile Channels
1 Introduction
2 Project Methodology
3 Results and Discussion
4 Conclusion
References
Classification of Fresh Vegetables Through Deep Learning and Neural Networks
1 Introduction
2 Project Methodology
3 Results and Discussion
4 Conclusion
References
Formal Techniques for Simulations of Distributed Web System Models
1 Introduction
2 Distributed Web System
3 System Architecture
3.1 Distribute Computing
4 Web System Models
4.1 Queuing Theory
4.2 Petri Nets
4.3 Timed-Colored Petri Nets
4.4 Proposed Models
5 Parallel and Distributed Calculations
5.1 Comparison of Queues Length for CSIM and TCPN Models
5.2 Comparison of Response Time for CSIM and TCPN Models
5.3 Conclusions After Simulations
5.4 Models Verification
6 Conclusions
References
Framework Towards Higher Data Privacy by Novel Data Integrity Scheme
1 Introduction
2 Related Work
3 Problem Description
4 Proposed Methodology
5 Proposed Methodology
6 Result Discussion
7 Conclusion
References
Big Data Security Using IoT-Based Smart Grid Communications
1 Introduction
2 Big Data Analytics Safety and Energy Issues IoT-Based Smart Grid Technology Issues
2.1 Big Data Analytics, and Smart Grid Applications
3 Performance Evaluation
4 Current Strategies for Development in the IoT Smart Grid
5 Conclusion
References
A Survey of Memristors and Its Applications
1 Introduction
2 Memristors
2.1 Historical Background
2.2 Unit of Memristor (Memristance)
2.3 Hysteresis Property of Memristors
3 Motivation Toward Memristors
4 Memristor Models
4.1 Model 1 Linear Ionic Drift Model
4.2 Model 2 Nonlinear Ion Drift Model
4.3 Simmons Tunnel Barrier Model: More Accurate Model in [26]
4.4 TEAM Model
5 Applications of Memristor
5.1 Memristors in Analog Circuits
5.2 Memristors to Implement Crossbar Arrays
5.3 Memristors in Communication
5.4 Memristor-Based Security Application
5.5 Memristor as Programmable Threshold Gate
5.6 Memristor as Synapse in Artificial Neural Networks
5.7 Memristor-Based Cellular Nonlinear Neural Networks (CNN)
5.8 Memristor for Chaos Generation Application
5.9 Memristor for Data Intensive Applications
5.10 Memristor Crossbars for Analog Computing in Three-Dimensional Space
5.11 Memristor for Biomedical Signal Processing
5.12 Memristors for Low-Power Self-rectifying High Density Crossbar Application
5.13 Memristor as Emulator
5.14 Memristors in Unsupervised Learning
5.15 Memristors Working as Perceptrons
5.16 Memristors in Cam Cell
5.17 Memristors for Neurorobotics
6 Conclusion
References
Obscuring of Data Leakage in Static Memory Cell and Optimization of WRITE Power
1 Introduction
2 Background
3 Static Memory
3.1 READ Operation
3.2 WRITE Operation
3.3 Precharge and Equalization Circuit
4 Optimizing Static Memory Performance
4.1 8 T SRAM READ Operation
4.2 8 T SRAM WRITE Operation
4.3 Performance Characteristics of SRAM
5 Results and Discussion
5.1 Power Calculations
5.2 Simulation Results
6 Conclusion
References
SVMBPI: Support Vector Machine-Based Propaganda Identification
1 Introduction
2 Related Work
3 Methodology
3.1 Data Collection
3.2 Annotation
3.3 Preprocessing
3.4 Feature Engineering
3.5 Classification
4 Experimental Results
5 Conclusion
References
Joint Reduction of Sidelobe and PMEPR in Multicarrier Radar Signal
1 Introduction
1.1 Envelope of OFDM Signal
1.2 PMEPR
1.3 Frequency Spectrum
2 Methods
3 Results
3.1 Comparison
4 Conclusion
References
Own HPC Cluster Based on Virtual Operating System
1 Introduction
2 Virtual Machines
3 HPC Cluster with Open MPI
3.1 PelicanHPC
3.2 Open MPI
4 Parallel and Distributed Calculations
5 Construction of the Environment
5.1 Virtual Root Node Configuration
5.2 Running Root Node
5.3 Configuring Persistent Storage Shared Within Cluster
5.4 Troubleshooting Problem with SSH Config File
5.5 Connecting to Virtual Machine via SSH
5.6 Virtual Computational Nodes Configuration
5.7 Configuring and Running Virtual Cluster
5.8 Managing Virtual Cluster When not in Use
6 Practical Usage of the Virtual Cluster—Performance Tests
7 Summary
References
An Efficient Technique for Image Captioning Using Deep Neural Network
1 Introduction
2 Motivation and Discussion
3 Data Preparation
4 Neural Network Architecture
4.1 Merge Model
4.2 Long Short-Term Memory (LSTM)
4.3 Model Designing
5 Model Training
6 Model Evaluation
7 Results
7.1 Training Loss
7.2 BLEU Performance
7.3 Testing on a Random Image
8 Conclusion
References
Secured Two-Layer Encryption and Pseudorandom-Based Video Steganography into Cipher Domain Using Machine Learning Technique
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Degree Ciphering the Frame
3.2 Data Hiding Algorithm
3.3 Data Retrieval
3.4 Dimensional Map
3.5 Two-Dimensional Map
4 Results
4.1 Training Set
4.2 Feature Values
4.3 Histogram
4.4 Information Entropy
4.5 MSE and PSNR
5 Conclusion
References
Automation Using Brain Signals and Machine Interface
1 Introduction
2 Review of Past Work
3 System Implementation
3.1 System Design of Synchronous BCI
3.2 Hardware Implementation
3.3 Software Utilized
4 Methodology
5 Implementation
6 Results and Discussion
6.1 Robot Control with BMI
6.2 Switching Home Appliances with BMI:
7 Conclusion
References
Design and Implementation of High-Speed Low-Power Carry Select Adder
1 Introduction
2 Literature Review
3 Adders
3.1 Ripple Carry Adder
3.2 Carry Look Ahead Adder
3.3 Brent Kung Adder
3.4 Regular Linear Brent Kung Carry Select Adder
3.5 Modified Square Root Brent Kung Carry Select Adder
4 Simulation Results and Comparison
5 Application
6 Conclusion
References
Mixed-Mode Time Delay Circuit Using CFOA
1 Introduction
2 Padé Approximation
3 Block Diagram
4 Implementation of the Proposed Circuit
5 Simulation Results
6 Hardware Results
7 Conclusion
References
Comparison of Clustering Algorithms Using KNIME Tool
1 Introduction
2 Literature Survey
3 Clustering Algorithms
4 KNIME
5 Proposed Work
5.1 Email Data from Kaggle
5.2 Survey Data on Wikipedia Usage by University Teachers
6 Implementation
7 Results
7.1 Email Data from Email Receiver File
7.2 Survey Data on Wikipedia Usage by University Teachers
8 Conclusion
References
Digital Video Watermarking Based on Hybrid Algorithm and Attack Detection
1 Introduction
2 Recent Work for Research
3 Preliminaries
3.1 Discrete Cosine Transform (DCT)
3.2 Lifting Wavelet Transform (LWT)
3.3 Particle Swarm Optimization (PSO)
4 Proposed Video Watermarking Algorithm
4.1 Altering Video into Frames
4.2 Watermark Embedding
4.3 Watermark Extraction
4.4 Reconvert Frames Into a Video
5 Results and Discussions
5.1 Robustness and Imperceptibility Test
5.2 Imperceptibility Analysis
6 Conclusion and Future Work
References
Trash Smart: An Innovative Automated Waste Segregating Trash Can
1 Introduction
2 Existing Technology
3 Proposed Methodology
4 Implementation of Trash Smart
4.1 Image Processing
4.2 System to Server Communication
5 Working of Trash Smart
5.1 Garbage Classification
5.2 Raspberry pi Processing
5.3 Database Updating
6 Results and Analysis
7 Classification
References
Protection of DDoS Attacks at the Application Layer: HyperLogLog (HLL) Cardinality Estimation
1 Introduction
1.1 HyperLogLog Algorithm
1.2 Vulnerabilities of HyperLogLog
1.3 Protecting HyperLogLog
2 Study Work
3 Proposed Method
3.1 Salted and not Salted Protection (SNS)
4 Results and Discussion
4.1 Dataset
4.2 Detection of Application Layer DDoS Attacks:
4.3 Manipulating HLL Estimates
4.4 SNS Protection
5 Conclusion
References
Spam Reduction in Smartphones for Arranging Tweets as Spam and No Spam Using Classification Algorithm in Machine Learning
1 Introduction
2 Related Works
3 Proposed Work System Architecture
3.1 Dataset
3.2 Feature Selection
3.3 Pre-processing
3.4 Model Selection
3.5 Training
3.6 Applying Algorithm
3.7 Prediction
3.8 Accuracy
4 Methodology
5 Results
6 Conclusion
References
Support Vector Machine-Based Reliable Route Formation in Mobile Wireless Sensor Network
1 Introduction
2 Related Work
3 Support Vector Machine-Based Reliable Route Formation
4 Simulation Analysis
5 Conclusion
References
Hybrid Ant Colony and Cuckoo Search Algorithm for Intelligent Routing in Wireless Sensor Network
1 Introduction
2 Related Work
3 Proposed System
3.1 CS Phase
3.2 ACO Phase
3.3 Optimal Path Selection Phase
4 Performance Evaluation
4.1 Packet Obtained Rate
4.2 Packet Drop Rate
4.3 Latency
4.4 Throughput
4.5 Average Energy
5 Conclusion
References
GIS-Based Agri-Waste Assessment for the Optimal Power Generation in the Ludhiana District, Punjab, India
1 Introduction
2 GIS Data Assessment
3 Punjab Profile
3.1 Punjab Map
3.2 Agriculture in Punjab
3.3 Biomass Categorization
4 Methodology and Calculations
4.1 Agriculture Residue Generation
4.2 Agriculture Residue Consumption
4.3 Overall Surplus Biomass Accessibility
5 Power Generation Potential
5.1 Power Conversion Factor
5.2 Power Generation in Punjab State
6 Analysis of Cost
6.1 Surplus Biomass in Ludhiana District
6.2 Cost of Fuel in District Ludhiana
6.3 Transportation Cost
7 Results and Discussion
8 Conclusion
References
Design and Analysis Performances of CPW-Fed Fractal Antenna for UWB Applications
1 Introduction
2 Coplanar Waveguide
3 Antenna Design
4 Simulation Results
4.1 Transmission Line Feed Antenna
4.2 Grounded Cpw Fed Antenna
5 Measurement Results
6 Conclusion
References
Reduction of PMEPR in Multicarrier Signals Using CBC Approach
1 Introduction
2 The Multitone Signal Structure
2.1 Mathematical Expression of MCPC Signal
2.2 Performance of MCPC Signal Based on P4 Codes
2.3 Peak-to-Mean Envelope Power Ratio of Multitone Signal
2.4 Ambiguity Function of MCPC Signal
3 MCPC Based on Complementary Block Coding
3.1 Algorithm to Implement MCPC Signal Based on Complementary Block Coding
4 Performance Results
5 Conclusion
References
A Real-Time Fuzzy Logic Based Accident Detection System in VANET Environment
1 Introduction
2 Related Work
3 Proposed System Model
3.1 Selection of Sensors and Other Hardware
3.2 Processing of Sensor Data
4 Conclusion
References
Currency Exchange Prediction for Financial Stock Market: An Extensive Survey
1 Introduction
2 State of the Art
3 Overall Analysis
4 Summary and Feature Scope
References
Predicting Stock Market Movements: An Optimized Extreme Learning Approach
1 Introduction
2 Methodologies for Prediction
3 Proposed Prediction Model for Stock Market Analysis
4 Experimentation and Result Analysis
5 Conclusion and Future Scope
References
Extensive Research on Adaptive Intelligence Cognitive and Machine Learning for Computing Technology
1 Introduction
2 Five Key Atributes of Congitive Computing Systems
3 Differences Between Congniti Computing and Ai
4 Application of Congitive Computing
5 Paradigm Shift Towards Cognitive Computing
6 Conclusion
References
A Highly Linear 2.4 GHz LNA with + 20 dBm IIP3 Operating at 600 mV Supply Voltage in 180 nm CMOS Technology
1 Introduction
2 Summary of Modified Derivative Superposition Technique
3 Proposed Method with Supplementary Path to Reduce the Nonlinearity
4 Design of Transistor to Enhance IIP3
5 Conclusion
References
An Optimized Area Efficient Implementation of FIR Filter Using Shift Add Multiplier with Carry Look Ahead Adder
1 Introduction
2 Proposed Methods
2.1 Vedic Multiplier
2.2 Ripple Carry Adder (RCA)
2.3 Shift Add Multiplier
2.4 Carry Look Ahead Adder (CLAA)
3 Results
4 Comparison Results
5 Conclusion
References
Advances in Machine Learning and Deep Learning Algorithms and Their Applications
1 Introduction
2 Difference Between Machine Learning and Deep Learning
3 Machine Learning Algorithms
3.1 Linear Regression
3.2 Logistic Regression
3.3 Naïve Bayes
3.4 Support Vector Machines
3.5 Principal component analysis (PCA)
3.6 Linear Discriminant Analysis (LDA)
4 Deep Learning Algorithms
4.1 Convolutional Neutral Network (CNN)
4.2 GoogLeNet Inception Model (GoogLeNet)
5 Conclusion
References
Feature Ranking-Based Prediction of Climatic Parameters for Enhancement of Agricultural Production: A Case Study in Rice Production
1 Introduction
2 Literature Survey
3 Proposed Method
4 Results and Discussion
5 Conclusion
References
Internet of Things and Blockchain-Based Demand Side Management of Smart Microgrids
1 Introduction
2 Energy Management of Microgrid
2.1 Smart Grid
2.2 Microgrid
2.3 Internet of Things (IOT)
3 Implementation of Microgrid Energy Management
4 Implementation of Block Chain in Microgrids
5 Conclusion
References
Regenerative Braking Control Methods of Interior Permanent Magnet Synchronous Motor for Electric Vehicles
1 Introduction
2 Mathematical Model of PMSM
3 Regeneration with MTPA
4 Regeneration with DTC-SVM
5 EV Modelling
6 Simulation Results
7 Conclusion
References
Data-Driven Power System Stability Analysis for Enhanced Situational Awareness
1 Introduction
2 Transient Stability Assessment for Situational Awareness
3 Proposed Anomaly Detection Architecture
4 Simulation Studies
4.1 Transient Stability Index Calculation
5 Results and Discussion
6 Conclusion
References
Optimization of Excitation and Commutation Angles of Switched Reluctance Motor Using Micro-Genetic Algorithm
1 Introduction
2 Mathematical Modeling of SRM
2.1 Symmetrical Converter
2.2 Flux Calculator
2.3 Current Look-Up Table
2.4 Torque Look-Up Table
3 Optimization Function
4 Genetic and Micro-Genetic Algorithms
4.1 Genetic Algorithm (GA)
4.2 Micro-Genetic Algorithm. (ΜGA)
5 Proposed Micro-Genetic Algorithm
5.1 Step-by-Step Process
5.2 Escaping Local Maxima
5.3 1 + [n/2 Random] Bit Mutation
5.4 Comparison Between 1 + [ n2Random ] Mutation and Dual Bit Mutation
6 Comparison and Validation of Results
6.1 Comparison
6.2 Limitation
7 Conclusion
References
Independent Power Management for Interlinked AC-DC MicroGrids
1 Introduction
2 Control of DC and AC Miniature Grid
2.1 Hybrid AC/DC Microgrid Configuration
3 Performance Validation
4 Conclusion
References
Underground Cable Fault Detection Using Internet of Things
1 Introduction
2 Design Methodology
3 Proposed Works and Result Discussions
3.1 Working Methodology
3.2 Simulation Results
4 Conclusion
References
Weather Data Visualization Using IoT and Cloud
1 Introduction
2 Literature Survey
3 Proposed System
3.1 Raspberry Pi
3.2 Humidity and Temperature Sensor (DHT11)
3.3 Barometric Pressure Sensor
3.4 ThingSpeak
4 Result and Discussion
5 Conclusion
References
Efficient Utilization of an IoT Device Using Bidirectional Visitor Counter
1 Introduction
2 Related Work
3 Proposed System
4 Description
5 Software and Hardware Implementation
6 Future Scope
7 Conclusion
References
Spammers Detection on Online Social Media Based on Machine Learning
1 Introduction
2 Related Work
3 Architecture Diagram
4 Existing System
5 Proposed System
6 Conclusion
References
Design of Cost-Effective Wearable Cardiac Monitoring System for Early Prediction
1 Introduction
2 Related Work
3 Existing System
4 Proposed System
4.1 Working of Proposed System
4.2 Methodology
4.3 Implementation
5 Application Snapshots
6 Results and Discussion
7 System Architecture
8 Conclusion
References
Innovative Gesture-Based Automation System for Home Appliances
1 Introduction
2 Related Works
3 Existing System
3.1 Disadvantages of Existing System
4 Proposed Work
4.1 Advantages of the Proposed System
5 Modules Description
5.1 Data Collection Process
5.2 Hand Segmentation
5.3 Training with the GestureNet
5.4 Optimization
6 Conclusion
References
Detecting Potholes Using Image Processing Techniques and Real-World Footage
1 Introduction
2 Related Work
2.1 Automatic Pothole Detection
2.2 Smart Sensing of Potholes and Obstacles in Automotive Applications Using Internet of Things
2.3 Detection, Notification of Potholes, Humps Using Android Application
3 Existing System
3.1 Disadvantages of Existing System
4 Proposed System
4.1 Advantages of Proposed System
5 System Architecture
6 Implementation
6.1 Canny Edge Detection
6.2 Gaussian Filter
6.3 Otsu’s Method
7 Results
8 Conclusion
References
Machine Learning Methods of Rainfall Prediction in Weather Derivatives
1 Introduction
2 Related Work
2.1 Indian Summers Monsoon Rainfall Forecasting Using Time Series Data
2.2 An Extensive Evaluation of Seven Machine Learning Methods for Rainfall Prediction with Weather Derivatives
2.3 A Hybrid Model for Statistical Downscaling of Daily Rainfall
3 Existing System
4 Proposed System
5 System Architecture
6 Implementation
6.1 Data Collection
6.2 Data Preparation
6.3 Training and Prediction
7 Results
8 Conclusion
References
A Smart Monitoring Industrial IOT Devices from Outsider Threats
1 Introduction
2 Literature Survey
3 Module Description
4 Conclusion
References
Auction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis
1 Introduction
2 Related Work
2.1 D2D-LB versus Smart User Association
2.2 D2D-LB versus Data Offloading: Technical Differences
2.3 Online Competitive Designs versus Similar Solutions
3 Proposed System
3.1 Existing System
3.2 Proposed System
4 Interface Design
5 The Auctioneer Interface
6 Results
7 Conclusion
References
Estimation of Vehicle Count, Class, and Speed on Highways Using a Computer Vision-Based Technique
1 Introduction
1.1 Limitations of Existing Work
2 Phases in Proposed System
2.1 Advantages of Proposed System
3 System Design and Implementation
3.1 Pre-processing Phase
3.2 Blob Detection and Tracking
3.3 Vehicle Counter
3.4 Speed Tracker
3.5 Vehicle Class Categorization
4 Experimental Results and Discussions
5 Conclusion
References
Heuristic Detection of Pharma Temperature Anomalies Using IOT and Machine Learning Techniques
1 Introduction
2 Related Work
2.1 Arduino-Based Hardware
2.2 General Purpose Input and Output (GPIO)
3 Proposed Methodology
4 Results and Discussion
5 Conclusion
References
A Machine Learning Approach to Predict and Classify the Levels of Autism Spectrum Disorder
1 Introduction
2 Related Work
3 Existing System
4 Screenshots
5 Conclusion
References
Survey of Cervical Cancer Prediction Using Machine Learning
1 Introduction
2 Related Works
2.1 Drawbacks
3 Methodology
3.1 The Used Neural Network
3.2 Removing Unnecessary Neurons
3.3 NEW Neural Network
3.4 Gaussian Mixture Model
3.5 Local Ternary Pattern
3.6 Gray Level Co-occurrence Matrix
3.7 Morphological Process
4 Results and Discussion
References
Author Index
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Advances in Intelligent Systems and Computing 1317
Pradeep Kumar Mallick Akash Kumar Bhoi Gonçalo Marques Victor Hugo C. de Albuquerque Editors
Cognitive Informatics and Soft Computing Proceeding of CISC 2020
Advances in Intelligent Systems and Computing Volume 1317
Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Nikhil R. Pal, Indian Statistical Institute, Kolkata, India Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba Emilio S. Corchado, University of Salamanca, Salamanca, Spain Hani Hagras, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK László T. Kóczy, Department of Automation, Széchenyi István University, Gyor, Hungary Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Jie Lu, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil Ngoc Thanh Nguyen , Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland Jun Wang, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Indexed by DBLP, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST). 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/11156
Pradeep Kumar Mallick · Akash Kumar Bhoi · Gonçalo Marques · Victor Hugo C. de Albuquerque Editors
Cognitive Informatics and Soft Computing Proceeding of CISC 2020
Editors Pradeep Kumar Mallick School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar, Odisha, India Gonçalo Marques Polytechnic of Coimbra ESTGOH Rua General Santos Costa Oliveira do Hospital, Portugal
Akash Kumar Bhoi Department of Electrical and Electronics Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar, India Victor Hugo C. de Albuquerque Graduate Program on Teleinformatics Engineering Federal University of Ceará Fortaleza/CE, Brazil Graduate Program on Telecommunication Engineering, Federal Institute of Education Science and Technology of Ceará Fortaleza/CE, Brazil
ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-16-1055-4 ISBN 978-981-16-1056-1 (eBook) https://doi.org/10.1007/978-981-16-1056-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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
Editorial Board Members
Chief Patrons Dr. Manmath Kumar Biswal, Hon’bl Founder-Chairman, BCET Group of Institutions Mrs. Subrata Biswal, Hon’bl Secretary FMECT
Patrons Dr. Sriman Srichandan, Director, BCET, Balasore Dr. Sibendra Kumar Ghadai, Principal, BCET, Balasore Mr. Abhishek Biswal, Trustee, FMECT
General Chairs Dr. Goo Soo Chae, Professor, Baekseok University, South Korea Dr. Valentina Emilia Balas, Professor, Aurel Vlaicu University of Arad, Romania Dr. Gonçalo Marques, Polytechnic Institute of Guarda, Portugal Dr. Prasanta K. Ghosh, Department of ECE, Syracuse University Engineering and Computer Science, USA
Honorary Conference Chair Dr. L. C. Jain, Professor, Founder KES International and adjunct Professor University of Canberra, Australia
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Editorial Board Members
Program Chairs Dr. Sriman Srichandan, Director, BCET, Balasore Dr. Pradeep Kumar Mallick, Assoc. Professor, SoCE, KIIT University, Bhubaneswar
Program Co-chairs Prof. Pritiranjan Bijayasingh, Vice-Principal BCET, Balasore Dr. Sandeep Kumar Satapathy, Department of CSE, VIT University, Chennai Dr. Akash Kumar Bhoi, Sikkim Manipal University, Sikkim Prof. Stobak Dutta, BCET, Balasore
Organizing Chair Prof. Jyoti Ranjan Rout, Professor and HOD-CSE, BCET, Balasore
Reviewer Board Dr. Sachidananda Dehury, F. M. University, Odisha Dr. Debahuti Mishra, ITER, SOA University, Odisha Dr. Bunil Kumar Balabantaray, NIT Meghalaya Dr. Bhabani Sankar Prasad Mishra KIIT University, Odisha Dr. Santos Das, NIT Rourkela, Odisha Dr. Preetisudha Meher, NIT Arunachal Pradesh Dr. Gitosree Khan, BPPITM, Kolkata Dr. Sachikanta Das, DRIEMS, Cuttack Dr. Saptarsi Goswami, Bangabasi Morning College, University of Calcutta Dr. Sandip Vijay, ICFAI University, Dehradun Dr. Shruti Mishra, Department of CSE, VIT University, AP Dr. Sashikala Mishra, IIIT, Pune Dr. Ebrahim Aghajari, Islamic Azad University of Ahvaz, Iran Dr. Manoj Kumar Mishra, KIIT University, Odisha Dr. Mohit Ranjan Panda, KIIT University, Odisha Dr. Amiya Ranjan Panda, KIIT University, Odisha Dr. Sudhakar Mande S, DBIT, Mumbai, India Dr. Anirban Mitra Amity, University of Kolkata Dr. Neelamadhab Padhy, GIET University, Gunupur, Odisha
Editorial Board Members
International Advisory Committee Dr. Atilla Elçi, Aksaray University, Turkey Dr. Hongyan Yu, Shanghai Maritime University, Shanghai Dr. Benson Edwin Raj, Fujairah Women’s College Fujairah, UAE Dr. Mohd. Hussain, Islamic University, Madina, Saudi Arabia Dr. Avinash Konkani, University of Virginia Health System, Virginia, USA Dr. Yu-Min Wang, National Chi Nan University, Taiwan Dr. Ganesh R. Naik, University of Technology, Sydney, Australia Dr. Steve S. H. Ling, University of Technology, Sydney, Australia Dr. Hak-Keung Lam, King’s College London, UK Dr. Frank H. F. Leung, Hong Kong Polytechnic University, Hong Kong Dr. Yiguang Liu, Sichuan University, China Dr. Abdul Rahaman, Debre Tabor University, Ethiopia Dr. Bandla Srinivasa Rao, Debre Tabor University, Ethiopia Dr. Ch. Suresh Babu, King Khalid University (KKU), K.S.A
National Advisory Committee Dr. Sabyasachi Patnaik, F. M. University, Odisha, India Dr. Kishore Sarawadekar, IIT-BHU, Varanasi, India Dr. T. Kishore Kumar, NIT Warangal, Warangal, AP, India Dr. Anil Kumar Vuppala, IIIT-Hyderabad, India Dr. Ganapati Panda, IIT-Bhuaneshwar, Odisha Dr. Inderpreet Kaur, Chandigarh University Dr. R. Gunasundari, PEC, Puducherry, India Dr. Ragesh G., SCE, Kuttukulam Hills, Kottayam, India Dr. Debashish Jena, NITK, India Dr. N. P. Padhy, IIT-Roorkee Dr. Subhendu Pani, OEC, Odisha Dr. Punal M. Arabi, ACSCE, Bangalore Dr. Mihir Narayan Mohanty, ITER, SOA, University Dr. K. Krishna Mohan, Professor, IIT, Hyderabad Dr. G. N. Srinivas, Professor, JNTUCE, Hyderabad Dr. M. Padmavathamma, Professor, SVUCCMIS, S. V. U. Tirupati Dr. S. Basava Raju, Regional Director, VTU, Karnataka Dr. R. V. Raj Kumar, Professor, IIT Kharagpur Dr. V. V. Kamakshi Prasad, COE, JNTUCE, Hyderabad Dr. M. Surya Kalavathi, Professor, JNTUCE, Hyderabad Dr. A. Govardhan, Rector JNTUH, Hyderabad Dr. K. Siva Kumar, Professor, IIT, Hyderabad Dr. S. Soumitra Kumar Sen, IIT Kharagpur
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Dr. N. V. Ramana, VTU, Karnataka Dr. D. V. L. N. Somayajulu, Professor, NIT Warangal Dr. Atul Negi, Professor, HCU, Hyderabad Dr. P. Sateesh Kumar, Asst. Professor, IIT Roorkee Dr. M. Sashi, NIT Warangal
Organizing Committee Prof. Basanta Kumar Padhi, BCET, Balasore Prof. Surendra Nath Bhagat, BCET, Balasore Prof. Niharika Mohanty,BCET, Balasore Prof. Santosh Mahallik, BCET, Balasore Prof. Saumya Ranjan Sahu BCET, Balasore Prof. Nilachakra Dash, BCET, Balasore Prof. Jayanti Dash, BCET, Balasore Prof. Banaja Basini Rath, BCET, Balasore Prof. Madhusmita Dey, BCET, Balasore Prof. Shrabani Pramanik, BCET, Balasore Prof. Tapas Das, BCET, Balasore Mr. Subransu Pradhan, BCET, Balasore Debasish Das, BCET, Balasore
Editorial Board Members
Preface
Cognitive informatics is a cross-disciplinary analysis of cognition and information processing technology that explores the structures and processes of human knowledge processing in various engineering applications. Cognitive technology is being extensively used in addressing the prevalent underlying problems of information processing in various domains like artificial intelligence, data science, cognitive science, Internet of Things, philosophy, and life sciences through natural intelligence of the human brain. Cognitive informatics gives a comprehensive collection of key hypotheses and modern-day mathematical models in solving the real-time information processing challenges. Soft-computing is the upcoming technology in the computer science technology that tackles the real-time problems that are indistinct and unpredictable natured problems that a collection of robust and computationally efficient approaches that would yield a least cost optimal outcome. There are various techniques that are part of the soft computing technology that includes neural networks, evolutionary computing, swarm intelligence, fuzzy computing, chaos models, heuristic models and probabilistic reasoning. Soft-computing models extensively used various interdisciplinary domains for effective handing of the problem. This book comprises of selected papers of the 3rd International Conference on Cognitive Informatics and Soft Computing (CISC-2020) which was held at Balasore College of Engineering and Technology, Balasore, Odisha, India, from December 12 to 13, 2020. We would like to extend our thanks to the authors and their active participations during CISC-2020. Moreover, we would like to extend our sincere gratitude to the reviewers, technical committee members and professional from national and international forum for extending their great support during the conference. Bhubaneswar, India Majitar, India Oliveira do Hospital, Portugal Fortaleza, Brazil
Dr. Pradeep Kumar Mallick Dr. Akash Kumar Bhoi Dr. Gonçalo Marques Dr. Victor Hugo C. de Albuquerque
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Contents
A Hybrid Face Recognition Scheme in a Heterogenous and Cluttered Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. R. Khansama, R. Priyadarshini, S. K. Bisoyi, Pradeep Kumar Mallick, and R. K. Barik Smart Critical Patient Care System with Doctor and Bystander Support with Wireless Sensor Network Using IoT and Intelligent Recommender Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Praveen C. Menon, P. R. Bipin, G. K. Ragesh, and P. V. Rao IoT-Enabled Toxic Gas Detection and Alarming System Using Wireless Sensor Network with TAGDS Smart Algorithm . . . . . . . . . . . . . . Praveen C. Menon, P. R. Bipin, and P. V. Rao A Deep Neural Network Model for Effective Diagnosis of Melanoma Disorder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pradeep Kumar Mallick, Sushruta Mishra, Bibhu Prasad Mohanty, and Sandeep Kumar Satapathy Sentiment Analysis and Evaluation of Movie Reviews Using Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pradeep Kumar Mallick, Priyom Dutta, Sushruta Mishra, and Manoj Kumar Mishra
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Risk Factors Analysis for Real Estate Price Prediction Using Regression Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Piyush Ranjan and Sushruta Mishra
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A Support Vector Machine Approach for Effective Bicycle Sharing in Urban Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saurabh Suman, Sushruta Mishra, and Hrudaya Kumar Tripathy
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Biosensor for Stress Detection Using Machine Learning . . . . . . . . . . . . . . . Arijit Dutta, Hrudaya Kumar Tripathy, Arghyadeep Sen, and Luina Pani
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An Accurate Automatic Traffic Signal Detector Using CNN Model . . . . . Ankush Sinha Roy, Lambodar Jena, and Pradeep Kumar Mallick
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Classification of Arrhythmia Through Heart Rate Variability Using Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 K. Srikanth and Md. Ruhul Islam U-INS: An Android-Based Navigation System . . . . . . . . . . . . . . . . . . . . . . . . 125 Suprava Ranjan Laha, Sushil Kumar Mahapatra, Saumendra Pattnaik, Binod Kumar Pattanayak, and Bibudhendu Pati LSTM-Based Cardiovascular Disease Detection Using ECG Signal . . . . . 133 Adyasha Rath, Debahuti Mishra, and Ganapati Panda Network Intrusion Detection Using Genetic Algorithm and Predictive Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Utsha Sinha, Aditi Gupta, Deepak Kumar Sharma, Aarti Goel, and Deepak Gupta A Comparison of Different Methodologies for Predicting Forest Fires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Kajol R. Singh, K. P. Neethu, K. Madhurekaa, A. Harita, and Pushpa Mohan Isolated Converters as LED Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Sumukh Surya and R. Srividya Fractional Order Elliptic Filter Implemented Using Optimization Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Ashu Soni and Maneesha Gupta Energy-Efficient MPLS-MANET Using Ant Colony Optimization and Harmony Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 B. J. Ambika and M. K. Banga Performance Evaluation of Novel Feature Selection Method for Classification of Diabetic Drugs Based on Twitter Data Using SVM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 S. Radha Priya and M. Devapriya Enhancing Periodic Storage Performance in IoT-Based Waste Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 S. Pavan Kumar, D. Naveen Kumar, M. Nishanth, Omkar Shrenik Khot, and P. I. Basarkod Oil Spill Detection and Confrontation Using Instance Segmentation and Swarm Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Manjunath R. Kounte, T. M. Raghavendra Kashyap, P. Rahul, M. K. Ramyashree, and J. K. Riya
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Impact of a Parameter Selection Using eARIMA and Ensemble by SKMC in Time Series Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 S. Gokila, Thiyagarajan C., and S. Deepa Secure Transfer of Images Using Pixel-Level and Bit-Level Permutation Based on Knight Tour Path Scan Pattern and Henon Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Kiran, B. D. Parameshachari, and H. T. Panduranga Performance Analysis of Irregular Shaped MEMS Switch with Gold and Aluminum as Composite Cantilever Beam Material . . . . . 285 Vijay Mallappa Peerapur and Anilkumar V. Nandi Gesture-Controlled System Using Convolutional Neural Network . . . . . . 295 Deekshith Shetty, Clifton Dsouza, Shrishail Navi, G. Rahul, and Abdul Haq A Survey on Security and Safety in Vehicular Ad hoc Networks (VANETs) Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 B. Shivanand, Shrikant S. Tangade, Geetha D. Devanagavi, and Sunilkumar S. Manvi Overview and Analysis of RPL Protocol Objective Functions . . . . . . . . . . 321 S. N. Vikram Simha and Rajashekhar C. Biradar Implementation of Low-Power High-Speed Clock and Data Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 K. J. Kumar and A. Raganna Unit Testing of Speech Recognition over Mobile Channels . . . . . . . . . . . . . 347 Arvind Vishnubhatla Classification of Fresh Vegetables Through Deep Learning and Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Arvind Vishnubhatla Formal Techniques for Simulations of Distributed Web System Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Tomasz Rak Framework Towards Higher Data Privacy by Novel Data Integrity Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 G. Anil Kumar and C. P. Shantala Big Data Security Using IoT-Based Smart Grid Communications . . . . . . 391 R. Ganesh Babu and G. Glorindal A Survey of Memristors and Its Applications . . . . . . . . . . . . . . . . . . . . . . . . . 403 G. L. SumaLata and Abhishek Kumar Shrivastava
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Obscuring of Data Leakage in Static Memory Cell and Optimization of WRITE Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Gopala Krishna Pasumarty, N. V. Ganapathi Raju, and Sankararao Majji SVMBPI: Support Vector Machine-Based Propaganda Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Akib Mohi Ud Din Khanday, Qamar Rayees Khan, and Syed Tanzeel Rabani Joint Reduction of Sidelobe and PMEPR in Multicarrier Radar Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 L. J. L. Sujan, Vishwanath D. Telagadi, C. G. Raghavendra, B. M. J. Srujan, R. B. Vinay Prasad, B. D. Parameshachari, and K. L. Hemalatha Own HPC Cluster Based on Virtual Operating System . . . . . . . . . . . . . . . . 465 Tomasz Rak and Łukasz Schiffer An Efficient Technique for Image Captioning Using Deep Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Borneel Bikash Phukan and Amiya Ranjan Panda Secured Two-Layer Encryption and Pseudorandom-Based Video Steganography into Cipher Domain Using Machine Learning Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 D. R. Vinay, Jogesh V. Motawani, and J. Ananda Babu Automation Using Brain Signals and Machine Interface . . . . . . . . . . . . . . . 505 Vidyadhar S. Melkeri and Gauri Kalnoor Design and Implementation of High-Speed Low-Power Carry Select Adder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 Kummetha Deepthi, Pratheeksha Bhaskar, M. Priyanka, B. V. Sonika, and B. N. Shashikala Mixed-Mode Time Delay Circuit Using CFOA . . . . . . . . . . . . . . . . . . . . . . . 531 Anurag Sau, Pamandeep Singh Puri, and Aniket Gupta Comparison of Clustering Algorithms Using KNIME Tool . . . . . . . . . . . . 545 Archana Boob, Sonali Deshpande, and R. R. Shelke Digital Video Watermarking Based on Hybrid Algorithm and Attack Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 T. Nagajyothi and Amit Kr. Jain Trash Smart: An Innovative Automated Waste Segregating Trash Can . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 S. Subburaj, R. Jaisuriya, M. Pukale Akshay Kumar, and P. Girish
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Protection of DDoS Attacks at the Application Layer: HyperLogLog (HLL) Cardinality Estimation . . . . . . . . . . . . . . . . . . . . . . . . 595 Balarengadurai Chinnaiah Spam Reduction in Smartphones for Arranging Tweets as Spam and No Spam Using Classification Algorithm in Machine Learning . . . . . 605 M. Meena, S. Jayashree, and R. Rupa Support Vector Machine-Based Reliable Route Formation in Mobile Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 Azath Mubarakali Hybrid Ant Colony and Cuckoo Search Algorithm for Intelligent Routing in Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625 J. S. Praveen and T. Kumanan GIS-Based Agri-Waste Assessment for the Optimal Power Generation in the Ludhiana District, Punjab, India . . . . . . . . . . . . . . . . . . . 637 Harpreet Kaur Channi, Manjeet Singh, Yadwinder Singh Brar, Surbhi Gupta, and Arvind Dhingra Design and Analysis Performances of CPW-Fed Fractal Antenna for UWB Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 J. Premalatha and D. Sheela Reduction of PMEPR in Multicarrier Signals Using CBC Approach . . . . 669 D. Ashish, C. G. Raghavendra, B. R. Prajwal, B. D. Parameshachari, and K. L. Hemalatha A Real-Time Fuzzy Logic Based Accident Detection System in VANET Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 Anita Mohanty, Subrat Kumar Mohanty, and Bhagyalaxmi Jena Currency Exchange Prediction for Financial Stock Market: An Extensive Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 Asit Kumar Das, Debahuti Mishra, and Kaberi Das Predicting Stock Market Movements: An Optimized Extreme Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711 Asit Kumar Das, Debahuti Mishra, and Kaberi Das Extensive Research on Adaptive Intelligence Cognitive and Machine Learning for Computing Technology . . . . . . . . . . . . . . . . . . . . 721 Lakshmi Maka, V. D. Mytri, and Kiran Maka A Highly Linear 2.4 GHz LNA with + 20 dBm IIP3 Operating at 600 mV Supply Voltage in 180 nm CMOS Technology . . . . . . . . . . . . . . 731 D. Sharath Babu Rao and V. Sumalatha
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An Optimized Area Efficient Implementation of FIR Filter Using Shift Add Multiplier with Carry Look Ahead Adder . . . . . . . . . . . . . . . . . . 745 Sripathi Samyuktha, Chaitanya Duggineni, N. Swetha, and Hima Bindu Valiveti Advances in Machine Learning and Deep Learning Algorithms and Their Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 Kiran Maka, Lakshmi Maka, and S. Pazhanirajan Feature Ranking-Based Prediction of Climatic Parameters for Enhancement of Agricultural Production: A Case Study in Rice Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767 Sandeep Kumar Satapathy, Shruti Mishra, and Pradeep Kumar Mallick Internet of Things and Blockchain-Based Demand Side Management of Smart Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773 Leo Raju and V. Balaji Regenerative Braking Control Methods of Interior Permanent Magnet Synchronous Motor for Electric Vehicles . . . . . . . . . . . . . . . . . . . . . 785 P. R. Abhijith and S. R. Mohanrajan Data-Driven Power System Stability Analysis for Enhanced Situational Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799 Twinkle Thobias, A. Rathinam, B. Saravanan, and S. Senthilmurugan Optimization of Excitation and Commutation Angles of Switched Reluctance Motor Using Micro-Genetic Algorithm . . . . . . . . . . . . . . . . . . . 817 Vinod Krishna Komaragiri and A. Vijayakumari Independent Power Management for Interlinked AC-DC MicroGrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837 S. Padmini and S. Aravind Kumar Reddy Underground Cable Fault Detection Using Internet of Things . . . . . . . . . . 843 S. Padmini, Prakhar Pandey, and Divyashree Tarafder Weather Data Visualization Using IoT and Cloud . . . . . . . . . . . . . . . . . . . . . 849 P. Sardar Maran, D. Krishna Vamsi, and D. Vidya Shankar Efficient Utilization of an IoT Device Using Bidirectional Visitor Counter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859 T. Sasikala, Mohammad Iliyaz Ahamad, and G. Nagarajan Spammers Detection on Online Social Media Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867 M. Pavani, Swapna Penteyala, and R. Sethuraman Design of Cost-Effective Wearable Cardiac Monitoring System for Early Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873 B. Dilip Kumar, B. Praneeth, A. Pravin, T. Prem Jacob, and G. Nagarajan
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Innovative Gesture-Based Automation System for Home Appliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883 Pranathi Lakshmi Sai Konda, Alekhya Kondapi, and A. Jesudoss Detecting Potholes Using Image Processing Techniques and Real-World Footage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 M. B. Sai Ganesh Naik and V. Nirmalrani Machine Learning Methods of Rainfall Prediction in Weather Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 903 L. Sandra Jose, P. Nidhusha, and V. Nirmalrani A Smart Monitoring Industrial IOT Devices from Outsider Threats . . . . 913 T. Anandhi, D. Radha Krishna, Koushik Pilli, P. Ajitha, A. Sivasangari, and R. M. Gomathi Auction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 925 B. Bharathi, G. HemanthChowdary, and G. Girish NagaKumar Estimation of Vehicle Count, Class, and Speed on Highways Using a Computer Vision-Based Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937 P. Asha, S. Prince Mary, D. Usha Nandhini, and Sathyabama Krishnan Heuristic Detection of Pharma Temperature Anomalies Using IOT and Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 951 Machavaram Bhavana, K. Indira, T. Anandhi, P. Ajitha, and A. Sivasangari A Machine Learning Approach to Predict and Classify the Levels of Autism Spectrum Disorder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961 T. Anandhi, A. Srihari, G. Eswar, P. Ajitha, A. Sivasangari, and R. M. Gomathi Survey of Cervical Cancer Prediction Using Machine Learning . . . . . . . . 969 Natasha Shereen Benita, S. Vaishnavi, and G. Kalaiarasi Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981
About the Editors
Dr. Pradeep Kumar Mallick is currently working as Senior Associate Professor in the School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Odisha, India. He has also served as Professor and Head of the Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad. He has completed his Postdoctoral Fellow (PDF) from Kongju National University South Korea, Ph.D. from Siksha ‘O’ Anusandhan University, M.Tech. (CSE) from Biju Patnaik University of Technology (BPUT), and MCA from Fakir Mohan University Balasore, India. Besides academics, he is also involved in various administrative activities, a member of Board of Studies to C. V. Raman Global University Bhubaneswar, a member of Doctoral Research Evaluation Committee, Admission Committee, etc. His area of research includes algorithm design and analysis, and data mining, image processing, soft computing, and machine learning. Now, he is an editorial member of Korean Convergence Society for SMB. He has published 13 edited books, 1 textbook, and more than 100 research papers in national and international journals and conference proceedings to his credit. He is currently editing several books with Springer Nature, Elsevier and Routledge & CRC Press. He is also serving as Guest editor for special issues of the journal like Springer Nature, Elsevier and Inderscience. Dr. Akash Kumar Bhoi has completed his B.Tech. (Biomedical Engineering) from Trident Academy of Technology, BPUT University, Odisha, and M.Tech. (Biomedical Instrumentation) from Karunya University, Coimbatore in the year 2009 and 2011, respectively. He received his Ph.D. from Sikkim Manipal University, India, in 2019. He is working as Assistant Professor (Research) in the Department of Electrical and Electronics Engineering at Sikkim Manipal Institute of Technology (SMIT), India, since 2012. He is a University Ph.D. Course Coordinator for “Research and Publication Ethics (RPE).” He is a member of IEEE, ISEIS, and IAENG, an associate member of IEI, UACEE, and an editorial board member reviewer of Indian and international journals. He is also a regular reviewer of repute journals, namely IEEE, Springer, Elsevier, Taylor and Francis, Inderscience, etc. His research areas are Biomedical Technologies, the Internet of Things, Computational Intelligence, xix
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Antenna, Renewable Energy. He has published several papers in national and international journals and conferences. He has 90+ documents registered in the Scopus database by the year 2020. He has also served on numerous organizing panels for international conferences and workshops. He is currently editing several books with Springer Nature, Elsevier and Routledge & CRC Press. He is also serving as Guest editor for special issues of the journal like Springer Nature and Inderscience. Gonçalo Marques holds a Ph.D. in Computer Science Engineering and is member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as Assistant Professor lecturing courses on multimedia and database systems. Furthermore, he worked as a Software Engineer in the Innovation and Development unit of Groupe PSA automotive industry from 2016 to 2017 and in the IBM group from 2018 to 2019. His current research interests include Internet of Things, Enhanced Living Environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 60 publications in international journals and conferences, is a frequent reviewer of journals and international conferences and is also involved in several edited book projects. Victor Hugo C. de Albuquerque [M’17, SM’19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Ceará, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. He has a Ph.D. in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), an M.Sc. in Teleinformatics Engineering from the Federal University of Ceará (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic.
A Hybrid Face Recognition Scheme in a Heterogenous and Cluttered Environment R. R. Khansama, R. Priyadarshini, S. K. Bisoyi, Pradeep Kumar Mallick, and R. K. Barik
Abstract The recognition of human faces is an old application which can detect, track, and identify or verify human faces from a still or motioned picture stored digitally and is taken by digital camera. Though a significant development has already been seen in this domain, still some challenging issues are present which are yet to be addressed. Some of such issues are recognizing human faces in low intensity lighting condition, the presence of noise in images, scales, masquerade, etc. The present work focuses on using a conglomerate approach using deep learning algorithms with a support vector machine (SVM) to recognize faces. In the present paper, it has proposed a model to do facial image detection by using of deep neural network (DNN) with triplet loss function to extract features and support vector machine (SVM) classification algorithms to identify the images. The aim of the present work is to recognize the people from a variety of sources like videos, pictures and even sketches in a heterogeneous environment. When a selected image is fed to the system the model can be able to recognize detected faces. After that these detected faces are extracted and supplied to the recognizer which works with these faces and recognizes them as the respective persons. This recognition is done using SVM classifier which is very efficient when it comes to image processing. DNN and SVM have significantly improved the performance in recognition of faces in a cluttered and heterogeneous environment. Keywords Fog computing · Performance analysis · IoT · Task scheduling
R. R. Khansama · S. K. Bisoyi Department of CS and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha, India R. Priyadarshini Department of CS and Information Technology, C.V. Raman Global University, Bhubaneswar, Odisha, India P. K. Mallick (B) School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India R. K. Barik School of Computer Applications, KIIT University, Bhubaneswar, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_1
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1 Introduction Human eyes do an incredible job at looking at people and recognizing them at once. This is also expected from machines, when we want them to recognize the person captured in the camera [1]. Recognizing people can be done effectively even with the help of machines if we train them well with an adequate and accurate dataset. However, if we consider it practically, there are many conditions which affect this scenario of image recognition. Some of these factors are illumination or the blurriness of the image and many more. And, a fully functional image detection algorithm is only highly useful when it detects the person in the image accurately even in a dim light or we can say in a heterogeneous environment which is not suitable for the normal image detection algorithm [1, 2]. We can come across a solution for this problem, by using OpenCV and deep neural network (DNN) together in our algorithm. It can assure to detect images more accurately as this works more effectively than the feed forward method which is not able to predict the results as accurately as this. The DNN also falls in the category of convolutional neural network (CNN) which on the other hand comprises of three layers. CNNs are commonly used in analyzing visual images. It forms the foundation of every model which decides to work with visual images as this algorithm best fits it [3]. The process of image recognition has different stages. Initial stage is to perform image acquisition in which data is collected from different sources and integrated it as one. The next is the preprocessing process in which the noise is eliminated from the collected data. In this case, the noise can be the improper size of the image, so in preprocessing resizing is needed to mold it the way the algorithm needs to accept it. Next part of the process will be diving the transformed data into two one will be the training set of data and another will be the testing set of the data. Now with the help of this training set of data, we will train our model to function accordingly in the way we want it to perform. When we have trained our model enough on all sorts of data, our model will be ready to show the results for the testing data. As no model made till date is hundred percent efficient or if we say more correctly that it is practically impossible to make a model which is hundred percent efficient no matter how we train it or with how much data. But CNN constitutes with deep learning gives us the most accurate prediction possible. Using DNN, OpenCV and support vector machine (SVM), we propose to make a model which detects human faces and identifies the person in the image even when the environmental conditions are not homogeneous for example even if there is low illumination or the picture is not very clear or it is the sketch if the same person. Our model works for all the above cases which fall in the category of heterogeneous environment. As there are several existing models for face recognition being used world wide using various technologies, still these models prevail some or the other errors and face some problems in recognizing the images in an heterogeneous environment, i.e. the images that are blurred, have low density of light, black and white images or the sketch images. The main contributions of our work are enlisted below:
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• The model makes use of a DNN with triplet loss function which helps to produce distributed embeddings of a given data points in such a way that the analogous data points are projected in a closer region and dissimilar images can be represented in a place far from the similar one. • This feature helps the model to easily recognize the blur images or the images with a very low density of light with a good confidence level or a good accuracy. • It can recognize faces even from the drawn sketches.
2 Related Work Object tracking is defined as keeping a trace on a particular kind of object. In this paper, as we are mainly concentrating on face, we track human faces based on the given input features. Continuous tracking makes us leave the problems like illumination, variation in pose, etc. Here, tracking of human faces in a video sequence is done and also live video tracking using a webcam is done. Kanade Lucas Tomasi (KLT) algorithm was introduced by Lucas and Kanade, and their work was later extended by Tomasi and Kanade. KLT algorithm is used for feature tracking. This algorithm is used for detecting scattered feature points which have enough texture for tracking the required points in a good standard [1]. Michael Jones and Paul Viola introduced an algorithm named as Viola-Jones. They made algorithm mainly for the issue of face detection. This algorithm helps us detect features of a face in a particular frame of a video sequence. This is the first object detection framework which gives competition to real-time detection rates [2]. “HOG” was introduced by Navneet Dalal and Bill Triggs for pedestrian detection. Histogram of oriented gradients is a descriptor which detects objects in computer vision and image processing. The main goal is to identify how dark is current pixel in comparison with neighboring pixels. Original image is converted into simple representation that captures basic structure of a face in a simple way. Detecting faces means to find the part of our image that looks the most similar to a known HOG pattern that was extracted from a bunch of other training faces [3–7]. Some researchers used holistic matching, where complete face region is taken into account as input data into face catching system. One of the best examples of holistic methods are Eigen faces, principal component analysis (PCA), linear discriminant analysis and independent component analysis, etc [8, 9]. In feature based, features likes nose, eyes, mouth are first of all withdrew and their geometry, locations, and appearance are fed into a structural classifier. Feature “restoration” is one of the major difficulties for feature extraction, this is when the system tries to retrieve features that are invisible due to large variations, e.g., head pose while matching a frontal image with a profile image. In hybrid model, a mixture of both holistic and feature extraction methods. Generally, 3D images are used in these methods. The image of a face is caught in 3D to note the curves of the eye sockets or the shapes of the forehead or chin. A face in profile would serve just because the machine uses an axis of measurement and depth, which gives it enough
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information to construct a full face. The 3D system includes position, detection, representation, measurement and matching [10–12].
3 Proposed Model The flow chart of the proposed model is depicted in Fig. 1. The proposed model comprises of three important steps, i.e. extraction, training and recognition. First step involves the extraction of the faces from the image dataset given by the model. The piece of code in the file named as extract_embeddings.py does this activity. The required packages, i.e., bumpy, argparse, imutils, pickle, cv2 and os are imported. User needs to have installed OpenCV and imutils. Next, the face detector and embedder are called. For this, a DL face detector module with triplet loss function is being used to extract the face embeddings. After the process of face detection and localization, the images are now passed with image Blob through the detector network. Blob basically resizes and crops the image and extracts only the facial part from the image. After that, the detections are processed which consists the list of probability and coordinate for localizing the face in the images. If there is only one face in the image, then it is extracted with the highest confidence, and the confidence here if meet the least probability threshold which is utilized for filtering the weak detections and extracting the faces ROI and checking the dimensions for confirming
Input Images: Black and White, Coloured, Blurred, noisy images, image sketches with different luminous environment
Extraction of relevant and important features
A CNN with Triplet loss function to compute distributed embeddings of the captured data points
Generated 128-d embeddings are fed to Linear SVM for classifying the images
Classifier generates the prediction probability of the input image
Fig. 1 Describing the control flow with a flow chart of the model
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Fig. 2 Describing the embedding flow control
Fig. 3 Description of images and their types
if the face ROI is large enough. It generates 128D vector describing the face, and this data will be used for recognizing new face using machine learning. Then, the names and the embeddings vector are added to known names and known embeddings, respectively, and subsequently the total number of faces which was kept track of is incremented (Figs. 2 and 3). This step only studies the images and detects the presence of faces in the file by using the defined feature of faces. It also converts the detected faces into 128D embeddings. When the network is trained, it is provided with a batch of images, one batch consists of three images, first the anchor image which is to be tested, a positive image which contains the face of the same person, and a negative image which can be any other face rather than the face to be tested. Now, when this batch passes the neural network, it computes the three 128D embeddings and fixes its weight such that the embeddings of 128D of the anchor is in minimum distance with the embeddings of the positive image and is in maximum distance with that of the embedding of negative image. In training phase, we train our model on the top of every embeddings. LabelEncoder, SVC, argparse and pickle packages present in scikit-learn are used for this purpose. Here, linear support vector machine (LSVM) is used for doing the image classification. Here, no embeddings will be generated, rather the embeddings
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generated earlier will be used and loaded. Then, the scikit-learn LabelEncoder is initialized, and the user’s name labels are encoded, on which SVM model is trained. Now, the face recognition can be performed with OpenCV library. This part of the model needs processing of all three main parts of the model which are to be called one by one which comprises of the detector, the embedder and lastly the recognizer. After that we load the image find the faces in it using the detector, then we loop over all the detected faces and its probability, here it is also important to find the faces which have a weak confidence value and then compare it to the one we have stored as threshold, If the confidence of the detected face is greater than the threshold or we can say the minimum probability then the face is considered for further processing. We also ensure that the dimensions of the faces are sufficiently large enough to carry on with the further proceedings. After we get the final faces to carry over the proceedings, the faces are first extracted and converted into 128D embeddings and fed to the recognizer which comprises of the SVM model and the result of which will tell us the identity of the face given as input to the model. Then, from the result, the highest of all the probability index is considered and then matched with the label encoder. The label encoder with the name of the person is then displayed as a result on the top of the box which surrounds the face of the person in the image after proper identification. The same can also be used for the detection and identification of the faces in streaming videos by doing the slightest of changes as the videos are nothing but a stream of images changing with a fraction of seconds which we see as moving. Only we have to include is the imutils.video package which will ensure the uninterrupted processing of the video. VideoStream is used to capture the frames from the video to be processed which in turn is captured from the camera, and we also use PFS (per frame statistics) to capture frames from the video for further processing. To show the working of the model in case of videos, this model right now uses the web cam installed in the device. We initialize the VideoStream according to that and then proceed with enabling all the three main parts the face detector, the embedder and the tool used for recognition the face recognizer. After the camera sensor has warmed up. All the frames are processed in a loop, and all the frames are initialized. The detector works on all the frames in a loop finding all the probable faces and passing it to the embedder which works on creating 128D embeddings of all of it if only it’s probability exceeds that of the minimum probability defined earlier or we can say the threshold. The embeddings are then fed to the recognizer which comprises of the trained SVM model which will be giving the final result by identifying the face of the person by referring to the label encoder with the names if the people when the probability index is greater than the minimum. The result is displayed with a text which is constructed by combining the name of the person identified and the probability of the correctness of the prediction. The face detected and identified is enclosed in a rectangular box with the text created on the top of it.
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4 Dataset Descriptions For training and validation of the proposed model, we have used three data sets. (i)
(ii)
(iii)
Labeled Faces in the Wild (LFW) is a dataset which is created especially for the face recognition of people in an unconstrained environment. This data is a set of simple labeled data which has been collected in different situations. It shows the data with a lot of variance which is at different ages, with different expressions, with dim and bright lights, with accessories, makeup, different backgrounds, and different photographic qualities. This has a very large data with images from real life which fits very well in the pipeline of the image recognition algorithms [13]. YouTube faces database is another dataset on which this model has been trained. YouTube Faces Database is a collection of videos of faces designed to help study the problem of recognition of faces in unconstrained environment. This data set positively consists of 3425 videos of different people numbered 1595. On an average, 2.15 video clips are present for representing each person. The duration of shortest clip is 48 frames, and the longest clip 6070 frames with average length of each video clip is 181.3 frames. This data set is used by the model to classify and identify faces even in the videos. This data set has also been created on the basis of LFW. A third kind of dataset was also proposed to be used which is the gallery dataset which has been created manually with the pictures taken from mobile phone and cameras and also some images and sketches which have been downloaded from the Internet to test the model if it is working efficiently. This dataset contains images of people with their names as labels. This was manually collected and dataset like the LFW contains the pictures of people in different positions and situations like with poor resolution, dim light, dark background, heavy makeup, blurred images, proper sketches and many more. All these datasets combined make a very proper combination for training our model. The datasets when combined fulfills all the requirements on which the model will be tested and as a result makes the mode more efficient and increases the probability of correct prediction.
5 Result Analysis The model works at its best in the clear image without any noise. The model is also tested on by adding different types of noises in the image. We performed various tests in different environments such as a blur image, image with low light, recognizing the sketches and using both, i.e., by feeding the image and using the live webcam, which are also shown in this section. As the above given output screenshots, our model can easily detect the faces without any noise. The average confidence level of the faces to be detected without noise is around 50%. It completely depends on the number
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of datasets provided. More the model will be trained, more accurate the results will be. As we are OpenCV to recognize the faces, it is a highly used algorithm in face detection models with a good accuracy. Figure 4 describes the image captured from the screenshot by recognizing the face using a still image, whereas Fig. 5 describes the image captured through screenshot through live video stream. Here as denoted, Fig. 6 signifies the low light image recognized by our model in a still image with confidence level 26%, and Fig. 7 signifies the image of a screenshot captured from the live video stream, where our model is used to identify the person’s face in a very low light with noise in it with the confidence level 53.64%. As soon as the live video stream starts, our model also starts to keep a counter of frames per second. As observed above, our model is still functioning well in a very low light image with noise in it and is responsive with a good confidence level. Although we face homeless confidence level in the Fig. 6, but our model gives correct output in the low light images. Here is a result analysis to black and white image being recognized by our model. Figure 8 represents the screenshot
Fig. 4 Describing the output of the clear image without any noise in it
Fig. 5 Describing the output of a clear video in the input format of still images
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Fig. 6 Image captured at low light and tested from the live video streaming at lowlight
Fig. 7 Image captured through screenshot using still image
captured while a still image is being recognized by our model with confidence level 43.34% which is a good level, whereas Fig. 9 represents the screenshot of a black and white image being recognized by our model using the live video streaming using the webcam with confidence level of 45.05%. Here, we can easily observe that our model is having a very nice performance while detecting a black and white image with a very good confidence level and that too in both the cases, while testing through the still image or while testing using live video streaming. Here, Fig. 10 signifies that using our model, the still blurred image can be identified. This image is given as a test image which is blurred and contains a lot of noise in it. This image is easily identified by our model with a confidence level of 75.96% which is a very good accuracy level. So, we can here conclude that our model is responsive also in the blurred images to identify the faces. A red box is formed around the face which is
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Fig. 8 Black and white still image recognized by model in live video stream
Fig. 9 Black and white image recognized by our model
extracted by our model along with the name of the identified person’s face. After this, using the OpenCV face recognition pipeline, faces are detected easily. Here, Fig. 11 signifies the screenshot which is captured while recognition of a sketch image using a still image. Figure 12 signifies the screenshot which is captured while recognition of sketch image using the live video streaming. Here, we observe that in Fig. 11, the confidence level denoted is 91.71% which is highly accurate, and in the Fig. 12, as observed, the confidence level is 49.36% which also quite good accuracy level.
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Fig. 10 Signifies the blurred image captured by screenshot in still picture
Fig. 11 Signifies the sketch image recognition using live video streaming
6 Conclusion and Future Works Image recognition has taken several steps from past decades until today as discussed in our literature survey. Earlier, technologies such as thermal cameras, skin texture analysis, 3D recognition systems were used as image recognition. But, those were not as accurate as the models used in recent days. We tried here to build a robust model which can identify images in a cluttered environment. The crucial role was
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Fig. 12 Signifies the sketch image recognition in still image
played by DNN with triplet loss function which extracts the right features. Using this model, the user will have to make the system detect faces initially. Then, the user will compute 128D face embeddings in order to quantify the faces. After that, the user will have to make the system train a SVM on top of the embeddings and finally recognize faces in images given as the test data set and also use the webcam to recognize the faces in live video streams. In order to build the user’s own OpenCV face recognition pipeline, the user will have to apply deep learning in the following steps: Initially, the user will have to use face detection, for detecting the presence and the exact location of a face in the provided image as the data set. It would not be able to identify the image itself. Secondly, the user will extract 128D feature vectors called as embeddings which will be used to quantify each face in an image. This model is a working prototype model, and we have some future work decided for it to take on a large-scale working model. Moreover, we are working over it to make it more accurate by providing it many images as a training dataset, as it can improve the accuracy of our model.
References 1. Suhr, J.K.: Kanade-Lucas-Tomasi (KLT) Feature Tracker. Comput Vision (EEE6503), pp. 9–18 (2009) 2. Barnouti, N.H., et al.: Face detection and recognition using Viola-Jones with PCA-LDA and square euclidean distance. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(5), 371–377 (2016) 3. Nguyen-Quoc, H., Hoang, V.T.: Rice seed image classification based on HOG descriptor with missing values imputation. TELKOMNIKA 18(4) 1897–1903 (2020) 4. Iranmanesh, S.M., et al.: Coupled generative adversarial network for heterogeneous face recognition. Image Vis. Comput. 94, 103861 (2020) 5. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition (2015)
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6. Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2015) 7. Kortli, Y., et al.: Face recognition systems: a survey. Sensors 20(2), 342 (2020) 8. Vishwakarma, V.P., Dalal, S.: A novel non-linear modifier for adaptive illumination normalization for robust face recognition. Multimedia Tools Appl. 1–27 (2020) 9. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E., (eds.): Bio-inspired Neurocomputing. Springer (2021) 10. Tamilselvi, M., Karthikeyan, S.: A literature survey in face recognition techniques. Int. J. Pure Appl. Math. 118(16), 831–849 (2018) 11. Huang, G.B., et al.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2008) 12. Marques, G., Bhoi, A.K., de Albuquerque, V.H.C., Hareesha, K.S., (eds.): IoT in Healthcare and Ambient Assisted Living. Springer (2021) 13. Martínez-Díaz, Y., et al. Toward more realistic face recognition evaluation protocols for the YouTube faces database. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops (2018)
Smart Critical Patient Care System with Doctor and Bystander Support with Wireless Sensor Network Using IoT and Intelligent Recommender Algorithm Praveen C. Menon, P. R. Bipin, G. K. Ragesh, and P. V. Rao
Abstract Wireless sensors and body area network are playing an important role in medical and healthcare field in this technological era. Communication between the doctor, the hospital and the bystander of the patient who is critically ill and whose health condition is unpredictable should be accurate, timely and precise. For a doctor, a complete physical monitoring of the critical patient by being with them is very difficult during his or her busy schedule. Even timely update to the bystander of the patient is equally important. PBDM application is based on a novel idea where the patient bystander and the doctor have been given equal priority in communication to each other. PBDM application uses state-of-the-art technology to implement the concept. Currently, no hospitals in India are using this technology to communicate between critical patient and the hospital. This paper proposes an ideal solution for the communication between the doctor, patient and bystander through IoT application, which is the first ever idea among IoT-based patient care solutions. The bystander updates become very important because he or she will be updated for a support like buying a medicine from the medical store, meeting the doctor quickly for a decision making, inform in the hospital about the patients previous medical history like that can be done online and live and remotely even when the bystander is away from the patients ICU for some other emergency. Also the hospital authorities need not worry about the presence of a bystander when they use PBDM application support. Here, a smart recommender system enabled with wireless body area network sensors, which communicates effectively with the doctor with dynamic patient data in 24/7 and updates the bystander with the patient status with the help of a smart prediction P. C. Menon (B) Ilahia College of Engineering and Technology, Muvattupuzha, Kerala, India P. R. Bipin · G. K. Ragesh Department of ECE, Adisankara Institute of Engineering and Technology, Kalady, Kerala, India e-mail: [email protected] G. K. Ragesh e-mail: [email protected] P. V. Rao Department of ECE, Vignana Bharathi Institute of Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_2
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algorithm, is proposed. Live clinical results showed that the proposed system works effectively with the timely communication with the doctor and plays an important role in saving a sinking patient. Keywords Body area network · Wireless sensor network · Body sensor units · Personal area network (PAN) · Patient bystander doctor monitoring (PBDM)
1 Introduction PBDM application is based on a novel idea where the patient bystander and the doctor have been given equal priority in communication to each other. PBDM application uses state-of-the-art technology to implement the concept. Currently, no hospitals in India are using this technology to communicate between critical patient and the hospital. This paper is organized as follows. After introduction about what is PBDM application in the beginning of the paper, the next chapter begins with the problem identification. But before the problem identification, the related work and literature review connected to the idea being introduced are discussed. In the problem identification chapter, the recognition of need of such a solution is identified. Despite many research ideas related to the problem, it is clear that there needs a more practical and useful yet simple solution for the existing problem. Problem identification chapter discusses how the use of PBDM application from the currently available solution proposals. It is clearly defined in this chapter that the criticality of communication between the doctor and the patient plays a vital role along with the communication to the bystander. Next chapter defines the methodology in which the way PBDM application is implemented with the state-of-the-art technology using IoT. This chapter shows the architectural diagram and the communication diagram which explains the structure of PBDM application enumerator with how sensors are deployed in this and also illustrates the actors in the application have communicated each other. This chapter shows the method adopted, strategies followed and protocols used to implement PDBDM using IoT. Chapter also explains the smart data mining algorithm used to define intelligent recommendations provided to the doctor in critical situations. Next chapter illustrates the results we received while PBDM application was really implemented and hospital. This includes the feedback from the patients, bystander and the doctor involved. Results are displayed in figures and diagrams which are shown in PBDM mobile application. The paper concludes with the last chapter, i.e., conclusion, in which the future enhancements and possibilities of this application are included. Wireless sensor network is the network of nodes containing sensors. Each node contains a sensor with a transceiver module. Sensor nodes communicate each other to transfer data to a central system. Data will be transferred based on specified routing protocols. Also sensor nodes will manage within the network if one of the nodes fails to transfer or receive data by re-routing with a new path avoiding the failed node. This happens based on the self-healing protocols applied to the node. Body
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area network is the network of body sensor units placed in the human body. This network communicates with a central system in it with the data received from the body where which sensors are placed.
1.1 Related Work There are a lot of researches happening in medical and healthcare field using wireless sensor network. Biosensors and body area sensors play a vital role in advancement of technology in healthcare field such as patient health prediction, patient critical monitoring, health updates and data management, pandemic disease control, health data analysis, classification and prediction of people according to health status, monitoring and control of lifestyle diseases [1] and so on. Many of these kinds of applications have been found when we had the literature review. One among those research papers on healthcare monitoring system using wireless sensor network discusses wearable sensors which transfer data to the base station for monitoring [2]. The patient wears a biosensor-enabled strip on his or her wrist which will retrieve data from the body. But this system more focuses on the health status which can be monitored and updated to the doctor remotely. Another study d aims at patient’s healthcare monitoring in the hospital using the hospital hierarchy, that is, from the ward to the ICU with the health status of the patient. In this system, the hospital administration is also considered which is relatively unimportant [3]. There are also studies like remote care and assistance to the elderly patients in which the system continuously monitors the health reading fluctuations office senior citizen and updates the hospital [4]. The study focuses on a particular group of people with age restrictions. Another study details with tracking the patients’ location wise and getting the health status so that separation may be monitored remotely and live by the hospital. Also the traffic load and accuracy of data precision when we consider the location plays a critical role [5]. Also studies show that there are solution proposals which gives patient recovery status and prediction of their health. Based on the symptoms and previous medical records, the patient may be alerted for extra health care or medication by the hospital authorities [6]. Also the system tries to predict the possibilities of another health issue based on the current health data and previous health problems. [7] Also the possibilities of recovery state of health may be shown to the stakeholders of the application. There are simple studies which just monitor the health of a particular patient in the hospital inside the ICU or outside the ICU happening [8]. Some wireless sensor network applications focus on the emergency e and casualty services, that is patient who needs and emergency support on the hospital at the time of admission, patients with accidents or trauma [9]. When a detailed analysis and detailed research on the recent studies on the patient monitoring system was done, it was found some studies which aim to develop a wearable WBAN application for health remote monitoring monitor patient’s health through the continuous detection, process and communicate of human physiological parameters [10]. Another review analyses the use of different wireless sensors to
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demonstrate how to check various health parameters of the user using various sensors like pulse sensor, blood pressure sensor, temperature sensor and ECG sensor which we will use to monitor heart rate of the person. But in this paper, no user interfaceenabled mechanism is introduced to alert doctor and consider the bystander [11]. There are also researches happened which show the feature comparison between WSN and WBAN and also show the routing protocols suitable for mobile wireless network [12]. Some recent proposal was found related to patient monitoring which gives comprehensive overview of the current state and the perspectives of wireless body sensors [13].
2 Problem Identification None of the above studies consider the bystander and never communicate the health status with the bystander. Also, the above-mentioned techniques do not consider the relevance of a user interface portable system to get updates and monitoring health state of a critical patient. Another solution proposes that the doctor might have three or four critical patients admitted in the ICCU who are connected to or biosensors in PBDM so that the doctor can verify their health status live from the mobile application and also there medical records and patient status are continuously recorded data server for future reference. Similarly, the bystander of the critical patient if getting a continuous update would be a big relief the bystander and the family of the patient. Because it is a common scene in all the hospitals of a bystander and the patient’s family is relentlessly waiting to get the patient status. The bystander updates become very important because he or she will be updated for a support like buying a medicine from the medical store, meeting the doctor quickly for a decision making, inform in the hospital about the patients previous medical history like that can be done online and live and remotely even when the bystander is away from the patients ICU for some other emergency. Also the hospital authorities need not worry about the presence of a bystander when they use PBDM application support. Recent studies and research focus on the patient monitoring. But communication with the doctor and the bystander is found to be equally important. Analysis reveals that there has not been much concentration on doctor’s communication with the bystander of the patient which makes the entire medical process completely transparent and clear. So, there is a possibility of research on this address the issue. The solution to this issue is the IoT-based patient monitoring system along with the doctor and the bystander communication. PBDM proposes a complete solution using an interface between the patient and doctor and bystander.
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3 Methodology PBDM proposes a state-of-the-art solution that uses IoT and wireless body area network of body sensors like pulse or heart rate sensors/temperature sensor/pressure sensor/blood sugar sensor which are installed in the body of a patient who needs critical medical care. Sensors read data and transfer to the central node (computer) where PBDM application works. PBDM application connects the critical patient with doctor and bystander. Doctor receives live health update of the patient at anytime from anywhere in the world. Bystander gets updates and information about patient’s health status from PBDM so that it’s needless for the bystander to completely dedicate himself/herself near the patient. Also doctor and bystander can communicate through PBDM such as the doctor can prescribe change of medicines or diet plans or hospital visit to the bystander if patient is shifted to home. The main feature of PBDM is that it’s useful not only at hospital but also can be applied to a patient who’s shifted back home and patient will be equally taken care of by the doctor and bystander.
Fig. 1 Actors in PBDM and communication architecture of PBDM
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Fig. 2 Sensor data architecture of PBDM
Diagrams in Fig. 1 show the actors/main stakeholders of PBDM application. Also the diagram depicts the communication between doctor and bystander through the PBDM app. Sensor data read from the patient body is sent to the base station over wireless body area network. Block diagram in Fig. 2 explains the sensor data communication between the patient, doctor and the bystander. The sensor data is read from the patient body continuously and transferred to the IoT controller for processing. The processed data is used by PBDM algorithm which is shared with the users of the application. Doctor Module Doctor can handle multiple critical patients through PBDM. Doctor can view patient’s health report and values like heart rate, pulse, temperature and other values from the sensors connected to the body. Arduino module is used to transfer data to the central system where PBDM algorithm works. Doctor reads the data taken from the patient’s body and reviews it and update accordingly. Any comments or information needed to pass to the bystander also is done at this level. Bystander Module Bystander can read updates from the doctor at any time. Also bystander can communicate back to the doctor in case if he/she has some queries/concerns. Change of medicines, change in diet plan, next hospital visit and current status of the patient will be displayed to the bystander. An alarm is set in PBDM in case if any sensor values go beyond the specified range, and alarm will be automatically alerted in PBDM. An administrator privilege is given in PBDM to manage doctors and bystanders and also update patient information. The PBDM application works in three-tier architecture in terms of the communication. In Tier 1, the sensed data from the biosensors is transferred to the base station where PBDM smart algorithm works over a wireless sensor body area network after the data is continuously send from the patient’s body. In Tier 2 communication, PBDM application communicates with the doctor about the patients’ current health status with the recommendations in case if the health seems to be sinking. The response of the doctor to the PBDM application is also a part of this level of communication. In Tier 3, the doctors update to the bystander, and all the systems’ update
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to the bystander with the permission of the doctor is executed. Also communication of the doctor to the hospital authorities such as keeping the operation theatre ready for making arrangements to shift the patient to another hospital and so on if the part of this communication level [14]. The PBDM application may be depicted in a two-layer architecture, such as communication layer and service layer. In communication layer, all communications as mentioned in the previous paragraph, that is communication of the sensor to the computer system where PBDM app runs, communication of the application with the doctor and the doctor’s response to the application and finally communication of the doctor or the system to the bystander, may be considered together comprising of a communication layer. The PBDM application interface, bystander updates, data transfer to the server may be considered as a service layer components [15]. Patient Information Security Patient health information is very important as it is the intervention of their private and personal matter. PBDM encrypts the data received from the sensors using triple DES algorithm. At the receiving end, same algorithm is used to decrypt data so that data is securely passed through the unsecured Internet. Pseudocode for PBDM 1. 2. 3. 4. 5.
Read Patient data in N seconds of intervals repeatedly Input specified range to stored_range Condition: IF values > stored_range THEN Start Alarm module Send Communication messages
Algorithm PBDM (P, HP, TD, SD) Step 1: Collect Dataset of PL, Patient List PL{P1, P2, …, Pn} Step 2: Collect Normal Dataset of health Parameter, HP{HP1, HP2, …, HPn} Step 3: Collect statistical Dataset of the disease (a) (b) Step 4: Step 5: THEN
TD, Statistical Reports ED, Expert data
Sensed data SD collected from PL IF < SD > HP > is TRUE
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IF < SD > {TD and ED} > THEN (a) (b) (c)
Alarm doctor and nurse in-charge of ICU with recommendations and suggestions Alert operation theatre and casualty services after the confirmation from the doctor Alert the bystander with the updates
ELSE Continue monitoring. Algorithm Analysis Algorithm PBDM receives inputs like patient data, health parameters, statistical data, expert data and sensor data to result in a summary of conclusions and recommendations to the doctor with almost like an artificial intelligence system. Algorithm produces smart suggestions and triggers the necessary operations to help the doctor in case of emergency. Algorithm continuously receives the patient health data from the sensors deployed in the body. The sensor data is continuously compared with the normal health parameters, and it is found varying, the sensor data is again compared with statistical data and expert data, i.e., data given by experts with previous historical data. When both comparisons show difference, algorithm alerts the doctor with recommendations and also alerts the bystander and emergency services like casualty and operation theatre after the confirmation from the doctor [16, 17] (Fig. 3). Arduino Mega 2560 Arduino is an electronic board with a microcontroller installed on that. It is a computer hardware. Arduino is based on ATMega 2560 which is devised with 54
Fig. 3 Arduino Mega 2560 with its PIN diagram
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Fig. 4 ESP8266 Wi-Fi module
Input/Output pins and 6 analog pins. Each pin in this designed to accept a maximum of 40 mA. This microcontroller contains a resistor of 20–50 K ohms. Arduino Mega can communicate with a computer, another Arduino Mega, or another microcontroller. Text information may be sent from and to the Arduino Mega IDE when we wanted to program on this chip. This microcontroller is designed to be coded in almost all popular coding languages such as C/C++/Java or Python. The microcontroller code is named sketch and may be carried using a USB cable (Fig. 4). ESP8266 Microchip ESP8266 is a relatively cheap Wi-Fi microchip. ESP8266 has adopted TCP/IP protocol array, which may be easily mounted to the chip. ESP8266 may be used to get access to the Wi-Fi network specified. I A ESP8266 chip is equipped with a Wi-Fi unit which works in a built set-up using an AT command. This module uses a rubber duck receiver in an antenna structure with a coverage of 479 m. Saturation sensor, blood pressure sensor, heart rate sensor and temperature sensor are deployed in the patient admitted in the ICU who needs critical monitoring. All sensors are connected to Arduino Mega 2560. The sensor data is transmitted over Wi-Fi using the chip E2323. Arduino Mega is connected to PBDM Program in the computer system where the algorithm processes the data received using the algorithm. The result is displayed in the end user Android user application in the form of alert and notification. The app updates the bystander and communicates with the doctor. Even the doctor can send special update to the bystander, if needed. Saturation Sensor MAX30102 Saturation sensor reads the oxygen percentage of human blood. We spoke to a chief physician and a paediatrician of a reputed hospital in Muvattupuzha, Kerala, to get the standard values of sensor readings. According to them, patient may fall critically sick if the saturation reading goes down beyond 90%. So we set the sensor data limit to 91%. Saturation sensor data is given high priority (Fig. 5).
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Fig. 5 Blood pressure sensor M30100
Blood pressure sensor Blood pressure consists of two values—systolic pressure and diastolic pressure. Systolic reading and diastolic pressure are 120/80 mm Hg [8] for a normal healthy human being, which may slightly vary from child to adult, male to female and young to old people. As per the doctors’ suggestion, we have set the data limit of systolic reading as 100 and diastolic pressure limit to 50. Below the specified limit, a patient can be critically sick and should be communicated to the doctor. Here in this case, the algorithm reads the sensor data two times in a specific interval to see how quickly the pressure variation happens. If the variation happens too quickly, emergency alert is given to doctor and some helpline will be informed to take care of the patient in case if doctor is away from the patient (Fig. 6). Heart Rate sensor This sensor counts the number of times the heart beats for a human being. Normal standard value is 72–80 times per minute for a healthy human being, which may slightly vary from child to adult, male to female and young to old people. Here, as Fig. 6 Heart rate sensor PZIN51000071
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Fig. 7 Temperature sensor MLX90614
per the doctor suggestion, we have set the critical value as 60, and any reading below 60 from the sensor will trigger the emergency alert to the doctor (Fig. 7). Temperature Sensor Temperature sensor reads the human body temperature which is 37 °C or 98.6 F. A patient can go critical when the temperature goes below 95 F or above 105 F, which is called a heat stroke in the patients body. Temperature sensor data is relatively least prioritized since the above-mentioned variation is very rare and variation in body temperature is seldom very critical.
4 Result and Discussion Android Application PBDM app is an android-based application. PBDM provides an interface to the doctor and bystander. This app provides alerts, notification, patient reports in respective views. For example, bystander login shows patient status, doctor updates and emergency alert provided in urgent situation by the doctor. Doctor view shows the sensor data, variations, patient history, emergency alert, option to communicate emergency contact. This app works in background so that the users get notifications even while they hide the app. Alerts and updates come in the form of notifications from the app. The doctor can handle up to four very critical patients simultaneously. The doctor will be able to see the direct status of the patient in his login and specific sensor data chart if needed. Also the app facilitates the doctor to make an emergency call to a preset number when it is highly urgent. In the bystander view, the user will be able
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to see the patient status and details of treatments provided till now and information about the medicines or requirements to be provided at the ICU. Comparison of accepting data and showing improvement accordingly is quite hard in this application since no such system exists in any hospital which directly communicates and updates the patient details with the doctor, as a recommender system. The doctor commented on PBDM application that he found it very helpful and interesting as the medical history along with the patient’s current status is made available through the application. When the system was tested, it is found that the application communicated with the doctor in 3 min 20 s when the patient’s sensor readings violated normality. The system is found to be very robust and consistent in different environments. When the sensor reading was set such that the patient is in a sinking state, PBDM recommended the doctor with suggestions like alerting operating theatre for ambulance services. Since there is no such application running in any hospital as of now in India, PBDM was most recommended by the users of the application. PBDM Application—User Screenshots Since PBDM application handles critical patients, we were unable to experiment our app in patients admitted in ICCU. We installed the system and sensors in a patient in ICU of the hospital at Muvattupuzha, Kerala, for an hour every day for 10 days where we installed sensors on his body and read the values. We installed PBDM app in the doctor-in-charge’s mobile phone and bystander mobile phone. Doctor was getting the readings and could review while he was away and on a specific time, the patient’s saturation level went critically low and eventually PBDM alarmed the doctor and he confirmed with ICU readings that PBDM suggestion was right and the patient was given proper medication in time. Bystander got updates from doctor and received patient status on time. Apparently, the test showed that patient was more protective while being in PBDM assistance when compared with other time where PBDM sensors were removed (Figs. 8, 9 and 10). The online service of PBDM made it easy for the doctor and bystander to have a closer review on the patient. Also we successfully tested the sensor performance individually, i.e., heart rate sensor and BP sensor in different in-patients. PBDM application was also tested with the patient’s data for the doctor’s future reference. Test data was taken from a particular patient who was connected to biosensors of PBDM. We try it to retrieve the data of that particular patient after two weeks of his discharge from the hospital. PBDM application successfully showed the patients previous health history with the medication details and health status from the time he was admitted till the date the patient was discharged. The doctor found it very helpful and easy to operate and manage rather than following the conventional paper printed chat and her quotes stored in the repository of the hospital. The doctor specifically commented on the ease of access of previous medical data in a single click when compared with the manual search of the medical chart of the patient which is extremely time consuming, tedious. Doctor also mention that, when a particular patient is discharged, the doctor might not always be completely satisfied with his health condition at the time of discharge. Sometimes it happens due to the special
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Fig. 8 Saturation sensor reading of a patient in doctor’s view
Fig. 9 All sensor readings with patient status in doctor’ view
request of the patient all of their family, once the patient seemed to be better. When he used PBDM application, the doctor felt very much satisfied that we can have a follow-up with the patient online and can get back to the patient with recent updates of his health status and having a plan what to do, in case if he is admitted in the same hospital for the same doctor.
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Fig. 10 Heart rate reading of a patient for four days
As per the suggestions and comments made by the doctor, we have decided to update the PBDM application with more wearable biosensors so that, in case of elderly patients or patients with continuous home monitoring is needed, more wireless devices and sensors are desirable for a hassle free execution of the application. When the live updates are made possible with PBDM application, it may be really helpful for the doctor and the family members. In normal cases, a person with probabilities of severe health issues, it may be a worry to the family members that what if something worst happens to the patient while being home. PBDM can provide a complete solution for this worry after making it completely wireless with wearable sensors. PBDM becomes very relevant when the doctor can communicate back to the application with the critical request to the hospital authorities, say, “Give him treatment x” or “Make surgery room Ready” Arrange an Ambulance for a quick shift to Hospital X” live and online even when the doctor is away. It is also convenient for the hospital authorities and management to do an action with the consent/request by the doctor. In a bystanders’ view, knowing that the patient who is critically ill is under the continuous online live supervision of the doctor will make him/her feel much relaxed.
5 Conclusion and Future Work Monitoring a patient in critical condition is always important. This system proposes an effective approach to efficiently monitor a patient either in hospital or at home. Bystander communication is very critical in patient monitoring as normally bystanders stretch a lot and struggle to get updates frequently and many times in an unmovable situation from near the patient. This issue has an emotional aspect as
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well, and that is the reason PBDM has given much importance to bystander along with the doctor. Updating and alerting doctor in critical time are very important for patients with fluctuating health conditions so that doctor can have the timely act. Also alarming the necessary services will enhance the possibility of rescuing the patient when it is highly required. The proposed PBDM algorithm acts as smart mechanism in critical healthcare monitoring. The future enhancement includes the completely wearable devices for the patient so that the system can be recommended to patient who’s not that critical but still needs medical attention. This solution proposal PBDM may be further extended by adding module is like external health workers, expert doctors in different hospitals, government health officials and so on to get benefited different dimensions of the live and online healthcare support using biosensors and body area networks. For example, suppose there is a pandemic situation arises in the society, PBDM application may be extended by adding external health workers and government representatives who wants to continuously monitor the health status of people who have more probabilities for exposure to the pandemic. Also a critical patient who survived may be continuously monitored independent situation to avoid unexpected mortality and spreading pandemic to anyone in his family ought to anybody whom he directly interacts with. Also the report may be sent to the government officials and health workers on a daily basis so that I can quickly identify in case if something goes wrong. Also since the data is recorded and stored for the future reference in the data server, data may be used for data analysis and pandemic prediction. PBDM data may be used for classification and prediction by mining the health data patient. Also the clustering of patience with similarities in their health records is possible. In that aspect suppose if the patient visits the doctor 2 years down the line after recovered from a severe health issue, then the doctor may be able to track and find health history in a single click in the PBDM application. Effective application of technology like a wireless sensor network and biosensors and body area network is not only for individual protection and care, but also so it may be extended as a social service which could save many human lives and that is something very much essential. That can be easily made possible through PBDM application because ultimately any technology should be able to aim at serving society and making the process execution smoother and helpful.
References 1. Baba, E., Jilbab, A., Hammouch, A.: A health remote monitoring application based on wireless body area networks. 978-1-5386-4396-9/18 (2018) 2. Chowhan, J., Bojewar, S.: Sensor networks based healthcare monitoring system. IEEE (2016) 3. Kumari, R., Nand, P.: Performance comparison of various routing protocols in WSN and WBAN. In: ICCCA 2016, pp. 427–431 (2016) 4. Chaari Fourati, L.: Wireless body area network and healthcare monitoring system, p. 362. IEEE (2014)
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5. Ghose, A., Sinha, P., Bhaumik, C., Sinha, A., Agrawal, A., Choudhury, A.D.: UbiHeld: ubiquitous healthcare monitoring system for elderly and chronic patients. In: Boulos, M.N.K., Wheeler, S., Tavares, C., Jones, R., (eds.) UbiCom’13: how smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX, vol. 10, p. 24. Biomedical engineering online 2011 6. Ghose, A., Sinha, P., Bhaumik, C., Sinha, A., Agrawal, A., Choudhury, A.D.: UbiHeld: ubiquitous healthcare monitoring system for elderly and chronic patients. In: UbiCom’13 7. Rajankumar, P., Nimisha, P., Kamboj, P.: A comparative study and simulation of AODV MANET routing protocol in NS2 & NS3. In: 2014 International Conference on Computing for Sustainable Global Development (INDIACom), pp. 889–894. IEEE (2014) 8. www.healthline.com/health/diastole-vs-systole 9. Smart healthcare monitoring system based on wireless sensor networks. In: 2016 International Conference on Computing, Analytics and Security Trends (CAST), College of Engineering Pune, India, 19–21 December 2016 10. Patient monitoring system using wireless sensor based mesh network. IEEE (2017) 11. Chen, M., Gonzalez, S., Cao, A.H., Victor, C.M.L.: Body area networks: a survey. Springer Science + Business Media, LLC. Published online: 18 August 2010 © (2010) 12. Nur, N., Ayu, F., Che, B.H.: Health care monitoring using wireless sensor network (H-Caring), Thesis, June 2012 13. Marques, G., Bhoi, A.K., de Albuquerque, V.H.C., Hareesha, K.S., (eds.): IoT in Healthcare and Ambient Assisted Living. Springer (2021) 14. Anoop, V., Bipin, P.R.: Super-resolution based automatic diagnosis of retinal disease detection for clinical applications. Neural Process. Lett. (2020). https://doi.org/10.1007/s11063-020-102 92-x 15. Anoop, V., Bipin, P.R.: Medical image enhancement by a bilateral filter using optimization technique. J. Med. Syst. 43, 240 (2019). https://doi.org/10.1007/s10916-019-1370-x 16. Bhoi, A.K., Sherpa, K.S., Khandelwal, B.: Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT) analysis of electrocardiogram. Cluster Comput. 21(1), 1033–1044 (2018) 17. Anoop, V., Bipin, P.R.: Spectral feature and optimization-based actor-critic neural network for arrhythmia classification using ECG signal. J. Exp. Theor. Artif. Intell. (2019). https://doi.org/ 10.1080/0952813X.2019.1652355
IoT-Enabled Toxic Gas Detection and Alarming System Using Wireless Sensor Network with TAGDS Smart Algorithm Praveen C. Menon, P. R. Bipin, and P. V. Rao
Abstract Recent studies and research say there have been many number of human lives expired after being exposed to toxic gas produced where they stayed. Recently, eight people were died inhaling toxic gas that is carbon monoxide produced from the air conditioner inside the room of the resort when they stayed in Nepal. This paper proposes a solution to this problem by implementing an IoT-enabled wireless network of toxic gas sensors at every room of the resort or hotel. The inmates of the room will be alerted using an alarm, where the toxic gas presence is detected and also communicates with the authorities of the hotel or resort. This system has TAGDS mobile application which helps people to get alerted even when they are away from the room in which toxic gas is detected. The importance of this paper is that we propose IoT-based state-of-the-art solution for detecting toxic gas presence in a hotel or restaurant where common people stay very frequently. The novelty of this idea is that there is no such wireless sensor-enabled and IoT-based solution for toxic gas detection is available till now. So we propose a unique solution which is technology-based, IoT-incorporated and wireless sensor network-enabled. TAGDS is a simple yet powerful mobile application-based technical system which detects the presence of eight different toxic gases that can communicate directly with the inmate of the room and also communicate with the hotel authorities. The smart algorithm used in this IoT-based state-of-the-art application recommends the user to what to do next. Also, TAGDS application has the provision to contact emergency services if needed. Results showed that inmates of the hotel could comfortably use the mobile application and the system could give the inmates a timely alert when the toxic gas was present in the room. The proposed smart recommender algorithm works on the
P. C. Menon (B) Ilahia College of Engineering and Technology, Muvattupuzha, Kerala, India P. R. Bipin Department of ECE, Adisankara Institute of Engineering and Technology, Kalady, Kerala, India e-mail: [email protected] P. V. Rao Department of ECE, Vignana Bharathi Institute of Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_3
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sensor data to verify the quality of air inside the room and trigger the IoT devices when the sensor readings violate the standard values. Keywords Toxic gas detection and analysis system (Acronym: TAGDS) · Wireless sensor · Internet of Things: IoT
1 Introduction There have been many studies and researches on toxic gas identification in the atmosphere and inside the rooms of the buildings, industries and apartments. There was a recent incident which happened in a resort at Nepal where eight people died being exposed to toxic gas produced inside the room they stayed. Presence of toxin and toxic gases can be there in the environment where we live at any time, maybe caused by the leakage from factories and industries, unexpected atmospheric changes or atmospheric pollution caused by vehicles, burning plastic or toxic materials and rarely from sun beams. There may be even more reasons which create toxic gas where people live. Hotels, resorts, restaurants and shopping malls, movie halls, conference halls are some of the areas where people gather very frequently. Toxic gas exposure on these areas can be deadly. It is very important to detect the presence of any kind of toxic gas which may be present in such areas where huge number of people are gathered. The importance of this paper is that we propose IoT-based state-of-the-art solution for detecting toxic gas presence in a hotel or restaurant where common people stay very frequently. The novelty of this idea is that there is no such wireless sensorenabled and IoT-based solution for toxic gas detection available till now. So we propose a unique solution which is technology-based, IoT-incorporated and wireless sensor network-enabled. TAGDS is a simple yet powerful mobile application-based technical system which detects the presence of eight different toxic gases that can communicate directly with the inmate of the room and also communicate with the hotel authorities. The smart algorithm used in this IoT-based state-of-the-art application recommends the user to what to do next. Also, TAGDS application has the provision to contact emergency services if needed. The organization of this paper is illustrated as follows. After the introduction, the literature review and related work are discussed where are similar studies are analyzed to reveal the relevance of our solution proposal. In the literature review, we have gone through many research studies and tried to identify the shortcomings of the research studies. TAGDS approach tries to overcome the shortfalls identified in the literature review of the related researches. The next chapter is problem identification, where the actual problem with the toxic gas detection and analysis of common places like restaurants, hotels are studied and confirmed that there needs a consistent solution for the problem. As of now, there is no technical solution proposed using wireless sensor network and IoT, to safeguard the human lives who stay in hotels or resort. Next chapter
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discusses the methodology that TAGDS application adopted to solve the problem identified. Here, all the tools technology, protocols used, sensors used are explained. Also the architectural diagram of gas sensor with the IoT system is depicted. The layered architecture diagram shows the information exchange between communication layer and service layer. In this chapter, TAGDS smart recommender algorithm is analyzed in detail, which is used to you recommend the TAGDS application what to do when toxic gas is detected. Upcoming chapter shows the result and discussion. Here, the test results are analyzed and discussed after the implementation of TAGDS application in a hotel. Also the review from the customer and hotel manager is verified for future updates and enhancements. Also the screenshots of the TAGDS mobile application are added. The final chapter concludes the paper with the future enhancements based on the results received from the guest and the hotel authorities.
1.1 Related Work Recent researches on death of humans in drains and trenches discussed a system to detect the presence of carbon monoxide inside the trench and alerts the workers inside the trench [1]. This paper targets specific group of workers focusing on protecting their lives through GSM communication. The solution of this work confined to a limited number of workers which will be relatively less in number. It does not mean that human life, which is even a single one, is not important but when the solution proposal becomes capable enough to save more number of lives that technical solution becomes more socially committed. TAGDS identifies the detection of toxic gas at very common places like hotels restaurants seminar halls where people tend to gather very frequently. This proposal identifies multiple number of toxic gases, which is again more critical. Another area where researches went very deeply is on toxic gas detection in chemical industries and factor is where there are more probabilities for people to have definite exposure to and healthy gas and chances of unsafe gas leak ages are high [2]. In that aspect also, the exposure to people is limited compared to those gathered in common places. Most researches show that there are no too much studies or analysis happened in toxic gas identification and remedy for places where common people generally meet, stay or gather [3]. Almost all the works related to toxic gas have been found to be either job specific or people specific. The disaster in Nepal resort which took eight human lives shows that common places have to be verified for purity of air without any possibility of toxic gas presence [4]. The situation of the people can be really dangerous in case if they are exposed to toxic gas at the above-mentioned places as there may not be emergency access for a large number of people to get out to fresh air [5]. Even it is very difficult to identify the presence of a toxic gas while we inhale it until we get unconscious. In case if toxic gas has been present while people sleeping in hotels or resorts, they may not even realize that they are exposed to a toxic gas and have to get out sooner and that might lead to severe number of mortalities
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[6]. Wireless sensor network is the network of nodes containing sensors. Each node contains a sensor with a transceiver module. Sensor nodes communicate each other to transfer data to a central system. Data will be transferred based on specified routing protocols. Also, sensor nodes will manage within the network if one of the nodes fails to transfer or receive data by re-routing with a new path avoiding the failed node. This happens based on the self-healing protocols applied to the node.
2 Problem Identification The main focus of this paper leads to the disaster happened to eight members of a family who expired breathing toxic gas produced from the heater they used inside the room of the resort they stayed in Nepal [2]. This is not an isolated case, and there are many other cases reported with mortality of people after being exposed to toxic gas leakage in rooms, auditoriums, factories, industrial areas and so on. After we communicated and visited many resorts and hotels, it is identified that there is no such mechanism or systems installed to detect and prevent the presence of toxic gas in rooms. It is found to be very important to detect such deadly gas leakage because it can harm and kill inmates very quickly and silently, special in situations like the gas is leaked while the inmates are sleeping. In our literature study, it is found that there are many studies about the gas leakage and its prevention at chemical industries, chemical factories, chemical labs and in places where chemical gas leakage is possible [1–3]. But no studies found exclusively on hotels and resorts where these kinds of toxic gas leakage are very rarely expected. There is a possibility of research and development of a technical solution to this issue by using IoT-based approach with smart sensors and the mobile application with the help of a smarter recommender algorithm. Here we have proposed a system which can detect the toxic gases like carbon monoxide and alert the inmates and the hotel authorities using the mobile application we have developed. The smart recommender algorithm we have used in this proposed technique is named TAGDS, the toxic gas detection and alarming system. This technical solution is proven to be very relevant since there is no such application found for hotels and resorts safeguarding the inmates with them [7]. Wireless sensor network forms a network of sensor nodes where each sensor node communicates between them. The sensor network is connected to a base station, typically a computer system. A sensor node is a transmitter–receiver device which will transfer the network data. The data collected from the sensors are processed in the base station, and it is used by the stakeholders of the application through Internet [8]. Also the information processed is stored in a data server. Here in our proposal, the sensor detects different types of toxic gases and the read sensor data, that is the value of the gas detected will be processed by the computer system for the further actions. Arduino is a microcontroller which can process sensor data [9]. Typically, Arduino is connected to a computer system to store and process information read from the sensor network which is connected to the Arduino. Arduino is a widely used microcontroller through which a variety of IoT applications can be performed.
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3 Proposed Methodology The proposed system consists of a wireless sensor network, Arduino Mega microcontroller and a base station (computer system) illustrated in Fig. 1. Multigas sensor MiCS6814 is capable of sensing eight toxic gases which are harmful to human life, such as carbon monoxide, nitrogen dioxide, ethanol, hydrogen, ammonia methane, propane and isobutane. This multisensor is installed in every room of the hotel/resort, the sensor continuously monitors the quality of air, and the data is transferred to Arduino Mega 2560. Zigbee technology is used for network data transmission in the system. All sensors are directly connected to the base station. Sensor data is verified in the base station computer system which is collected from the Arduino Mega. The smart recommender algorithm TAGDS works on the sensor data with rule-based data mining approach. The algorithm is designed to alert the inmates using an alarm placed in every room, in case if toxic gas is present. Also, an alarm notification will be triggered in the mobile application with the message explaining exactly what is happening in the room with the name of the identified toxic gas. Simultaneously, the system will alert the authorities of the hotel with room number in which toxic gas is identified. Android mobile application developed to work as an interface between the user and the system. When the user/guest of the hotel installs the mobile application in their mobile phones, they will get updated about the room situation even while they are away from the room. This will be helpful while someone temporally leaves the room with their relatives or friends staying there. Suppose if the guest who is out of the hotel receives an alert, he or she can get back to the hotel sooner to safeguard the inmates. The proposed system is composed of two layers such as (i) (ii)
Communication layer Service layer.
Fig. 1 Layered architecture of TAGDS
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The system consists of the following hardware (i) (ii) (iii) (iv) (v)
Sensor module Alarm unit Base station typically a computer system Mobile application Server for data storage.
The architecture diagram shows the layered communication of the TAGDS implementation. This diagram depicts the connection between the communication layer where the actual sensors and hardware is installed and detection of toxic gas is happening and the service layer where the system interacts with the user. Rule-Based Pseudocode of TAGDS Application TAGDS (Sensor Reading) If toxic gas = no then Air quality = fine else If toxic gas = yes then (a) (b) (c)
Trigger room alarm Send notification to hotel authorities Send notification to inmates through mobile application TAGDS.
Trigger alarm in the mobile application TAGDS If emergency situation = yes then (a) (b)
Confirmation from hotel authorities= yes then Inform emergency services.
Figure 2 shows the sensor information transfer to the base station, which in turn shares the processed information to the user communication layer where the user action is executed. Arduino Mega 2560 Arduino is an electronic board with a microcontroller installed on that. It is a computer hardware. Arduino is based on ATMega 2560 which is devised with 54 Input/Output pins and 6 analog pins. Each pin in this designed to accept a maximum of 40 mA. This microcontroller contains a resistor of 20–50 K. Arduino Mega can communicate with a computer, another Arduino Mega or another microcontroller (Fig. 3). Text information may be sent from and to the Arduino Mega IDE when we wanted to program on this chip. This microcontroller is designed to be coded in almost all popular coding languages such as C/C++/Java or
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Fig. 2 Sensor communication architecture
Fig. 3 Arduino Mega 2560
Python. The microcontroller code is named sketch and may be carried using a USB cable. ESP8266 Microchip ESP8266 is a relatively cheap Wi-Fi microchip. ESP8266 has adopted TCP/IP protocol array, which may be easily mounted to the chip. ESP8266 may be used to get access to the Wi-Fi network specified. A ESP8266 chip is equipped with a Wi-Fi unit which works in a built setup using an AT command. This module uses a rubber duck receiver in an antenna structure with a coverage of 479 m.
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Fig. 4 MiCS 6814 multigas sensor
Table 1 Gas sensitivity of MiCS 6814
Gas name
Chemical formula
Sensitivity (Range) (ppm)
Carbon monoxide
CO
1–1000
Nitrogen dioxide
NO2
0.005–0
Ethanol
C2 H5 OH
10–500
Hydrogen
H2
1–1000
Ammonia
NH3
1–500
Methane
CH4
1–1000
Propane
C3 H8
1–1000
Iso-butane
C4 H0
1–1000
MiCS 6814 multigas sensor A multigas sensor, MiCS 6814, shown in Fig. 4, is used in this approach to detect the presence of any toxic gas as this sensor is capable of detecting eight harmful gases listed in Table 1. Table 1 describes different gases that are toxic to the human body which is identified by the sensor MiCS 6814. This sensor is capable of identifying eight toxic gases such as carbon monoxide, nitrogen dioxide, ammonia, methane, propane, isobutane, ethanol and hydrogen. The table also shows the minimum to maximum range of values detectable by this particular sensor.
4 Results and Discussion Android Application The Android mobile application has three modules—a guest module, an employee module and an emergency module. All the employees of the hotel should install and login to the employee module receive updates. Guest can directly install the
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application by giving their name. Also the guest can add and emergency contact number to which the application will send the message in case of an emergency. The application has a third module called an emergency module. This module stores the contact of the nearest fire station/rescue team. An emergency notification will be sent to the stored contact after the confirmation from the authorities of the hotel. TAGDS application was tested at a private hotel at Muvattupuzha, Kerala. MiCS multigas sensors were placed in five rooms in the first floor of the hotel, and each sensor was connected individually and directly to the Arduino Mega 2560. An electronic alarm compatible with the Arduino Mega was also installed in every room where sensor is connected. Every sensor has a sensor ID which is connected with the room number. TAGDS is programmed in such a way that, when a sensor reports toxic gas presence, sensor ID will be used to identify the affected room. We made the hotel manager and all the guests in five rooms TAGDS application in their mobile phones. We set the carbon monoxide standard value to be 40 ppm as presence of carbon monoxide with value 50 PPM is harmful to human life [10–12]. We have concentrated on carbon monoxide only here because it is a toxic gas which cannot be identified smelled by human beings directly [13]. We set the algorithm in such a way that the application will alert the customer when any of the toxic gases identified by the sensor. We generated carbon monoxide fumes in a room for testing the proposed system. TAGDS application precisely detected the toxic gas presence and alerted the guest and the hotel manager through notification. Mobile phone of the guest raised an alarm. Also the alarm installed in the particular room worked without trouble, when CO was detected. When TAGDS application was tested in the hotel, we set the minimum reading of carbon monoxide to be 100 ppm so that anybody going beyond the set level will trigger the system. When the application identified the carbon monoxide presence detected beyond the expected limit of 100 ppm, TAGDS application trigger the alarm in the room and send messages to the inmates TAGDS mobile application asking to evacuate the room as soon as possible (Figs. 5 and 6). At the same time, the system communicated with the hotel manager and recommended the user you called an emergency number which was reset. The whole communication happened within 10 s detection of carbon monoxide in the room. The guest and the hotel management found it very helpful since it requires a very low cost for implementation and its simplicity of usage. There is no such wireless sensor network-enabled system available in India and so that no comparison with the previous system is possible.
5 Conclusion TAGDS may be used as a fine solution to identify leakage of toxic gases from room heaters or air conditioners or any electronic or electrical devices installed in a hotel or resort room. This application will clearly safeguard the inmates of the room so that disasters can be avoided. Since this application is online, simple and portable, guest can easily manage it. No personal data is needed for this application to work.
40 Fig. 5 Alert shown in user login when toxic gas detected
Fig. 6 User options when toxic gas is detected
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In future, multiple sensor node failure shall be considered and can be made sure that information loss is avoided at any cost. Also of an extension of TAGDS application, the next version shall be upgraded to identify the reason of gas leakage and cut off the power through the mobile application when needed. Technology is effectively used in this application to safeguard human life.
References 1. Jain, P.C., Khushwaha, R.: Wireless gas sensor monitoring for detection of harmful gases in utility areas and industries. In: 2012 Sixth international conference on sensing technology (ICST) 2. Al Rasyid, M.U.H., Nadhori, I.U., Sudarsono, A., Alnovinda, Y.T., Pollution monitoring system using gas sensor based on wireless sensor network. Int. J. Eng. Technol. Innov. 6(1), 79–91 (2016) 3. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E., (eds.): Bio-inspired Neurocomputing. Springer (2021) 4. http://www.thehindu.com/todays-paper/tp-national/tp-tamilnadu/their-life-clogged-with-dan gers/article1357153.ece 5. Khalaf, W.: Sensor array system for gases identification and quantification. In: Strangio, M.A. (ed.) Recent Advances in Technologies. InTech, Rijeka, Croatia (2013). Fig. 3.7. LCD to Arduino circuit. Fig. 3.8. GSM to Arduino connection. 113 K. Visvam, Ambeth, D.: Sens. Bio-Sens. Res. 7, 107–114 (2016) 6. Anoop, V., Bipin, P.R.: Super-resolution based automatic diagnosis of retinal disease detection for clinical applications. Neural Process. Lett. (2020). https://doi.org/10.1007/s11063-020-102 92-x 7. Anoop, V., Bipin, P.R.: Medical image enhancement by a bilateral filter using optimization technique. J. Med. Syst. 43, 240 (2019). https://doi.org/10.1007/s10916-019-1370-x 8. Anoop, V., Bipin, P.R.: Spectral feature and optimization-based actor-critic neural network for arrhythmia classification using ECG signal. J. Exp. Theor. Artif. Intell. (2019). https://doi.org/ 10.1080/0952813X.2019.1652355 9. Marques, G., Bhoi, A.K., de Albuquerque, V.H.C., K.S., H. (eds.): IoT in Healthcare and Ambient Assisted Living. Springer (2021) 10. https://www.huffingtonpost.in/entry/nepal-tragedy-kerala-tourists-here-is-what-happened_ in_5e2917b5c5b67d8874ac9b28 11. https://www.environment.gov.au/protection/publications/factsheet-carbon-monoxide-co 12. https://www.environment.gov.au/protection/publications/factsheet-national-standards-cri teria-air-pollutants-australia 13. https://www.nfpa.org/-/media/Files/News-and-Research/Fire-statistics-and-reports/Hazard ous-materials/osgasexposure.ashx?la=en
A Deep Neural Network Model for Effective Diagnosis of Melanoma Disorder Pradeep Kumar Mallick, Sushruta Mishra, Bibhu Prasad Mohanty, and Sandeep Kumar Satapathy
Abstract Skin cancer is one of the most critical diseases, and melanoma is the most widely observed skin cancer occurring in major population. Hence, early detection and treatment of melanoma disease are very important. This research study presents the classification of melanoma images using deep neural network. Principal component analysis (PCA) was used as a feature extraction algorithm in this study. Wavelet transform is also applied to remove noise and inconsistencies in the dataset. Performance was compared with various other classifiers like SVM, RBS, random forest and Naive Bayes. Deep neural network gave an optimal performance with performance indicators. Classification accuracy, precision, recall and F-score value recorded with deep neural network are 98.4%, 97.8%, 98.5% and 98.1%, respectively. Hence, our proposed work can be an effective classification framework in categorization and diagnosis of melanoma disease. Keywords Malignant melanoma · Computer-aided diagnosis · Feature extraction · Classification
1 Introduction Skin is the surface of much of our body which is likely to be exposed to the atmosphere and can be subjected to dust, bacteria, microorganisms as well as UV radiation. It P. K. Mallick (B) · S. Mishra School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, Odisha, India S. Mishra e-mail: [email protected] B. P. Mohanty Department of Computer Science and Applications, Utkal University, Bhubaneswar, Odisha, India S. K. Satapathy School of Computer Science and Engineering, VIT University, Chennai, Tamilnadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_4
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is one of the key causes for skin disease which is also caused by the instability in the genes, which complicates the skin diseases. Skin cancer is the deadliest form of disease in human beings in all forms of cutaneous illnesses. It can develop anywhere in the cells among less pigmented cells. Skin cancer is a serious and common disease. Millions of raw case studies of skin-related tumors in the west alone are reported each year [1, 2]. Equally troubling are global statistics [3, 4]. Melanoma is a very dangerous kind of cutaneous cancer [5]. Melanoma is a kind of skin-related cancer. It has been projected that nearly 97,480 more new melanoma-based cases and 7230 people are estimated to lose life due to this disease, as per American Cancer Society’s annual report [6]. Melanoma is a very deadly kind of skin cancer, and it holds for around 75% of skin cancer deaths [7]. With an abnormal mole, melanoma usually occurs. On an existing mole that has varied, on a newly developed mole, Melanoma can develop but can also appear without a sign on an other skin sign or skin portion. Further, lesions may be inflamed, ulcerated, itched or bleeding. Many melanomas, however, do not have a typical mole hue. It can be less than 5 mm, while moles are generally more than 5 mm. It may take place in places not exposed to the sun. At this stage, a thorough local lesion evaluation by a dermatologist is carried out. Tumors with irregular appearance, rapid growth in size, asymmetrical pigmentation varying between brown and black (violet tint) and hyper-pigmentation islands are suspected to cause malignant melanoma. Anatomical–pathological study of the excised tumor is used to determine certainty. The concern is that lesion treatment is an invasive operation. However, around 90% of melanoma cases are cured if detected and removed on time. The rate of survival after surgery is reduced well below 20% if it is diagnosed later, if metastases of the liver or lungs are already present. An intelligent model that is able to detect melanoma by patients is therefore essential. This system is not invasive. Precise early melanoma identification will dramatically improve patients’ survival rate. Manual melanoma identification, therefore, creates an enormous demand for well-trained experts and undergoes changes between observers. It is worth developing a robust and automated model for identifying melanoma to increase pathologists’ preciseness and effectiveness.
2 Literature Survey Due to deep learning and machine learning methodology, various researchers have attempted to build skin lesion detection systems. Extraction features and grading techniques are still being developed. This section discusses the research related to the diagnosis of melanoma using different methodologies. The authors in [8] have established a method for classifying the skin lesion, whose first step consisted of preprocessing data. Followed by this process was the extraction of features by a pre-trained CNN neural network from AlexNet. The decision was eventually made with the use of a K-nearest neighbor (KNN) algorithm. The findings from a collection of 399 images depicted good values of bio-statism. The accuracy of the model was 95.18%, a 92.1% sensitivity and 93.64% precision. In order to remove certain
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functionality, Codella et al. [9] have used an AlexNet CNN. This article used one of the most well-known skin lesion detection image databases, the data samples which constitute 2624 dermoscopic images, which include melanoma and non-melanoma images. The outputs of AlexNet CNN have been modified, and features including sparse scripting, small-level drafting and a deep residual network have been used. A support vector machine has been used to identify the images. The precision was 93.1%, the precision 92.8% and the recall 94.9%. In [10], more kinds of skin lesions were classified on the basis of ResNet-152. Outcome generated from 956 pictures was arranged between 89% for warts and 96% for acnytic keratosis. The author contrasted the findings of three forms of CNN: the restal networks (ResNet), VGG19 and the hybrid VGG19 with the support vector machine ( SVM), and another paper addressed the topic of skin injury classification in multiple CNNs [10]. Within this database, 10,000 images of benign and malignant lesions were identified. The optimum output was generated by using VGG19, with a 95% recall [11]. The high accuracy of a linear classifier demonstrated by Kawahara et al. [12] and was trained on CNN features pre-trained on natural images. Up to ten skin lesions can be successfully separated. Some researchers [13] focused on the exact segmentation of lesions in the skin. The technology used in this document is also linked to the method of deep learning. Initially, noise was to be reduced by using certain filters, which then resulted in a CNN image. Results have been obtained, with a sensitivity of 95, 98.9% and a precision of 98.5%, but only for segmentation and not classification. In recent years, a new technique for segmenting skin injuries [14] has been developed where a complex de-convolutionary network is equipped to hold input and output image resolution without making any difficult image post-processing. In order to obtain contextual information, a chained residual pooling is then used. A hierarchical control is used to achieve a strong forecast mask. A 0.939% precision , a Jaccard index of 0.756% and a Dice coefficient of 0.866% are then achieved. The ISBI 2017 database has been used for the algorithm. The ISBI 2017 database has been used for the algorithm. In [15], the author used the histogram of oriented gradients (HOG) and the descriptors of texture, such as fractal proportions, to detect skin lesions, as well as classic features, such as geometrical features. The fractal test showed the best results in skin lesions classification [16]. The best result of validation score of 76% through the use of PNASNet-5-Large was obtained with deep learning models such as InceptionResNetV2, InceptionV4, PNASNet-5-Large and SENet154 for categorization of skin lesions from the ISIC 2018 database. On the 2016 ISBI challenge dataset for the classification of malignant melanoma cancer, Yu et al. [17] established a CNN with over 50 layers. This challenge recorded the best classification accuracy of 85.5%. In 2018, Haenssle and others [18] used a deep convolution neural network to identify melancholic images with an 86.6% sensitivity and classification characteristic of a binary diagnostic group. Dorj et al. in Ref. [19] have developed a multi-class classification using ECOC SVM and CNN. ECOC SVM with AlexNet deep learning CNN pre-trained and categorized multi-class data was the strategy. This research records an average accuracy of 95.1%. [20] In a deep neural network, Han et al. used 12 skin diseases to identify clinical photos. The best classification precision instance recorded varies between 96.0% [21, 22]. Similarly, several other popular
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works related to the domain are being undertaken to evaluate the impact of machine learning on disease diagnosis.
3 Proposed Methodology The proposed methodology of diagnosis among benign and malignant melanoma lesions is shown in Fig. 1. Skin dataset is the input to the system model. The proposed model sequentially processes different phases like dataset preprocessing, extraction of features, classification of data records into benign and malignant melanoma and finally performance evaluation. Dataset collection is done from ISIC dataset. It comprises a larger collection of skin cancer data samples with approximately 23,000 image samples. In this study, 3500 images are aggregated from the dataset [23]. Initially, data preprocessing is done to eliminate noise and inconsistencies
Dataset Input
Dermoscopy Images from ISIC
Data PreProcessing
Resizing, transformaon, contrasng and filtering
Feature Extraction
Wavelet transform & PCA
Classification
Deep Neural Network
Performance
Accuracy, Precision and Recall
Evaluation
Fig. 1 Proposed melanoma detection model
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from dataset so as to enhance its quality. At first, hair removal method was used through Hough transform approach. The result gives a clear image of tumor on skin layer, thereby enhancing the quality of data. It is followed by shade removal. Darker and lighter shades around tumor in images are removed so that the tumor is very well visible. MATLAB filters are successfully applied for this process. Removal of glares is done after this. The presence of glares sometimes degrades the classification performance. Thus, it is also removed using MATLAB filters. In feature extraction phase, unique and more relevant features are retained to optimize the dataset and improve the performance. Here, at first, image transformation is done with the use of 2D wavelet transform. Images are divided into detailed replicas representing basic information, vertical and diagonal sections. Subsequently, these features are retrieved with principal component analysis. It is used to reduce wavelet coefficient dimensions [24–26]. The resultant vector set constitutes less components, thereby needing less execution time and storage requirements. Finally, classification is performed using deep learning model to categorize the retrieved features into benign or malignant melanoma. Figure 2 demonstrates the CNN model data flow for melanoma detection. A deep learning studio (DLS) is deployed to develop the classification prototype. Here, a deep learning algorithm is chosen by dragging the important dashboard constituent. Sequential steps needed to construct a deep learning prototype are as follows. 1. 2.
Input block is selected and dragged within work space. It is further normalized.
Dropout_1 Input_1 Convolution2D_3 BatchNormalization_1 Convolution2D_4
Dense_2
Dropout_2
Output_1
Convolution2D_1
Convolution2D_2 Flatten_1 MaxPooling2D_1 Dense_1
Fig. 2 Convolution neural network data flow
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3. 4. 5. 6.
Convolution, pooling and dropout blocks are applied serially. Step 3 is iterated again and again until optimum result is obtained. Flattening block is applied. Dense block core layer is used to fine-tune the output metrics to suit the class label. Output block is applied. At the end, it is ensured that prototype development selects green ‘ok’ otherwise selection is gain fine-tuned.
7. 8.
4 Results and Discussion Our research study presented an analysis of skin cancer images to classify it into malignant and benign tumor. Deep neural network is used for the analysis. The dataset constitutes around 3500 images. PCA method is applied for feature extraction and then classified with deep neural network model. The proposed model is compared with other popular machine learning algorithms to determine its effectiveness. Various parameters are used to evaluate its performance of classification. An accuracy analysis was performed, and it was observed that proposed model using deep neural network generated 98.4% accuracy. Among other algorithms, RBF gave 96.15% accuracy, while least accuracy was generated by SVM classifier on the dataset. Figure 3 highlights the result obtained. Precision analysis was also performed using the series of classifiers on a 60:40 ratio data samples. A very impressive precision value of 97.8% was recorded with deep neural network, while SVM recorded a low 78% precision value. In general, there was a consistent performance throughout. The comparative analysis for precision metric is illustrated in Fig. 4. Similarly, the recall value was noted for all classifiers. A very high value of 98.5% recall was observed with deep neural network as compared to Classificaon Accuracy (%) 90
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others. SVM gave a relatively low score of 80.5% recall value. Figure 5 illustrates the overall process. F-score is the harmonic average value of precision and recall. It is a vital metric for evaluation of a machine learning model. 98.1% F-score value was recorded with deep neural network, while a low value of 79.2% was noted with SVM classifier.
5 Conclusion and Future Scope The research discusses a new classification approach using deep neural network for skin cancer image categorization. DWT and PCA methods were used as feature extractors. Around 3500 images were collected for the task. The proposed model was
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implemented with other classifiers like SVM, RBF, Naive Bayes and random forest for evaluation of performance. It was observed that classification with deep neural network generated an optimum performance in classification of melanoma images. 98.4% accuracy rate was generated in the classification. Precision and recall values noted were 97.8% and 98.5%, respectively, with deep neural network. 98.15 was the F-score value recorded. Results indicate that classification with deep neural network can provide a maximum performance in dealing with melanoma disease detection.
References 1. Siegel, R., Naishadham, D., Jamal, A.: Cancer statistics. Cancer J. Clin., pp. 10–29 (2012). https://doi.org/https://doi.org/10.3322/caac.20138 2. Esteva1, A., Kuprel1, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S. :Dermatologist-level classification of skin cancer with deep neural networks. Nature, pp. 115– 126 (2017). https://doi.org/https://doi.org/10.1038/nature21056. 3. Siegel, R., Miller, K.D., Jamal, A.: Cancer statistics. Cancer J. Clin., pp. 7–30 (2018). https:// doi.org/https://doi.org/10.3322/caac.21442. 4. W. H. Organization. WHO. [link]. https://www.who.int/uv/faq/skincancer/en/in dex1.html. 5. Zhang, X.: Melanoma segmentation based on deep learning. Comput. Assist. Surg. 22(Suppl. 1), 267–277 (2017). https://doi.org/10.1080/24699322.2017.1389405 6. Siegel, R., Miller, D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 69, 7–34 (2019). https:// doi.org/10.3322/caac.21551 7. Jerant, A.F., Johnson, J.T., Sheridan, C.D., Caffrey, T.J.: Early detection and treatment of skin cancer. Am. Fam. Physician 62(2), 381–382 (2000) 8. Pomponiu, V., Nejati, H., Cheung, N.M.: Deepmole: deep neural networks for skin mole lesion classification. In: Proceedings of the IEEE International Conference on Image Processing (ICIP); Phoenix, AZ, USA. 25–28 Sept 2016. 9. Codella, N., Cai J., Abedini M., Garnavi R., Halpern A., Smith J.R. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In: Proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, pp. 498–503, Munich, Germany, 5–9 Oct 2015
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10. Mendes, D.B., da Silva, N.C.: Skin Lesions Classification Using Convolutional Neural Networks. arXiv. 20181812.02316 11. Kwasigroch, A., Mikołajczyk, A., Grochowski, G.: Deep neural networks approach to skin lesions classification—2014A comparative analysis. In: Proceedings of the 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 28–31 Aug 2017 12. Kawahara, J., BenTaieb, A., Hamarneh, G.: Deep features to classify skin lesions. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 1397–1400, Prague, Czech Republic, 13–16 Apr 2016 13. Jafari, M.H., Karimi, N., Nasr-Esfahani, E., Samavi, S., Soroushmehr, S.M.R., Ward, K., Najarian, K.: Skin lesion segmentation in clinical images using deep learning. In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) Cancún Center; Cancún, México, 4–8 Dec 2016 14. Li, H., He, X., Zhou, F., Yu, Z., Ni, D., Chen, S., Wang, T., Lei, B.: Dense deconvolutional network for skin lesion segmentation. IEEE J. Biomed. Health Inform. 23, 527–537 (2018). https://doi.org/10.1109/JBHI.2018.2859898 15. Jianu, S.R.S., Ichim, L., Popescu, D., Chenaru, O.: Advanced processing techniques for detection and classification of skin lesions. In: Proceedings of the 22nd International Conference on System Theory, Control and Computing (ICSTCC), pp. 498–503, Sinaia, Romania, 10–12 Oct 2018. [Google Scholar] 16. Milton, M.A.A.: Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin lesion analysis towards melanoma detection challenge. arXiv. 20191901.10802v1. 17. Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 994–1004 (2016). https://doi.org/10.1109/TMI.2016.2642839 18. Haenssle, H.A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A.B., Thomas, L., Enk, A., Uhlmann, L.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29(8), 1836–1842 (2018). https://doi.org/10. 1093/annonc/mdy166 19. Dorj, U.O., Lee, K.K., Choi, J.Y., Lee, M.: The skin cancer classification using deep convolutional neural network. Multimedia Tools Appl. 77(8), 9909–9924 (2018). https://doi.org/10. 1007/s11042-018-5714-1 20. Han, S.S., Kim, M.S., Lim, W., Park, G.H., Park, I., Chang, S.E.: Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Invest. Dermatol. 138(7), 1529–1538 (2018). https://doi.org/10.1016/j.jid.2018.01.028 21. Mishra, S., Dash, A., & Jena, L. Use of Deep Learning for Disease Detection and Diagnosis. In Bio-inspired Neurocomputing (pp. 181–201). Springer, Singapore. 22. Mishra, S., Tadesse, Y., Dash, A., Jena, L., Ranjan, P.: Thyroid disorder analysis using random forest classifier. In: Intelligent and Cloud Computing, pp. 385–390. Springer, Singapore (2019) 23. Mishra, S., Tripathy, H. K., Acharya, B.: A precise analysis of deep learning for medical image processing. In: Bio-inspired Neurocomputing, pp. 25–41. Springer, Singapore 24. Mishra, S., Mallick, P.K., Jena, L., Chae, G.S.: Optimization of skewed data using samplingbased preprocessing approach. Front. Public Health 8, 274 (2020). https://doi.org/10.3389/ fpubh.2020.00274 25. Bhoi, A.K., Sherpa, K.S., Khandelwal, B.: Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT) analysis of electrocardiogram. Clust. Comput. 21(1), 1033–1044 (2018) 26. Bhoi, A. K., Mallick, P. K., Liu, C.M., Balas, V.E (eds.): Bio-inspired Neurocomputing. Springer (2021)
Sentiment Analysis and Evaluation of Movie Reviews Using Classifiers Pradeep Kumar Mallick, Priyom Dutta, Sushruta Mishra, and Manoj Kumar Mishra
Abstract The analysis of the opinions and likelihood as well as emotions in any form either in the form of text is called as sentiment analysis. Opinion mining is termed as sentiment analysis. The analysis of the data is very helpful in expressing the likelihood of the options for group of persons or individuals. With the advancement of the Internet, a huge collection data is being generated. Facebook, Twitter, YouTube, LinkedIn, Instagram and other social sites are gaining a lot of popularity as they allow users from different parts of the world to share their views upon various topics through comments, posts, tweets and tags. This paper provides a survey of existing technique for sentimental analysis like machine learning-based approaches like Naïve Bayes, logistic regression and SVM. Among them, decision tree recorded the best performance. Decision tree generated a classification accuracy of 91.6% and a minimum execution time of 1.12 s. Hence, it can be concluded that sentiment analysis with decision tree algorithm is an optimal concept for efficient movie review analysis. Keywords Sentiment analysis · Logistic regression · Naïve Bayes · Likelihood
1 Introduction The advancement of the social technology like Facebook, Twitter reviews, LinkedIn, Instagram allows people to communicate and share their opinions through status and stories and share their feelings and posting pictures as well as sharing views, votes and opinions. Social sites have huge chunks of data in the form of news feed, blogs, story and status updates, pictures, personal posts, etc. The sentiment analysis determines likelihood of opinions, views as well as likelihood of emotions from P. K. Mallick (B) · P. Dutta · S. Mishra · M. K. Mishra School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, Odisha, India S. Mishra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_5
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reviews as well as text reviews posts that are supplied by the users in this platform. Opinion mining finds the text that is given. It is the difficult to recognize the sentiment against each word (token). It works on the basis of emotions and options in the form of any text. We have made an analysis of the movie reviews using SVM, logistic regression and Naïve Bayes. Doing research and analysis is the most active research area. Data mining also requires the same kind of research. So, across many domains such as business, real estate or social platforms, data mining is applied. The accuracy and efficiency are the broad reasons for considering that sentiment analysis is the best option. It is the most used analysis because of its efficiency. Thousands of documents can be processed by this analysis. Quality improvement of a product or merchandise, recommendation systems or Decision making in Marketing research or online products shopping prediction in all these field sentiment analysis has given good accuracy. It generally takes three steps: Initial Processing State: The data obtained is first prepared, and it includes data cleaning and noise reduction. Feature Selection: Every feature in the word (keywords) has to be allocated a token, and the token is put under analysis. Cluster the data: On applying several algorithms on those tokens, it allocates different categories. So, in order to classify movie based on its review and ratings and genre, one can find whether the movie would be his likelihood of interest and can be watched because he likes such and such genre of movies. In this paper, we are going to analyze the algorithms, namely Naïve Bayes, logistic regression and SVM and check its performance. All these algorithms work perfectly fine and give us better classification result and good classification score. Our motive is to classify sentiment and further analysis, solely relied on enhancing analysis techniques that currently prevail. Sentiment plays important act in the field of decision-making aspects of any business to grow. In the field of sentiment analysis, various research has been done through different machine learning techniques like classification as well as clustering techniques such as SVM, Naïve Bayes and logistic regression. Using sentiment analysis, huge amount of document can be processed that resulted in good accuracy, and hence, it has wide range of applications. Since it is an efficient process which provides good accuracy, therefore it has various application. This paper consists of several parts based on the research done, and it is mentioned below (a) (b) (c)
(d)
Literature Review: It consists of the related work till date based on the research topic called sentiment analysis for the movie reviews Proposed Model: It consists of the model for which we have designed and implemented algorithm based on the model Algorithms used for the model: The algorithm used after the feature selection process to find out the better accuracy through the algorithm for sentiment analysis Result Analysis: The result analysis obtained after the implementation of the machine learning algorithm and hence to find out the good score.
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2 Literature Review Nanda et al. [1] used sentiment analysis for the Hindi language movie reviews and also used machine learning for this. So, the likelihood of the reviews either positive or negative is used in the sentiment analysis. They used sentiment analysis on Hindi movie for the review. The paper also stated to have used sentiment analysis for huge volumetric amount of data. Permatasari et al. [2] used sentiment analysis on Indonesian movie review which they found from Twitter. They used Naïve Bayes, and instead of bag of words features, they used ensemble learning included with Naïve Bayes. They found the accuracy as 0.94 measurable value as bag of words whereas ensemble learning as 0.88. Firmanto and Sano [3] took reference of tomato meter which recommend films based on rotten tomatoes which is referenced from the Web site called Rotten Tomato. But the method to determine the score was not publically available. So, the paper proposed a model based on which sentiment of the movie from the website rotten tomato and adding it up with the Sentiwordnet. Zhao and Jin [4] used sentiment analysis for Chinese movies. They combined a collaborative model where they combined sentiment labels with SVM. Rangkuti et al. [5] used sentiment analysis using bag of words and ensemble feature selection which used Pearson correlation. It resulted in dimensionality reduction, and they received the optimal feature selection. They got 82% accuracy prediction score. Singh et al. [6] had made a experimental work which includes new domain in the field of featurebased heuristic level sentiment analysis for movie review. They used SentiWord to differentiate different linguistic feature. Timani et al. [7] made a prediction for success of a movie based on social media review analysis. They used the comments of the movie from the trailers posted on YouTube, and from those, they used bag of words for sentiment analysis. Bandana [8] in their paper has made an analysis that using features such as Naïve Bayes or LSVM using this kind of heterogeneous features and collaborative approach can give more accurate sentiment analysis. Malini and Sunitha [9] stated that sentiment analysis can be referred as option mining and used sentiment analysis of the tweets based on the movie reviews and classified tweets as positive, negative as well as neutral. Sahu and Ahuja [10] used IDMB database as sentiment analysis for movie review prediction. They used Figs. 0 as most disliked and 4 as most liked movie. They followed informal jargon and stated that their approach to sentiment analysis has best accuracy which is 88.95%. Rahman and Hossen [11] used movie review either positive or negative from various sources and used several machine learning techniques. They found that BNB classifier achieves better accuracy. Wankhede and Thakare [12] used Times of India database for movie reviews and used sentiment analysis and found that random forest algorithm gives 90% accuracy. Ahuja and Anand [13] used dual training and dual prediction for the movie sample which are original as well as for the opposite and used bag of words approach. Sushruta et al. [14] proposed an attribute optimization model for effective classification of diabetes disorders. It served as an optimization model for machine learning computation. Sushruta et al. [15] presented a sampling-based approach that
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helped in optimization of data skewing in healthcare datasets. This sampling approach proved to be an efficient model for dimensionality reduction.
3 Proposed Model We proposed a model based on the sentiment analysis, and we use movie reviews from the dataset from the box office, Bollywood movies. The box office of Bollywood movies uses various types of ratings based on the user votes. The comments which are positive as well as negative or neutral are picked and are used as bag of words. Those reviews are used as a bag of tokens to analyze. Those reviews which are not useful and are inappropriate are removed as a part of data cleaning and used only those which are essential and rich in features. Sentiment analysis uses bag of words for the feature prediction and uses algorithms to provide better accuracy score. Similar to our proposed model, we are using sentiment analysis for movie reviews. The user is having a movie profile that would work like knowledge discovery and uses the user’s likelihood of the genre of the movie. For a movie, which we received reviews from the dataset, we are applying sentiment analysis and collect reviews from comments, stories, status, hash tags about the movie. The reviews are then obtained after the data cleaning process and feature extraction process begins. The feature extraction process uses special features to be ready for the next process. The feature selection is an important part which selects important features out of the reviews. Now, the data is to be put into machine learning algorithm and is to be checked which algorithm gives better accuracy score for the movie review based on the data obtained from dataset. This model is proposed for our sentiment analysis in which the user searches for a movie, and based on the type of search, the similarity of genre of movie is displayed. The knowledge base displays similar kind of movies and processes the movie reviews. After the reviews are processed, the sentiment analysis uses the data cleaning to remove the noisy redundant data and allows it for feature selection. After feature selection, algorithms such as Naïve Bayes, logistic regression and SVM are applied to get the more accuracy score for the accuracy of the data [16, 17, 19–21].
4 Result Analysis We have used algorithms such as SVM, Naïve Bayes, logistic regression that were performed on the dataset. We obtained the results which are presented in the form of tables, and Naïve Bayes gives the good accuracy score. We can see that Naïve Bayes performs more perfectly than logistic regression and support vector machine [18]. We compared it with the other machine learning algorithms from the dataset which are obtained by previous research papers. The comparison chart is as below which depicts which algorithm is more stable and more accurate for sentiment analysis. This is a result which is purely based on the dataset of IDMB, Bollywood movies. The
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dataset includes the reviews and comments and user votes against a particular movie. After the data clean up of the resulting dataset, the following results are obtained. We have obtained the above score from various machine learning algorithms such as Naïve Bayes, SVM, logistic regression and compared it with the previous accuracy obtained on the dataset from the same model. We can see that Naïve Bayes has accuracy score of 76.2% whereas SVM has 80.4% and logistic regression has the least 68.5%, so in our case, we can say that SVM performs best than other machine learning algorithms. We can clearly see that decision tree has 91.6% accuracy. It is shown in Fig. 2. For a good classification, we can see that SVM and Naïve Bayes have more and logistic regression has more execution time than KNN. Decision tree gave the minimum time of 1.12 s, while RBF algorithm provided a high execution time of 2.98 s. Figure 3 highlights the execution time delay analysis. But considering the accuracy, we can see that SVM, logistic regression and Naïve Bayes have good accuracy but decision tree algorithm has the maximum accuracy rate with minimum execution time delay. So, it can be concluded that decision tree should be recommended for the sentiment analysis. This would give us higher accuracy and best movie review analysis since bag of tokens works more accurately on these algorithms.
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5 Conclusion In this research, attempt was made to develop an automated classification model to categorize movie reviews using machine learning algorithms. Research on various algorithms which would give good accuracy score for sentiment analysis in movie prediction was conducted. Decision tree algorithm gave the best accuracy score of 91.6%, while it also took least time to classify the movies taking only 1.12 s model execution time. The future scope of this paper is to analyze and compare all the algorithm and check with permutation and combination that which algorithm accuracy is suitable and will be considered for best accuracy for sentiment analysis.
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References 1. Nanda, C., Dua, M., Nanda, G.: Sentiment Analysis of Movie Reviews in Hindi Language Using Machine Learning (2018) 2. Permatasari, R.I., Fauzi, M.A., Adikara, P.P., Sari, E.D.L.: Twitter Sentiment Analysis of Movie Reviews Using Ensemble Features Based Naïve Bayes (2018) 3. Firmanto, A. and Sarno, R.: Prediction of movie Sentiment Based on Reviews and Score on Rotten Tomatoes Using Sentiwordnet (2018) 4. Zhao, K., Jin, Y.: A Hubrid Method For Sentiment Classification in Chinese Movie Review Based on Sentiment Labels (2015) 5. Rangkuti, F.R.S., Fauzi, M.A., Sari, Y.A., Sari, E.D.L.:Sentiment analysis on Movie Reviews using Ensemble Features and Pearson Correlation Based on Feature Selection (2018) 6. Singh, V.K., Piryani R., Uddin A., Waila, P.: Sentiment Analysis of Movie Reviews : A New Feature-Based Heuristic for Aspect-Level Sentiment Classification (2013) 7. Timani, H., Shah, P., Joshi, M.: Predicting Success of a Movie from YouTube Trailer Comments using Sentiment Analysis (2019) 8. Bandana, R.: Sentiment Analysis of Movie Reviews using Heterogeneous Features (2018) 9. Malini, R., Sunitha, M.R.: Opinion Mining on Movie Reviews (2019) 10. Sahu T.P., Ahuja, S.: Sentiment Analysis of Movie Reviews: A Study on Feature Selection and Classification Algorithms (2016) 11. Rahman, A., Hossen, M.S.: Sentiment Analysis on Movie Review Data Using Machine Learning Approach (2019) 12. Wankhede R., AThakare, A.N.: Design Approach for Accuracy in Movies Reviews Using Sentiment Analysis (2017) 13. Ahuja, R., Anand, W.: Sentiment Classification of Movie Reviews Using Dual Training and Dual Prediction (2017) 14. Mishra, S., Tripathy, H.K., Mallick, P.K., Bhoi, A.K., Barsocchi, P.: EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20, 4036 (2020) 15. Mishra, S., Mallick, P.K., Jena, L., Chae, G.S.: Optimization of skewed data using samplingbased preprocessing approach. Front Public Health 8, 274. Published 2020 Jul 16. 16. Mishra, S., Koner, D., Jena, L., Ranjan, P.: Leaves shape categorization using convolution neural network model. In: Intelligent and Cloud Computing, pp. 375–383. Springer, Singapore (2019) 17. Mishra, S., Tripathy, H.K., Mishra, B.K., Sahoo, S.: Usage and analysis of big data in E-health domain. In: Big Data Management and the Internet of Things for Improved Health Systems, pp. 230–242. IGI Global (2018) 18. Mishra, S., Tripathy, N., Mishra, B.K., Mahanty, C.: Analysis of security issues in cloud environment. In: Security Designs for the Cloud, Iot, and Social Networking, pp. 19–41 (2019) 19. Bhoi, A.K.: Classification and clustering of Parkinson’s and healthy control gait dynamics using LDA and K-means. Int. J. Bioautom. 21(1) (2017) 20. Bhoi, A.K., Sherpa, K.S., Khandelwal, B.: Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT) analysis of electrocardiogram. Clust. Comput. 21(1), 1033–1044 (2018) 21. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E. (eds.): Bio-inspired Neurocomputing, Springer (2021)
Risk Factors Analysis for Real Estate Price Prediction Using Regression Approach Piyush Ranjan and Sushruta Mishra
Abstract The researchers of Harvard University found in a study that India has the potential to become one of the largest economy over next decade. As real estate is the second highest job creating sector, the first one is agriculture. The real estate sector will play a very important role in economy growth of India. A study of oxford economics tells that India has the potential to become world largest real estate market with around 11.5 million new houses per annum. Real estate sector has seen a boom in last two decades with increase in demand of new offices and residential buildings. Private investment reached around 1.47 billion US$ in 2019. In real estate sector in India, FDI reached around 25.04 billion US$. There are different factors which can affect the price of real estate like amenities, infrastructure, availability of land, affordability, and many more. Importance of the factors also depends upon the location and one should be careful at the time of selection of significant factors. It is very important to identify the important factors so that the approximation of the real estate price can be done. Here, regression techniques such as multiple linear regression, stepwise regression, and support vector regression are used for real estate price prediction. Keywords Real estate · Price estimation · Price prediction · Multiple linear regression · Stepwise regression · Support vector regression
1 Introduction The researchers of Harvard University found in a study that India has the potential to become one of the largest economy over next decade. As real estate is the second highest job creating sector, the first one is agriculture. The real estate sector will play a very important role in economy growth of India. A study of oxford economics tells that India has the potential to become world largest real estate market with P. Ranjan · S. Mishra (B) School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, Odisha, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_6
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around 11.5 million new houses per annum. There are different deciding factors of real estate price or property price. Here, we are going to discuss some of the most relevant factors which effect the price of property. Below there are some factors which effect the prices and attracts the buyer. Infrastructure [1] Infrastructure around the property plays a very important role in determining the property prices. People generally look for good transport facility that is good roads, railway, and air connectivity. There must be electricity, nearby hospitals, shopping complex, and people also look for good educational institutes around the location in which the property is located. Location [2] Location plays a very important role in price determination of real estate. A property available in heart of the city will have many features which will attract the buyers and motivate them to pay higher prices for the property. But in case of property which is situated in outskirt of the city will not have that features, and hence it will not attract a particular group of buyers. Good location mainly has good transport facility, healthcare facility, and good educational infrastructure. Amenities The value of property also depends upon the amenities provided with them. The more the amenities, the more the price of property. Amenity includes dedicated lift facility, alternate power supply, gym, swimming pool, community hall, shopping area, children park, and many more. Generally, buyers opt for property having more amenities. Commercial Real Estate Real estate which is located in rich industrial location is of high price. Properties in places like Gurugram, Noida, Hyderabad, and Pune have high value with respect to property which are in Patna, Cuttack, Ranchi, and Raipur. Availability of land Availability of land is an important factor for price estimation of real estate. If a prime location has scarcity of land, then the price of available property in that area will be very high. Affordability Affordability is related to the asking price of the property by the owner. The asking price should be in such a way that there exists certain concession. The EMI of the property should be affordable for the group of buyers which is targeted by the owner.
2 Related Work ANN can be used to model input and output relationship of data. There are two main input parameters in this paper, first one is transport system and second is environmental quality attributes. There are also some input parameters such as local land use and building characteristics are used here. Three layers are used here in ANN. First two layers consist of 20-neurons each and last layer consists of one
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neuron. For training, testing, and validation, the dataset is divided as 70% for training, 15% for testing, and 15% for validation. They have used correlation coefficient as a fit index which will evaluate the ANN goodness. Here, R-value is calculated for training, testing, and validation set. The output of the ANN approach is the price range of the real estate as discussed by Caggiani et al. [1]. In economy growth, the real estate plays an important part. So, we need an accurate forecasting of real estate prices. Researchers have found out that SVM is suitable for limited sample problem. In this paper, the researchers have used PSO-SVM for property price prediction. Here, for determining the parameter of SVM, PSO is used. Eight input features are extracted in SVM model for real estate, and for regression, output average price is used. For nonlinear processing, SVM is used because it has strong nonlinear processing capability and it is highlighted by Wen et al. [3]. This paper compares power of prediction between ANN and MRA. Multiple comparison is carried out between ANN and MRA with varied function specification, sample size of data, and temporal prediction. When a data sample of moderate to large size is used, then ANN shows better performance than MRA. Data sample is varied from 13 to 39% of total data. In this case, it is 506 to 1506 out of 3906 observation. Although the standard feed forward neural network with backpropagation is used, experiment with other learning variation such as backpropagation with weight decay, enhanced backpropagation, and quick propagation has been done. After many experiment, the standard backpropagation was found to perform better as presented by Nguyen et al. [4]. Price estimation process of real estate can be called as real estate property assessment. Client can get detailed information of the property specification through online or through brokers. Regression is generally used for price prediction. Here, deep neural network is used to get more improved and accurate result. A systematic method is used to derive a layered knowledge graph which is explained by Shinde et al. [5]. For real estate stakeholder, an accurate real estate price prediction is very helpful. A location-centered framework is used with different data profiling and prediction model. Location plays a very significant role in data profiling which is considered here. A multi-task learning approach is used here where each partition is related to a task which addresses the association in real estates as discussed by Gao et al. [2]. In real estate sector, house price estimation is one of the most important trading decisions. Here, in FairFax County, VA housing data of five thousand houses is analyzed. Here, Naive Bayesian, Ripper, C4.5, and AdaBoost machine learning algorithm are used for price prediction as highlighted by Park et al. [6]. Here, house prices of Turkey are determined using hedonic method that uses both ANN and multiple linear regression. By further comparison, it is found out that ANN performs better than multiple linear regression and is outlined in the research work by Selin et al. [7]. Here, historic market price method is used which is a mixture of math, statistic, and database algorithms. The real estate data is updated with 6 lakh offer prices. It includes historical data of real estate market as presented by Hromada [8]. Here, ADAM model which stands for asset dividing appraisal model is used for real estate price estimation. The ADAM objective includes evaluation of real estate investment and the resulting cash flows which is correctly highlighted by Krabec et al. [9]. For real estate price prediction, here, fuzzy modeling technique is used. Some
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parameter for price prediction is area of premises, no. of rooms, age of building, and floor of flat. Two model building process are used which are SparseFIS and FLEX FIS and is discussed by Lughofer et al. [10]. Many such research works are being carried out in computational domain [11–16].
3 Proposed Work From UCI repository and the Ageron, the data is taken. The data consists of 2000 rows. There are 10 attributes in the dataset which are latitude, longitude, housing_median_age, total_rooms, total_bedrooms, population, household, median_Income, median_house_value, and ocean_proximity. The data is taken from the UCI repository and data was in format of text file. Data is extracted and saved it as a comma-separated file (.csv). Data has many null values, outliers, and extreme values so data is preprocessed to get high accuracy [17]. Here, WEKA is used for data pre-processing and support vector regression. RStudio is a free development tool and it is open source also. It is used for the processes like machine learning, statistical analysis, and graphical analysis. Here, RStudio is used for multiple linear regression and stepwise regression (Fig. 1). Multiple Linear Regression MLR is a technique which uses more than one explanatory variable to guess the outcome of a response variable. Through MLR, we can model linear relationship between independent and dependent variable [18]. Assumptions for the Multiple Linear Regression are as follows: i. ii. iii.
Between dependent and independent variable there exists a linear relationship. Correlation between the dependent and independent variable is not too high. Form the population Yi, observations are selected randomly.
Stepwise Regression This regression can be used when we have multiple independent variables. The selection of independent variables in stepwise regression is done by an automatic process which does not require human intervention. Regression model is fitted by adding or removing independent factors one at a time on a specific criterion in stepwise regression. Through stepwise regression, we can maximize the prediction power of model with minimum number of predictors. This method can handle the high dimensionality of dataset. Most commonly used stepwise regression is as follows: 1. 2. 3.
Standard stepwise regression—It performs two things, it adds and removes independent variables as per need. Forward regression—It starts to add the most significant predictors first and then adds predictors in each step. Backward regression—It starts with all predictor variables and removes the less significant predictors in each step.
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Fig. 1 Proposed work
Support Vector Regression Sahoo et al. [19] used SVM for classification, but the extension of this technique can also be used for regression problem. Support vector regression (SVR) is the adjustment of SVM for regression. For minimizing the error of cost function, the SVR finds a line of best fit. For doing this, it uses the optimization process. It only considers those instances in training set that are closest to the line with minimum cost. We name these instances as support vector. A line always does not represent the best fit, so a margin is added around the line which tolerates some bad predictions so that we get an overall better result.
4 Result and Analysis 4.1 Implementation of Multiple Regression In RStudio to implement multiple regression, we have to use NLME and caret library. Linear and nonlinear mixed effect model (NLME) is used to fit and compare Gaussian
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Fig. 2 Diagnostic plots
linear and nonlinear mixed effects models. The caret is a group of functions which can be used to smooth the process of creating predictive models. Caret contains tool for data splitting, pre-processing, and visualizing. I have used the independent variables from the dataset to fit the regression model and used lm function to build the model. Plot() function is used build diagnostic plot. When fitting a multiple regression model, we have multiple x variables so these diagnostic plots allow us to check the validity of assumptions we make when we have too many variable to visualize.
4.1.1
Residual Versus Fitted Plot
This plot in Fig. 2 shows if residuals have indiscriminate pattern. There can be a nonlinear relationship between independent variable and dependent variable, and pattern can be seen in this plot if the model does not capture the nonlinear relationship. In Fig. 3, residual versus fitted plot, positive values for the residual on y-axis means the prediction was too low, and negative value means the prediction value was too high. 0 means the prediction or guess were exactly correct. Looking at the above residual versus fitted plot, we can see the red line is curved which means there is a nonlinear trend to the residuals.
4.1.2
Normal Q-Q Plot
The normal Q-Q plot helps us to understand whether the residuals are normally distributed. The Q-Q plot is a graphical tool which allows us to understand that if a set of data possibly came from some theoretical distribution such as normal distribution and polynomial distribution.
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Fig. 3 Residual versus fitted plot
In Fig. 4, normal Q-Q plot, the points fall along the line in middle of the graph but curve off in the extremities. The Q-Q plot which exhibits this kind of behavior usually mean that our data has more extreme values that could be expected if they truly came from a normal distribution.
4.1.3
Scale Location Plot
This plot indicates us about the spread of points across the predicted values range. If plot has a horizontal red line, then it indicates that residuals have a uniform variance across the range. Through this, we can check the assumption of equal variance (homoscedasticity). In Fig. 5, scale location plot, a horizontal red line will be ideal which indicates equal variance. But in the above plot, we see a curved red line which indicates that we have not met the linearity assumption.
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Fig. 4 Normal Q-Q plot
4.1.4
Residual Versus Leverage Plot
This plot helps us in identifying the influential data points which are present in our model and also helps us to detect hetroskedasticity and nonlinearity. In Fig. 6, residual versus fitted plot, we can see the standardized residual spread changes with the increase in leverage. In above plot, it is visible that the value of standardized residual is decreasing in y-axis with increase in leverage in x-axis which indicates hetroskedasticity. The points with the high leverage in the plots may be significant, deleting those points will change a lot the model. The Cook’s distance in the above plot which measures the effect of deleting the significant point of the combined parameter vector (CPV). Dotted red line in the above plot is Cook’s distance. Points outside the dotted line will have high significance. In our plot, we can see that there are no points outside the red dotted line.
4.2 Implementation of Stepwise Regression In RStudio to implement stepwise regression, we have to use tidyverse, caret, leaps, and mass library. stepAIC() comes under mass library which can choose the best
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Fig. 5 Scale location plot
model by AIC. It has option named direction which can take three values first is “both” for stepwise regression for both forward and backward regression, second is “backward” for backward regression and last is “forward” for forward regression. It returns the best model. Regsubsets() from leaps package has the tuning parameter nvmax. nvmax specifies the maximum number of predictors to be used in the model. I have used five predictor variables in the model. It returns multiple model with different size of predictors. We have to perform the performance comparison of different models in order to choose the best one. I have used cross-validation of tenfold in order to guess the average prediction error (RMSE) of each of the five models. From Table 1, we can clearly see that the RMSE is decreasing with increase in nvmax value. And it clearly visible that the model having five predictor variables shows lowest RMSE error which is best suited for the model.
4.3 Implementation of Support Vector Regression In Weka to implement support vector regression, we have to use SMOreg algorithm which is present inside function folder of classifier. I have used 66% split which is
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Fig. 6 Residual versus fitted plot
Table 1 Stepwise RMSE table
nvmax
RMSE
Rsquared
1
0.1190981
0.6222472
2
0.1094128
0.6800529
3
0.1088262
0.6834651
4
0.1064198
0.6976187
5
0.1060761
0.6992613
66% data used for training purpose and the rest 34% is used for testing. The kernel function is changed from poly which is default to Puk. The optimization process used here is RegSMOImproved. The reason behind changing the kernel from poly to Puk also known as Pearson VII Universal Kernel has capability of easily changing from a Gaussian to a Lorentzian peak shape and more by adapting its parameter. The RMSE of support vector regression model is 0.0933 which is quite low from others.
Risk Factors Analysis for Real Estate Price Prediction … Table 2 Evaluation table
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S. No.
Regression model
RMSE error
1
Multiple regression
0.108
2
Stepwise regression
0.106
3
Support vector regression
0.093
5 Parameter for Performance Analysis Root Mean Square Error—RMSE RMSE is standard deviation of the residuals (prediction errors). Residuals can be defines as the measure of the distance of data points from the regression line. RMSE is the measure of the spread out these residuals. It tells us how data is concentrated around the best fit line. Larger the RMSE, larger the spread of residuals around best fit line, and lower RMSE indicates concentrated data points around the best fit line. To compute error rate of a regression model, RMSE is used. Lower RMSE indicates better model. From Table 2, it is clearly visible that the support vector regression model shows less RMSE than two other models. So, for my real estate price prediction model, the support vector regression model performs best with only 0.093 RMSE error.
6 Conclusion Main goal is the prediction of real estate prices which is done using multiple regression, stepwise regression, and support vector regression, and found that support vector regression shoes less RMSE in comparison with multiple regression and stepwise regression. Intention with this research is to help both buyer and investor and they can spend their money wisely. To predict the prices, new software technology can always help in future. Price estimation can be refined by considering other significant attributes like groundwater level, marketplaces, air quality index, earthquake index, and many other factors with the houses, and it will help people to invest wisely.
References 1. Chiarazzo, V., Caggiani, L., Marinelli, M., Ottomanelli, M.: A neural network based model for real estate price estimation considering environmental quality of property location. In: 17th Meeting of the EURO Working Group Transportation, EWGT2014, 2–4 July 2014, Sevilla, Spain 2. Gao, G., Bao, Z., Cao, J., Qin, A.K., Sellis, T.: Location-Centered House Price Prediction: A Multi-Task Learning Approach (2019). arXiv:1901.01774v1 [cs.LG] 3. Wang, X., Wen, J., Zhang, Y., Wang, Y.: Real Estate Price Forecasting Based on SVM Optimized by PSO (2013)
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4. Nghiep, N., Al, C.: Predicting housing value: a comparison of multiple regression analysis and artificial neural network. Journal of real estate research 22(3), 313–336 (2001) 5. Shinde, A., Dange, N., Patane, N., Ghopal, S., Beera, V.: Real Estate Properties Assessment using Deep Neural Network, IJRSEM (2019) 6. Park, B., Bae, J.K.: Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia Housing data. Expert Syst. Appl 42(6), 2928–2934 (2015) 7. Selim, H.: Determinants of house prices in turkey: Hedonic regression versus artificial neural network. Expert Syst. Appl. 36(2), 2843–2852 (2009) 8. Hromada, E.: Real estate valuation using data mining software. In: Creative Construction conference 2016, CCC 2016, 25–28 June 2016 9. Krabec, T., Schafer, C.: Valuating direct real estate investment by using the ADAM modeling approach. In: 16th Annual Conference on finance and Accounting, ACAF Prague 2015, 29th May 2015 10. Lughofer, E., Trawinski, B., Trawinski, K., Kempa, O., Lasota, T.: On employing Fuzzy modeling algorithms for the valuation of residential premises. Inf. Sci. 181, 5123–5142 (2011) 11. Mishra, S., Mishra, B.K., Tripathy, H.K.: A neuro-genetic model to predict hepatitis disease risk. In: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–3. IEEE (2015) 12. Mallick, P.K., Mishra, S., Chae, G.S.: Digital media news categorization using Bernoulli document model for web content convergence. Pers. Ubiquit. Comput. (2020). https://doi.org/10. 1007/s00779-020-01461-9 13. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E. (eds.): Bio-inspired Neurocomputing. Springer (2021) 14. Bhoi, A.K., Sherpa, K.S.: QRS Complex detection and analysis of cardiovascular abnormalities: a review. Int. J. Bioautom. 18(3), 181–194 (2014) 15. Bhoi, A.K.: Classification and clustering of Parkinson’s and healthy control gait dynamics using LDA and K-means. Int. J. Bioautom. 21(1), 19 (2017) 16. Bhoi, A.K., Sherpa, K.S., Khandelwal, B.: Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT) analysis of electrocardiogram. Clust. Comput. 21(1), 1033–1044 (2018) 17. Sahoo, S., Mishra, S., Panda. B., Jena, N.: Building a new model for feature optimization in agricultural sectors. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 2337–2341 (2016) 18. Mishra, S., Dash, A., Mishra, B.K.: An insight of Internet of Things applications in pharmaceutical domain. In: Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach, pp. 245–273. Academic Press (2020) 19. Sahoo, S., Mishra, S., Mishra, B.K.K., Mishra, M.: Analysis and implementation of artificial bee colony optimization in constrained optimization problems. In: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms, pp. 413– 432. IGI Global (2018)
A Support Vector Machine Approach for Effective Bicycle Sharing in Urban Zones Saurabh Suman, Sushruta Mishra, and Hrudaya Kumar Tripathy
Abstract Bicycle-sharing system is a modern personalized public transport network through which people can rent a bicycle from one bicycle stand to the other in their network. It is an environment-friendly mode of transport for a healthier society. Today, the bicycle-sharing system is getting popular all over the world but inconsistent demand in different time slot and unreasonable bicycle allocation at different bicycle stand is a potential concern for any governing company. Thus, bicycle demand on a particular day and particular time slot is very important for the distribution of limited available resources. In this paper, we have predicted the bicycle demand based on a few important attributes like temperature, humidity, and time that affect the performance. We have used a support vector machine for this regression task where predictions are made both on a daily and hourly basis. So that the business administrator can effectively redistribute the bicycle across the location based on the demand, and end user can effectively plan their trip to nearby catchment areas. The evaluation result indicates that our method achieved an acceptable accuracy on both daily and hourly bicycle-sharing dataset. However, effective re-balancing at particular bicycle stand is possible if a potential destination or raw sensor data of a particular bicycle stand is known beforehand. Keywords Bicycle-sharing system · Bicycle demand prediction · Machine learning · Support vector machine · Support vector regression
1 Introduction Bicycle is part of our day-to-day communication from ages, but with the innovated approach, public bicycle-sharing business is flourishing in India. It has effectively S. Suman (B) · S. Mishra · H. K. Tripathy School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, Odisha, India H. K. Tripathy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_7
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extended the scope of public transport service and continuously contributing to a cleaner and greener environment. Besides that, the bicycle-sharing system nowadays is not limited to, rent and ride facility, instead, they provide real-time health and activity tracking via simple applications which ultimately help the users to be fit as a fiddle. While working on this project, we realize that the technology-driven mobility platform should provide real-time bicycle demand forecast as it will be helpful for both the end user and the business administrator. In the sense that if people know the number of bicycles that will be available in the particular area, they can plan their trip easily, whereas the business administrator can effectively arrange the bicycle across that particular area. In this paper, we analyse and predict casual and registered users bicycle demand on a daily and hourly basis using a support vector regression algorithm. Overall, we have considered all potential attributes like temperature, and humidity that can affect the dynamic forecasting. The remaining paper is divided into four sections: the literature survey is carried out in Sect. 2; methodology is outlined in Sects. 3 and 4 explains the proposed prediction model and the result obtained from the same; Sect. 5 concludes the paper and suggests future research direction.
2 Literature Review Bicycle-sharing system is an emerging industry all over the world. It has greatly enhanced the urban mobility but affected by spatial and time-related demand. Till now, various studies have been conducted to improve the bicycle demand, in and around the city. Patil et al. [1] presented a random forest model to predict the hourly bike-sharing demand. To improve the accuracy of the model, they carried out a few feature engineering steps where they categorize the humidity, temperature, and wind speed variables. Besides that, they partitioned the season variables into spring, summer, and winter factors. Overall, the continuous variables have been normalized in their experiment, and TuneRF function had enhanced the model by determining the optimal parameters. Feng et al. [2] proposed random forest-based bike rental demand model where they used GBM package to improve the capacity of the decision tree. The resultant accuracy improved in contrast to multiple linear regressions because of strong random forest generalization ability and decision tree is independent of each other. Xu et al. [3] presented a bicycle demand prediction model. They collected both trip and stand-based usage pattern to understand the usage pattern. The K-mean clustering method has been used to analyse the hourly pickup and return fluctuant pattern. Besides that, different back-propagation neural networks are trained under various conditions. The resultant experiment shows that prediction improves by making the distinction between stations and working–non-working days in comparison to an individual. Li et al. [4] proposed a hierarchical model to predict the bike demand from each cluster. They leverage gradient boosting regression tree (GBRT) to learn traffic model with the help of historical check-in/check-out and meteorology data.
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GBRT is based on bipartite clustering algorithm that clusters the station according to their geographical locations and transition patterns. Besides that, they used a multisimilarity-based inference model to learn across or inter-cluster transition effectively. Overall, the experiment has significantly reduced the error rate when carried out on New York City and Washington D.C. bike-sharing datasets. Cui et al. [5] presented a passenger flow forecast model that relies on advanced XGBoost method and sliding window concept. At first, they collected passenger flow data at a particular subway station with the help of automated fare allocation system, which is later used along with spatial-temporal feature and historical bicycle share data to recommend bicycle allocation. Feng et al. [6] presented a hierarchical traffic prediction model. At first, they divided the bike stations into different clusters with the help of iterative spectral clustering algorithm as nearby stations are frequently compared to determine bike usage pattern. The second most important thing they used is the gradient boosting regression tree to understand cluster and inter-cluster transition proportion, which is taken along with timeslot data to improve the city bike prediction. Po-Chuan et al. [7] presented a recurrent neural network-based model to predict rental and return bicycle demand both at the station and global level, in which high-dimensional time series demand data at the station level and hourly weather data are considered for the same. Authors in [8] explained the significance of bio optimization methods in real-time applications. A dynamic and intelligent CPU scheduling strategy was discussed by researchers in [9]. Authors in [10] stressed upon the factors helpful in disease diagnosis using deep learning. Qian et al. [11] proposed a gradient boosting regression model to predict rental and return demands. It starts by creating weak learner which keeps learning with every iteration. The core idea behind the same is to minimize the residual error by considering the various combination of features. Mrazovic et al. [12] proposed a long short-term memory network-based model to predict user demand and effectively handle the inventory spatial imbalance caused by topographical conditions, different commuting pattern, etc. The idea is to separate sequential and none sequential data so that a single model for all operational station in a given bicycle-sharing system can be trained, which will be helpful in reducing the redistribution and operational cost.
3 Methodology In this paper, we have followed the flowchart shown in Fig. 1 to analyse and predict the bicycle demand in a particular area.
3.1 Dataset The daily and hourly basis bicycle-sharing datasets used here are collected from the UCI repository. The day dataset consists of 731 instances, whereas hour dataset
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Fig. 1 Bicycle demand prediction flowchart
consists of 17,379 instances. Both the datasets have similar attributes except hour attribute, which is not available in day dataset. Each of these attributes is described in Table 1.
3.2 Data Pre-processing Data pre-processing is an important factor for any classification or regression model [13]. In most of the cases, data are usually inconsistent, noisy, or incomplete, but the bicycle-sharing dataset that we used here is already normalized. Besides that, there are no null values found in both day and hour dataset. In this experiment, we have partition both datasets into 60:40 ratio for bicycle demand prediction. Here, we have made a few hypotheses based on layman point of view to understand and explore the data [14]. We thought that the bicycle demand by registered users during college or office hours would be more, whereas bicycle demand by casual users would be higher during evening time because this is the appropriate time when most of them visit nearby catchment areas. Moreover, the business days would observe more demand by registered users, whereas the weekends would observe more demand by casual users. To validate our first hypothesis, we have generated boxplot diagram of casual users and registered users demand in different time frames which are shown in Figs. 2 and 3, respectively. We randomly divided the 24 h into a seven-time frame where ‘1’ denotes time frame of 0 to less than 8 hundred hours, ‘2’ denotes 8 hundred hours, ‘3’ denotes 9 hundred hours, ‘4’ denotes time frame of greater than 9 and less than 18 hundred hours, and ‘5’ denotes time frame of 18 hundred hours or 19 hundred hours, and similarly, ‘6’ denotes time frame of 20 hundred hours or 21 hundred hours, and ‘7’ denotes greater than 21 hundred hours. Later, we created a new variable named ‘days’ where ‘0’ denotes weekends if that
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Table 1 Data description S.
Attributes
Description
1
instant
Record ‘Indexing’
2
dteday
Denote ‘Date’
3
season
Denote ‘Season’ where ‘1’, ‘2’, ‘3’, and ‘4’ indicate winter, spring, summer, and fall, respectively
4
yr
Denote ‘Year’ where ‘0’ indicates 2011, and ‘1’ indicates 2012
5
mnth
Denote ‘Month’ from ‘1’ to ‘12’
6
hr
Denote ‘Hour’ from ‘0’ to ‘23’
7
holiday
Denote ‘Holiday’ where holiday is represented by 1 and 0 otherwise
8
weekday
Denote ‘Week Day’ from ‘0’ to ‘6’
9
workingday Denote ‘Working Day’ where working day is represented by ‘1’ and ‘0’ otherwise
10
weathersit
Denote ‘Weather’ where ‘1’ represents clear, few clouds, partly cloudy; ‘2’ represents mist, cloudy and mist, broken clouds and mist, few clouds and mist; ‘3’ represents light snow and rain, thunderstorm, scattered clouds, light rain, scattered clouds; ‘4’ represents heavy rain, ice pallets, thunderstorm, mist and snow, fog
11
temp
Denote normalized ‘Temperature’ in degree Celsius. The values are obtained by mentioned method (Temp–TempMin)/(TempMax–TempMin) where TempMin = −8 and TempMax = +39
12
atemp
Denote normalized ‘Feel Like Temperature’ in degree Celsius. The values are obtained by mentioned method (Temp–TempMin)/(TempMax–TempMin) where TempMin = −16 and TempMax = +50
13
hum
Denote normalized ‘Humidity’ which is obtained by dividing the values to maximum 100
14
windspeed
Denote normalized ‘Wind Speed’ which is obtained by dividing the values to maximum 67
15
casual
Denote number of ‘Casual Users’
16
registered
Denote number of ‘Registered Users’
17
cnt
Denote ‘Total Count’ of rented bicycles
No.
day is not a holiday and not a working day, ‘1’ denotes holiday, and ‘2’ denotes business day if that day is not a holiday but normal working day, to satisfy our second hypothesis. Figures 4, 5, 6, and 7 denote casual and registered users demand on the weekend, holiday, and business days basis on both daily and hourly datasets. There are a lot of natural outliers in the hourly dataset graphs, which are a result of different work shift patterns or group trips by the registered or casual user. After analysing these boxplot diagrams, we could say that it satisfies our hypothesis.
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Fig. 2 Casual user versus hours slot trend on hourly dataset
Fig. 3 Registered user versus hours slot trend on hourly dataset
4 Proposed Prediction Model Mostly, bicycle-sharing demand is influenced by demographics and environmental factors. But the empirical model reveals that bicycle lanes, parking stations, and city road networks also affect the bicycle rental frequency [15]. In this work, we have considered all potentially available attributes and support vector machines for this regression task. The main reason for using support vector regression is that in this algorithm, one attempts to fit the errors inside a specific threshold, whereas in simple regression, one reduces the error rate. The support vector regression algorithm is
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Fig. 4 Casual user versus days trend on daily dataset
Fig. 5 Registered user versus days trend on daily dataset
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Fig. 6 Casual user versus days trend on hourly dataset
Fig. 7 Registered user versus days trend on hourly dataset
based on the principle of support vector machine with a few minor differences which include the use of continuous values. Moreover, this non-parametric technique relies on the kernel function, which helps to map the lower-dimensional data into higherdimensional space [16]. The support vector regression considers maximum numbers
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of points within a boundary line and on our best fit hyperplane. We have tested linear, polynomial, and radial basis function (RBF) kernels for our work. The linear kernel is mostly used on linearly separable data and comparatively faster than any other kernel. The polynomial kernel generates curved lines in the input space. It is useful when all the training data is normalized. The RBF kernel creates a nonlinear combination of given features. But the training itself is a more expensive task because data become linearly separable when projected in higher-dimensional space. The Gaussian radial basis function is one of the most popular members of the RBF kernel family that have been used by default in Scikit-learn Python module. These aforementioned kernels are better understood by mathematical equations mentioned below. Linear Kernel: k(xm , xn ) = (xm · xn + c)
(1)
where c
Regularization parameter
Polynomial Kernel: k(xm , xn ) = (xm · xn + c)d
(2)
where d c
Degree Regularization parameter.
Radial Basis Fuction (RBF) Kernel: k(xm , xn ) = exp(−γ xm − xn 2 )
(3)
where γ
Determine the influence of a single training sample.
Here, we have calculated the R 2 regression score to determine the variation of dependent variable like registered and casual users predicted by the independent ones. Table 2 reflects the accuracy obtained by different kernels on both datasets. We have obtained a higher R 2 score of 81.16 and 65.24% by registered and casual users on the daily bicycle-sharing dataset, whereas a higher R 2 score of 81.42 and 81.19% by registered and casual users on the hourly bicycle-sharing dataset. The predicted individual demands are later concatenated to obtain daily demand and hourly demand. Overall, a decent accuracy has been achieved without depending on the principle of gradient boosting.
82 Table 2 Accuracy table
S. Suman et al. Predicted variables
Kernel
R2 score
Registered users demand on daily basis
Linear kernel
0.811686
Casual users demand on daily basis Registered users demand on daily basis
0.652467 Polynomial kernel
Casual users demand on daily basis Registered users demand on daily basis
0.656661 Radial basis function (RBF) kernels
Casual users demand on daily basis Registered users demand on hourly basis
Linear kernel
Casual users demand on hourly basis
0.342118 0.351411
Polynomial kernel
Casual users demand on hourly basis Registered users demand on hourly basis
0.549998 0.672116
Casual users demand on hourly basis Registered users demand on hourly basis
0.757147
0.578517 0.641901
Radial basis Function (RBF) kernels
0.814253 0.811904
5 Conclusion and Future Work Bicycle-sharing system plays a key role in a smart city. It provides the last mile connectivity to the catchment area and ultimately reduces the traffic congestion and air pollution. Overall, our proposed method can effectively assist the end user and business administrator by predicting the bicycle demand as it has achieved a decent R2 score on both day and hour dataset. But, outliners can affect the demand prediction if a large number of scan-to-ride requests can be made from any bicycle stand in a particular location on any random day or time. Hence, in future, we will work on relevant vision and imaging sensor data attributes for demand prediction at particular bicycle stand. Besides that, we will consider the employability of different machine learning techniques to improve the performance.
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References 1. Patil, A., Musale, K., Rao, B.: Bike share demand prediction using RandomForests. IJISET Int. J. Innov. Sci. Eng. Technol 2 (2015) 2. Feng, Y., Wang, S.: A forecast for bicycle rental demand based on random forests and multiple linear regression. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), Wuhan, 2017, pp. 101–105. https://doi.org/10.1109/ICIS.2017.795 9977 3. Xu, X., Zhirui, Y., Li, J., Xu, M.: Understanding the usage patterns of bicycle-sharing systems to predict users’ demand: a case study in Wenzhou, China. In: Computational Intelligence and Neuroscience, 2018, pp. 1–21.https://doi.org/10.1155/2018/9892134 4. Li, Y., Zheng, Y., Zhang, H., Chen, L.: Traffic prediction in a bike-sharing system, vol. 33. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’15). Association for Computing Machinery, New York, NY, USA, 2015, pp. 1–10 5. Cui, Y., Lv, W., Wang, Q., Du, B.: Usage demand forecast and quantity recommendation for urban shared bicycles. In: 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Zhengzhou, China, 2018, pp. 238–2388 6. Feng, S., Chen, H., Du, C., Li, J., Jing, N.: A hierarchical demand prediction method with station clustering for bike sharing system. In: 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, 2018, pp. 829–836 7. Chen, P., Hsieh, H., Sigalingging, X.K., Chen, Y., Leu, J.: Prediction of station level demand in a bike sharing system using recurrent neural networks. In: 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, 2017, pp. 1–5 8. Mishra, S., Mishra, B.K., Tripathy, H.K.: Significance of biologically inspired optimization techniques in real-time applications. In: Robotic Systems: Concepts, Methodologies, Tools, and Applications, pp. 224–248. IGI Global (2020). 9. Mishra, S., Sahoo, S., Mohapatra, S., Mishra, B.K.: CPU Scheduling Using an Optimized Round-Robin Scheduling Technique (2017) 10. Mishra, S., Dash, A., Jena, L.: Use of deep learning for disease detection and diagnosis. In: Bio-inspired Neurocomputing, pp. 181–201. Springer, Singapore 11. Qian, J., Comin, M., Pianura, L.: Data-driven smart bike-sharing system by implementing machine learning algorithms. In: 2018 Sixth International Conference on Enterprise Systems (ES), Limassol, 2018, pp. 50–55 12. Mrazovic, P., Larriba-Pey, J.L., Matskin, M.: A deep learning approach for estimating inventory rebalancing demand in bicycle sharing systems. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, 2018, pp. 110–115 13. Mishra, S., Tripathy, H.K., Mallick, P.K., Bhoi, A.K., Barsocchi, P.: EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20(14), 4036 (2020) 14. Jena, L., Patra, B., Nayak, S., Mishra, S., Tripathy, S.: Risk prediction of kidney disease using machine learning strategies. In: Intelligent and Cloud Computing, pp. 485–494. Springer, Singapore (2019) 15. El-Assi, W., Salah Mahmoud, M., Nurul Habib, K.: Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto. Transportation 44, 589–613 (2017) 16. Mallick, P.K., Mishra, S., Chae, G.S.: Digital media news categorization using Bernoulli document model for web content convergence. Pers. Ubiquit. Comput. (2020). https://doi.org/10. 1007/s00779-020-01461-9
Biosensor for Stress Detection Using Machine Learning Arijit Dutta, Hrudaya Kumar Tripathy, Arghyadeep Sen, and Luina Pani
Abstract Biosensor analytics is a crucial tool for monitoring health conditions for patients and individuals with availability of wearable sensors and other devices. Biosensor studies can depict the detection of stress with precision. However, in this study, a review of feasible machine learning algorithms is reviewed with their comparative analysis as per healthcare data analytics. To design and implement a biosensor, suitable machine learning algorithm should be selected in order to detect anxiety and stress levels. Using machine learning algorithms, the analysis can be fault-tolerant and stress detection could be effective in terms of convenience. Keywords Stress detection · Biosensors · Machine learning models · Machine learning techniques
1 Introduction 1.1 Brief About Biosensors Biosensor is an essential tool for current healthcare organizations to administer patients’ stress monitoring, mental emotion assessment and psychological reaction prediction. Biosensors are providing certain ways to detect stress and seizure for patients to potentially diagnose major trauma and predict nervous system failure symptoms. Besides, stress monitoring sensor data can be helpful for several fields including environmental quality prediction, disease diagnosis, food quality assessment, industrial process evaluation and others. Over a general aspect, sensor development incorporates major components such as analytical instruments and data analysis techniques [1]. Machine learning approaches can be used over sensor data to perform data pre-processing and system modelling, aimed at identifying a certain A. Dutta (B) · H. K. Tripathy · A. Sen · L. Pani School of Computer Science Engineering, KIIT (Deemed to be University), Bhubaneswar, India H. K. Tripathy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_8
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pattern to predict the desirable results. The electrode usually can be an enzyme, microorganism, tissue or an organelle connected with physico-chemical transducer performing the translation process [2]. Two fundamental types of wearable biosensor exist such as embedded in clothes, as in smartwatches, bracelets, fitness bands and others. The biosensors can measure the anxiety, stress, emotion and mental health monitoring. The skin sensors can keep track of electro-dermal activity (EDA) from certain attribute such as skin conductivity assessment [3]. Again, the heart rate monitoring (HRM) or heart rate variability (HRV) sensor can store cardiovascular data and nervous system impulses to estimate individual stress levels [4, 5].
1.2 Objectives of the Study The study is aimed to perform a survey on machine learning algorithms that can be applicable for biosensors for stress detection. The study is aimed to provide a comprehensive discussion about the algorithms useful for biosensor design. The objectives of the study are mentioned as; 1. 2. 3. 4.
To identify case studies on the agenda of stress detection To explore case studies with use of biosensors in stress detection as applications To demonstrate the machine learning model used for biosensor design To review the machine learning algorithms useful for biosensor design.
1.3 Applications of Biosensors and Types of Biosensors Biosensors comprise biological element and physiochemical detector elements; the device is used for detection and analysis [6]. Some fields that can integrate biosensors are as follows: • • • • • •
Clinical applications over disease diagnosis Healthcare monitoring process Stress screening procedure Pollution control over environment aspects Agricultural applications Industrial monitoring and processing (Fig. 1).
Moreover, biosensors have recent applications in carbohydrate measurement, alcohol-level detection and acids over food industries. For instance, in quality control procedures, biosensors are used for testing fermentation level for beer production, yogurt and soft drinks manufacturing. Types of biosensors are mentioned in Table 1.
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Fig. 1 Applications of biosensors
1.4 Advantages and Disadvantages of Biosensors Emotion or stress detection system can be broadly divided under three categories such as: systems based on electroencephalogram (EEG) signals, systems based on multiple physiological attributes and multimodal systems. Study [7] opined that advantages and disadvantages can be considered for EEG-based biosensors as follows. Advantages of EEG-based biosensors: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Better accuracy than other modalities as signals are from central nervous system. Insights gathering over functional status of neural networks in human brain and insights can be assessed over individual person’s mind. Faster response of signal towards stimuli and faster data collection can be performed. Physical or mental treatment tolerant and physiological signal quality can be inhibited. Lesser impact from social status and unbiased real data can be obtained. More robust. Popular non-invasive method. Highly temporal solutions. Low intrusiveness. Objective evaluations are obtained.
Disadvantages of EEG-based biosensors:
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Table 1 Types of biosensors based on applications Nature of biosensor
Objective
Devices exist
Electrochemical biosensors
Electrochemical biosensor reacts with analyte of interest multiplied by proportional electrical signal to analyte concentration
Potentiometric—it obtains changes and variations in open-circuit potential values Amperometric—based on reduction or oxidation of electroactive species, it measures current
Optical biosensors
Optical biosensors are either label-free or label-based; it measures interaction between bio-recognition sensing element and optical field
Colorimetric—it obtains light absorption changes Photometric—it obtains intensity of light
Immunosensors
Based on affinity ligand, these – biosensors couple immunochemical reaction with a transducer
Magnetic biosensors
Magnetic biosensor measures variations in magnetically induced effects or magnetic properties
Thermometric biosensors
Biological reactions associated – with heat release; thermometric biosensors obtain variations in temperature of solution with enzymatic reactions caused by an analyte
Acoustic biosensors
Acoustic biosensors known as piezoelectric biosensors; obtains change in an acoustic wave’s physical properties
I. II. III. IV.
V. VI. VII. VIII. IX.
–
–
Highly expensive. Data collection can face connectivity issues. Sophisticated techniques of signal processing are necessary for pattern extraction from EEG data. EEG-based stress classifier can be trained over a certain stimulus and cannot be generalized over unseen EEG data on other stimuli. Therefore, building model is quite challenging and specific training data is required. Electrode placement is a crucial work; failure of this work could lead to discrepancies in data. Non-stationary inherent characteristic brings more complexity in the research. Data acquisition process is lengthier. Data processing can be affected from different values of impendences. Distribution discrepancy between subjects exists.
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In case, collected data is sampled differently or collected from different sources; then training and test data mismatch could occur.
2 Stress Detection Using Biosensors and Other Applications: Literature Review 2.1 Stress Detection: Aspects and Its Use Anxiety or stress management is a major attribute to detect the stress level for an individual, and stress level is a hindrance to normal lifestyle as in social behaviour. As per World Health Organization’s research results, stress is considered as mental health issue that can put adverse effect on life over one out of four individuals [8]. Stress is considered as physical response to mental, physical or emotional issues that individual noticed. Immediate threats to stressor response are acute stress reaction [9] (Fig. 2). Certain hormones are triggered with excessive stress level encountered; adrenaline in bloodstream can cause concentration. Physical changes are induced from acute stress such as increased heartbeat, automatic reflexes and others. Healthy condition suggests the body should come back to normal state after providing acute stress response to the stressor [11]. Traditional approach of detecting stress is to provide questionnaire to stressed individual and collect their responses, whereas automatic stress detection can minimize the health risks and it can easily automate the process of automating stress-level detection for an individual [12]. However, in current scenario, medical and physiological experts can predict the stress level based on the patient inputs and can provide certain recommendations to reduce stress levels. To design sensor and to collect signal data, machine learning is much easier, hardware can be smaller, and IoT devices can be cheaper and widely available. These devices can provide several working applications to evaluate predictive maintenance and help in user behaviour monitoring. In certain conditions, stress causes several medical issues such as diseases or mental disturbance that are known to the patients and they are not ready to or not to confront a physician for opinions [13]. Obesity, Fig. 2 Aspects in stress detection [10]
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overweight, high blood pressure, irregular heartbeat or diabetes is certain example of physical conditions that can be induced by stress levels and stressors. In such scenarios, people usually suggested to visit physician periodically and get feedback based on their individual situations. Frequent and periodical physician visit can even lower their precious time and medical bills, and they can gain personal control over the personal well-being control over stress levels [14]. Biosensor is chemical sensing device that is biologically derived element linking a transducer for allowing quantitative development of complicated biochemical parameters.
2.2 Related Work and Case Studies About Stress Detection Using Biosensors Researchers [10] opined about stress analysis over respiratory rate, heart rate, facial electromyography (EMG) and galvanic skin response (GSR), and this data is a part of substantial outcome to stress detection. It served as an optimization model for machine learning computation. Researchers [15] depicted that mental stress prediction can be considered as stand-alone process over interfacing the GSR data over physiological sensors. Sushruta et al. [5] proposed an attribute optimization model for effective classification of diabetes disorders. Study [16] has proposed about electrocardiography (ECG)-based study for stress-level detection. Multi-model sensor provides efficiency over the stress analysis procedure in working people and drivers on regular basis (Table 2). Study [17] cited over sensor data on pressure distribution, heart rate, blood volume pulse (BVP) and electro-dermal activity (EDA). Stress and stressor are different by terminological aspect as stress is mental state under certain mentally perceived pressure, whereas stressor is an agent that causes stress such as noises, Table 2 Case study of stress overload signs and symptoms Cognitive symptoms Emotional symptoms
Physical symptoms
Behavioural symptoms
Poor judgement
Agitation relaxation inability
Increased frequency of urination
Isolating oneself from others
Memory problems
Moodiness
Aches, pains
Eating more/less
Constant worrying
Depression, general unhappiness
Nausea, dizziness
Nervous habits—nail biting, pacing and others
Anxious/racing thoughts
Sense of loneliness/isolation
Changes in levels Using alcohol, cigarettes, of blood glucose drugs to relax
Pessimistic approach/thoughts
Feeling overwhelmed
Indigestion
Procrastinating/neglecting responsibilities
Concentration inability
Irritability, short temper
Diarrhoea, constipation
Sleeping more/less
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Fig. 3 Flow chart of stress detection algorithm [13]
unpleasant colleagues, speeding vehicle, fear of deadline miss and even a first date. Eye movement tracking sensor can help with systematic analysis on stressors such as stoop word test and information related to tasks. Sushruta et al. [18] presented a sampling-based approach that helped in optimization of data skewing in healthcare datasets. Researchers [19] performed some experiments over non-invasive sensors for perceived stress detection and collecting physiological signals such as ECG, GSR, electroencephalography (EEG), EMG and peripheral oxygen saturation (SpO2 ) (Fig. 3). Continuous stress-level detection is carried out with performing physiological sensors over collecting data such as GSR, EMG, HR and respiration in a study. Stress detection is conducted based on skin conductance-level (SCL) data, facial EMG sensors and HR by ICT-related stressors. Findings from [20] have aimed to detect stress levels from individual reading using wearable sensors. BioNomadix module from Biopac can be worn as wristband, and electro-dermal activity can be measured through fingers and pulse plethysmograph (PPG) signals. PPG is known as blood volume pulse (BVP) that is obtained from pulse oximeter illuminating skin and measuring differences from light absorption. The device connects to a computer wirelessly and subject can easily move freely, while the readings are recorded in computer conveniently. Saliva secretion rate and components of saliva can be affected by automatic receptor activation, and increase in blood flow is associated with higher saliva secretion. Researchers [21] conducted a study on stress detection by analysing changes in level of protein or salivary alpha-amylase (sAA) considering sAA as stressor (Table 3). Some researchers have made attempts for predicting individual personality based on mobile phone study. Researchers [22] claimed that tests over extraversion, agreeableness, conscientiousness, neuroticism and self-esteem and mobile phone interaction can assess the association between stress level and personality category. Based on mobile phone usage such as call and SMS history tracking, smartphones have
92 Table 3 Case study of causes of stress [14]
A. Dutta et al. Common external cause
Common internal causes of stress
Major life changes
Chronic worry
Work/school
Pessimism
Relationship difficulties
Negative self-talk
Financial problems
Unrealistic expectations/perfectionism
Being too busy
Rigid thinking, lack of flexibility
Children, family
All-or-nothing attitude
employed accelerometers. Smartphone usage can detect individual mood and individual mental trait understanding and study [23] estimated mood defined by displeasure, tiredness and tense behaviour from daily living lifestyle from mobile phone data and previous subjective mood state. Smartphone data is used to understand about relation among sleep, mood and sociability. iPhone data and wearable HRV data can be considered for classification of low, moderate and highly perceivable stress level and conditions. Mobile phone and smartphone in-built accelerometers can be a part of stress detection so that user can follow the application features to reduce or take control of stress. As per [24] viewpoint, voice can be considered as stress indicator and voice-based stress detector application uses microphones for recording voice of the participant and classifies the voice features into Gaussian mixture model. Multi-model approaches are used for stress detection and actions, and voice signals are part of physiological measurements for stress-level detection improvement. This sampling approach proved to be an efficient model for dimensionality reduction. Researchers [25] conducted study over deception detection by considering measurement of voice variation, skin conductance and heart rate, while a poker gamer is playing. The study showed linear model can be developed to identify stress and bluffing at the same time for real money no-limit Hold’em tournaments where stakes are high. However, this study is conducted only for poker players as participants where stress level is high at most of the time.
3 Machine Learning Model Used in Biosensor Design Generated sensor data often comprises irrelevance and additional information rather than context information; however, sensor data handling could be more complex with irrelevant information. However, the sensor data is much detailed information for performing sophisticated data processing. Machine learning is considered as core technique to deal with sensor data to explore patterns or rules for controlling the system. Machine learning procedure is followed with three major steps as in data pre-processing, feature extraction and dimension reduction. Furthermore, the
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Signal Pretreatment
Feature extraction
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System modelling
Fig. 4 Machine learning process flow chart [17]
system modelling is designed to determine the pattern behind the extracted information. The process is shown in Fig. 4. In a brief, data pre-processing includes noise filtering, normalization of data, alignment of signals and others. After application of relevant data treatment procedures, feature extraction step is conducted. Feature extraction helps to extract sensor signals to low-dimensional feature space from highdimensional feature space. Feature extraction can select a pertinent variable to put characterization over the system. Final step of the procedure is to establish model for classifying quantitative estimation problems such as chemical concentration prediction or disease diagnosis and stress detection. These steps are major part in order to control stress detection application.
3.1 Data Pre-processing Raw sensor data is collected for data pre-processing in order to set up the machine learning model, and sensor data has some discrepancies over modelling effects. Data pre-processing includes some additional steps as follows: Noise removal: Noisy data removal is initial stage to enhance relevant signal data. Several sensor data includes time series data; wavelet analysis is used for examining the data over time frequency domain rather than Fourier transform approach as it only focuses on frequency domain. Wavelet analysis requires setting a suitable threshold value for enhancing the signal data as higher-frequency domain data composed with more noisy data. Auto-regressive analysis coefficients can build original data with appropriate chromatic signal enhancement representation. Outlier detection: The possible outliers are part of strange data points, questionable outcomes and doubtful observations. The outliers are generally obtained from instrument and procedural error; however, under a range or threshold, the outliers are gathered under data collection process. Statistical observations produce outliers in linear systems determining the residual of each observation. Self-organizing map (SOM) is considered as a tool to detect outliers for nonlinear systems depending on the data point distances. However, distance cannot be only way to obtain outlier from a set of data points for nonlinear systems as distance cannot verify the outliers or cannot confirm them either. Therefore, the radial basis function neural network (RBFNN) is considered as outlier detecting algorithm for complicated nonlinear systems. Outliers are not part of noisy data; hence, excluding outlier from main analysis can affect the mechanism outcome at some point.
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3.2 Feature Extraction and Dimension Reduction Here are some commonly used machine learning algorithms that are used for biosensor design. Wavelet analysis: Rather traditional Fourier transform, wavelet analysis is considered to examine the signal simultaneously over time and frequency domain. Wavelet analysis is essential for analysing non-periodic, intermittent signals and noisy data and can spawn several wavelets for signal analysis and interrogation. Researchers [26] claimed that wavelet sensor selection transformation obtained 100% accuracy for classification of odours for daily beverages. Based on feature extraction, wavelet analysis should be conducted based on decomposition level and wavelet type. Wavelet coefficients can be determined for both low and high frequency over case-dependent usefulness. Auto-regressive modelling: Auto-regressive modelling is another efficient feature extraction tool for speech analysis. AR model can be defined as: x n = a0 +
p
ai xn−1 + εn
i=1
X is considered as time series, a is AR coefficient, and epsilon is error rate estimate. AR model helps in estimating the original data series over optimization process. AR coefficients characterize the changes in data series trends with common effects of signal treatment. Noise removal and dimension reduction are considered as advantage to deal with mild signal shifting for time series. AR modelling is useful for using time alignment with pre-processing. The advantage is required for GC/MS-based sensor design for identification of bacterial growth.
3.3 Feature Subset Selection Genetic algorithm: Genetic algorithm (GA) is considered as optimization strategy for imitating natural evolution features, and it includes three operators such as selection, mutation and crossover. The algorithm attempts in selecting an optimum feature set detection, and binary vector is utilized for evolution process. Another objective function is used over classification process, and fitness function is used for testing the suitability of the objective function. Differential evolution: Differential evolution (DE) is a popular search-based method with scheme of generating population vectors. DE utilizes three population vectors with new population vector generation. The crossed-over target vector g is used for new vector v; the v can gather better quality than g, and g will be replaced with next-generation elements. Probabilistic neural network can be used for determining cost in this procedure. DE is applicable for selection of wavelength of sound for patients’ respiration. The
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method can reduce the data dimensions by 50–60%, and DE has better performance capability than GA in terms of minimal classification rate 0.0175 over GA.
4 Various Challenges of Using ML in Biosensor Design Machine learning models are formulated for identifying the patterns, employ them over the system mechanisms and sort out rules for controlling the system. Probabilistic and artificial intelligence-based rules and patterns are mainly based on continuous self-adjustment process, and machine learning model is entirely based on accuracy value. However, based on continuous trials of the model, the accuracy can either increase or decrease, adjusting towards perfection of the model. The real-time applications can have tendencies to obtain results from “grey box” and “black box” models. Therefore, real-time applications and models require a validation model for reliability checking. Model parameter updating process is essential for validation of the model based on a certain dataset. Validation model and parameter update are part of adjustment procedure as well.
5 Conclusion and Future Work Stress and anxious mental condition are major threats causing several health issues affecting individuals in daily life. Life behind a computer screen, life inside a factory, life behind wheel; each and every person encounters stress in daily life. To find a solution to resolve the stress-related physical issues, biosensor should be used. The solution should be fed with correct dataset from the biosensor, and the sensor data should be analysed and utilized for better pattern design. In current research era, physiological signals can be collected and can be easily utilized for detecting stress levels for any individual. In order to select the useful features, the gathered signals should be treated with machine learning algorithms. The treatment will consist of pre-processing implementation, feature determination and classification model determination. The accelerometer can be used for collecting data with significant measure to identify the physical movements and daily activities. Skin temperature, Galvanic skin response and heart rate variability can easily reflect autonomic nervous system activities. Therefore, the features are important for predicting individual stress levels. Support vector machine and Naïve Bayes classifier are best solution for using in biosensor design. Besides, these two algorithms; random forrest and decision tree can be employed to detect stress causes and conditions. Moreover, the thermal imaging technology is effective for development of wearable sensor design. Furthermore, deep learning can be utilized as individual stress level as deep learning is becoming a potential field for development.
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An Accurate Automatic Traffic Signal Detector Using CNN Model Ankush Sinha Roy, Lambodar Jena, and Pradeep Kumar Mallick
Abstract The main objective is to train and build an efficient artificial neural network model, such that the model’s accuracy is high enough to be able to apply it in real life. If the detector is not trained well or rather we can say, if the accuracy of the detector is not high enough, the automated car/driver will end up doing a wrong recognition. This can cause many accidents. Our main aim here is to check and increase the accuracy of the designed model and also, at the same time, ensuring minimal data loss. This can be achieved by proper preprocessing of the data and preventing overfitting. The result shows and compares how the accuracy of the machine learning model increases after proper data preprocessing is performed before feeding it into the model. Also, keeping into account that model overfitting is prevented. Keywords Machine learning · Image processing · Traffic automation · Image grayscaling · Feature selection
1 Introduction The most talked about topic in the world of innovation and technology for the future is probably about the future of the automobile industry. Now, when it comes to automation in automobiles, we think about fully automatic or self-driving cars. Here, a passenger has to depend totally upon the AI of the car for traveling. It is vital for the car to stick to the traffic norms, which includes following the traffic signals on roads one might come across, in order to maintain the smooth flow of traffic and to avoid unnecessary traffic violation fines. For this, we need to have an efficient traffic signal detector with good accuracy and ability to correctly understand the traffic A. S. Roy · P. K. Mallick School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India e-mail: [email protected] L. Jena (B) Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_9
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signs installed inside the car. There exist many kinds of traffic signals such as speed limits, no entry, turns, sensitive crossing, etc. Traffic signal categorization refers to the approach to identify the class label of a traffic signal. Training and building an efficient detector, able to detect all the signs correctly, are a very challenging task. An automatic traffic signal detector can find its use in a manned vehicle as well. It will help drivers be cautioned about the traffic rules and the traffic conditions that lay ahead. This will be specially useful for people who are new to that certain area or country and is not aware of the traffic signals that are being used there. As a result, one will be able to keep the flow of traffic smooth and can also prevent themselves from paying traffic rules violation fines at the same time.
2 Literature Survey There are several extensive works carried out in this domain. Many researchers and academicians have developed and proposed different machine learning models for efficient traffic signal detection. This section presents some papers related to that domain. Authors in [1] used a Hough transform for detection of edges of panels and for selection of closed channels to make the method noise sensitive. It was observed that it identified 97% of speed limits signals on the way and around 300 danger signs at an instant between 20 and 200 ms/mg as per the quantity of total outline. In another work [2], authors applied radial symmetry transformation for detection of speed limit signals in path. The method was a variation of circular Hough transform, and it was present to identify various circular signs in way. A radial symmetry transformbased method was applied in [3] for identification of similar geometric patterns such as square and triangle [4, 5]. Traffic signs detection is generally resolved in two phases which include localization and subsequent categorization. In another work [6], image processing is proposed for traffic sign identification in a real-time scenario. It used a generalized Hough transform approach to compute exact coordinates of a traffic sign in the desired image. It produced an accuracy of 97.3%. Authors in [7] showed efficient application of an algorithm approach to remove noise with the use of CUDA. Sushruta et al. [8] proposed an attribute optimization model for effective classification of diabetes disorders. It served as an optimization model for machine learning computation. Sushruta et al. [9] presented a sampling-based approach that helped in optimization of data skewing in healthcare datasets. This sampling approach proved to be an efficient model for dimensionality reduction. Other related works demonstrated the use of computational techniques in various application domains [10–14].
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3 Dataset Description The training dataset consists of 43 sets where each set denotes a distinct label. It consists of a total of 39,209 unique RBG images distributed accordingly in the 43 folders. These RBG images will be normalized and further converted into grayscale images, respectively. The testing dataset contains a total of 12,630 unique RBG images. The following Table 1 shows the list of classes of traffic signals in which the input image is to be classified.
4 Proposed Work/model with Description The proposed work in Fig. 1 constitutes feeding the training dataset into the convolution neural network (CNN) model after proper preprocessing of the data, which includes gray scaling, normalization, etc. [15] After the model is trained using the training dataset, performance of the model will be analyzed based upon its prediction when tested with a testing dataset. Each step of the model is discussed in detail.
4.1 Preparing the Training Dataset Grayscale and Normalization: The three-channel RBG image is converted into one link grayscale graphics. After which, it is normalized in order to get uniform image input from the dataset into the model. The final shape of the image that is given as input to the model is 30 × 30 × 1. Moreover, we have used python 3.7 and Tensorflow.
4.2 Proposed Machine Learning Model The model architecture is described in detail as follows:
102 Table 1 Classes of traffic signals
A. S. Roy et al. Traffic signal class (km/h) Threshold speed (20) Threshold speed (30) Threshold speed (50) Threshold speed (60) Threshold speed (70) Threshold speed (80) Threshold speed termination (80) Threshold speed (100) Threshold speed (120) No passing No passage of vehicles over 3.5 tons Intersection right way Priority-based path outcome End No vehicles Vehicles >3.5 tons restricted No entry General alert Danger left curve Danger right curve Double path Rough road Slippery path Narrow roads right Road work Traffic signs Pedestrians Children crossing Vehicle passing Snow alert Animals passing Passing velocity Right ahead Left ahead Across only Right-straight (continued)
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Traffic signal class (km/h) Left-straight Right mode Left mode Roundabout compulsory End_no-pass End pass vehicles >3.5 weight
Fig. 1 Proposed model
There are two numbers of 2D layers are used for feature extraction (combining multiple features to create one feature) of the images. The number of filters used = 32. The kernel size is kept large (5 × 5), considering the fact that this is the first layer and making sure no data is lost. The rectified linear unit (ReLu) is used as an activation function to keep the output of the layer always positive. Max pooling is used to further minimize the features in the image. The size of max pooling = 2 × 2 (Maximum of the four pixels is termed as an output of the max pooling layer). Stride is that parameter of the neural network’s filter that is responsible for modifying the amount of movement over the image. Size of stride is equal to that of max pooling = 2 × 2.
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Dropout functions by resetting the out-edges of neurons in hidden layers to 0 in every updation step of training thereby preventing the model from overfitting (model learning from the noise present along with data). Dropout rate = 0.25.
There are two numbers of convolve 2D layers that are used for feature extraction (combining multiple features to create one feature) of the images. The number of Filters used = 64. Kernel size is kept small (3 × 3), considering the fact that already feature extraction has been done, and this is the second layer. The rectified linear unit (ReLu) is again used as an activation function to ensure a clean and positive output. Max pooling, also is done keeping the pool size same as that in the first layer (2 × 2). Thus, size of stride will also remain same. Again, the dropout rate is set to 0.25 to prevent overfitting. Flattening Layer The flatten function (Flatten()) is added, which is used to squeeze the layers and the 2D output from them into one dimension, fit to be fed into the dense layer. Dense Layer Dense (256, activation = ‘relu’). The dense fully connected layer is created having 256 nodes, and ReLu is utilized as an activation function. Dropout rate is kept at 0.5 to prevent the model from overfitting. Output Layer Dense (43, activation = ‘softmax’). A dense output layer is created having 43 nodes, and activation function softmax is used for multi-classification. The detail about the model is depicted in Table 2 which describes the layer type, output shape, and the number of parameters. From the above table, the following values are obtained. Total metrics: 236,348. Training set metrics: 236,348. Non-training set metrics: 0
An Accurate Automatic Traffic Signal Detector Using CNN Model Table 2 Summary of the model
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Layer
Shape of output
Param #
conv2d_1 (Conv2D)
(None, 26, 26, 30)
780
conv2d_2 (Conv2D)
(None, 22, 22, 30)
22,530
max_pooling2d_1 (Max Pooling2D)
(None, 11, 11, 30)
0
dropout_1 (Dropout)
(None, 11, 11, 30)
0
conv2d_3 (Conv2D)
(None, 9, 9, 64)
17,344
conv2d_4 (Conv2D)
(None, 7, 7, 64)
36,928
max_pooling2d_2 (MaxPooling2D)
(None, 3, 3, 64)
0
dropout_2 (Dropout)
(None, 3, 3, 64)
0
flatten_1 (Flatten)
(None, 576)
0
dense_1 (Dense)
(None, 256)
147,712
dropout_3 (Dropout)
(None, 256)
0
dense_2 (Dense)
(None, 43)
11,051
5 Result and Analysis The accuracy vs. epoch, loss vs epoch rate for both RBG, and grayscale images are shown in the following figures from Figs. 2, 3, 4, and 5. By comparing the above graphs, we can infer that the model accuracy shown in Fig. 2, which was around 95% when an RBG (normalized image) was given as input,
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increases to around 97% after the converted grayscale image is taken as an input to the model (Fig. 4). Also, if we compare Fig. 3–loss versus epochs (for normalized RBG image) and Fig. 5–loss versus epochs (for normalized grayscale image), we can observe a significant decrease in the data loss in the later graph.
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Grayscale(Normalized) Image input 2 1.8 1.6 1.4 LOSS
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6 Conclusion The objective of this paper is to discuss how to create a traffic signal recognition system and how its efficiency can be increased by designing a system that will convert the input RBG image into grayscale format. The traffic signs classifier has been successfully classified with 97% accuracy. The change in accuracy and data loss is also highlighted which is quite good in CNN model. Professionals suggest that different technologies using different number of testing and training datasets reach results of varying accuracy. The accuracy depends upon the tools used for implementation.
References 1. Garcia-Garrido, M.A., Sotelo, M.A., Martin-Gorostiza, E.: Fast traffic sign detection and recognition under changing lighting conditions. In: IEEE Intelligent Transportation Systems Conference, 2006 ITSC’06, IEEE, pp. 811–816 2. Barnes, N., Zelinsky, A.: Real-time radial symmetry for speedsign detection. In: IEEE Intelligent Vehicles Symposium, 2004, IEEE, pp. 566–571 3. Loy, G., Barnes, N.: Fast shape-based road sign detection fora driver assistance system. In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1. 2004 (IROS 2004), IEEE, pp. 70–75 4. Nikonorov, A., Yakimov, P., Petrov, M.: Traffic sign detection on GPU using color shape regular expressions, VISIGRAPP IMTA-4, Paper 8 (2013) 5. Belaroussi, R., Foucher, P., Tarel, J.P., Soheilian, B., Charbonnier, P., Paparoditis, N.: Road sign detection in images, a case study. In: 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 484–488
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6. Fursov, V., Bibkov, S., Yakimov, P.: Localization of objects contours with different scales in images using Hough transform. Comput. Opt. 37(4), 502–508 (2013). (in Russian) 7. Yakimov, P.: Preprocessing of digital images in systems of location and recognition of road signs. Comput. Opt. 37(3), 401–405 (2013). (in Russian) 8. Mishra, S., Tripathy, H.K., Mallick, P.K., Bhoi, A.K., Barsocchi, P.: EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20, 4036 (2020) 9. Mishra, S., Mallick, P.K., Jena, L., Chae, G.S.: Optimization of skewed data using samplingbased preprocessing approach. Front Public Health 8, 274 (2020). https://doi.org/10.3389/ fpubh.2020.00274 10. Mishra, S., Koner, D., Jena, L., Ranjan, P.: Leaves shape categorization using convolution neural network model. In: Intelligent and Cloud Computing, pp. 375–383. Springer, Singapore (2019) 11. Mishra, S., Tripathy, H.K., Acharya, B.: A precise analysis of deep learning for medical image processing. In: Bio-inspired Neurocomputing, pp. 25–41. Springer, Singapore 12. Marques, G., Bhoi, A.K., Albuquerque, V.H.C. de, K.S., H. (eds.): IoT in Healthcare and Ambient Assisted Living, Springer (2021) 13. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E. (eds.): Bio-Inspired Neurocomputing. Springer (2021) 14. Bhoi, A.K., Sherpa, K.S., Kalam, A., Chae, G.-S. (eds.): Advances in Greener Energy Technologies. Springer (2020) 15. Mishra, S., Pradhan, A., Mishra, S., Raj, A.: Analysis of Software Development Lifecycle Models and Its Significance in Software Industries
Classification of Arrhythmia Through Heart Rate Variability Using Logistic Regression K. Srikanth and Md. Ruhul Islam
Abstract The fundamental part of electrocardiogram (ECG) is to detect cardiac abnormalities by measuring the electrical activity generated by the heart as its contracts and expands. The ECG signal generated by a healthy heart has n uniform characteristic shape. The signal may vary haphazardly due the malfunction heart rhythm of the subjects suffering from arrhythmia. Accurate classification of the abnormal ECG signal from normal signals helps the clinical practitioners to diagnose arrhythmia patients. This article proposed an arrhythmia detection scheme using feature selection and logistic regression. Keywords ECG (electrocardiogram) · MATLAB · Cardiac disease · Heart rate variability (HRV) · Standard deviation of normal-to-normal RR intervals (SDNN) · Root mean square of successive NN interval differences (RMSSD) · NN50 (number of R peaks greater than 50 peaks)
1 Introduction The modern lifestyle and hectic work schedule of human being play an important role behind the cardiovascular diseases (CD). In the field of medical science, the diagnosis of CD and anomalies related to heart becomes a prominent area of research among medical practitioners. Most of the incongruities related to heart begins with irregularities of heartbeat and ends with an unavoidable cardiac arrest. The condition of heartbeat irregularities of patients is clinically termed as arrhythmia. Patients suffering arrhythmia reported to have a sudden slow heartbeat below 60 beats per minute and a fast heartbeat above 100 beats per minute. Therefore, an abnormal heartbeat should be diagnosed as early as possible to avoid arrhythmia. The highest mortality rate [1–4] and long-lasting and expensive treatment caused by chronic course of the CD [4, 5] become the major source of concern for the progressive aging of the population. K. Srikanth (B) · Md. Ruhul Islam Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_10
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Many modern arrhythmia detection approaches exist, that employs ECG signal as the basis for arrhythmia detection. Those methods for the detecting of heart abnormalities are based on the calculation of the dynamic or morphological features of single QRS complexes and heart rate variability (HRV) of ECG signals. This QRS complexes for arrhythmia detection is error-prone and difficult because of the variability of morphological characteristics in different persons [6]. For these reasons, the methods currently presented in the scientific literature do not obtain sufficient performance output [7]. In spite of performance anomalies, the modern generalized automated arrhythmia detection (GAAD) is getting popular day by day. It is because, along with the clinical identification of probable arrhythmia in patients complaining about cardiac anomalies, an automated process of classification of ECG signals can be conducted for accurate decision making. It should be noted that, the automated process cannot replace the clinical test of arrhythmia; but the automated process acts as an aid to identify the arrhythmia accurately. A GAAD mechanism takes the ECG signal as input and the detection process carried out through three prominent stages: feature extraction, feature selection, and classification [1, 2, 8–12]. Detection and potent of irregular heartbeats can be determined through manual observation of electrocardiogram (ECG) signals. A typical ECG signal of normal and abnormal heartbeats has been presented in Fig. 1. The heartbeat amplitude generated by ECG in patients suffering arrhythmia clearly evident intermittent amplitude. The ECG signal is non-stationary in nature, i.e., the amplitude of the signal varies along with the change in heartbeats [1].
2 Materials and Methods There are several steps to follow for detecting the heart defects and classification using multiple ECG signals for developing a new algorithm. suitable features are achieved from optimizing existing algorithms to achieve performance [2, 8–12].
2.1 ECG Database ECG database is an important part of signal processing. The database is collected from different sites. Our databases are collected from the PhysioNet. In this project, we took abnormal database as long-term atrial fibrillation, atrial fibrillation challenge termination database and normal database as MIT—BIH arrhythmia database. In Physio, the net ECG database contains the (.mat) MATLAB file which contains the row matrix for further signal processing. In MATLAB, file which contains combined multiple ECG databases can be considered any row matrix for analysis and classification.
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(a) Normal ECG Signal
(b) Abnormal ECG Signal Fig. 1 Visualization of normal and abnormal ECG signal
2.2 Plotting In case of plotting, first start with loading the ECG signal and then plot the signal in MATLAB [3]. Before plotting it is important to smooth by removing the base and gain form the signal. ECG database has the (.info) information file.in the information file it contains the base and gains unit values to smooth signal. The formula for removing the base and gain units.
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xi =
xi − base gain
(1)
where xi is the ECG sample, base = baseline value, gain = gain factor [6] After loading and removing the gain, base units can plot the ECG signal using plot function which contains the smooth signal.
2.3 Preprocessing ECG signal contains various noises. The most common noises are power line interference and the baseline wandering. Power line interference Power line interference can easily recognize by interfering voltage that is less than or equal to 50 Hz frequency. interference occurs due to loops in patients’ cables and wandering effects are in the alternate current fields. There are several reasons for interference occurring. The main reason is the 50hz interference that produces a strong disturbing signal. To detect accurately, several noise filters are used. There are two types of filters: (1) analog filters (2) digital filters. the problem with the analog signal is to introduce the nonlinear phase shifts and skewed the signal. Digital filters are good enough to bring more advantages than analog signals [13]. The notch filter is used to correct the power line interference [4] (Fig. 2). In this project, notch filter function is used to extract the required information from the ECG signal. These filters are rejecting/attenuate signals in a specific frequency band [5]. It is one of the best filters to use in multiple ECG signals to extract the required information. notch filters use the window method to fetch the desired frequency range with fewer coefficients. The efficiency of FIR filters is computed by removing the noise using the SNR technique. The SNR can be calculated by (x denoised)2 SNR = 10 log10 2 xoriginal − xdenoised
(2)
2.4 Thresholding After removing all noise reduction using a specific noise filter, the very next step is to put some certain threshold point for ECG smooth signal. The threshold point is required to consider the required signal and not consider an unnecessary signal. For
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(a) Filtered Normal ECG Signal
(b) Filtered Abnormal ECG Signal Fig. 2 Visualization of normal and abnormal ECG signal after preprocessing
detecting the RR intervals, only the RR interval is taken using a certain threshold point. Threshold point helps to find what we required and it considers only those points. It cannot take the remaining unnecessary information. In this paper, the threshold point is above 45% ECG signal to consider certain intervals. Because the ECG signal is a combination of P, Q, R, S, T wave form to consider a certain interval try to use the threshold.
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2.5 Peak Points Detection in Ecg Signal In ECG signal, after setting a threshold to detect the certain peak points, ECG signal contains different peaks. Here, different peak points are detected in the ECG signal. 1.
R peak I. II.
III.
IV. 2.
RR Interval I. II.
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II. III. IV. V. VI. VII.
Pan Tompkins algorithm is used to detect the QRS complex in the ECG signal. In this algorithm, first step is to filter the ECG signal using the bandpass filter for removing the noise. The slope information is got from the filtering signal. After that, we want to square the ECG signal for all signal values to positive. The amplifying previous output is in a nonlinear manner. After squaring moving the window integration to find the waveform feature information [15, 16]. In the next step, we put the threshold to find the QRS complex.
Q And S Peaks Point Detection I. II. III. IV.
5.
RR interval is the distance between the one R peak point to another R peak point. Using the RR interval can find out the standard deviation of normal-tonormal RR interval and root mean square of successive heartbeat difference between the intervals, heart rate, and heart rate variability.
QRS Complex Detection I.
4.
R peak points can easily find out which contains the largest amplitude and it is the sharpest one compared to other peaks. detecting the R peak point put the adaptive threshold which contains a pair of threshold limits named up limited threshold and down-limited threshold [7, 14]. the adaptive threshold is used because for finding the higher it leads to a lack of proper detection and defining low value causes the incorrect detection of peaks. An R peak changes direction within predefined intervals.
Q and S peaks points are minimum points of the R peak. Q peak is present at the left side of the R peak and S peak is present on the right side of the R peak. If these are minimum are cannot be found then these Q and S peaks are not detected. Q and S peaks are part of the QRS complex.
P And T Peak Point Detection
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P and T peak points are detected using the QRS complex. The p wave is the present left side of the QRS complex containing the maximum points from the left side of the Q wave. T wave is the present right side of the QRS complex containing the maximum points from the right side of the S wave. Use a threshold technique to detect the P and T peak points.
(a) Peak points detected in ECG normal signal
(b) Peak points detected in ECG abnormal signal Fig. 3 Visualization of peak points detected in normal and abnormal ECG signal
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2.6 Feature Extraction In feature extraction, multiple features can be extracted from ECG signal. After detecting the R peak, extract some features like heart rate, root means successive heartbeat interval, the standard deviation of RR interval, and heart rate variability. Heart Rate Heart rate is the number of times a person’s heartbeat per minute. The heartbeat can vary based on size, age, medication taken, and temperature. Normal heartbeat range per person is 60–100 BPM. After detecting the R peak the heart rate is calculated by using the R peaks. Several R peaks = Number of cycles = Number of beats [6]. 1.
Heart Rate Variability • It shows the variation in the time interval between heartbeats. • It varies from every beat to beat interval. • After detecting the R peak find out the maximum and minimum points of RR interval. • The mean value of the RR interval is to be calculated dividing 60 by average RR interval. • Calculate the HRV max and min by subtracting both from mean and divided by average RR interval [6]. • The formula for HRV is shown below HRV = ( HRV max − HRV min) ∗ 100(4)
2.
(3)
Root Mean Successive Heatbeat Intervals • RMS is calculated by using the RR intervals. • It takes the difference between the successive RR intervals by squaring for all RR intervals and the square root of the mean of the RR intervals [17]. • The formula is shown below (r 1 − r 2)2 + (r 3 − r 4)2 + · · · + · · · (r 1 − r 2)2 + (r 3 − r 4)2 + · · · + · · · RMSSD = N
(4)
2.7 HRV Analysis ECGs were recorded at the baseline (pretest) and the end of the experimental period (post-test). The recorded ECGs were divided into two trails, each comprising a 150-s window without overlap. An RR tachogram was generated by detecting the R peak in each ECG trail. The HRV time-domain features were subsequently calculated in
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the time and frequency domains from the RR tachogram. The HRV time-domain features include the mean of RR intervals (RR), HR, SDRR, the coefficient of the variance of RR interval (CVRR), RMSSD, and the proportion of consecutive N–N intervals that differ by more than 5 ms(NN5) [17]. The HRV Analysis is shown in Fig. 4.
(a) HRV of abnormal ECG signal
(b) HRV of normal ECG signal Fig. 4 Visualization of heart rate due to HRV in normal and abnormal ECG signal
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3 Classification In this case, classification is done using one of the machine learning techniques. For classification, logistic regression was used. Logistic regression comes under supervised learning. • In supervised learning, training data includes the desired output. • It is a learning mechanism where you have two variables x and y wherein x is input variable and y is output variable. • Supervised learning maps input variable to the output variable by building a mapping function defined by y = f (x). • The goal is to approximate the mapping function, to a degree of precision that, when you have new input data x, you can predict the corresponding variable y. • Types of logistic regression. 1.
Binary Logistic Regression
The categorical response has only two possible outcomes, e.g., Spam or Not. 2.
Multinomial Logistic Regression
Three or more categories without ordering, e.g., predicting which food is preferred more (veg, non-veg, vegan). 3.
Ordinal Logistic Regression
Three or more categories with ordering, e.g., movie rating from 1 to 5. Decision Boundary To predict which class a data belongs, a threshold can be set. Based on this threshold, the obtained estimated probability is classified. Say, if predicted_value ≥ 0.5, then classify an e-mail as spam else as not spam. In this, the binary logistic regression was to predict the patient has heart disease or not. Point Plot of ECG Signal
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Logistic Regression Analysis Using Cross-Entropy Sigmoid For a scalar real number Z. The sigmoid function (standard logistic regression) is defined as σ (Z ) =
1 1 + e−Z
(5)
Its output value is in range of (0, 1) , not inclusive. This is very useful for interpreting a real value score Z as a probability. • As z → −∞, then σ (z) →0 • When z = 0, σ (z) = 1 2 • As z → +∞, then σ (z) → 1. The derivative of the sigmoid function is σ (z) = σ (z)(1 − σ (z)). Softmax This function is useful for converting an arbitrary vector of real numbers into a discrete value distribution. For a vector x ∈ Rn . The softmax function is defined as e xi soft max = n j=1
xj
(6)
Each element in softmax (x) is squashed in range [0,1], and the sum of the elements is 1.
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Numerical Stability Let c be some scalar. Then e xi +c soft max(x + c) = n x j +c j=1 e c
e xi e xj c j=1 e e
n
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e xi e xj c j=1 e e
= n
e xi = n j=1
ex j
= soft max(x)i e xi = n j=1
(7)
ex j
= soft max(x)i The softmax function are often exploited to improve numerical stability. If choosing c = − maxi xi then maxi e xi +c = 1. And also having mini e xi > 0 since the exponential is always positive, thus constrain each term of the denominator of the range (0, 1]. It helps to avoid underflow when they xi are very small. When xi are all very small e xi → 0 means that e xi may underflow is 0, leading to division by 0. When we substitute the xi by c = − maxi xi at least one of the terms in the denominator is maxi e xi +c = 1, thus avoiding the division by zero error. Substituting the xi by c also helps avoid overflow, since the exponential function grows very fast. When we substitute xi by c = − maxi xi we have maxi e xi +c = 1, so no single term will overflow. For any size of xi the sum of the denominator will not overflow. Cross-Entropy For two probabilities p(x) q(x) and, the cross-entropy function of how different they are. It is defined as H ( p , q) = −Ex≈ p [log(q(x))]
(8)
If p and q are discrete then we have H ( p , q) = −
( p(x)) log(q(x))
(9)
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Whenever loss function was used, put p = y (the labels) and q = yˆ (the predictions). In the classification setting where each class belongs to 1 class, Y is the one-hot vector with 1 at the index of the true class and yˆ is a vector representing a discrete probability distribution over the possible classes. Convex Functions A function f : R n → R is convex if for all x, y in its domain, with 0 ≤ α ≤ 1, we have f (ax + (1 − a) y) ≤ a f (x) + (1 − a) f (y)
(10)
A function f is strictly convex if strict inequality holds x = y and 0 < α < 1. Assuming the following true statements. If t is convex, performing gradient descent on f (w) with small enough step size is guaranteed to converge to a global minimum f . If f is strictly convex, then it has a unique global minimum. Binary Logistic Regression Data: For all (x, y) pairs, when each x is a feature length M and the label y is 0 or 1. Goal: Predict y for a given x. Model: For an example x, calculated score as z = w T +b where vector as w ∈ R M and scalar as b ∈ R are to be learned from data. For further prediction of the binary value y, then setting a threshold on result yˆ = 1[z > 0]. Set any threshold since always changing the value of scalar b. the most commonly used threshold is 0. There are two issues with the model which use the sample threshold on the result. • First, it is difficult to differentiable loss function loss(y, yˆ ) when both y and yˆ are discrete values. • Second, they frequently want a probabilistic interpretation of the result. Thus we introduce the sigmoid function σ which maps all results in the range (0, 1). p(y = 1) = σ (z) = σ w T + b For a given x p(y = 1) ≥ 0.5, if then predicting yˆ = 1 otherwise yˆ = 0. In the equation above if solving the result z, interpreting the results as log-odds of y = 1. p(y = 1) = σ (z) = 1 + e−z =
1 1 + e−Z
1 p(y = 1)
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e−z =
1 1 − p(y = 1) −1= p(y = 1) p(y = 1) ez =
p(y = 1) 1 − p(y = 1)
z = log
p(y = 1) 1 − p(y = 1)
Loss function: The logistic loss function is loss = −y log(σ (z)) − (1 − y) log(1 − σ (z))
− log σ (z), y = 1; = − log(1 − σ (z)), y = 0; where z = 1 + e−z
(11)
In the second line of the equation above, it is clear that the probabilistic interpretation of our model, this loss function is exactly the negative log probability of single example x having true label y. Thus, minimizing the sum of loss over the training is equivalent to maximizing log-likelihood. We can see this as follows p(y|x; w, b) =
σ (z), y = 1 1 − σ (z), y = 0
= σ (z) y (1 − σ (z))(1−y)
log(y|x; w, b) = −y log(σ (z)) + (1 − y) log(1 − σ (z))
(12)
The parameters w and b perform gradient descent on the loss function concerning these parameters. The logistic loss function is convex in the parameters w and b, so it is guaranteed to converge to a globally optimal value with a small enough learning rate. The loss function is strictly convex and has a global minimum. Generalization: multilabel classification In this case, we generalize where x can belong to many classes simultaneously with. y ∈ {0, 1}c where c is the no of classes. General model: for example x ∈ R M , calculating result z = w T + b s as to where w ∈ R C×M and vector b ∈ R C are parameters to learn the data. the probabilities of each class are given by the sigmoid of each class result. p(yc = 1) = σ (z c ) = σ (wcT + b)
(13)
This is a vectorized implementation of c separate binary logistic regression, one for each class.
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Multiclass Logistic Regression Given a binary classification algorithm, there are two common approaches to use them for multiclassification one versus rest and one versus one. there is no clear best multiclass classification it depends on the dataset. In one versus rest, we train c separate binary classification models. Each classifier f c c ∈ {1 . . . C} is trained to determine whether are not an example is part of a class or not. To predict for new example x, then run all c classifiers on x and choose the class with the highest score. ∧
y = arg max f c (x) c∈{1...c}
(14)
c In one vs one train the = c(c − 1)/2 separate binary classification models, 2 one for each possible pair of each class. To predict the class for a new example x,
c classifiers on x choosing the class most votes. then run all 2 Decision Boundary of Normal ECG Signal Decision Boundary of ECG Signal
Results and Accuracy Using Features Finally, after doing the classification the final accuracy is 90.36%.
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4 Conclusion This paper has presented a novel approach for heart disease detection of the ECG signal by extracting features like heart rate, standard deviation, and adaptive threshold technique. Our approach for heart disease detection introduces the logistics regression technique to improve accuracy.
References 1. Thulasi Prasad, S., Varadarajan, S.: ECG signal analysis: different approaches. Int. J. Eng. Trends Technol. (IJETT) 7(5) (2014) 2. Shrivastava, A., Chandra, V., Sinha, G.R.: Diagnosis of ECG signal and prediction of critical diseases for cardiac patients using MATLAB. Int. J. Adv. Res. Comput. Commun. Eng. 5(12) (2016) (ISO 3297:2007 Certified) 3. Signal Processing Toolbox, For Use with MATLAB, Users Guide Version 5 4. Islam, M.K., Haque, A.N.M.M., Tangim, G., Ahammad, T., Khondokar, M.R.H.: Study and analysis of ECG signal using MATLAB LABVIEW as effective tools. Int. J. Comput. Electr. Eng. 4(3) (2012) 5. Ojha, D.K., Subashini, M.: Analysis of electrocardiograph (ECG) signal for the detection of abnormalities using MATLAB. World Academy of Science, Engineering and Technology 6. Mayapur, P.: Detection and classification of heart defects. Int. J. Sci. Healthcare Res. 3(4) (2018) 7. Jiao et al.: Modified Log-LMS adaptive filter with low signal distortion for biomedical applications. In: 34th Annual International Conference of the IEEE EMBS San Diego, California USA, pp. 5210–5213 (2012) 8. Bhoi, A.K.: Classification and clustering of Parkinson’s and healthy control gait dynamics using LDA and K-means. Int. J. Bioautom. 21(1) (2017) 9. Bhoi, A.K., Sherpa, K.S., Khandelwal, B.: Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT) analysis of electrocardiogram. Clust. Comput. 21(1), 1033–1044 (2018) 10. Bhoi, A.K., Sherpa, K.S.: Statistical analysis of QRS-complex to evaluate the QR versus RS interval alteration during ischemia. J. Med. Imaging Health Inform. 6(1), 210–214 (2016) 11. Bhoi, A.K., Sherpa, K.S.: QRS complex detection and analysis of cardiovascular abnormalities: a review. Int. J. Bioautom. 18(3), 181–194 (2014) 12. Bhoi, A.K., Sherpa, K.S., Khandelwal, B.: Ischemia and arrhythmia classification using timefrequency domain features of QRS complex. Proc. Comput. Sci. 132, 606–613 (2018) 13. Ponnusamy, M., Sundararajan, M.: Detecting and classifying ECG abnormalities using a multimodel methods. Biomed. Res. Artif. Intell. Techn. BioMed. Signal Process. (2017) 14. Kazi et al.: Least mean square algorithm based adaptive filters for removing power line interference from ECG signal. IEEE/OSA/IAPR Int. (2012) 15. Bawa, K., Sabherwal, P.: R-peak detection by modified Pan-Tompkins algorithm. Int. J. Adv. Res. Technol. 3(5) (2014) 16. Pan, J., Tompkins, W.J.: A Real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME 32(3) (1985) 17. Pak, D., Lee, M., Park, S.E., Seong, J.-K., Youn, C.: Determination of optimal heart rate variability features based on SVM—recursive feature elimination for cumulative stress monitoring using ECG sensor on July 23, 2018
U-INS: An Android-Based Navigation System Suprava Ranjan Laha, Sushil Kumar Mahapatra, Saumendra Pattnaik, Binod Kumar Pattanayak, and Bibudhendu Pati
Abstract Many people are coming to a campus for different types of work throughout the year. They can come to the campus with ease due to the GPS-based smart phone. But it is very difficult for them to find the exact room or the department within a huge campus. This proposed system designs the simple and robust Android-based ultrasonic indoor navigation systems (U-INSs) that allow the user to get their desired location within the campus. Android application allows estimating the position of the person in a flexible and robust way mixing ultrasound signals emitted by a set of U-INSs. The face recognition is used to improve the system’s accuracy by analyzing the visitor’s face at different sensors to reach the destination. The proposed system can achieve high accuracy within the range of 10 m, and it can be utilized in any Android-based smart phone. Keywords Indoor navigation system · IoT · Ultrasonic sensor · ESP32 · Android app
1 Introduction In this twenty-first century, every people moves to new places to get better education. The visitors may get the geographical position of different places through satellite map that is provided by different service providers such as Google map. But the place of visit inside a city may be too big to explore. This leads to increase in the demand of positioning and navigation day by day [1]. When any outsider comes to visit the campus, they do not know the exact position of different buildings they want S. R. Laha (B) · S. K. Mahapatra · S. Pattnaik · B. K. Pattanayak Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India B. K. Pattanayak e-mail: [email protected] B. Pati Department of Computer Science, Ramadevi Womens University, Bhubaneswar, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_11
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to visit. To solve that problem, indoor navigation system is highly necessary. Some of these systems use lights, radio waves, magnetic fields, signals or other sensory objects to locate their positions. Indoor navigation is highly challenging in finding the exact position of the searched object due to several reasons: multipath errors, presence of moving people, sundry objects (such as walls, equipment and people) that reflect signals, which leads to multipath and delay in finding the destinations. In 2012, Hong et al. implemented a GPS indoor-based navigation system. Global Positioning System (GPS) is a dedicated technology to find the exact position of the searched object [2]. However, the system failed to find the exact location in the indoor using GPS due to the presence of many obstacles. In 2004, Pahlavan et al. explain about radio frequency identification-based navigating system. The system is used to find the exact position of object located indoor. It uses RFID tags to find the receiver location. But it fails to find the position of the object which is far from the system due to its short range, i.e., 1–2 m, and the cost is high [3]. Using mobile tower communication signals or finding nearby Wi-Fi access points does not provide much accuracy to distinguish two buildings in the same campus. In this paper, an ultrasonic face recognition indoor navigation system (U-FINSs) is proposed which provides positioning and navigating with the exact location that enhances the accuracy. The system is implemented for one of the departments in S ‘O’A Deemed to be University situated at Bhubaneswar, Odisha. This system can be further developed for the other departments of the University as well as for the other universities across the globe. The whole navigation system is connected through wireless network. The primary customer of our project is students and the parents along with the visitors who are coming to the university. The proposed system is developed by using image processing technique and ultrasonic sensor to facilitate the navigation system. Ultrasonic system is used to find the exact location of the destination place. Here, image processing is used to increase the accuracy of the system, and it also removes error from the system. The paper is organized in the following way. Sections 1 and 2 represent the introductory part and the previous work that has been done by some researcher in the same field. The proposed work is presented in Sect. 3 which includes the different components used for the indoor navigation system. The result and discussion are elaborated in Sect. 4. Finally, the conclusion is drawn, and the future work is presented in Sect. 5.
2 Related Works For indoor navigation and positioning, several works are done. In 2018, Adam Satan et al. implemented Android-based indoor navigation mobile system using Bluetooth module. The system calculates the distance by using the radio frequency signals. The Bluetooth navigation system emits frequency signals by estimating the distance between the beacons system and the user location [4]. Binu et al. in 2016 implemented an Android-based application by using magnetic field map to determine the exact
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position of the system. The system uses simplified magnetic map matching algorithm and the Route Boxer algorithm to determine the exact location of the people in the building. But, the system failed to track and navigate for an end user from any particular source inside the building [5]. In 2016, Rizwan Muhammad et al. implemented an indoor positioning system based on iBEACON which is a protocol based on a class of BLE that transmits a beacon of a universal identifier within a certain range to measure the proximity of the smart devices. The system makes use of Gaussian filter method and unscented Kalman filter method to find the position of the system. The system is based on mobile application with Bluetooth technology. It uses terminals of the mobile to show the position [6]. In 2016, Andrei Popleteev proposed a human-based indoor positioning system. This system is designed by assuming the truth error of human which are negligibly small. The system uses laser-based positioning method to determine its location [7]. Trong et al. in 2020 implemented a wireless indoor positioning and navigation based on cloud computing. The system uses movement decision algorithm to give the directions in the system. The system uses cloudlet Wi-Fi approach to determine the exact position as well as to navigate through it [8–11].
3 Proposed System Nowadays, the visitors are coming to the school, colleges and the universities for different personal and professional work. With the rise of online communities, school, colleges, meet ups for communities have become more common. In some cases, the meet ups are regular and occur periodically every month, or every year. Once the meet ups reach a level of popularity, there are a lot of members who come to the event, colleges to visit from other cities, states or even countries. As the publicly available map applications do not have map data for the interiors of the buildings, members unfamiliar with the location often cannot find their way around. Even if the map data were available to the map applications, some event locations have multiple events taking place on the same day, with events taking place almost every day. Thus to facilitate the navigation system within a specified area in this case inside a university, an indoor navigation system with the help of image processing technique and Internet of Things (IoT) is proposed. The proposed system comprises of three main parts, i.e., ultrasonic sensor with IoT module to measure the distance from one point to another within a building, camera module connected with IoT module to implement face recognition of the visitor connected to the server and Android app to facilitate the user to navigate within the building. Ultrasonic sensor with IoT module In this proposed system, a set of ultrasonic sensors are used to measure the distance of the visitor from a specified point. Usually, an ultrasonic sensor is transmitting sound wave of about 40–70 kHz frequency. The transmitter is open for 10 µs. The sound
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wave propagates in the air, and when it hits the target, it bounces back to the receiver which is at the same sensor module as transmitter. The time of travel is calculated, and the distance is thus calculated by using the formula given in Eq. 1. Distance = 0.5 × Time of travel × Speed of sound wave in air.
(1)
The entire ultrasonic module, i.e., URM05 is attached to different IoT devices at various point of the campus. The range of the sensor module is 10 m. In this system, ESP32 IoT device is used to transmit the collected data from the ultrasonic sensor to the server [12]. Camera module The camera module, i.e., ESP32 CAM Wi-Fi module with OV2640 camera module 2 mega pixel for face recognition, is connected with the IoT module to take the snapshot of the visitors who want to navigate within the campus [13, 14]. The camera module facilitates the administrator to identify the visitor at each node within the campus. Android App In this system to facilitate the visitor to navigate within the campus, an Android app is developed using Android SDK and Android Development Tools [15].
4 Result and Discussion When a visitor enters into the campus, an auto generated SMS is triggered to his/her mobile that will ask for the user’s permission to use the navigation utility Android app to visit the campus. If the user agrees to use the facility, then the android app will be get installed into his/her smart phone. The use case diagram for the interaction between the user and the app and the data in the server are shown in Fig. 1a, and use case diagram for the interaction between the Admin of the server and the database are shown in Fig. 1b. Initially, the user has to enter his source and destination of
Fig. 1 a Use case diagram for the interaction between the user and the app and the data in the server. b Use case diagram for the interaction between the admin of the server and the database
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visit along with their mobile number. The server will generate a user ID against the mobile number, and simultaneously the camera will take a photo of the visitor. The ultrasonic sensor fitted at different vital points of the campus will trace the distance of the visitor to a particular point. The server traces the location of the visitor from different access point and sends the notification to the visitor via SMS. Figure 2a explains class diagram for the maps depicting how various data members and functions interact with each other. Figure 2b explains class diagram for the events depicting how various data members and functions interact with each other. Figure 3a explains sequence diagram depicting the workflow of the module that works on buildings. In Fig. 3b, sequence diagram depicting the workflow of the module that manages event data is presented. Camera module at each point takes the photo of the visitor and sends the data to the server. The server matches the photo with the previous data using a face recognition algorithm. Here the face recognition algorithm is written in Python to facilitate the IoT device. When the visitor comes to a particular point, its distance will be calculated as zero by the ultrasonic sensor and hence the server comes to know
Fig. 2 a Explains class diagram for the maps depicting how various data members and functions interact with each other. b Explains class diagram for the events depicting how various data members and functions interact with each other
Fig. 3 a Explains sequence diagram depicting the workflow of the module that works on buildings. b Sequence diagram depicting the workflow of the module that manages event data
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Fig. 4 Explains sequence diagram of the system. It shows the workflow of the module that shows a location to the user
that the visitor has reached the particular point. Then it triggers the other ultrasonic sensor to trace the distance of the visitor. The visitor is guided by the server through SMS service. When the visitor reaches the destination, then the server terminates the user ID. Figure 4 explains sequence diagram of the system. It shows the workflow of the module that shows a location to the user. The proposed system is very useful to the different types of visitors which includes students, parents, resource person from different colleges and university, Govt. Officials, workers, etc., who are coming to campus. This facility avoids the unnecessary traffic due to the ignorance of the path. The sequence diagram of the system which shows the workflow of the module is presented in Fig. 4. Sometimes during a special event within the campus, large no. of people may come to the campus, and they are just roaming in the campus to get their destination while the academic session is going on in different rooms of the different departments. The user interface of the android app is presented in Fig. 5. This type of situation generally hampers the academic session unknowingly. Thus, the proposed system facilitates the visitors to get their destination within the campus which also avoids the unusual gatherings within the campus.
5 Conclusion and Future Work The proposed system is a novel system that facilitates the visitors coming to a campus for different purposes. So a low-cost indoor navigation system is proposed in this paper named as U-INS. The utilization of ultrasonic sensor with ESP32 IoT module in the proposed system keeps the cost of the system low, and the use of image processing feature, i.e., face recognition algorithm enhances the accuracy of the system. Due to
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Fig. 5 User interface of the Android app
the low cost and less maintenance, this system can be implemented in any campus. As this system comprises different modules which are independent of each other, it can be scalable. Further the proposed system can be developed by different cutting-edge technologies like AI and ML along with cloud computing.
References 1. Deng, Z.L., Yu, Y.P., Yuan, X., Wan, N., Yang, L.: Situation and development tendency of indoor positioning. China Commun. 42–55 (2013) 2. Hua, T.Y., Hong, N.L., Gu, M.: Recent research and applications of GPS-based monitoring technology for high-rise structures. Struct. Control Health Monitoring 20, 649–670 (2012) 3. Kanaan, M., Pahlavan, K.: A comparison of wireless geolocation algorithms in the indoor environment. Wireless Commun. Netw. Conf. 1, 177–182 (2004) 4. Satan: Bluetooth-based indoor navigation mobile system. In: 19th International Carpathian Control Conference (ICCC), Szilvasvarad, pp. 332–337, 2018 5. Binu, P.K., Krishnan, R.A., Kumar, A.P.: An efficient indoor location tracking and navigation system using simple magnetic map matching. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, pp. 1–7, 2016
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6. Li, X., Xu, D., Wang, X., Muhammad, R.: Design and implementation of indoor positioning system based on iBeacon. In: International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, pp. 126–130, 2016 7. Popleteev: HIPS: human-based indoor positioning system. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, pp. 1–7, 2016 8. Khanh, T.T., Nguyen, V., Pham, X., et al.: Wi-Fi indoor positioning and navigation: a cloudletbased cloud computing approach. Hum. Cent. Comput. Inf. Sci. 10, 32 (2020) 9. Android Developers: https://developer.android.com. Accessed on 12.2.2013 10. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E. (eds.): Bio-inspired Neurocomputing, Springer (2021) 11. Marques, G., Miranda, N., Kumar Bhoi, A., Garcia-Zapirain, B., Hamrioui, S., de la Torre Díez, I.: Internet of things and enhanced living environments: measuring and mapping air quality using cyber-physical systems and mobile computing technologies. Sensors 20(3), 720 (2020) 12. Espressif Systems, ESP32 Datasheet, 10 April 2019. https://www.espressif.com/sites/default/ files/documentation/esp32_datasheet_en.pdf. Accessed 11 June 2019 13. Allan, A.: Face Detection and Recognition on the ESP32, 10 December 2018. https://blog.hac kster.io/face-detection-and-recognition-on-the-esp32-3b4b9a35c765. Accessed 7 June 2019 14. Erich11, GitHub, 8 May 2019. https://github.com/espressif/arduino-esp32/issues/2538. Accessed 14 June 2019 15. Marques, G., Bhoi, A.K., de Albuquerque, V.H.C., Hareesha, K.S. (eds.): IoT in Healthcare and Ambient Assisted Living, Springer (2021)
LSTM-Based Cardiovascular Disease Detection Using ECG Signal Adyasha Rath, Debahuti Mishra, and Ganapati Panda
Abstract Out of all diseases, the cardiovascular disease (CVD) kills the largest number of human beings in the world. This has drawn the attention of many researchers to address this vital issue and to suggest appropriate early detection method of heart diseases so that many valuable lives can be saved. The CVDs are mainly detected from various test data as well as signals such as ECGs, heart sounds, and cardiac computerized tomography scans. In this chapter, attempt has been made to propose an efficient diagnose coronary heart disease (CHD) from the ECG recordings of the subjects employing a simple but robust LSTM network method of detection of CHD. The standard PTB diagnostic database version 1.0.0—PhysioNet comprising the ECG signals recordings of 268 subjects are used in the model. Three stages of LSTM network with 256, 128, and 64 modules in each stage and using 20% random dropouts of weights between modules are employed to develop the detection model. Two types of training and validation schemes (80 and 20%) and (70 and 30%) of the datasets have been carried out. The simulation-based experiments of the developed model using the standard ECG signals of healthy and HD patients exhibit the best performance of 86.598%, 93.817%, 88.015%, 71.597%, and 0.915 in terms of accuracy, precision, sensitivity, specificity, and F1-score, respectively. The proposed model can be conveniently used by doctors for early diagnosis of the CVD. Keywords CVD detection · ECG signal · LSTM model · Health care · Diagnosis of CHD
A. Rath · D. Mishra (B) Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, Odisha 751030, India e-mail: [email protected] G. Panda Department of Electronics and Tele Communication, C. V. Raman Global University, Bhubaneswar, Odisha 752054, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_12
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1 Introduction The coronary heart disease (CAD) is one form of heart diseases (HD) which is caused due to smoking, unhealthy diets, and irregularity in working hours. In the initial phase of this HD hardly, any symptom of CAD is exhibited. The electrocardiogram (ECG) recording which demonstrates the abnormal activity of the heart is mostly used as the diagnostic tool of CAD. Similarly, analysis of heart sound also plays a crucial role for detection of CVD. In recent past, the deep learning convolution neural network (CNN) and long short-term memory (LSTM) neural network have been employed and proven to be powerful methods for detection of the CVD and CAD. Out of these three techniques, the LSTM method is chosen as the basic tool for classification of CVD. The LSTM model has been used for many applications including time series prediction, speech recognition, robot control, natural language processing, and healthcare applications. Based on the potential applications of LSTM model, important articles published between 2018 and 2020 on the diagnosis of CVD using LSTM methods are identified and the associated literature has been reviewed and presented. In [1], the authors have proposed a hybrid stacked convolutional and LSTM network for identifying CAD patients from the ECG signals. The simulation-based results show very high accuracy. In an interesting paper [2], the authors have used diagnostic variable length heart beats for automatic diagnosis of arrhythmia. The CNN-LSTM method has been used with tenfold cross-validation strategy for the CAD detection with appreciable accuracy, sensitivity, and specificity using MITBIH arrhythmia database. For classification of ECG signal, the authors [3] have used a deep bidirectional LSTM network model. Atrial fibrillation is a common cardiac rhythm disorder in adults. Accurate and automatic diagnosis of this disorder employing LSTM network is reported in [4] using MIT-BIH atrial fibrillation database. Recognition of arrhythmia from ECG signals by using different feature extraction methods and LSTM network model is reported in [5]. The authors in this paper have used wavelet transform, higher-order statistics, morphological descriptor, and R-R intervals for extracting different features from ECG signal. They have simulated the proposed model using MIT-BIH arrhythmia database and obtained different classification performances. These methods have yielded very high precision, accuracy, specificity, sensitivity, and F1 score. In [6], the authors have proposed a novel approach to classify cardiomyopathy and cardiomyopathy with arrhythmia using CNN and LSTM techniques. The results of simulation study using MIT-BIH database are compared with those obtained by decision tree, support vector machine, and neural network. It is reported that the proposed model shows better performance compared to other three models. In a recent work [7], the authors have suggested a method of segmentation of heart sound using duration LSTM method. It is demonstrated that for PTB diagnostic database version 1.0.0—PhysioNet, the proposed approach outperforms other two methods. It is very important to assess percutaneous coronary intervention (PCI) to detect whether surgery is required or not. For diagnosis of PCI, the authors [8] have proposed generative adversarial network
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(GAN) and LSTM models. For this purpose, they have used cardiac computerized tomography scans. Another hybrid model using CNN and LSTM is suggested in [9] for analyses of the ECG signal to facilitate detection of heart diseases. The CNN deep learning model extracts the spatial and temporal features of the heartbeat signals. Using the extracted features, the ECG signal is faithfully reconstructed by the proposed method. The features of ECG signals are extracted [10] by multi-fractal detrended fluctuation analysis. These features are employed for detection and classification of arrhythmia. The simulation results using the same database show that the proposed method outperforms other deep learning methods. There are several other methods which discusses on the classification and detection of cardiovascular conditions [11–14]. A detailed review of the literature on diagnosis of HD published between 2018 and 2020 and using LSTM network as the basic model is presented in Table 1. The published articles are divided into three categories which are based on types of input signals used such as ECG signal, heart sound and cardiac computerized tomography scan. This is presented in Table 2. Table 1 Detail review of the literature between 2018 and 2020 on diagnosis of HD using LSTM network References
Authors
Machine learning techniques used
Methods of feature extraction used
[1]
Jen Hong Tan et al.
CNN, LSTM
First few layers of CNN
[2]
Shu Lih Oh et al.
CNN, LSTM
First few layers of CNN
[3]
Ozal Yildirim
DWT-Deep bidirectional LSTM
Wavelet transform
[4]
Oliver Faust et al.
Deep LSTM-based RNN
Bidirectional LSTM
[5]
Saroj Kumar Pandey et al.
LSTM
Wavelets, higher-order statistics, morphological descriptor, R-R intervals
[6]
Mythili Thirugnam et al.
CNN-LSTM
Time and frequency domains heart rate variability features
[7]
Yao Chen et al.
Duration LSTM
Homomorphic, Hilbert, wavelet, and power spectral density
[8]
Zi-Zhuang Zou et al.
Generative adversarial network, LSTM
–
[9]
Kohei Yamamoto et al.
CNN, LSTM
First few layers of CNN
[10]
Biswarup Ganguly et al.
bi-LSTM
Unidirectionally processed multifractal detrended fluctuation analysis
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Table 2 Grouping of articles based on the types of input signals
ECG signal
Heart sound
Cardiac computerized tomography scans
[1, 3, 5, 6, 9, 10]
[2, 4, 7]
[8]
Table 3 Sources of data References
Sources
[1]
Open source PhysioNet database, normal and CAD ECG data were from Fantasia and St. Petersburg Institute of Cardiology Technics 12-leads arrhythmia
[2]
PhysioNet public database, MIT-BIH arrhythmia database
[3]
PhysioBank MIT-BIH arrhythmia database
[4]
MIT-BIH Atrial Fibrillation database
[5, 6, 10]
MIT-BIH Arrhythmia database
[6]
PTB diagnostic, European ST-T, Sudden cardiac death Holter, long-term ST database
[7]
MITHSDB
[8]
Datas are collected through experiments
[9]
Data was collected using doppler sensor with a sampling frequency of 1000 Hz
Present chapter
PTB Diagnostic database version 1.0.0—PhysioNet. 268 total subjects, 216 healthy, and 52 HD patients. ECG recording is sampled to produce 1000 samples. Average of 12 recording of each subject is considered
Table 1 mainly outlines the methods employed and the features extraction methods. Most of the articles use the hybrid models of CNN and LSTM. In these hybrid models, CNN is used for extraction of features and LSTM model for classification. The various articles which have been reviewed in this chapter have used different data sources for training and validation purposes. The details of these data sources used in various articles are listed in Table 3.
2 Research Gap, Motivation and Objectives 2.1 Research Gap Early detection of HDs will save the lives of lot of people globally. Keeping this in view, many research works are being carried out using machine learning techniques. It is noticed that out of various machine learning methods the deep learning approach and is highly potential for detection and diagnosis of HDs. Accordingly, there is a need to further develop some simple machine learning method which can be employed
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for diagnosis of signal-based heart diseases. The method chosen should be simple and effective and can be conveniently used by medical experts for HD detection.
2.2 Motivation The requirement of effective HD detection method has motivated to propose an LSTM-based diagnosis model which employs the ECG signal of the patient as input and can detect the HD reliably and accurately. The LSTM approach is chosen for HD detection purpose because of few advantages associated with LSTM network are: the absence of vanishing gradient problem and relative insensitivity to gap length in a time series to be predicted or classified.
2.3 Objective • To identify and use standard arrhythmia database which provides details of ECG signals for training and validating the HD detection model. • To suitably choose number of LSTM stages and appropriate dropouts of the weights of the proposed model, to achieve best possible classification performance. • To determine various performance metrics using standard database of ECG signals of healthy subjects and HD patients. • To analyze the results and present the contribution of the chapter. • To present the concluding remarks highlighting the contribution and limitation of the work as well as scope for future research work.
2.4 Organization The organization of the chapter proceeds as follows. In Sect. 1, the introduction and the review of related recently published articles on LSTM-based HD detection is presented. Section 2 deals with the research gap, motivation, and objectives of proposed research work. Section 3 outlines the details of the dataset used in the chapter. Section 4 presents the development of the proposed LSTM detection method. It includes both training and validation of the model. The simulation results obtained from the standard datasets, and the associated analysis are presented in Sect. 5. Finally, in Sect. 6, the chapter is concluded by including contribution of the paper and scopes for further extension of the current work.
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3 Source and Details of the Data For the development of the proposed HD detection model, the required ECG data is taken from PTB diagnostic database version 1.0.0—PhysioNet. In the simulation study, the ECG signals of the 268 subjects are used. Each subject has twelve recordings. Each recording is sampled to obtain 1024 discrete samples, and 1024 averaged samples are obtained from the twelve ECG recordings of each patient to be used as inputs for classification purpose.
4 Development of the Proposed LSTM Detection Method In this section, the detail of the development of the LSTM classification model is dealt. The diagram of LSTM model which comprise of N 1 -N 2 -N 3 structures. The block diagram of this structure is shown in Fig. 1. In the diagram, the following notations are used, the odd and even subjects whose samples are simultaneously applied are denoted by St (2p − 1) and St (2p) where ‘St’ stands for odd and even pairs of subjects whose ECG samples are applied simultaneously where 1 ≤ p ≤ P/2 and P = total number of subjects. N 1 , N 2 and N 3 stand for number of LSTM modules in first, second and third stages, respectively. The numbers of connecting weights between stages are 2 M, N 1 N 2 , N 2 N 3 and N 3 N 4 . The input to the model
L1,1
L2,1
Predicted class of St (2p-1)
L3,1
Actual class of St (2p-1)
∑ SM1
∑
-
+ e (2p-1, k)
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Predicted class of St (2p)
L3,n3
∑
∑ W (2M-1)
W (N1N2)
W (N2N3)
W (N3N4)
Actual Class of St (2p)
SM2
-
+ e (2p, k)
L1,N1
L2,N2
L3,N3
Learning Algorithm N1
N2
N3
2M
Fig. 1 Block diagram of LSTM-based model for HD detection from ECG signal
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comprises of 2 M samples corresponding to two consecutive subjects. N 4 stands for number of output nodes which corresponds to number of patients whose ECG samples are simultaneously used in the input. To facilitate training of the model, the total number of training sets is pairwise grouped. The total number of ECG samples of each pair is fed in the same time. This arrangement has been made to achieve faster training of the proposed model. In the first layer of the architecture, N 1 LSTM modules are provided. In the second and third stages, the numbers of LSTM modules are N 2 and N 3 . To determine the class of each subject N 4 , output nodes are provided to assess the class of each pair of subjects. In the present case, N 4 = 2 because the datasets belongs to two classes (healthy and diseased). Since the LSTM is a supervised training model, the desired class of each subject is simultaneously applied and then compared with the corresponding output class obtained from the two nodes of the model. The resultant two error terms e (2p − 1, k), e(2p, k) are used to update the connecting weights between different layers where 1 ≤ p ≤ P/2 and k is the iteration number. The conventional backpropagation learning algorithm is employed to adjust the weights connected between different layers. In the training phase of the model, both 70 and 80% of the total available datasets are used and the remaining 30 and 20% of the datasets which are not used for training are employed for validation purpose. To minimize the overfitting problem of the LSTM network, 20% weights between layers are randomly dropped out. Out of 2048 samples (corresponding to two odd and even subjects) serially eight samples are connected to each LSTM module through connecting weights. For the selected LSTM network model, the number of connecting weights between various layers are 2048, 256 × 128, 128 × 64, 64 × 2. In the output node, softmax function is used to transfer the input into values between 0 and 1. In the validation stage, the test datasets are used in the trained model to determine the actual class of each subject. The various performance metrics obtained from the proposed model using reallife data of patients as the input. These are sensitivity, specificity, accuracy, F-score, and precision. The formula of each of the performance metrics is presented in Table 4. Table 4 Performance metrics
Performance metrics
Formulae
Explanation of symbols
Sensitivity
T1 (T1 +F2 )
True positive = T1
Specificity
T2 (F1 +T2 )
True negative = T2
F-score
2T 1 (2T 1 +F1 +F2 )
False positive = F1
Accuracy
(T1 +T2 ) (T1 +F1 +F2 +T2 )
False negative = F2
Precision
T1 (T1 +F1 )
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5 Simulation-Based Experimental Results and Discussion In this section, the simulation-based experiments of the proposed LSTM-based HD detection model is simulated. The block diagram shown in Fig. 1 is used for HD detection purpose. The source of ECG datasets from which the input samples are generated to be used in this chapter is presented in Table 3. For training purpose, the ECG samples of 188 subjects (comprising of both healthy and diseased) are used. The remaining ECG samples of 80 subjects are used for validation purpose. The structure of the LSTM model employed in the simulation consists of 2048 input nodes (corresponds to 1024 number of samples of each of the two subjects), 256 LSTM modules in the first layer, 128 LSTM modules in the second layer, and 64 LSTM modules in the last layer. The final output comprises of two nodes (one per subject). Softmax function is used at each output node. The desired class of the two subjects for which the input samples are applied is fed as the target class. For simulation purpose, 0.1 and 0.9 are used to represent healthy and diseased class, respectively. Two error terms are produced by comparing the predicted classes and target classes. The generated two errors are used to update the weights of different layers following the backpropagation learning algorithm. To avoid the overfitting problem, 20% of the neurons are randomly dropped and the remaining weights are updated. The input samples of the two ECG sets are sequentially applied, and the weights are updated until the convergence is achieved. The learning coefficient is suitably selected to achieve the best possible convergence. The convergence characteristic obtained from the simulation is plotted in Fig. 2. After the completion of the training phase, the weights are frozen and then the model with trained weights is ready for validation purpose. During this phase, the remaining 20% ECG samples are used as input to the models and the corresponding class of each subject is determined from the model. From the validation results, the performance metrics listed in Table 4 are computed. These metrics for the given datasets using the proposed LSTM model is presented in Table 5. The same table also presents the results of the model when the Fig. 2 Learning characteristics of LSTM detection model during training phase using ECG samples of 70% subjects
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Table 5 Comparative performance metrics of the LSTM method Training and Accuracy in % Precision in % Sensitivity in % Specificity in % F1 score testing samples 70 and 30% of total datasets
84.556
92.708
87.767
71.126
0.902
80 and 20% of total datasets
86.598
93.817
88.015
71.597
0.915
ECG samples of 70% of the subjects are used for training and the remaining 30% are used for validation. The convergence plot is presented in Fig. 2 indicates that the mean square error of the model decreases from a higher value to almost zero value as the number of epochs increases. The comparisons of the results presented in Table 5 demonstrate that with 80% training and 20% validation samples-based LSTM model yields higher performance metrics as compared to the performance metrics of the model developed using 70% input data during training phase.
6 Conclusion A simple but efficient CAD detection model using LSTM network is presented in this chapter. Five different standard performance metrics obtained by using reallife standard ECG datasets reveal the effectiveness of the proposed method. The LSTM model developed using 80% of the total datasets exhibits superior performance compared to the model built with 70% training of the datasets. The proposed LSTM model can serve as an important tool for clinical practitioners dealing with HD treatment. The robustness of the proposed model can be further assessed by using similar other standard medical datasets. The concept of the proposed model can also be applied for diagnosis of other diseases and for other medical applications. Further, different types transform domain features can be obtained and then used as inputs to develop hybrid model for HD detection.
References 1. Tan, J.H., Hagiwara, Y., Pang, W., Lim, I., Oh, S.L., Adam, M., Acharya, U.R.: Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Comput. Biol. Med. 94, 19–26 (2018) 2. Oh, S.L., Ng, E.Y., San Tan, R., Acharya, U.R.: Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput. Biol. Med. 102, 278–287 (2018) 3. Yildirim, Ö.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96, 189–202 (2018)
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4. Faust, O., Shenfield, A., Kareem, M., San, T.R., Fujita, H., Acharya, U.R.: Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput. Biol. Med. 102, 327–335 (2018) 5. Pandey, S.K., Janghel, R.R.: Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model. Signal Image Video Process. 1–9 (2020) 6. Thirugnanam, M., Pasupuleti, M.S.: Cardiomyopathy-induced arrhythmia classification and pre-fall alert generation using convolutional neural network and long short-term memory model. Evolut. Intell. 1–11 (2020) 7. Chen, Y., Lv, J., Sun, Y., Jia, B.: Heart sound segmentation via duration long-short term memory neural network. Appl. Soft Computing, 106540 (2020) 8. Zou, Z.Z., Xie, K., Zhao, Y.F., Wan, J., Lan, L., Wen, C.: Intelligent assessment of percutaneous coronary intervention based on GAN and LSTM models. IEEE Access. 8, 90640–90651 (2020) 9. Yamamoto, K., Hiromatsu, R., Ohtsuki, T.: ECG signal reconstruction via doppler sensor by hybrid deep learning model with CNN and LSTM. IEEE Access 8, 130551–130560 (2020) 10. Ganguly, B., Ghosal, A., Das, A., Das, D., Chatterjee, D., Rakshit, D.: Automated detection and classification of arrhythmia from ECG signals using feature-induced long short-term memory network. IEEE Sens. Lett. 4(8), 1–4 (2020) 11. Mishra, S., Mallick, P.K., Tripathy, H.K., Bhoi, A.K., González-Briones, A.: Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl. Sci. 10(22), 8137 (2020) 12. Bhoi, A.K., Sherpa, K.S., Khandelwal, B.: Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT) analysis of electrocardiogram. Clust. Comput. 21(1), 1033–1044 (2018) 13. Bhoi, A.K., Sherpa, K.S.: QRS complex detection and analysis of cardiovascular abnormalities: a review. Int. J. Bioautom. 18(3), 181–194 (2014) 14. Bhoi, A.K., Sherpa, K.S.: Statistical analysis of QRS-complex to evaluate the QR versus RS interval alteration during ischemia. J. Med. Imag. Health Inform. 6(1), 210–214 (2016)
Network Intrusion Detection Using Genetic Algorithm and Predictive Rule Mining Utsha Sinha, Aditi Gupta, Deepak Kumar Sharma, Aarti Goel, and Deepak Gupta
Abstract In the modern digital world, there are over 4.3 billion Internet users contributing to the ever-increasing network traffic. With the huge network traffic growth, there is an increase in security threats which usually stem from external and/or internal hosts. As a consequence, it becomes indispensable to secure the system, but the dynamic nature of diverse attacks makes it strenuous to build a completely secure system. Therefore, using a technology that monitors the traffic and identifies any potential security threat gives an edge to the user and that is where the network intrusion detection system (NIDS) comes into play. An intrusion detection system (IDS) utilizes its ability to recognize any type of malicious network which cannot be detected by a conventional firewall. In this paper, we proposed a NIDS based on genetic algorithm (GA) using predictive rule mining. The novel idea of updating the crossover and mutation rate is applied maintaining a good balance of exploitation and exploration during evolution. The training set has been varied to evolve the rule base over a broader set of attacks. The proposed algorithm is implemented on the network security laboratory-knowledge discovery and data mining (NSL-KDD99) benchmark dataset. The results evaluate the performance of the network intrusion detection model using accuracy and detection rate metrics. The proposed system can be deployed in both wired and ad hoc networks. Keywords Genetic algorithm · Intrusion detection system · Weka · Predictive rule mining · NSL-KDD · Network security
U. Sinha · A. Gupta · D. K. Sharma (B) · A. Goel Department of Information Technology, Netaji Subhas University of Technology, New Delhi, India D. Gupta Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_13
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1 Introduction With rapidly changing environment, network security becomes the most important factor to protect an individual’s data from hackers or malware attacks. Although new protection technologies are being developed and used, a completely secure system is an unachievable goal [1]. Therefore, IDS becomes a priority for multiple organizations to safeguard their classified data and avoid any unwanted security breach. The foremost objective of the IDS is to monitor and analyze the network traffic and alert the administrator or the system in case of any policy breach [2]. These are of two types, namely network-based IDS and host-based IDS. The networkbased IDS are further classified into anomaly detection and signature-based detection depending on the nature and method of analyzing traffic. The anomaly detection uses statistical procedures to point out the deviation in the behavior of the network traffic, whereas signature-based method uses the defined network patterns that effectively detect the known intrusions and it utilizes various techniques which are grouped in the following three classes: 1. 2. 3.
The expert system The signature-based approach Genetic algorithm.
This paper introduces a GA-based approach in NIDS using a set of rules which evolves with time in order to reach the optimum solution. GA balances the rate of exploration with the rate of exploitation and also has the ability to adapt to the changing environment. This makes it a good option for NIDS. The proposed model has incorporated the concept of predictive rule mining and GA to generate optimum rule set for intrusion detection and can be deployed in both TCP/IP and ad hoc networks. Our implemented system has two stages. The training stage involves generation of a set of rules using non-uniform rates of crossover and mutation in genetic algorithm model. The generation of initial rule set using predictive rule mining makes the system generic in terms of the network dataset used. For our study, we have utilized the NSL-KDD dataset [3] after preprocessing in Weka, incorporating both continuous and discrete features of the network. This generated rule set is then used to detect any anomalous or intrusive connection in the detection stage. This work has not only eliminated the drawbacks of IDS based on machine learning (ML) algorithms but also improved the accuracy to more than 5% when compared to previous works of GA-based IDS. The system has also focused on achieving a high detection rate while keeping the number of false positives low. The rest of the paper is organized as follows. Section 2 briefly describes some previous works. In Sect. 3, we give a brief introduction on IDS, including their types and discuss the drawbacks of the existing systems. Section 4 explains the step-wise application of GA and predictive rule mining in IDS which is implemented and tested in Sect. 5. Section 6 shows the results of the proposed work and further discussions. Section 7 concludes the paper and discusses the future works.
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2 Related Work According to Crosbie et al. [4], the implementation of the network IDS through GA is a concept as old as 20 years. They applied genetic programming (GP) in order to detect network intrusion. The implementation of GP to derive classification rules for network intrusions was shown by Lu et al. [5]. They used a support confidence framework to produce satisfactory detection rates. Goyal et al. [6] in his paper employed GA to classify certain categories of smurf attacks in which the detection rate of the malicious attacks was as efficient as it could be, i.e., close to 100% while having a very low false positive rate. A GA-based anomalous IDS using both quantitative and categorical features of the network was proposed by Li [7]. However, it lacked any experimental results. It was shown by Xia et al. [8] that network intrusion can be detected by identifying the network features and comparing it with the known malicious attacks on the network. The common characteristics exhibited by both help in the formation of the linear rule which uses GA. Even though this approach was effective but it could not handle the continuous features. Gong et al. [9] were able to show software implementation of the NIDS which used GA. They followed the support confidence framework and calculated fitness according to the same framework. Benaicha et al. [10] proposed an IDS based on GA focusing mainly on increasing detection rate and decreasing the false alarms. They developed a rule to select and evolve the elite population to achieve the mentioned objective. Some recent works have addressed the application of blockchain technologies in IDS. The authors of [11] and [12] and [13] have explored the new domain of collaborative GA, IDSs, and the application of blockchain technology is to build secure and accountable collaborative IDSs. The tabular representation of related work is shown in Table 1.
3 Intrusion Detection System Intrusion detection system can be defined as the system that monitors the network traffic for malicious activity and also generates an alert to the system by following a well-defined methodology. The IDS generally handle any data regarding the malicious attack in two ways (1) processing it to the administrator or (2) assimilating the information into security information and event management system (SIEM). The mechanism of IDS can be classified into the following three components: 1. 2.
The event generator The analysis engine: It has two approaches: a. b.
3.
Signature-based method Anomaly based method
The response manager.
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Table 1 Related works in IDS Year
Highlights
Limitations
2004 Wei Li
Authors
A GA-based anomalous IDS was proposed that used both quantitative and categorical features of the network
The model is stated theoretically and there are no experimental results
2004 Lu et al.
A NIDS model using GP was proposed to obtain a set of rules from the existing network data with the help of support and confidence. The detection rate of the model has a satisfactory high value
The use of made the implementation of the model difficult. Also, the data and time required to train the system is quite high
2005 Gong et al.
It proposed a genetic algorithm-based NIDS and the software implementations of the approach. The usability of each rule to be used to classify was determined using support and confidence function
It uses DARPA dataset which has redundant records. This results in the generated rules that are more biased toward the training dataset and decrease model efficiency
2005 Xiao et al.
This paper stated that network intrusion can be detected by identifying the network features and comparing them with the known malicious attacks on the network. The common characteristics exhibited by both help in the formation of the linear rule which uses GA
This model considers only discrete features of the network. The important continuous network features cannot be included in this system
2008 Goyal et al.
This work employed a GA-based NIDS to generate a classification rule set for identifying network intrusions. The detection rate of the model is close to 100% with very low false positives
The model is built to detect only two classes of attacks, namely denial of service (DoS) and probing attacks
2014 Benaicha et al.
It proposed an IDS based on GA focusing mainly on increasing detection rate and decreasing the false alarms. They developed a rule to select and evolve the elite population to achieve the mentioned objective
This work employs basic genetic algorithm operators and focuses mainly on elitism of generated rules
(continued)
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Table 1 (continued) Year
Authors
Highlights
2018 Alexopoulos et al. This paper discussed the application of blockchain technology in the development of collaborative IDSs. It proposed a blockchain-based design architecture for implementation in CIDS and analyzed the design decisions
Limitations This work does not discuss the implementation details of the design proposed
Network Intrusion Detection System (NIDS): These systems are designed to analyze the network traffic of all the connected devices at present fixed intervals of time. It conducts a passing traffic analysis on the complete subnet and compares the traffic passed to the list of documented attacks on the subnet. After identifying the malicious attack or the deviant behavior, the system then sends the alert signal to the system admin. One practical example is fixing NIDS near the firewall on the subnet in order to recognize if the firewall is being compromised. Host Intrusion Detection System (HIDS): It operates on the network’s individual hosts or computers. This type of system controls and manages only the incoming and outgoing packets of a particular device, and in case of any suspicious activity, the system alerts the administrator. It also compares the current version of system files with previous versions for enhanced detection.
3.1 Drawbacks of the Existing System Most of the systems undergo two of the five following shortcomings: 1.
2. 3. 4.
Fidelity Issue: The IDSs mechanism works on the principle of identifying any kind of malicious activity or deviant activity of the system apart from normal. Since the data which assists the system to compare the suspicious behavior with a normal one has to cross a long path from origin to the system, there is a possibility that the hacker might corrupt the data or even destroy it in some cases before it reaches its destination. Resource Usage Issue: Since the IDS has to monitor the system’s network all the time for any malicious activity, it uses extra resources for smooth conduct. Reliability Issue: The components of IDS are executed independently which makes it vulnerable to any modifications by the attacker. False Positives: It is one of the most common issues any IDS faces. It is a condition when the system generates a false alarm.
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4 Proposed Work: IDS Using Genetic Algorithm and Predictive Rule Mining 4.1 NSL-KDD Dataset NSL-KDD dataset models the real-world network traffic with a total of 41 features. It is a revised cleaned up version of the KDD’99 dataset removing all the redundancy and other drawbacks. It is a standard dataset which makes it ideal for IDS comparison. Due to the aforementioned advantage, it becomes easier to execute the algorithm in the complete dataset without compromising the quality of the data. It comprises four classes of attack: denial of service (DOS), remote-to-local (R2L), user-to-root (U2R), and probe, and four categories of data: continuous, discrete, categorical, and binary. The instances of different attack classes are present in equal proportions. The NSL-KDD dataset provides the following edge: 1. 2. 3.
Elimination of redundant sets from the training data. Repeated data from test sets are discarded to make the IDS more efficient. The number of records for training as well as testing is sufficient to build an efficient generalized model.
4.2 Feature Selection and Predictive Rule Mining Not all the features in the NSL-KDD dataset have the same predictive ability and the high correlation among some features of the dataset and thus arises the need for feature selection. Weka has been used for feature selection, predictive rule mining, and preprocessing of the dataset. Feature selection in Weka consists of two parts: attribute evaluator and search method: (1)
(2)
Attribute Evaluator: It predicts the ability of the column or the feature to predict the class. The algorithm CfsSubsetEval has been used as the basis of subset selection. It evaluates the value of a subset concerning its ability to predict the class and its correlation with the class. It also takes into account the redundancy between the features and intercorrelation among the features. Search Method: It searches for different sets of attributes or features to derive the optimal set of features. The BestSearch method has been opted as it searches the search space using greedy hill climbing with a backtracking facility [14].
These methods have led to the selection of eight features after evaluating 462 subsets. The merit of the best subset is 0.589. The selected eight features along with label are displayed in Table 2. The continuous features have been discretized into integer ranges using Weka for easy formation of genetic rules. Predictive rule mining is the process of finding patterns in the data that helps in building predictive rules where a predetermined set of values of certain features predict the class of the rule. It has been applied by using the association rule mining
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Table 2 Table showing features and their descriptions Feature name
Description
Type
Integer range (for chromosomes)
Flag
Status of the connection – Normal or error
Categorical
1–11
src bytes
Number of bytes from the source
Continuous
1–23
dst bytes
Number of bytes from destination
Continuous
1–18
logged in
If logged in? 1:0
Discrete
0–1
srv error rate
Percentage of connections having “SYN” error
Continuous
1–5
same srv rate
Percentage of connections to Continuous the same service
1–14
diff srv rate
Percentage of connections to Continuous different services
1–11
dst host srv diff host rate
Percentage of connections to Continuous serve same service coming from different hosts
1–9
Label
Class: attack or normal
0–1
Categorical
feature of Weka on records of anomaly class and normal class, separately. The lower bound support has been set as 0.1 and metric type as lift. If {condition1 and condition2 ...} then {consequence}
This generates a set of 20 rules which after evolution are utilized to differentiate real-time intrusive behavior from the usual behavior of the system. Each rule is in the form of an if-else statement connected by a logical AND as shown above. The features constitute the condition part and the label forms the outcome part.
4.3 Data Representation The rules developed in Weka are converted into chromosomes to build the initial population of the model. A chromosome having a fixed length represents only one rule and each gene stands or one feature. The string values are encoded according to predefined integer values. Wildcard entries (−1) are used for generalizations. The continuous features which have been discretized in Weka are numbered into groups containing a range of integers. A sample rule is shown as follows:
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If Flag = SF and logged_in=0 and srv_serror rate = (0-0.01] and same_srv_rate = (0.99-1] and diff_srv_rate = (0-0.04] and dst_host_srv_diff_host_rate = 0, then label = attack
The rule says that if the flag indicates normal establishment and termination, the login status is unsuccessful(0), percentage of srv counts that activated the flags is in range 0–0.01, 99–100% of the aggregated connections are to the same server and 0–4% of the aggregated connections are to different server and dst_host_srv diff_host_rate is 0, then there is an attack in the system. The above-mentioned sample rule can be represented genotypically as a chromosome as shown below: ([3, −1, −1, 0, 1, 10, 1, 0, 1]).
4.4 Evaluation Function The fitness function or the evaluation function assesses the ability of an individual to compete in the environment among other individuals to propagate its chromosomes in the next generation. It essentially indicates the fitness of an individual and also implies the optimality of the solution. The fitness score lies in the range of [−1, + 1] with the positive side tending to the ideal side. The chosen evaluation function in the implementation is support confidence function. In this, if a rule correctly classifies the vector as an attack or not, it is rewarded otherwise penalized. The more correctly it classifies the higher the fitness value it receives. Based on these fitness values, it is used to select the parents for crossover and mutation. The support confidence function is defined as: Support: The fraction of the number of records matching completely to a rule to the total number of records in the dataset. Support represents the proportion of dataset classified correctly. Confidence: The fraction of the number of records matching completely to a rule to the total number of records matching only the precedent of the rule. Confidence stands for the accuracy of the rule. If the rule is represented as: If Ψ , then Φ (Ψ = precedent, Φ = subsequent), and N is the total number of records, we have Suppor t =
φ+ψ N
Con f idence =
φ+ψ ψ
(1) (2)
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.The fitness function is given as, Fitness = w1 ∗ Suppor t + w2 ∗ Con f idence
(3)
Weights w1 and w2 are parameters in the range [0,1]. They are initialized with values of 0.2 and 0.8, respectively. Though changing weights does not affect the final evolved rules, it has been found that changing the weights gives information that helps in identifying network intrusion or classifying the intrusion [15].
4.5 Genetic Operators Selection: We have adopted tournament selection for selecting the parents in the population for breeding. In this, K (taken to be 3 in the implemented system) random individuals are selected from the population and the fittest individual among them is chosen as the parent that creates the new offspring. Out of every k individuals, the best fit is chosen. Crossover: The crossover technique used in the proposed algorithm is a two-point crossover, it is also dynamic which changes as a function of generation, also called non-uniform crossover. The non-uniform crossover starting with a lower value is used to maintain the rate of exploitation in the earlier stages and avoid getting trapped in local maxima. This value increases in the later stages, consequently increasing the exploitation to arrive at an optimal solution and achieve convergence. Mutation: The number of chromosomes to be mutated is selected by generating as many random numbers as the product of the population and mutation rate. A mutation threshold is determined between 0 and 1. This threshold value is multiplied with the population and the chromosome length to determine the number of gene positions to be mutated. Random numbers are generated corresponding to the number of gene positions, and those genes are mutated by replacing with random values in line with the corresponding data attributes. Survivor Selection: In the proposed algorithm, n is taken to be 20 based on the number of features chosen. The fitness-based method is employed in the implemented system to select individuals based on fitness.
5 Implementation and Testing The proposed algorithm is implemented in two phases: the learning and the testing. The evolved rule set was evaluated on the 20% NSL-KDD test dataset, consisting of 22,544 records, as shown in Table 3. The various metrics that have been used to test optimality are true positive rate, false positive rate, and accuracy [3].
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Table 3 Tabular representation of detection metrics in 20% NSL-KDD test dataset Actual Predicted
Total
Attack
Normal
Attack ara>
TP = 10,944
FP = 1360
12,304
Normal
FN = 1889
TN = 8351
10,240
12,833
9711
22,544
Total
Fig. 1 Detection metrics
The heuristic-based initial rule set of 20 rules obtained is evolved using NSL-KDD training dataset and the steps of GA. This results in generation of 20 best fit rules which are used to classify the test data and calculate the various metrics involved in measuring the performance of the algorithm.
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6 Results and Discussion The objective is to achieve a higher value of true positive rate (TPR) and a low value of false positive rate (FPR), implying that most of the anomalies present are detected with low misclassification of normal data as an anomaly [16]. Globally, the results, presented in Fig. 1, were found to be satisfactory with a high detection rate or TPR of 98.75% along with a low FPR of less than 15% (14.45%). Another metric, accuracy is used to check the cost of misclassification of anomalies by the implemented IDS. For the proposed model, the highest accuracy obtained is 85.59%. The variation of accuracy metric with a change in the number of generations for which rules is evolved as shown in Fig. 2. It depicts the general trend of an increase in the accuracy of the model with an increase in the number of generations until a saturation point or convergence point is reached, where the accuracy becomes constant. Results in Fig. 3 show a global accuracy of 56.92% for 10 generations which increases to 63.32% for 15 generations. The highest accuracy of 85.58% is obtained for 25 generations after which the model converges.
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Fig. 2 Accuracy versus number of generations
Fig. 3 Comparison between the proposed algorithm and the existing algorithm
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6.1 Comparative Analysis The comparison between the proposed approach with modified operators and general genetic algorithm has been shown in Fig. 3. Experimental studies proved that the implementation of modified genetic operators like a non-uniform crossover and nonuniform mutation in IDS using GA helps increase the accuracy when compared to fixed crossover and mutation rates. Non-uniform operator rates are a function of a number of generations.
7 Conclusion and Future Work In this work, an improved intrusion detection system has been implemented for networks using GA and predictive rule mining. Experimental results have positively shown the optimality of the proposed model. The training and the testing of the system have been done on NSL-KDD dataset which has resulted in an increased accuracy of more than 5% when compared to previous implemented works. It has also eliminated the drawback of ML-based IDSs which are unable to evolve and identify new network threats. This system can be further applied on other datasets modeling different network features and attacks or on real-world network traffic using simulators. Further enhancements can be made in the system itself by using different fitness functions to evolve the rules. Variants of non-uniform crossover and mutation operators can also be applied to enhance the performance of the system.
References 1. Chhabra, A., Vashishth, V., Sharma, D.K.: A game theory based secure model against Black hole attacks in Opportunistic Networks. In: Proceedings of 51st Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA (2017) 2. Patel, P.: Geeksforgeeks, https://www.geeksforgeeks.org/intrusion-detection-system-ids/ (2018) 3. NSL-KDD data set: https://nsl.cs.unb.ca/NSL-KDD/ (2013) 4. Crosbie, M., Spafford, E.: Applying genetic programming to intrusion detection. In: AAAI Fall Symposium (1995) 5. W. Lu, I. Traore: Detecting New Forms of Network Intrusion Using Genetic Programming: The 2003 Congress on Evolutionary Computation CEC’03, pp. 2165–2172, Vol.3. Blackwell Publishing, Canberra, Australia (2003) 6. Goyal, A., Kumar, C.: GA-NIDS: a genetic algorithm based network intrusion detection system. In: Electrical Engineering Computer Science, North West University, Technical Report (2008) 7. Li, W.: Using genetic algorithm for network intrusion detection. In: United States Department of Energy Cyber Security Group 2004 Training Conference, pp. 1–8, USA (2004) 8. Xia, T., Qu, G., Hariri, S., Yousif, M.: An efficient network intrusion detection method based on information theory and genetic algorithm. In: 24th IEEE International Performance Computing and Communications Conference, Phoenix, AZ (2005)
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9. Gong, R.H., Zulkernine, M., Abolmaesumi, P.: A software implementation of a genetic algorithm based approach to network intrusion detection. In: Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, Towson, MD, USA (2005). https://doi.org/https://doi.org/10.1109/SNPD-SAWN.2005.9 10. Benaicha, S.E., Saoudi, L., Guermeche, S.E.B., Lounis, O: Intrusion detection system using genetic algorithm. In: Science and Information Conference, pp. 564–568. IEEE, London, UK (2014) 11. Meng, W., Tischhauser, E.W., Wang, Q., Wang, Y., Han, J.: When intrusion detection meets blockchain technology: a review. IEEE Access 6, 10179–10188 (2018). https://doi.org/10.1109/ ACCESS.2018.2799854 12. Alexopoulos, N., Vasilomanolakis, E., Ivanko, N.R., Muhlhauser, M.: Towards blockchainbased collaborative intrusion detection systems. In: Critical Information Infrastructures Security. CRITIS 2017. Lecture Notes in Computer Science, vol. 10707. Springer, Cham (2018) 13. Mishra, S., Tripathy, H.K., Mallick, P.K., Bhoi, A.K., Barsocchi, P.: EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20(14), 4036 (2020) 14. Frank, E., Hall, M.A., Witten, I.H.: The WEKA Workbench (2016). https://waikato.github.io/ weka-wiki/documentation. 15. Chhabra, A., Vashishth, V., Sharma, D.K.: A fuzzy logic and game theory based adaptive approach for securing opportunistic networks against black hole attacks. Int. J. Commun. Syst. Wiley 31(4) (2018). https://doi.org/https://doi.org/10.1002/dac.3487 16. Ojugo, A.A., Eboka, A.O., Okonta, O.E., Yoro, R.E., Aghware, F.O.: Genetic algorithm rulebased intrusion detection system. J. Emerg. Trends Comput. Inf. Sci. 3(8), 1182–1194 (2012) 17. Chiba, Z., Abghour, N., Moussaid, K., Omri, A.E., Rida, M.: New anomaly network intrusion detection system in cloud environment based on optimized back propagation neural network using improved genetic algorithm. Int. J. Commun. Netw. Inf. Secur. (IJCNIS) 11(1), 61–84 (2019)
A Comparison of Different Methodologies for Predicting Forest Fires Kajol R. Singh, K. P. Neethu, K. Madhurekaa, A. Harita, and Pushpa Mohan
Abstract Forest fires in recent years have been one of the most common disasters. The effects of forest fires on the ecosystem are permanent as they contribute to deforestation and global warming, one of their main causes. Forest fires are managed by weather data collection or by the processing of satellite forest photographs. Rapid detection can help us control damage effectively. Earlier work has shown that forest fires can be caused for many causes and it is therefore very important to accurately make predictions. Many machine learning techniques were applied and compared in order to construct a prediction model. The SVM model provides reliable forecasts for the use of the environment in small forest fires, which inevitably lead to larger fires. When forecasting forest fires using weather information, the forest weather predictor along with basic weather parameters must be taken into account. We show that through parallel computation with vector support engines, the accuracy of this predictive model can be enhanced. Keywords Support vector machine · MapReduce · Data mining · Artificial neural network
1 Introduction Forest fires have recently become one of the most common disasters that have been recorded to cause destruction of hectares of forests. They pose a threat not only to the forest resources, but to the entire regime, to the fauna and plants, which seriously disrupt biodiversity, the ecosystems and the environment of an area. In summer, when there is no rain for months, it is packed with dry senescent leaven and dwelling that can burst into flames caused by a small spark. The Himalayan Forests, Garhwal Himalayas in particular, were frequently burnt with a huge vegetation loss in the region. One of the primary factors for forest fires is that global warming is one of the key causes for the Earth’s surface temperature. The other causes are rain, floods, and K. R. Singh · K. P. Neethu · K. Madhurekaa · A. Harita · P. Mohan (B) Department of CSE, CMR Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_14
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people’s ignorance. Fire can cause deforestation, which has a great deal of negative effects on human society. In fighting fires, early detection of forest fires is critical. Different features of forest fires indicate that the forest fire center will be aware of the danger no more than 6 min after fire starts in order to eradicate the fire without causing permanent harm to the forest. Forest fire prediction includes environmental conditions, atmosphere, the dryness of flameproof materials, flammable material forms, and ignition sources for the evaluation and prediction of combustion hazards of flammable products in the wood. The authorities use many methods to track forest fires, and satellite detection is popular among world authorities. Few forestry authorities use forest fire alarms and reporters as human observers. There are also model machine learning for forestry fires forecasting and forestry alerts. Logistic regression, random forest, K-nearest neighbor, vector support, neural artificial nets, bagging, etc., are the techniques used for detection of such fires [11–17]. Figure 1 displays a data flow map for forest fires and climate change worldwide. In this article, we compare the various methods used in related research. In each of the methods used, there are different advantages and disadvantages. Data mining and machine learning techniques may provide an effective mitigation strategy, where forest-related data can be used to predict forest fires. Owing to changes in Earth’s orbital parameters, solar intensity, atmospheric composition, the environment and associated weather are not continuous. Our atmosphere has warmed up in recent years due to changes in human behavior in the air in radioactively active gases (carbon dioxide, methane, etc.). Such warming is likely to have a profound impact on fire activity in the forest zone. Based on weather altering factors, we restrict our research to forest fires. From recently on, it appears that they greatly impact the frequency of wildfires. By
Fig. 1 Data flow diagram
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analyzing these causes, we are developing a stronger and more efficient way of predicting forest fires. The IMD dataset is split into training data and test data. In the Indian Meteorological Department (IMD), the training data were used in the supervised learning model. This model will now forecast forest fires on the basis of the input test results. For testing various test data to forecast the occurrence of forest fires, a user interface is created.
2 Related Work The literature review indicates that the highest accuracy and precision are obtained using the SVM algorithm which is illustrated by the following study. The predictive model proposed by Rishikesh et al. [1] is based on a dataset found in the Montesinho Park in Portugal in a UCI machine learning repository consisting of both physical and climatic conditions. Various algorithms, such as logistics regression, vector support, random forests, K-nearest neighbors, and bagging and boosting predictors, both with and without the principal component analysis (PCA), are used. The logistic regression of PCA was the highest F-1 scoring of 68.26, and the gradient booster of 68.36.st was the highest score of PCA in those models. Al_Janabi et al. [2], Bhoi et al. [19] proposed a model which would be the most powerful method of forest fire prediction by exploring five techniques of soft computing under artificial neural networks. The data were collected from the UCI learning system database, which collected at different times 517 different entries for the Montesinho natural park (MNP). The first step was the preprocessing of the data collection. The principal component analysis (PCA) was used to identify important patterns and to pick the fire regions (clusters) by using PSO technique. The following process was followed by five neural network software (SC) technology which established best techniques for predicting forest fires, namely the neural network polynomial (PNN), cascade correlation network (CCN), radial base functions (RBF), neural perceptron multilayered (MPNN) network, and support vector machine(SVM). The final step was measured based on five consistency metrics including the root mean square error (RMSA), the MSE, the relative absolute error (RAE), the MAE, and the information gain (IG). In the final step, the predictors were evaluated. The results suggest that the RBF, MPNN, PNN, and CCN predictors are more efficacious and efficient. The results also show that the SVM algorithm provides predictive insight with a more reliable measurement error compared to other predictors. The results indicated that the SVM improves the precision of the forecast in contrast to other approaches and is ideal for forest fire predictions. The results show that SVM predicts fire probability well. SVM has the smallest MSE of 2926.4, RMSE of 54.0, MAE of 2.656, and RAE of 10.5 and highest IG of 2.656. In order to predict the labeled region of forest fires Cortez et al. [3], Mallick et al. [22] explore a data mining (DM) approach, e.g., five DM techniques. Recent real-world data collected from the north east of Portugal were tested in support of vector machines and random forests and four distinct selection features (using spatial,
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temporal, FWI components, and weather attributes). The best configuration utilizes an SVM and four meteorological inputs (that is to say, temperature, relative humidity, rain, and wind) and is able to predict the more frequent burnt area in small fires. This knowledge is particularly useful for better management of firefighting resources. Sun et al. [4], Naga Srinivasu et al. [21] show that, in case of large datasets resulting in increasing the number of training vectors, SVM parallel can be implemented to render SVM faster in terms of machine speed. The big data collection is managed first using MapReduce, built in software tools such as Hadoop and Twisters. Hadoop’s MapReduce does not support task reduction in the iterative map. Twisters help reduce and merge the tasks in both the iterative and non-iterative graphs. A large range of SVM models is used for the applications, such as libSVM, light SVM, ls-SVM, and the libSVM models. Training samples are separated into subparagraphs by using parallelization. A libSVM model is used for each subsection. Sub-SVMs filter the non-support vectors. The support vectors of each sub-SVM are taken as the input for the next sub-SVM layer. This shows that SVM with MapReduce reduces the time required to compute. Suresh Babu [5, 5] provides a comprehensive explanation of various worldwide indices of forest fire conditions, groups of forests and their areas, classification of different rates of hazards, fire accidents in various classes of land cover, level of fire danger of different types of property, categories of vegetation fire accidents, fire hazards for Uttarakhand vegetation categories. In addition, he has described different class and FWI rating systems, such as the Canadian Forest Fire Danger Rating System, the Canadian Fire Risk Index System, and the National Fire Danger Rating System. The different forms of forest in India are also being studied. We plan to use his work to measure forest fire weather indicators and measure fire hazard rates thresholds to improve the performance and accuracy. Artés et al. [6] offer a way to prevent the spread of flames. This approach is based on genetic algorithms involving multiple simulations. However, certain input parameters cannot be considered consistent throughout the entire fire if the fire is big and additional models need to be added. Wind is one of these uniform parameters. Therefore, a wind field model is provided in this work. This model requires more time for estimation, while the key constraint is response time. The prediction must be made to be useful as soon as possible and thus all available computational resources must be utilized. Mittal et al. [7] use wireless sensor networks to detect forest fires. They provide a review of various machine learning techniques for forest fire detection. These techniques include support vector machines, artificial neural networks, decision trees, and feed-forward neural networks. Zhang et al. [8] and Mishra et al. [20] proposed a model of spatial fire susceptibility prediction using a convolutional neural network. Past forest fire locations in Yunnan, China, from 2002 to 2010, and a collection of 14 factors influencing fire were mapped using a geographic system. Several statistical measures were used to measure the model’s predictive performance—the Wilcoxon signed-rank test, the receiver operating characteristic curve, and the area under the curve. The results have
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shown that CNN has a higher accuracy than random forest, vector support, multilayer neural perceptron network, and kernel logistic regression benchmark classifiers. The multilayer perceptron (MLP) techniques based on backpropagation algorithms for data on physical, climate, anthropogenic, and fire incident facts were explored by Onur et al. [9] in the Mediterranean forest in Turkey. It was concluded that each area should be studied separately for exact fire risk maps in order to accurately track risks posed by forest fires with the most concise features being tree canopy covering, temperature, and digital elevation map (DEMs) and according to cause of fire, vegetation dynamic, climatic conditions, and physical environment structures. In a model suggested by Daniela et al. [10] different data mining approaches were used to forecast forest fires in different regions of Slovenia, such as forest random, logistic regression and the bagging of forests and the boosting of decision trees, divided into three mainly GIS-based, multitemporal MODIS, and Meteorological ALADI data systems, such as Kras, Primorska, and Slovakia. GIS data cover geography, structures, etc., GIS data. ALADIN data includes temperature, humidity, wind speed, etc., and MODIS data contains data depending on the day of the year, such as average temperature for a specific quadrant for a specific day. The experimental results showed that the baggage of decision trees provided the highest accuracy for all three datasets. A model based on the Galician region (north-western Spain) has been proposed by Bisquert et al. [18], and image MODIS was used to track the status and the acquiring land surface temperature (LST). The following have been found: LST 8 days, fire and year history. The LST is an important parameter because higher temperatures correlate with lower humidity, which enables vegetation to ignite In order to assess forest fire hazards from remote sensing and fire history data, artificial neural network (ANN) and logistics regression were used for this work. The land surface temperature and enhanced vegetation index (EVI) were remote sensing inputs used. Different input combinations with logistic regression have been tested. In an artificial neural network, combinations of variables have been implemented and results obtained by the two techniques have been compared. Increased accuracy and recall in artificial neural networks are compared to logistic regression. This description was helpful in determining maps of fire hazards that can prevent fires.
3 Methodology Table 1 describes different methods with pros and cons in detail.
4 Comparison of Results A comparative analysis of various methods is conducted. This paper presents many mining algorithms that can be applied to the specified domain and define successful
Key idea
Method for linking independent variables with dependent variables and finds a linear relationship between them. It aims to find the value of Y such that the difference between the predicted value and the actual value is minimum. The cost function can be used to find the best values for and which provides the best fit line for the points
The dependent variables act as a nonlinear part of model parameters and one or more independent variables. It typically generates a curve where Y is a random variable. It is more complex to develop because it uses multiple iterations
Algorithm
Linear regression
Nonlinear regression
The equation is of the form, Y = f (X, β) + ε Transformed to intrinsically linear Y = θ1 + θ2 ∗ 1/ X
The equation is of the form, Y = α + βX Where Y is dependent variable and X is independent variable The cost function is given by, n C = n1 i=0 ( pr ed i − Yi )2
Formulas
Table 1 Comparison of different methodology with pros and cons Cons
Well deals with small datasets and effectively uses information to predict unknown parameters
(continued)
The most productive model research can be established because the experimental information does not have concrete cinematic values
Simple and easy to implement Most problems in the real world are not linear and are not realistic
Pros
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Key idea
Many decision trees act as a group in which each individual decision tree gives a class prediction, and this is the final prediction for the class with the highest votes. There is low correlation between the trees/models and the predictions are more accurate
Similar to how the brain has interconnected neurons and processes information, we have multiple interconnected elements that work together simultaneously to solve a problem. They are multilayer connected nets. There are three layers, the input layer, hidden layer, and output layer
Algorithm
Random forest
Artificial neural networks
Table 1 (continued) Works well for large datasets and avoids over-fitting problems by averaging or combining decision tree results
Pros
In general if there are n It is durable, versatile, and variables then the equation for can be used in complex ANN will be designs. The result is obvious f (x) = b + w1· × 1 + w2· × 2 + … + wn·xn Where w is the weights and x is the data points
To make a prediction at a new point x then, f ˆB(x) = 1/B(b1,B)(Tb(x)) Where Tb is the random forest tree and we take the average of B such trees
Formulas
(continued)
Requires high data and the chances of over-fitting are high
Random forests are difficult to construct and timely
Cons
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Key idea
Every data component is drawn as one point in an n-dimensional space and a hyperplane can be defined to differentiate between the classes by maximizing its margin. Having maximum margin between the support vectors and hyperplane is important as this will ensure accurate predictions
Parallel methods for large-scale classification are implemented to speed up the SVM process. Here, the model has multiple SVMs working in parallel on divided datasets. Each SVM will produce support vectors which will be sent to another set of SVMs. This will continue till we get a refined set of support vectors which will not change further
Algorithm
Support vector machine
Parallel support vector machine
Table 1 (continued)
SVM is implicitly parallelized to get smaller SV sets. Parallelizing SVM with split dataset to get refined set of support vectors
to the hyperplane and x is the set of points. The width of the margin is (2/|w|)
Where w the normal is vector
w .x + b = 0
The hyper plane can be found using the equation:
Formulas
Reduces memory from O(n2) to O(np/m) and improves computation time to O(np2/m) Where n is the number of instances, p is the reduces matrix, and m is the number of machines
Functions well in large spaces. It is efficient in memory because it supports vectors which form part of a group of training vectors
Pros
Reformulations are too memory intensive
If the number of features is much greater than the number of samples, chances of over-fitting are possible no memory and processing time portable
Cons
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Algorithm
Accuracy
Linear regression
Citations
0.840
Daniela et al. [10]
Random forest algorithm 0.843
Daniela et al. [10]
Artificial neural networks
0.76
Bisquert et al. [16]
Support vector machine
0.663
Rishickesh et al. [1]
Parallel support vector machine-Hadoop
0.99
Sun et al. [4]
algorithms. On the basis of our research in Table 2, we found that support vector machines are the best way to forecast forest fires. The SVM is a supervised data collection algorithm used to classify datasets into good and bad instances. By comparing classification methods and total accuracy, the best methodology and the best data mining tool can be established. The discourse is intended for the development of a new or modified software tool or mining algorithm with the best performance.
5 Conclusion and Future Scope Forest fires may happen because of many causes; accurate predictions are our target. Our work shows that the easiest way to predict forest fires is by using support vector machines. For small fires, which ultimately trigger bigger fires, there are more reliable tests. But support for large datasets on support vector machines would have several support vectors to minimize accuracy. We need accurate and reliable data. It is critical. Via parallel computation, we can boost the support vectors. We conclude that by modifying the algorithm and using parallel calculation, performance and calculation time are improved. There is, however, a higher memory requirement and an increase in computational time. For this, we make use of the Apache Spark framework. Spark and MapReduce have a symbiotic relationship. The ease of use, high computational speed, and swiftness of Spark are a perfect accompaniment to MapReduce. In order to make forest fire prediction more efficient and effective, we will use a redesigned parallel SVM for our future model.
References 1. Rishickesh, R., Shahina, A., Nayeemulla Khan, A.: Predicting forest fires using supervised an ensemble machine learning algorithms. Int. J. Recent Technol. Eng. (IJRTE) 8(2), 3697 (2019) 2. Al_Janabi, S., Al_Shourbaji, I., Salman, M. A.: Assessing the suitability of soft computing approaches for forest fires prediction. Appl. Comput. Inf. 14, 214–224 (2018). 3. Cortez, P., Morais, A.: Data Mining Approach to Predict Forest Fires using Meteorological Data. https://www.dsi.uminho.pt/pcortez
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4. Sun, Z., Fox, G.: Study on Parallel SVM based on MapReduce (2014). 10.1.1.300.9800 5. Suresh Babu, K.V.: Developing Forest Fire Danger Index Using Geo-Spatial Techniques. IIT Hyderabad-03-09. Report no: IIIT/TH/2019/21, (2019) 6. Artés, T., Cortés, A., Margalef, T.: Large forest fire spread prediction: data and computational science. In: The International Conference on Computational Science, vol. 80, pp. 909–918 (2016) 7. Anupam, M., Sharma, G., Aggarwal, R.: Forest fire detection through various machine learning techniques using mobile agent in WSN. Int. Res. J. Eng. Technol. 3 (2016). 8. Zhang, G., Wang, M., Liu, K.: Forest fire susceptibility modeling using a convolutional neural network for Yunnan Province of China. Int. J. Disaster Risk Sci. 10, 386–403 (2019). https:// doi.org/10.1007/s1753-019-00233-1 9. Onur, S., Berberoglu, S., Donmez, C.: Mapping regional forest fire probability using artificial neural network models in a Mediterranean forest ecosystem. Geomat. Nat. Haz. Risk 7, 1645– 1658 (2016) 10. Daniela, S., Panov, P., Kobler, A., Džeroski, S., Taškova, K.: Learning to predict forest fires with different data mining techniques. In: Conference on data mining and data warehouses (SiKDD 2006), pp. 255–258 (2006) 11. Mohindru, P., Khanna, V., Singh, R.: Various approaches in forest fire detection (2013) 12. Kaihua, Z., Wang, H., Bai, H., Li, J., Qiu, Z., Cui, H., Chang, E. .: Parallelizing support vector machines on distributed computers. In Advances in Neural Information Processing Systems, pp. 257–264 (2008) 13. Tyree, S., Gardner, J.R., Weinberger, K.Q., Agrawal, K., Tran, J.: Parallel support vector machines in practice (2014). arXiv preprint arXiv:1404.1066 14. Hema, P., Raghavan, N.R.S.: A support vector machine based approach for forecasting of network weather services. J. Grid Comput. 4, 89–114 (2006) 15. Tarun, R., Rajasekhar, N., Rajinikanth, T.V.: An efficient approach for weather forecasting using support vector machines. In International Conference on Computer Technology and Science, (ICCTS) IPCSIT, vol. 47, pp. 208–212 (2012) 16. Jian-Pei, Z., Li, Z.W., Yang, J.: A parallel SVM training algorithm on large-scale classification problems. Int. J. Mach. Learn. Cybern. 3, 1637–1641 (2005) 17. Meijer, N.A.: Fine Fuel Moisture Code: Creating a Predicative Regional Fire Weather Model for the Mediterranean Area La Peyne, France. Master’s thesis. 18. Bisquert, M., Caselles, E., Sánchez, J.M., Caselles, V.: Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data. Int. J. Wildland Fire 21, 1025–1029 (2012). https://doi.org/10.1071/WF11105 19. Bhoi, A.K., Mallick, P.K., Liu, C.M., Balas, V.E.: Bio-Inspired Neurocomputing. Springer Nature (2021) 20. Mishra, S., Tripathy, H.K., Mallick, P.K., Bhoi, A.K., Barsocchi, P.: EAGA-MLP-An enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors 20, 4036 (2020) 21. Naga Srinivasu, P., Srinivasa Rao, T., Dicu, A.M., Mnerie, C.A., Olariu, I.: A comparative review of optimisation techniques in segmentation of brain MR images. J. Intell. Fuzzy Syst. 38, 1–12 (2020). https://doi.org/10.3233/JIFS-179688 22. Mallick, P.K., Balas, V.E., Bhoi, A.K., Chae, G.-S. (eds.): Cognitive informatics and soft computing. In: Proceeding of CISC 2019, vol. 768 (2020)
Isolated Converters as LED Drivers Sumukh Surya and R. Srividya
Abstract In this study, comparative analyses of performance of isolated flyback and forward DC-DC converters for output voltage and current of 12V and 300mA, respectively, are reported, based on solutions of the governing ordinary differential equations. Results of simulations using MATLAB / Simulink are presented. The importance of selecting a proper step size is shown. For simulations, the LED is considered as a resistive load and its current and voltage characteristics are studied under loaded conditions. The modeling is based on ‘Commonly Used Blocks’ and hence the dependence on SimPower System tool box is reduced. Closed-loop analyses of the converters are carried out using a PI controller. The simulations show that flyback converter is better suited than forward converter as the response time was better. Further, the flyback converter shows lesser value of filter inductance, lesser duty cycle and hence reduced conduction losses. Keywords Forward converter · Flyback converter · LED driver · MATLAB/Simulink
1 Introduction A converter is a device used to transform electrical power from one form to another. Converters can be of various types, such as AC to AC, AC to DC, DC to DC, and DC to AC. Among DC-DC converters, switched mode converters and linear regulators are more commonly used. Since the latter has the disadvantage of operating the load at full load current and consequent I2R losses, the former is preferred. Among switched mode converters, several types categorized as isolated and nonisolated can be used as LED drivers. These converters are differentiated based on S. Surya (B) e-PowerTrain, KPIT, Bangalore, India e-mail: [email protected] R. Srividya Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 P. K. Mallick et al. (eds.), Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 1317, https://doi.org/10.1007/978-981-16-1056-1_15
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voltage levels and isolation at the input. Buck, boost, buck-boost, Cuk and SEPIC are some of the non-isolated/transformerless converters. Isolated converters are further classified as unidirectional (e.g., flyback and forward) and bi-directional (e.g., pushpull, full bridge and half-bridge). Isolated converters possess a transformer at the input side, which offers the advantage of isolation against short circuits and large surges. Hence, the load which is downstream of the transformer would be protected. These transformers operate at high frequencies (typically kHz) and hence the design of such transformers is a challenge. The flyback and forward converters are the isolated versions of buck–boost and buck converters. In this paper, voltage and current specifications for a typical LED downlight are selected and performance of two typical LED drivers, viz., flyback and forward converters, is mathematically modeled and analyzed. The advantages of mathematical modeling are that (a) transients in voltages and currents can be easily predicted (b) selection of suitable inductor and capacitor is enabled. Section II of this paper contains a literature review. In Section III, the specifications of the LED are indicated. Section IV presents the values of duty cycle, inductance and capacitance, calculated using KVL and KCL to the flyback converter circuit. In Section V, results of closedloop analysis for constant output voltage of the converter using a PI controller are presented. In Section VI, a forward converter is modeled using ‘Commonly Used Blocks’. The duty cycle, inductance and capacitance values are obtained based on the governing equations. In Section VII, the closed-loop analyses of the converter for constant voltage are presented.
2 Literature Survey In [1], a large signal model was derived to obtain constant current and constant voltage at the output. Circuit averaging technique was used to control the output voltage and current. In [2], working of isolated converter has been discussed. However, transients in voltages and currents during open and closed-loop operation were not discussed. In [3] new forms of forward and flyback converters were developed to increase the power factor and efficiency. But the mathematical modeling was not shown in the paper for the proposed topology. In [4] mathematical modeling for a forward converter was proposed. The mutual inductance effect was also considered. However, the closed-loop analysis for the forward converter was not shown. In [5] a universal flyback converter was proposed wherein the supply AC voltage was rectified to DC voltage and fed to the converter. The significance of snubber circuit was indicated and the steady-state analysis of the converter was proposed. In [6], the waveforms for the CCM (expand) and DCM (expand) are shown. The design of the primary inductance was shown for CCM and DCM. In [7], state space averaging technique was proposed for finding out the transient and steady-state responses of the converter. The converter was designed for supply voltage variation of 10% by fixing the duty ratio as 45%. In [8], comparative analysis of a flyback and forward converters was indicated. The inductor designs for the converters were shown in [8]. The software
Isolated Converters as LED Drivers Table 1 Specifications of the down light
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Specifications
Values
Input voltage
216 V
Output current
300 mA
Output voltage
12 V
Current ripple
1%
Voltage ripple
1%
Switching frequency
80 kHz
and hardware implementations for the converters were demonstrated. Various control strategies have been incorporated to achieve constant voltage at the output. In [9], sliding mode control was incorporated to regulate transformer output current and output voltage. In [10], a Cuk converter was mathematically modeled indicating the transients and steady-state behavior under open and closed-loop conditions. A Cuk converter would have higher duty ratio for the specified ratings and hence it not recommended. The isolated drivers operate at lesser duty ratio than the buck and boost converters for specified voltage and current ranges. The turn on time of switch would decrease and hence the conduction loss decreases. In the present approach, the control was achieved using a simple PI controller. The magnetizing inductance in the forward converter was neglected as it had no effect on the performance of the converter. Isolated flyback and forward converters were mathematically modeled using ‘commonly used blocks’. The advantage of using this approach is that C-Code for embedded applications can be easily extracted. Further, the dependence on Simscape electrical components is reduced and transient response can be obtained.
3 Specifications of LED Down Light The design specifications of the down light are shown in Table. 1. The specifications were taken from [11].
4 Mathematical Modeling of Flyback Converter Figure 1 shows the circuit diagram of a flyback converter. For analyses, flyback and forward, the following assumptions were made (a) operation in continuous conduction mode, (b) ideal components, (c) large capacitance such that it consumes no current and the entire load voltage appears across it [12]. When the switch is closed, VL = V g
(1)
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Fig. 1 Circuit diagram
i c = i L (N1 /N2 )
(2)
VL = −Vo ∗ (N1 /N2 )
(3)
i c = −Vo /R
(4)
When the switch is opened,
By using the principle of inductor volt sec balance and capacitor amp balance, V0 /Vg = (N2 /N1 ) ∗ (D/(1 − D))
(5)
where duty ratio is D. The switching functions can be written as Ldi L /dt = Vg ∗ s − (1 − s) ∗ Vo ∗ (N1 /N2 )
(6)
Cdv0 /dt = il (N1 /N2 ) ∗ s + (1 − s)(−V0 /R)
(7)
By solving the differential Eqs. (6) and (7), the output voltage and the inductor current were determined [12]. By solving the differential equations, current and voltage in inductor and capacitor can be obtained. In MATLAB, an auto solver technique was used to solve the differential equations. The authors observed that the solver step size had to be set to e-6 to capture the initial transient. Since MATLAB solves differential equations numerically, the step size must be made small to obtain accurate result and minimize the error. Figure 2 shows the mathematical model of a flyback converter. Figures 3 and 4 show the variation of the output voltage and inductor current over time.
Isolated Converters as LED Drivers
Fig. 2 Mathematical model
Fig. 3 Output voltage
Fig. 4 Inductor current
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4.1 Calculation of Inductance and Capacitance Based on Table 1, the duty ratio, inductance, load resistance, and capacitance were calculated. V0 /Vg = (N2 /N1 ) ∗ (D/(1 − D))
(8)
I L = Vo2 / Vg ∗ D ∗ R
(9)
i L /I L = 0.01
(10)
Vo /V0 = D/(R ∗ C ∗ f s )
(11)
D = 0.1428, L = 332.37 mH, R = 40 and C = 4.46 uF. From Fig. 3, it can be observed that during open loop operation, the time taken to reach the desired output voltage is