Embracing Machines and Humanity Through Cognitive Computing and IoT 9811945217, 9789811945212

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Advanced Technologies and Societal Change

Mohammed Usman Xiao-Zhi Gao   Editors

Embracing Machines and Humanity Through Cognitive Computing and IoT

Advanced Technologies and Societal Change Series Editors Amit Kumar, Bioaxis DNA Research Centre (P) Ltd, Hyderabad, Telangana, India Ponnuthurai Nagaratnam Suganthan, School of EEE, Nanyang Technological University, Singapore, Singapore Jan Haase, NORDAKADEMIE Hochschule der Wirtschaft, Elmshorn, Germany Editorial Board Sabrina Senatore, Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy Xiao-Zhi Gao , School of Computing, University of Eastern Finland, Kuopio, Finland Stefan Mozar, Glenwood, NSW, Australia Pradeep Kumar Srivastava, Central Drug Research Institute, Lucknow, India

This series covers monographs, both authored and edited, conference proceedings and novel engineering literature related to technology enabled solutions in the area of Humanitarian and Philanthropic empowerment. The series includes sustainable humanitarian research outcomes, engineering innovations, material related to sustainable and lasting impact on health related challenges, technology enabled solutions to fight disasters, improve quality of life and underserved community solutions broadly. Impactful solutions fit to be scaled, research socially fit to be adopted and focused communities with rehabilitation related technological outcomes get a place in this series. The series also publishes proceedings from reputed engineering and technology conferences related to solar, water, electricity, green energy, social technological implications and agricultural solutions apart from humanitarian technology and human centric community based solutions. Major areas of submission/contribution into this series include, but not limited to: Humanitarian solutions enabled by green technologies, medical technology, photonics technology, artificial intelligence and machine learning approaches, IOT based solutions, smart manufacturing solutions, smart industrial electronics, smart hospitals, robotics enabled engineering solutions, spectroscopy based solutions and sensor technology, smart villages, smart agriculture, any other technology fulfilling Humanitarian cause and low cost solutions to improve quality of life.

Mohammed Usman · Xiao-Zhi Gao Editors

Embracing Machines and Humanity Through Cognitive Computing and IoT

Editors Mohammed Usman King Khalid University Abha, Saudi Arabia

Xiao-Zhi Gao School of Computing University of Eastern Finland Kuopio, Finland

ISSN 2191-6853 ISSN 2191-6861 (electronic) Advanced Technologies and Societal Change ISBN 978-981-19-4521-2 ISBN 978-981-19-4522-9 (eBook) https://doi.org/10.1007/978-981-19-4522-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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

Introduction

This book sheds light on systems that learn extensively, with purpose and naturally interact with humans. Improving operations and increasing competitive differentiation among manufacturing organizations by harnessing the power of cognitive abilities, IoT can help build and influence the flow of information—making the shop floor more cognitive through effective processing, analysis and operational optimization. Now, we are seeing first-hand the potential of cognitive computing—its ability to transform businesses, governments, and society. The real potential of the cognitive age can be realized by combining data analysis and statistical reasoning of machines with uniquely human qualities, such as self-directed goals, common sense, and moral values. Improving operations and increasing competitive differentiation among manufacturing organizations. By harnessing the power of cognitive abilities, IoT can help build and influence the flow of information—making the shop floor more cognitive through effective processing, analysis and operational optimization. Cognitive initiatives come in all shapes and sizes, from change to strategic and everything in between. What most successful projects have in common, no matter how ambitious, is they start with a clear view of what technology can do. Therefore, the first job of cognitive scientists is to gain a firm understanding of cognitive abilities. Internet of things (IoT) allows communication of various devices around the world through a network. This is widely used in this era of growing technology. IoT is being used in a lot of crucial fields like agriculture, medicine, smart living, etc. Different types of electronic devices, sensors, and software can be combined to form a system. IoT-based project able to demonstrate the current humidity and temperature information on the LCD and the ThingSpeak cloud stage utilizing Raspberry Pi. Machine learning techniques can be used to diagnosis diseases based on patient symptoms. Many times, we see that a patient dies due to a disease because it was not identified at the beginning stages of its occurrence. So, in order to reduce such cases, we made a machine learning model using python which can predict diseases based on the symptoms so that necessary precautions can be taken accordingly. Disease

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predictor uses machine learning which also contains GUI that helps user to easily interact when he enters symptoms as input, and the output is given based on the prediction. Machine learning algorithm can be used forecasting of COVID-19 virus spread. Modeling the spread and the impact of COVID-19, infection can be especially significant in understanding its effect. While conventional models, ML-based methods could be the way to discovering good forecast models. In this study, ML-based technique called MLP-ANN is designed to approach stretch of the virus, which anticipates max amount of individuals who gotten the virus per region in apiece time unit, max amount of individuals who recovered per region in apiece time unit, and max amount of demises per area in a piece time unit. MLP has chosen for its straight forwardness in contrast with further AI strategies, because of limited preparing time related among such strategies, since the brisk generation of outcomes is significant when demonstrating infections, because of the as-quick-as-conceivable necessity for approaches with sufficient regression precision. Modeling should be possible on existing data, utilizing statistical examinations. But, with regards to amazingly complex approaches, statistical examination fails to fathom the complexities limited in the examined data. ML-based techniques can be utilized to “learn” the overall pattern, however the complexities of the data, which brings more precise results. Models got by ML strategies change their boundaries to accommodate their expectations to existing data, regardless of what it contains. This capacity to consider hard to watch complexities put away inside data ought to loan itself well, when utilized in an endeavor of regression an intricate model, for example, stretch of COVID-19. Presently, presented strategies of COVID-19 stretch have generally helpless outcomes or have made expectations which were demonstrated to not relate to genuine data. The research implicates that the chronic disease has affected our lives in a way that cannot be taken back. So, predicting the COVID-19 cases could help in achieving the prevention of the calamities. The evidence of machine learning (ML) and artificial intelligence (AI) application on the previous epidemic encourages researchers by giving a new angle to fight against the novel corona virus outbreak. Application of support vector machine and traditional model is to predict the monthly stream flow. The stream flow is very important component of any river system hydrodynamics. Due to their consequences for water-based influx control, dam structural architecture, and river engineering studies, the prediction of flow values is very significant in the engineering of water supply and hydrology. Stream flow includes various complex processes which effected by many hydro-geo-climatically parameters. A major problem of food and agriculture has emerged in extreme climate change and the vast global population increase. To evaluate appropriate techniques and crops for the region, our studies propose using a smart agricultural platform to track environmental factors. For such factor a decision-making boom, artificial neural remote networks (ANN), and support vector machines (SVM) can be used. Studies show that the assessment of crop yields can be based on all the machine learning techniques under consideration. These technologies were evaluated in five parameters: precision, recall, specificity, and fashion.

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One of the applications of artificial intelligence is a medical diagnosis. The CNN architecture has been developed to extract the functionality and has adapted to automate mass breast abnormality the faster R-CNN segment of the proposal area network (RPN) and the interest region (ROI). Computer-assisted classification leads to inappropriate diagnosis and forecasting. It is an extremely tiring job because there are a lot of data to be referenced. Researchers of this study empirically analyzed a typical classifier routine to measure its accuracy, studied its dimensions, positions, image qualities, and shapes. Goals of artificial intelligence are to replicate human intelligence, solves knowledge-intensive tasks, and intelligent connection of action, where it performs task like playing chess, driving car, performing surgical operation. Thus, the AI and deep convolution neural network can be used automatic attendance management system. The Internet of things (IoT) is a fast-growing number of physical devices (which have a unique identifier) interconnected with some devices over the Internet. These physical devices are combining and transmit information and transfer data via connected devices exclusive of any person communication. If IoT is implemented in transportation vehicles for pre-programmed accident detection and transmission of victim’s location and information to concerned officials, then the loss of life in accidents may gradually decrease. Deep brain stimulation (DBS) is a therapeutic-surgical procedure, protected, effectual, in addition neurosurgical intercession for a range of neurologic, neurodegenerative disorders plus Parkinson disease (PD) with a high cardinal tremor in the course of micro-neuro-chips embedded into the PD brain. Artificial intelligence (AI) and machine learning techniques (MLT) can be employed to well predict these outcomes. Artificial intelligence (AI) technologies are being part in the human life. Currently, developments in computational capabilities have led to substantial evolution in AI, developments of ANN are capable of organizing human and machine natural communications. Speech, communication, gestures, facial expressions, etc., are gaining popularity. Artificial intelligence cam is used to implement sustainable smart voice assistance. Similarly, dementia disease can be detected from psychiatric disorders based on automatic speech analysis.

Contents

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Publishing Temperature and Humidity Sensor Data to ThingSpeak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Sai Surya Teja, G. Venkata Hari Prasad, I. Meghana, and T. Manikanta Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Pardhasaradhi, G. V. Ganesh, V. V. S. Krishna, M. Naga Sai Kiran, and K. Manikanta Sai

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Stuck-At Fault Detection in Ripple Carry Adders with FPGA . . . . . P. Pardhasaradhi, K. V. K. V. L. Pavan Kumar, Raghava Yathiraju, Yadalapalli Sri Sai Sumanth, Surisetty Nishith, and Tiyyagura Vishnu Vardhan Reddy

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Prediction of COVID-19 Spreaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Siva Ganga Prasad, Rohitha Mikkilineni, N. Sampath, K. Yashwanth, and G. V. Ganesh

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Smart Agricultural Solutions Through Machine Learning . . . . . . . . K. V. Daya Sagar, Jasti Lalitha Sai, Shaik Sadiq, and Malladi Krishna Prasad

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Low-Cost ECG-Based Heart Monitoring System with Ubidots Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Vamseekrishna, M. Siva Ganga Prasad, P. Gopi Krishna, P. Bhargavi, S. Rohit, and B. Tanmayi

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Automatic Attendance Management System Using AI and Deep Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . J. RajaSekhar, Sridevi Sakhamuri, A. Dhruva Teja, and T. Siva Sai Bhargav

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Automatic Vehicle Alert and Accident Detection System Based on Cloud Using IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pachipala Yellamma, P. G. Sandeep, R. Revanth Sai, S. Rohith Reddy, and D. Mahesh AEFA-ANN: Artificial Electric Field Algorithm-Based Artificial Neural Networks for Forecasting Crude Oil Prices . . . . . . Sarat Chandra Nayak, Subhranginee Das, Biswajit Sahoo, and B. Satyanarayana

10. A Critical Survey on Machine Learning Paradigms to Forecast Software Defects by Using Testing Parameters . . . . . . . . Y. Prasanth, T. Satya Sai Vinuthna, P. Komali, K. Kavya, and N. Aneera

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11. Low-Power Comparator-Triggered Method of Multiplication for Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 K. Mariya Priyadarshini, C. Santosh, G. U. S. Aiswarya Likitha, I. B. V. Sai Srikar, and Peram Ramya 12. Assembly Line Implementation for IOT Applications . . . . . . . . . . . . 117 N. Siddaiah, P. Pardhasaradhi, M. Phanigopi, Y. Vasanthi, and Y. Deepika 13. Dementia Disease Detection from Psychiatric Disorders Based on Automatic Speech Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Merugu Suresh, Abdul Subhani Shaik, and Manir Ahmed

Chapter 1

Publishing Temperature and Humidity Sensor Data to ThingSpeak T. Sai Surya Teja, G. Venkata Hari Prasad, I. Meghana, and T. Manikanta

Introduction Internet of things [IoT] allows communication of various devices around the world through a network. This is widely used in this era of growing technology. IoT also allows users to handle the devices from remote places which in turn helps in handling the situation over there. It is handy when it comes to monitoring an area or manipulating diverse conditions. IoT is being used in a lot of crucial fields like agriculture, medicine, smart living, etc. Different types of electronic devices, sensors, and software can be combined to form a system. Raspberry Pi, which can mimic most of the operations performed by a computer, is available in a compact size and low price. Sensors communicate with cloud platforms through Raspberry Pi. Raspberry Pi uses Python as its default programming language. It is used here for acquiring the data from the input sensor and publishing it to the ThingSpeak platform. In this IoT project, humidity and temperature data is collected and published to ThingSpeak. Using ThingSpeak, this prototype monitors humidity and current temperature in an area. This is done by communication between Raspberry Pi, DHT22 sensor module, ThingSpeak server, and LCD. The humidity and temperature sensor (DHT22) displays the value of temperature and humidity through the LCD and simultaneously sends the value to the ThingSpeak for monitoring directly from anywhere in the world. ThingSpeak analyzes information and displays it in graphics format. LCD is also used to display temperature and humidity. This project contributes to achieving the purpose of the IoT as an effective monitoring system and a versatile system by making any future modifications easy. Monitoring comes in handy by binding the hardware using IoT. Any post-analysis of the situation over a place also comes easy with the collected parameters. T. Sai Surya Teja (B) · G. Venkata Hari Prasad · I. Meghana · T. Manikanta CMR College of Engineering & Technology, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_1

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Literature Review NodeMCU is the main controller for the system and DHT11 is used here for the temperature and humidity measurement. ThingSpeak is used here for uploading the data for observance [1]. NodeMCU has the advantage of having an inbuilt WiFi module. DHT11 lacks an inbuilt analog to digital converter and in NodeMCU CPU’s power is utilized a lot by the code. Another system proposed Arduino with a DHT22 sensor. The output is displayed over an LCD [2]. This is a low-cost and simple system. This is only used for nearby monitoring and lacks online monitoring. A climate monitoring system uses multiple sensors to monitor the climatic conditions of an area. This proposed system uses DHT22 as a humidity sensor and BMP180 for temperature measurement. Raspberry Pi is the main part and MQTT messaging system is used where the user has to be subscribed to the channels [3]. With the MQTT messaging system, it is difficult to form a global scalable network. IoT-based sensing and monitoring [4] employs multiple sensors like DHT22, LDR, Rain sensors, etc., and Arduino is used as the central component for the communication with the ThingSpeak. Analyzing the data is also done using MATLAB software [4]. A better powerful central controller makes it more batter for faster operations. The smart agriculture and monitoring system based on IoT also uses the ZigBee technology and GPRS data transmission or communication. It uses a DHT11 sensor and also a camera for surveillance [5]. This is advantageous for local communication but the data transfer rate is low. This prototype uses a gsm module for communication with the user and also an alert system regarding the humidity and temperature conditions [6]. Design of temperature and humidity monitoring system based on Zigbee technology [7]. Zigbee technology is used for communication with the end-user. This proposed system uses an SHT11 sensor for temperature and humidity measurement that requires mathematical calculations for getting accurate output [7]. The implementation of this prototype is easy and also has a flexible network structure. But this ZigBee has a low transmission rate. The replacement becomes costlier and has less coverage. Restful API-based data transfer to the AT&T M2X IoT platform has been shared in [8]. SHT11 sensor is used to gather the temperature and humidity data and the data has been stored in physical storage devices like micro SD cards rather than a cloud [9], whereas ESP-8266 NodeMCU is used in [10] for internal device communication. The detailed usage instructions and limitations of Raspberry Pi and ThingSpeak have been mentioned in [11]. Reference [12] consists of a central Arduino UNO board that interfaces with the input sensor DHT11, and an ESP-8266 Wi-Fi module that transmits the sensed data to the open IoT API ThingSpeak. Selecting the desired cloud platform for the applications is made easier with the comparison and information provided in [13]. Reference [14] uses ATMEGA328 between the sensor DHT22 that senses the data and LCD to display results. Reference [15] using ATMEGA328 to

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control temperature and humidity sensor DHT11 and wireless transceiver module nRF24L01 forming the temperature and humidity monitoring system.

Existing System Presently, all the systems are designed for a specific task but not for scalable tasks. Some existing systems use separate sensors for humidity sensing and temperature sensing. Some of the sensors have an analog output which again needs to be converted to digital values by the controller. These sensors also do not support long-range communication. Online monitoring is done through various platforms and some of them are not open to all the users. Some prototypes also do not have online monitoring but alerting through messaging techniques. These messaging alerts do not help in storing the data for future analysis. The cloud platforms that are being used are not open for every user to analyze the data and most of the data is unencrypted. Most of the present systems use Arduino as their controller. WIFI module connectivity will be done but it is a time taking process. But it is not compatible with all the programming languages, and also, it is less scalable for many operations.

Proposed System To overcome the drawbacks, the below system has been proposed. DHT22 sensor is used for sensing the humidity and temperature of an area. DHT22 has been chosen because it has an inbuilt analog to digital converter and can measure a wide range of temperatures. The DHT22 sensor also supports long-range communications up to 20 m with just a 5 V power supply. Raspberry Pi collects the data from the sensor. It supports various programming languages and employs multiple sensors. It provides low latency in communication due to its faster processing which helps in uploading the data faster to the cloud platform. This IoT-based project has three sections. Firstly, the humidity and temperature sensor, DHT22, senses the humidity and temperature data. Secondly, Raspberry Pi extracts the DHT22 sensor data as a suitable number in percentage and Celsius scale and sends the data to ThingSpeak server. And finally, ThingSpeak analyzes the data and shows it in a graph form. LCD is connected to display temperature and humidity (Fig. 1.1). Considering the flow diagram, DHT22 is the first component that connects with the surroundings and collects the data. This data is passed to the Raspberry Pi. Raspberry Pi is an intelligent device that communicates between input and output. It provides the data to the LCD and, through the Internet, it also provides the data to the ThingSpeak. This data can be accessed using LCD for a general view or ThingSpeak

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Fig. 1.1 Flow diagram

for an overall view. ThingSpeak is accessible through mobile phones, PCs, laptops, and work stations. Cloud platform displays data in an easy-to-understand format, such as graphics that will help you analyze and perform tasks remotely. ThingSpeak is used here because it is easily accessible to all users of this system. It also provides data in various ways that are used. The LCD is used for offline installation. One can use the LCD screen in places that do not have Internet access. Here, the user can see data on temperature and humidity and understand the situation. This system can be upgraded as it uses various device settings for different purposes.

Implementation In this system, DHT22 is employed for sensing. This device has one humidity sensing component and a negative temperature coefficient (NTC) temperature sensor, also called thermistor. The IC here makes the information understandable by the Raspberry Pi. The humidity sensor comes into play for measuring humidity and has a moisture-holding substrate that is sandwiched between two conducting plates. The conduction between the electrodes changes with respect to the humidity around the sensor. This transformation in resistance is then given to IC and then read by the Raspberry Pi. NTC thermistor is employed for temperature measurement. The resistance of this semiconductor device is reciprocal to the temperature which means their resistance decreases with the rise in temperature. These values are then converted into digital data by the ADC within the DHT22. It is a four-pin IC. The first pin is a VCC pin which is connected to the VCC line. The second pin is the data pin which can be connected to any one of the GPIO pins of a Raspberry Pi. The fourth pin is grounded using a ground line (Fig. 1.2). Raspberry Pi receives the data from the DHT22 sensor through the generalpurpose input–output (GPIO) pin. The data received by the Raspberry Pi is digital data. Raspberry displays the data output through two components, one is liquid crystal display (LCD) and the other is ThingSpeak. This data transfer is done through the general-purpose input–output (GPIO) pins. Uploading is done through API keys

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Fig. 1.2 Temperature and humidity sensing module [DHT22]

Fig. 1.3 Raspberry Pi 3B+

provided by ThingSpeak. Figure 1.3 is the Raspberry Pi 3B+ which is being used in this prototype. The liquid crystal display, used for the data display, is a 16 × 2 LCD. It has 16 pins and 8 of them are data pins. The contrast of the LCD is varied using a 10 k potentiometer. The fifth pin of the LCD is an R/W pin which decides whether the LCD either reads or writes. For this project, the fifth pin is set to the ground as it has to perform only a read operation. Data pins are connected to the GPIO pins of the Raspberry Pi for the communication between LCD and Raspberry Pi. This LCD has two lines where one is used for the temperature display and the other for the humidity value display. The ThingSpeak platform is also used here for observance purposes. The data is uploaded to ThingSpeak through Raspberry Pi using the Internet. This data will be analyzed here and represented in various forms. The graphical format makes the data more understandable for any user. A channel is created in ThingSpeak using the name Publishing temperature and humidity data to ThingSpeak. This channel has its API keys. We use the Write API key for writing into the ThingSpeak server (Fig. 1.4).

Results and Discussion Based on the results, the efficiency of the system will be known. This IoT-based system provides fast and accurate results. It is also a safe and economical system. The results below are accurate and understandable. The graphical format which is

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Fig. 1.4 New channel creation in the ThingSpeak platform

Fig. 1.5 LCD displaying temperature and humidity values

used in representing the output makes any person easily grasp the variations in the data which is grouped together. Using LCD, we can modify what to display and how to display it as per the requirements. There are two outputs for this system, one is LCD and the other is ThingSpeak. LCD is used for offline displaying of the data and ThingSpeak is for the online monitoring of the data.

LCD Output Figure 1.5 shows the LCD screen displaying both temperature values in degrees Celsius and humidity values in percentages. LCD is added to this system because it is simple to quickly gain knowledge about the situation at a particular time. This LCD is used for offline monitoring and requires no Internet. This LCD costs less power for displaying the values and is also easy to implement. It is used to present the data understandably for a normal person.

ThingSpeak Output Though the LCD provides us with data, we cannot get to know the variations in temperature and humidity. The user also has to be near the LCD to understand the

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situation. Adding an online source that stores and displays the data can help, which is ThingSpeak. Figure 1.6 shows the temperature graph in the ThingSpeak platform. In this way, we represent the temperature values with respect to time. And the temperature in that area fluctuates at 32° C and the lowest up to now is 31° C. The temperature is seen to be nearly constant over a period of time. Figure 1.7 shows the humidity graph in the ThingSpeak platform. In this way, we represent the humidity values with respect to time. Humidity values are represented in percentages. The humidity that we get now is relative humidity and varies from 0 to 100% says that the air contains the maximum amount of water vapor. In the above image, humidity values are constantly fluctuating between 13 and 14%. This ThingSpeak server requires an Internet connection for the real-time uploading of the data. Then the values are plotted on the graph and vary with time. This helps in taking certain actions required in a specific situation. We can say that Fig. 1.6 Temperature [°C] versus time graph from ThingSpeak

Fig. 1.7 Humidity [%] versus time graph from ThingSpeak

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anyone can use this data for analysis over the area and can also predict the conditions that may occur. ThingSpeak enables visualization of the changes that are occurring from the start or from a point of time in the past. This data can also be used for research purposes. This system provides monitoring access to the temperature and humidity values from anywhere with Internet access.

Conclusion and Future Scope An efficient humidity and temperature monitoring system has been designed using Raspberry Pi which provides accurate results at any point in time. Raspberry Pi is a powerful computer that makes this prototype more productive and more scalable. This proposed system is evolvable and can be improved for any other tasks that have an effect on humidity and temperature on them. DHT22 sensor being a longrange transmitter and accurate sensing device helps in making this system faster in collecting the data. ThingSpeak displays the data in a more organized and understandable manner for analyzing and taking necessary actions. IoT-based monitoring of an area is the handiest, but it also allows the consumers to research the correct modifications within the surroundings and for taking possible action. This project can be extended into many fields as it is not for a specific field. Numerous attachments can be done to this proposed system for various tasks. Raspberry Pi can handle any future operations easily. This can be extended and implemented in many fields where humidity and temperature play crucial roles. This can be used in agricultural systems by connecting water pumps to this system. We can also control the cooling systems in industries by handing control over the air conditioners in industries. We can add control over the ventilation systems in medical fields where people with lung diseases have problems because of the humidity levels. This system can be taken as the main part in any kind of future projects where they just need to take care of the attachments and this can do the overall control. This system can be further expanded to forecast the parameters based on the historical data and to monitor the developing cities and industrial zones for monitoring, collecting the data, and analysis.

References 1. Khaing, K.K.K.: Temperature and humidity monitoring and control system with ThingSpeak. Int. J. Sci. Res. Eng. Dev. (2019) 2. Bogdan, M.: How to use the DHT22 sensor for measuring temperature and humidity with the arduino board. Acta Univ. Cibiniensis. Tech. Ser. 68(1), 22–25 (2016) 3. Singh, N., Gunjan, V.K., Chaudhary, G., Kaluri, R., Victor, N., Lakshmanna, K.: IoT enabled HELMET to safeguard the health of mine workers. Comput. Commun. 193, 1–9 (2022) 4. Pasha, S.: ThingSpeak based sensing and monitoring system for IoT with Matlab Analysis. Int. J. New Technol. Res. (IJNTR) 2(6), 19–23 (2016)

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5. Singh, N., Ahuja, N.J.: Bug model based intelligent recommender system with exclusive curriculum sequencing for learner-centric tutoring. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 14(4), 1–25 (2019) 6. Rashid, E., Ansari, M.D., Gunjan, V.K., Ahmed, M.: Improvement in extended object tracking with the vision-based algorithm. In: Gunjan, V., Zurada, J., Raman, B., Gangadharan, G. (eds.) Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol. 885. Springer, Cham (2020). https://doi.org/10.1007/978-3030-38445-6_18 7. Ke, L., Ting, H., Li, L.: Design of temperature and humidity monitoring system based on Zigbee technology. In: 2009 Chinese Control and Decision Conference, 2009, pp. 3628–3631 8. Singh, N., Gunjan, V.K., Nasralla, M.M.: A parametrized comparative analysis of performance between proposed adaptive and personalized tutoring system “Seis Tutor” with existing online tutoring system. IEEE Access 10, 39376–39386 (2022). https://doi.org/10.1109/ACC ESS.2022.3166261 9. Simi´c, M.: Design and development of air temperature and relative humidity monitoring system with AVR processor based web server. In: 2014 International Conference and Exposition on Electrical and Power Engineering (EPE). IEEE, 2014 10. Lakshmanna, K., Shaik, F., Gunjan, V.K., Singh, N., Kumar, G., Mahammad Shafi, R.: Perimeter degree technique for the reduction of routing congestion during placement in physical design of VLSI circuits. Complexity 2022, 11p (2022), Article ID 8658770. https://doi. org/10.1155/2022/8658770 11. Maureira, M.A.G., Oldenhof, D., Teernstra, L.: ThingSpeak–an API and web service for the Internet of Things. World Wide Web (2011) 12. Singh, O., Bommagani, G., Ravula, S.R., Gunjan, V.K.: Pattern based gender classification. Int J 3(10) (2013) 13. Ray, P.P.: A survey of IoT cloud platforms. Future Comput. Inf. J. 1(1–2), 35–46 (2016) 14. Kashyap, A., Gunjan, V.K., Kumar, A., Shaik, F., Rao, A.A.: Computational and clinical approach in lung cancer detection and analysis. Procedia Comput. Sci. 89, 528–533 (2016) 15. Wang, Y., Chi, Z.: System of wireless temperature and humidity monitoring based on Arduino Uno platform. In: 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2016

Chapter 2

Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell P. Pardhasaradhi, G. V. Ganesh, V. V. S. Krishna, M. Naga Sai Kiran, and K. Manikanta Sai

Introduction In today life, the portable electronic devices like mobile phones, calculators and laptops are the integral part of our life. The designers struggle is for high speed, energy efficient and smaller circuits for making best out of the electronic devices. These electronic devices mostly consist of the arithmetic circuits. These arithmetic circuits are mostly in devices and consumes almost one third of the total power in the microprocessors. So, we can improve the system’s performance by the increasing the performance of the subtractor. There are several CMOS logic are there to realize the full subtractor (FS) circuit. The logic styles have been classified into two categories. They were few designs like classical and the other one hybrid design. By using the MOS transistors and the full subtractor is designed within a single module in classical design style as shown in Fig. 2.1. The complementary CMOS (C-CMOS) FA with 28 transistors is an example of classical approach. This approach provides robustness and full swing outputs, but the main drawback is the high input capacitance at every input which is connected to gates of ‘PMOS’ and ‘NMOS’ transistors which result in degradation of speed of the circuit. There is also another style in classical approach which is pass logic FS and can also be designed by this logic. However, there is a problem with this logic that is when there is logic ‘1’ and ‘0’ are given to pMOS and nMOS, respectively, at the output of the circuit, full swing logic cannot be obtained. The logic of transmission gate (TG) is used to resolve this issue. PMOS and NMOS are controlled by using the complimentary gate signals which are connected parallelly. The full swing outputs

P. Pardhasaradhi (B) · G. V. Ganesh · V. V. S. Krishna · M. Naga Sai Kiran · K. Manikanta Sai Department of ECE, Antennas and Liquid Crystals Research Center, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_2

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Fig. 2.1 Full subtractor circuit block diagram

are provided by the PMOS and NMOS when they are turned on at a time and the inputs of logic 1 and logic 0 are given. So coming to the other design style called hybrid design style, the basic FS is split up into three different elements which can be seen in Fig. 2.1. The full swing outputs of XOR and XNOR will be generated by module I by providing two inputs simultaneously. These XNOR and XOR signals need to drive the next two modules so they must have good capability to drive. The module II is the difference module and module III is borrow module which produces both difference and borrow, respectively. The number of transistor can be reduced, and each and every module can be simplified at the individual level which is the main advantage of this hybrid logic style. The power dissipation also decreases as this design reduces the power dissipating in internal nodes. There are many researchers who presented several full adder designs by using the hybrid logic which provides very good production without any degradation of the output. An XOR – XNOR module is presented by Vastrabacka et al. using pass transistor logic in which he used 2 × 1 multiplexers to realize sum and carry module. A hybrid style full adder cell is which PTL is used to generate the signals of the XOR – XNOR and to implement carry C-CMOS is used has been presented by Zhang et al. PTL and 2 × 1 multiplexer is used to implement sum and carry modules, respectively. In the performance of FA and FS designs, the production of XOR – XNOR plays a crucial part. In these recent years, there are various XOR – XNOR designs of the circuit have been presented. All these ways can be divided into two categories. In one approach, initially the circuit of XOR is synthesized, and then the outputs of XNOR will be generating by the simple inverter, but driving capability was the main drawback by using this approach. Because, the outputs of XOR – XNOR are not generated simultaneously. At the outputs of modules II and III glitching are occurred by this. In the other way, the XOR – XNOR cell is designed in a way that both the XOR and the XNOR signals will be generated where the delay between the signals in minimized. In [1] by using CPL, the XOR – XNOR circuit is presented which simultaneously generates the XOR and the XNOR signals. But the power of the

2 Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell

13

Fig. 2.2 Circuits of XOR – XNOR [2, 4]

presented circuit remains high due to feedback transistor which is used to recovers the output voltages. There is another design which is presented by Radhakrishnan [2]; it has two complimentary feedback transistors. When both inputs are same (00 and 11), we use feedback transistor which restores the weak logic in XOR and the XNOR output nodes as shown in Fig. 2.2. Because of these feedback transistors for inputs 00 and 11, this circuit gets the high delay; by using two extra nMOS and pMOS transistors, this slow response in output is solved by Chang et al. [3] and also provides good driving capabilities. The drawback is it adds extra parasitic capacitance due to its cross-coupled structure. Valashani and Mirzakhuchaki used the inverter for the circuit by Chang et al. [3] to improve its structure which gives lower path delay, but still the power dissipation is high. A preferable design of the XOR – XNOR circuit with the 12 transistors is presented by Naseri and Timarchi [5–8]. In [9], a new XOR – XNOR circuit is proposed, which provides good driving capabilities and full swing XOR – XNOR outputs without using any external inverter. The circuit performance is measured in the terms of the power, and the delay is better than all the other circuit we have discussed before. Yet, in this circuit, there is an external inverter; if we remove the inverter, we can improve the performance of this circuit by further bit. In [10], they propose a systematic approach to designing many 10-transistor full adders. Using a novel set of XOR – XNOR gates in combination with existing ones, a total of 41 new 1-bit full adders are created. In recent years, research communities are showing their keen interest to propose several kinds of different logic styles for implementing 1-bit full adder cell [11–13]. We get full swing outputs for all transitions with the help of internal not gate and feedback circuitry; by sizing of the different transistors, the power consumption and delay of the circuit can be reduced. In this paper, by using proposed the proposed circuit, i.e., XOR – XNOR circuit, the three distinct full subtractor (FS) models were presented which had good power consumption and driving capabilities.

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XOR – XNOR Circuit The XOR – XNOR cell is a fundamental function in various circuits for example, adders, blowers, comparators, and equality checkers and so on. Thusly, the behavior of them influences circuit execution enormously. Since, the base element size of MOSFET is downsizing into a couple of nanometers, and the inventory voltage should be diminished into forestall hot-carrier impacts in the CMOS circuits. In this way, the significance of upgraded plans for circuits to diminish power utilization, accelerate the activity, and furthermore to stay away from any decrease in the yield signal levels is not indisputable Likewise, XOR – XNOR cells and the multiplexers are imperative piece of the practical circuits, for example, the compressors and the full adders.

FS Cell From this hybrid style, the full subtractor cell using the XOR – XNOR circuit, difference circuit, and borrow circuit is discussed. Proposed XOR – XNOR cell is shown in Fig. 2.3. The performance of this FS cell relies upon these three elements. So, an efficient XOR – XNOR cell is been suggested in Sect. 2.1. In this section, the distinct varieties of developing difference circuits (module II) and borrow circuit (module III) are discussed, and by using the suggested XOR – XNOR circuit, the three FS designs are proposed.

Fig. 2.3 Proposed XOR – XNOR cell [6]

2 Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell

15

Fig. 2.4 Module II circuit using a TG [5], b TG with inverter, c CMOS [5]

Module II We use an external input as a buffer to increase the driving capabilities of the circuit in the cascading form. The expression for the difference shows that the module (II) circuit is only an XOR circuit for an XOR gate; when input will be at logic ‘0,’ the output will follow another input and is shown in Fig. 2.4. DIFF = ( A ⊕ B) ⊕ C  + (A ⊕ B) ⊕ C.

Module III This module is about the borrow circuit, which was designed with the same hybrid logic, in cascading form as shown in Fig. 2.5. In the module III, borrow circuit is implemented by providing C in which is an external input and the outputs of XOR – XNOR circuit as the input signals. BORROW = C(A ⊕ B) + A B When the value of ‘A’ or ‘B’ or ‘C in ’ is passed at the output which is relied on the intermediate signals (XOR and the XNOR outputs of module I), BOUT is generated to make the driving capability better the buffer can be added at the either side which increases the power consumption gradually. The module I which is XOR – XNOR cell has to drive the borrow circuit along with the difference circuit. The borrow circuit consists of four transistors in which 2 pull up and 2 pull down transistors. In this circuit, we used 250 nm technology with reduced number of transistors, and hence, it is efficient in area and power consumption. The module III of this FS is a borrow circuit (BOUT ). The output of borrow circuit of the FS can be calculated

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Fig. 2.5 Module III designs with similar structure

by using the XOR and XNOR outputs of the module I and previous borrow Cin. The designs of module III are discussed here.

Proposed FS Cell The three different modules (XOR – XNOR cell as a module I and difference as a module II and borrow as a module III) here combined to an hybrid FS circuit have been developed as discussed in Sect. 2.1. In Sect. 2.2, a XOR – XNOR cell is proposed. The other circuits in the modules II and III are proposed in the above section. The three designs of hybrid style FSs are shown in this section with proposed XOR and XNOR cell, and three different module II and module III as shown in Fig. 2.6. In Fig. 2.6a, the hybrid FS cell design 1 is presented. It consists of proposed XOR – XNOR cell and transmission gate-based module I and simple circuits of module III. It contains of around 20 transistors and provides high robustness and full swing outputs. But it has drawback with the driving capability. The design 2 of the FS cell is presented in the Fig. 2.6b which consists of 22 transistors, and it solves driving capability problem since it has buffers at output. But it increases the power consumption. In the design 3 shown in Fig. 2.6c of the full subtractor cell, the module II is implemented by using the CMOS logic style [8], but the module III is based on pass logic and it gives better performance among others in terms of power. This design consists of 20 transistors.

2 Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell

Fig. 2.6 Proposed FS cell a Design 1, b Design 2, c Design 3

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Results Anddiscussion Simulation Environment All the circuits presented in this article are simulated using Tanner EDA 2019.2 in 250 nm (GPDK) CMOS process technology. The 2.5 V and 500 MHz are the supply voltage and maximum operating frequency, respectively. Spice simulator used to observe the waveform is T—spice, and power estimation of the circuits is also done by T—spice with a simple commands.

Simulation Results XOR – XNOR Circuit: This requires two inputs ‘A’ and ‘B’ as pulses (Vpulse) in the XOR – XNOR circuit. The inputs are applied to the XOR – XNOR cell, and correspondingly, the XOR – XNOR full swing outputs are obtained parallelly which is a primary requirement of the hybrid FS design as shown in Fig. 2.7. The performance of the new circuit is compared with the previous ones in the terms of power consumption and the area which directly proportional to the no. of

Fig. 2.7 I/O waveform for XOR – XNOR circuit

2 Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell Table 2.1 Performance comparison of XOR – XNOR circuit in terms of power at the power supply of 2.5 V and 500 MHz operating frequency

19

XOR – XNOR circuits

No. of transistors

Power (µW)

Aguirre [1]

12

12.30

Radhakrishnan [2]

6

15.12

Chang [3]

10

14.93

Valshani [4, 8]

10

14.85

Naseri [5]

12

13.23

Proposed [6]

10

13.09

Goel [7]

8

14.59

transistors as shown in Table 2.1. The XOR – XNOR circuit which is proposed by Radhakrishnan in [2] has lesser number of transistors but, in the terms of power consumption, it has high value. In term of power consumption, the XOR – XNOR circuit presented in [1] has better performance than remaining circuits. But, no. of transistors are more which gives us more delay. FS: The three FS designs have been simulated in Tanner tool at 2.5 V supply voltage and 500 MHz operating frequency, and the performance of the design has been monitored. The design 3 gives best execution among the other designs. The input–output waveform of the FS design 3 is as shown in Fig. 2.8. Comparison of FS designs among themselves at 2.5 V supply voltage and 500 MHz operating frequencies is as shown in Table 2.2.

Fig. 2.8 Input and output waveform for the FS design 3

20 Table 2.2 Comparison of FS designs among themselves at 2.5 V supply voltage and 500 MHz operating frequencies

Table 2.3 Power consumption of full subtractor for different voltages

P. Pardhasaradhi et al. Full subtractor circuits

No of transistors

Power (µW)

FS Design—1

20

102.8

FS Design—2

24

104.34

FS Design—3

20

91.12

Voltage (v) 1

Power (µW) 250 nm technology 2.42

1.25

5.97

1.5

14.23

1.75

22.91

2

38.57

2.25

60.6

2.5

91.12

Comparison of the proposed FS designs in terms of no. of transistors and power consumption is shown Table 2.3. For the calculation of power consumption, some modules have been used (Fig. 2.9).

Fig. 2.9 Power plot against different voltages for FS design 3

2 Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell

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Conclusion In this article, we have used the proposed 10 T XOR – XNOR cell which gives the full swing outputs continuously. Also we have proposed three hybrid FS designs using 10 T XOR – XNOR circuit. The proposed FS design has been tested in Tanner EDA 2019.2 tool at 2.5 V supply voltage and 500 MHz operating frequency using GPDK 250 nm technology. The XOR – XNOR circuit shows better performance in terms of power and delay then other circuits which helped the FS design to improve their performance. Among all the three designs, the design 3 shows the better result.

References 1. Aguirre-Hernandez, M., Linares-Aranda, M.: CMOS full-adders for energy-efficient arithmetic applications. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 19(4), 718–721 (2011) 2. Radhakrishnan, D.: Low-voltage low-power CMOS full adder. In: IEE Proc.-Circuits, Dev. Syst. 148(1), 19–24 (2001) 3. Zargar, A.J., Singh, N.: Digital watermarking using discrete wavelet techniques with the help of multilevel decomposition technique. Int. J. Comput. Appl. 975, 8887 (2014) 4. Valashani, M.A., Mirzakuchaki, S.: A novel fast, low-power andhigh-performance XORXNOR cell. In: Proceedings IEEE International Symposium on Circuits and Systems (ISCAS), May 2016, pp. 694–697 5. Singh, N., Ahuja, N.J.: Bug model based intelligent recommender system with exclusive curriculum sequencing for learner-centric tutoring. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 14(4), 1–25 (2019) 6. Kandpal, J., Tomar, A., Sharma, K.K., Agarwal, M.: High-speed hybrid-logic full adder using high-performance 10T XOR–XNOR cell. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 28(6), 1413–1422 (2020) 7. Goel, S., Kumar, A., Bayoumi, M.A.: Design of robust, energyefficient full adders for deepsubmicrometer design using hybrid-CMOS logic style. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 14(12), 1309–1321 (2006) 8. Singh, O., Bommagani, G., Ravula, S.R., Gunjan, V.K.: Pattern based gender classification. Int. J. 3(10) (2013) 9. Kandpal, J., Tomar, A., Agarwal, M., Sharma, K.K.: High-speed hybrid-logic full adder using high-performance 10-T XOR–XNOR Cell. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 28(6), 1413–1422 (2020). https://doi.org/10.1109/TVLSI.2020.2983850 10. Tien Bui, H., Wang, Y., Jiang, Y.: Design and analysis of low-power 10-transistor full adders using novel XOR-XNOR gates. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 49(1), 25–30 (2002) 11. Singh, N., Ahuja, N.J.: Empirical analysis of explicating the tacit knowledge background, challenges and experimental findings. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2019, 4559–4568 12. Kashyap, A., Gunjan, V.K., Kumar, A., Shaik, F., Rao, A.A.: Computational and clinical approach in lung cancer detection and analysis. Procedia Comput. Sci. 89, 528–533 (2016) 13. Guduri, M., Mehra, R., Srivastava, P., Islam, A.: Current-mode circuit-level technique to design variation-aware nanoscale summing circuit for ultra-low power applications. Microsyst. Technol. 22, 1–12 (2016)

Chapter 3

Stuck-At Fault Detection in Ripple Carry Adders with FPGA P. Pardhasaradhi, K. V. K. V. L. Pavan Kumar, Raghava Yathiraju, Yadalapalli Sri Sai Sumanth, Surisetty Nishith, and Tiyyagura Vishnu Vardhan Reddy

Introduction Integrated circuits are fabricated with billions of transistors on a single silicon chip to compute a large number of operations that enhance speed, diminish area, and power. It is achieved by the CMOS process and by choosing an appropriate logic. The ICs were first application was developing a microprocessor for handling multiple applications in the demanding world [1, 2]. ASIC-based design circuits are increasing their demand to a large extent and the length of the transistor is miniaturized to accommodate a huge number of devices on a single silicon chip. Scaling down the channel length of a transistor imparts faults in the results of the circuit in an unexpected output. Interconnection between these devices uses minimum wiring leads to manufacturing defects. Because adders are a processor’s most important processing unit, ensuring their fault-free functioning is critical. It is vital to employ cost-effective online defect detection technologies. Overhead and performance penalties in the area fault detection in real-time methods identify a fault in a system’s ongoing operation without interrupting it as with offline testing, the system is shut off. As a result, it is gaining popularity. Because both permanent and transient faults are discovered at the same time, this is of critical importance. The regular course of events, furthermore, online defect detection is the most efficient. Designing a self-repairable system requires this step. The Internet the hardware redundancy technique is used to discover faults. When a flaw occurs, fix it right away. Furthermore, these methods have a smaller influence on timing performance. P. Pardhasaradhi (B) · K. V. K. V. L. Pavan Kumar · Y. S. S. Sumanth · S. Nishith · T. V. V. Reddy Department of ECE, Antennas and Liquid Crystals Research Center, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, India e-mail: [email protected] R. Yathiraju Department of ECE, St. Mary’s Group of Institutions, Guntur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_3

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An adequate cell fault model can encompass any problem in the inter-cell carry signals or input and output lines. As a result, the ripple carry adder is fault-free if all cells are fault-free. This study uses the broad fault assumption in [1–7], as opposed to the confined fault assumption in [8, 9]. A faulty cell can change its behavior in any arbitrary way as long as it remains a combinational circuit, according to the generic fault assumption at the cell level. The cell truth table is used to thoroughly test a cell. Knowledge about actual realizations of each cell is unnecessary. The faults and manufacturing defects need to be minimized to hike the throughput, to attain the desired output. A fault-free circuit is obtained by implementing different fault-tolerant algorithms on the faulty circuits during the testing stage and is carried out after the fabrication of circuits and consume time [3, 4]. Existing manufacturing defects or the occurrence of faults in the design or fabrication process leads to circuits faulty. The occurrence of faults on the circuits may be classified as stuck-at faults, memory faults, transistor open faults, transistor short faults, functional faults, delay faults, analog faults, and bridging faults. Of these, stuck-at fault, bridging fault, delay faults can be developed at input or output of the circuits. Stuck-at faults can be a single stuck-at fault or multiple stuck-at faults [5]. The testing process is generally formed by applying the input vectors to the circuits and verifying the expected output, and this testing process follows a process of developing the algorithms to develop the minimal test vector generation. To reduce the complexity of the testing process. Then deriving this algorithm for every path of the circuit is some backend time-taking process [6]. The faults which we are stressing is the stuck-at-fault, the stuck-at-fault are classified into two types: the stuck-at-0 and stuck-at-1 fault; these faults in the circuits are leading to the manipulation of the output further developing a faulty circuit [7]. The stuck-at-0 fault (SA0) will make a path of the circuit or the interconnections of the circuits are being grounded or the path will always resemble the logic ‘0’; if further changes in the inputs the path will not acquire a logic of input state of it and producing faulty outputs [8]. The stuck-at-1 (SA1) is a fault similarly the path will be in the logic ‘1’. These are the faults in which output manipulation leads to the development of a faulty circuit. In the process of testing the ripple carry adder (RCA) architecture, it develops in breaking of architecture into modules. Then unit under test (UUT) testing process will develop this in general with the usage of the Verilog test bench generation module, A N-bit ripple carry adder constitutes N-bit full adders [9].

Proposed Methodology The verification is the process of evaluating the circuits for the development of faultfree circuits. Many algorithms came into existence to find the keys for testing the circuit at particular faults, like the path sensitization method, Kohavi algorithm to attain fault-free circuits [9]. The algorithm of finding the stuck-at faults in this paper

3 Stuck-At Fault Detection in Ripple Carry Adders with FPGA

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Fig. 3.1 Ripple carry adder

is to give all input test vectors to the RCA architecture and find the algorithmic paths required that minimizes the testing turnaround time in the verification of the circuits. The algorithm to be proposed and developed in the paper is that vector verification for finding the faults in the circuits [10]. This algorithm will divide the work in the finding of the faults in the circuit by just the number of input vectors does the architecture has all the test vectors are processed to the architecture [11]. A verification algorithm is to find the possibility of these faults in the circuit and verify the path of fault in the circuit that indicates the expected output. The N-bit ripple carry adder constituting N-bit full adders, in the subsystem of the RCA is the single-bit adder [12]. The faults in the architecture are the stuck-at faults, and then each net must be taken care of in the consideration of the finding the stuck-at faults [13]. Figure 3.1 is the 4-bit ripple carry architecture in the addition process that takes the carry from the previous adder propagated to the next adder and performs 4-bit addition. The subsystem of this RCA is the single-bit adder element [14]. The single-bit adder is the main subsystem in the entire N-bit addition process. The subsystem under it is the main faulty generation position because of due to the different parameters in the circuit designed. The single-bit adder is the basic arithmetic circuit for the additional operations in the circuit; it is a module generating SUM and CARRY. The gate-level implementation of the single-bit adder in Fig. 3.2 is used to find the stuck-at faults in the circuit, and the net is the main reason for the propagating and rising a stuck-at fault in the circuitry [15]. Figure 3.2 presents the gate-level model of single-bit adder produces faulty outputs when any line is stuck-at SA-0 or SA-1fault on the input or output lines. In Fig. 3.2, each net is mapped with its path number and marked the path of the input and output of one gate to other gates are mentioned so because of the stuck-at fault in the path is leading in the development of the faulty output in the end [16]. The outputs of the faulty-free single-bit adder are given below. Table 3.2 demonstrates stuck-at faults in the circuit and stuck-at fault of each path. Tables 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 and 3.7 illustrate the way, the stuck faults are generated in the circuits then here comes how the analyzing the stuck-at faults in the

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Fig. 3.2 Gate level model of single-bit adder

circuit and the implementation of the vector verification algorithm for finding the paths in the circuit. Then consider the test vector of the bit 3-bit binary zero, in this process, the stuck-at fault all paths of the around the circuit in the SA0 are giving the faulty-free outputs so the neglect the test vector when output is giving the correct output whether if there is a stuck-at fault the output is expected is correct, then the test vector is faulty-free even though there is a fault in the circuit [4]. Then for the same test vectors enter the scenario of it so for SA1 fault in the circuit is resulting the faulty output so the all cases of the SA1 are giving the faulty outputs then test each and every path of the circuit for the stuck-at fault present or not in the circuit, by example consider a SA1 in the path of the circuit in the path 4 in the circuit, the validation starts in the process is the path carrying the same input values accordingly to the input test vector assigned to that vector is enviable then the path is ignored then case of contradiction then the path is supporting in the propagation of fault is evaluated then the true value of the each path is processed in the backend of the program verification in order to follow and find the test vectors for the each path of the verification of the fault in the circuits[1, 6, 9], this case scenario is developed Table 3.1 Fault table of a single-bit adder with a fault on path 1 Single-bit adder A

B

C

0

0

0

Fault-free Sum

Carry

0

0

0

0

1

1

0

0

1

0

1

0

0

1

1

0

1

1

0

0

1

0

1

0

1

0

1

1

1

0

0

1

1

1

1

1

1

3 Stuck-At Fault Detection in Ripple Carry Adders with FPGA

27

Table 3.2 Fault table of a single-bit adder with a fault on path 1 Stuck at ‘A’ (or) ‘1’ A

B

C

Fault-free Sum

SA0 Carry

Sum

SA1 Carry

Sum

Carry

0

0

0

0

0

0

0

1

0

0

0

1

1

0

1

0

0

1

0

1

0

1

0

1

0

0

1

0

1

1

0

1

0

1

1

1

1

0

0

1

0

0

0

0

0

1

0

1

0

1

1

0

0

1

1

1

0

0

1

1

0

0

1

1

1

1

1

1

0

1

1

1

Table 3.3 Fault table of a single-bit adder with a fault on path 2 Stuck at ‘B’ (or) ‘2’ A 0

B 0

C 0

Fault-free

SA0

SA1

Sum

Carry

Sum

Carry

Sum

Carry

0

0

0

0

1

0

0

0

1

1

0

1

0

0

1

0

1

0

1

0

0

0

1

0

0

1

1

0

1

1

0

0

1

1

0

0

1

0

1

0

0

1

1

0

1

0

1

0

1

1

1

1

1

0

0

1

1

0

0

1

1

1

1

1

1

0

1

1

1

Table 3.4 Fault table of a single-bit adder with a fault on path 3 Stuck at ‘c’ (or) ‘3’ A

B

C

Fault-free Sum

SA0 Carry

Sum

SA1 Carry

Sum

Carry

0

0

0

0

0

0

0

1

0

0

0

1

1

0

0

0

1

0

0

1

0

1

0

1

0

0

1

0

1

1

0

1

1

0

0

1

1

0

0

1

0

1

0

0

1

1

0

1

0

1

1

0

0

1

1

1

0

0

1

0

1

1

1

1

1

1

1

1

0

1

1

1

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Table 3.5 Fault table of a single-bit adder with a fault on path 4 Stuck at ‘d’ (or) ‘4’ A

B

C

Fault-free Sum

SA0 Carry

Sum

SA1 Carry

Sum

Carry

0

0

0

0

0

0

0

1

0

0

0

1

1

0

1

0

0

1

0

1

0

1

0

0

0

1

0

0

1

1

0

1

1

0

0

1

1

0

0

1

0

0

0

1

0

1

0

1

0

1

1

0

0

1

1

1

0

0

1

0

1

1

1

1

1

1

1

1

1

1

0

1

Table 3.6 Fault table of a single-bit adder with a fault on path 5 Stuck at ‘5’ A 0

B 0

C 0

Fault-free

SA0

SA1

Sum

Carry

Sum

Carry

Sum

Carry

0

0

0

0

0

1

0

0

1

1

0

1

0

1

1

0

1

0

1

0

1

0

1

1

0

1

1

0

1

0

0

0

1

1

0

0

1

0

1

0

1

1

1

0

1

0

1

0

0

0

1

1

1

0

0

1

0

1

0

1

1

1

1

1

1

1

1

1

1

Table 3.7 Fault table of a single-bit adder with a fault on path 6 Stuck at ‘6’ A

B

C

Fault-free Sum

SA0 Carry

Sum

SA1 Carry

Sum

Carry

0

0

0

0

0

0

0

0

1

0

0

1

1

0

1

0

1

1

0

1

0

1

0

1

0

1

1

0

1

1

0

1

0

1

0

1

1

0

0

1

0

1

0

1

1

1

0

1

0

1

0

1

0

1

1

1

0

0

1

0

0

0

1

1

1

1

1

1

1

0

1

1

3 Stuck-At Fault Detection in Ripple Carry Adders with FPGA

29

in the each every case of the input test vector of all the acceptable combinations, thus the circuit is identified by the fault in the circuit or not then verification of the circuit will take a turnaround time of the number of combinations of the circuit required [17, 18].

Results and Discussion A 4-bit RCA is implemented in Xilinx Vivado 2018.2 for the particular input test vectors, and the respective outputs in bitwise are in Figs. 3.3, 3.4. The subsystem of the RCA is a single-bit adder, and the interconnections are extended up to six paths or nets which are interconnected to the logic gates. Simulation results present identifying the stuck-at faults in the circuit. The above-simulated results from Figs. 3.3, 3.4 represent the 4-bit RCA stuck-at faults for the single-bit adders FA0, FA1, FA2, and FA3, respectively. It constitutes the input lines of A, B with a 4-bit size of the input and the outputs of this circuit are SUM and CARRY-OUT. The different paths visualize the stuck-at faults in the circuit, and the output paths or wires that are interconnected in the circuit in which the single-bit adder is connected to the logic gates and paths are acting as carrier paths for the logical operations. The paths signal active high represents there is a fault in the circuit and to acknowledge it by the above graphs indicates there is a fault in the single-bit adder. Signals

Fig. 3.3 Simulation results for 4-bit RCA with SA-0/1 at FA0

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Fig. 3.4 Simulation results for 4-bit RCA with SA-0/1 at FA1

in the above simulation constitute FA0, FA1, FA2 and FA3 path vector of size 6bit. This path vector will simulate the signal whether it is active high or active low the active high represents the path consisting stuck-at fault or vice versa and other signals to indicate whether it is stuck-at 0 or stuck-at 1 so these output signals are represented as the SA0, SA1 in the simulated timing graphs. The simulation results represent whether the circuit is fault-free or not. The different input test vectors are applied to the RCA in the form of a test bench for Xilinx Vivado 2018.2 and the respective timing graphs represent whether a circuit path is affected by the stuck-at fault or not and to represent in which input test vectors the faulty path is displayed in the above simulation. Therefore, every stuck-at fault is identified in the circuit.

Conclusion The identification of the stuck-at faults in the 4-bit RCA is discussed with and fast and scalable implementation of the testing process is presented with the path sensitization used in the verification of the faulty path in the circuits. A new implementation of the input vector verification for finding the faults is used in the identification of the single stuck-at faults in the circuits and also the identification of the multiple stuck-at faults in the circuitry. The design of a 4-bit RCA and identification of the faults are simulated and verified by Xilinx Vivado 2018.2 with appropriate input test vectors applied at the inputs of the RCA. The faults in the circuit are validated in the testing process, and multiple faults are also easily detected.

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References 1. Abramovici, M.: Digital Systems Testing and Testable Design (1990) 2. Bushnell, L., Agrawal, V.D.: Essentials of Electronic Testing for Digital, Memory & MixedSignal VLSI Circuits. Springer (2002) 3. Abramovici, M., Breuer, M.A., Friedman, A.D.: Digital Testing and Testable Design. IEEE Press, Piscataway-New Jersey (1994) 4. Zargar, A.J., Singh, N.: Digital watermarking using discrete wavelet techniques with the help of multilevel decomposition technique. Int. J. Comput. Appl. 975, 8887 (2014) 5. Khera, V.K., Sharma, R.K., Gupta, A.K.: A heuristic fault-based optimization approach to reduce test vectors count in VLSI testing. J. King Saud Univ.—Comput. Inf. Sci. (2019) 6. Singh, N., Ahuja, N.J.: Bug model based intelligent recommender system with exclusive curriculum sequencing for learner-centric tutoring. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 14(4), 1–25 (2019) 7. Dhanabalan, G., SelviS, T.: Design of parallel conversion multichannel analog to digital converter for scan time reduction of the programmable logic controller using FPGA. Comput. Stand. Interfaces 39, 12–21 (2015) 8. Singh, N., Ahuja, N.J.: Empirical analysis of explicating the tacit knowledge background, challenges and experimental findings. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 4559–4568 (2019) 9. Pavan Kumar, K.V., Sravanthi, G.L., Suresh Kumar, N., Prabhakar, V.S.V.: Performance analysis of 6transistor single bit adder element. Int. J. Innov. Technol. Explor. Eng. 8(6), 1677–1681 (2019) 10. Kumar, A., Choudhary, R.R., Bhardwaj, P.: Universal pattern set for arithmetic circuits. Int. J. Comput. Appl. 40(15) (2012), (0975-8887) 11. Singh, O., Bommagani, G., Ravula, S.R., Gunjan, V.K.: Pattern based gender classification. Int. J. 3(10) (2013) 12. Sauer, M., Jiang, J., Reimer, S., Miyase, K., Wen, X., Becker, B., Polian, I.: On optimal power-aware path sensitization. In: IEEE 25th Asian Test Symposium, pp. 179–184 (2016) 13. Pavan Kumar, K.V.K.V.L., Sravanthi, G.L., Prabhakar, V.S.V., Vijaya Lakshmi, P., Bindu Priya, K., Sai Akhil, K., Sai Nikhil, K., Hari Kishore, K.: Performance comparison of dynamic bias comparators. Int. J. Innov. Technol. Exploring Eng. 8(7S), 110–114 (2019) 14. Nelson, Nagle, Carroll, Irwin: Digital Logic Circuit Analysis & Design, pp. 739–757. PrenticeHall, Chapter 12 (1995) 15. MojtabaValinataj, A.M., Nurmi, J.: A low-cost high-speed self-checking carry select adder with multiple fault detection. ELSEVIER Microelectron. J. 81, 16–27 (2018) 16. Kashyap, A., Gunjan, V.K., Kumar, A., Shaik, F., Rao, A.A.: Computational and clinical approach in lung cancer detection and analysis. Procedia Comput. Sci. 89, 528–533 (2016) 17. Singh, R., Chauhan, R., Gunjan, V.K., Singh, P.: Implementation of elliptic curve cryptography for audio based application. Int. J. Eng. Res. Technol. (IJERT) 3(1), 2210–2214 (2014) 18. Kumar, P., et al.: Design and analysis of CMOS Schmitt trigger. Int. J. Innov. Technol. Exploring Eng. 8(7S), 106 (2019)

Chapter 4

Prediction of COVID-19 Spreaders M. Siva Ganga Prasad, Rohitha Mikkilineni, N. Sampath, K. Yashwanth, and G. V. Ganesh

Introduction The global economy has a SARS-CoV-2 epidemic in the current year called COVID19 (2020). On December 31, 2019 [1], the virus started circulating in Wuhan, China, and in January 2020 [2], it was recognized as a pandemic by the World Health Organization [2] due to the spread of these viruses around the world. A singlestranded RNA virus is the name of COVID-19, which is symptomatic of COVID-19, which is symptomatic headache, dry cough, malaise, fever, and aero are borne disease [3]. However, asymptomatic individuals have been identified, so health institutes are faced with a real challenge. The epicenter of the pandemic, with Italy, Spain, China, and Germany, was confirmed as March 31, 2020, 186, 265, 105.792, 95, 923, 82, 278, and 71.690 cases [4]. The five countries make up 63.4% of the confirmed cases worldwide. 42.8, 10.8, 9.1, 8.8, and 8.2 percent are led by China, Spain, Germany, Italy, and Iran to recover with this new pandemic. Due to the expansion of COVID-19 in Europe and America worldwide [5], there could be discrepancies between health facilities, air transport, and human and other sociocultural factors [6]. Several teams currently defined the transmission rules and safety steps of COVID-19 in [5, 7, 8], with several relevant findings [9–11]. This article uses machine learning (ML) to create a new model of coronavirus propagation. This results from the characteristics of transmission of infectious diseases cases in the next coming 15 days.

M. Siva Ganga Prasad (B) · R. Mikkilineni · N. Sampath · K. Yashwanth · G. V. Ganesh Department of ECE, Koneru Lakshmaiah Educational Foundation, Guntur, AP, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_4

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Methodology The methodology is identification of more widely distributed COVID-19 across countries through machine learning techniques. Machine learning (ML) is a type of (artificial intelligence) AI that allows software applications to be more accurate at predicting the results. They are expected without explicitly programming. The machine learning algorithms of different types are supervised, unsupervised, semi-supervised, and reinforcement learning algorithms. This paper uses both supervised and semi-supervised algorithms (Fig. 4.1). The main algorithms that we use in predicting and training are: . . . . .

Arima Prophet Random forest XGBoost LGBM.

The above are the algorithms that we used to predict the ICMR data. The main idea is to analyze the datasets that we have taken to train and expect them for knowing the increase in the number of COVID-19 cases in the next 15 days. This is the sole idea behind the paper.

Data Exploration

Label Encoder

Data Preprocessing

Visualization

Pediatric Asymptomatic

Start

Prediction of active cases for next 15 days

Identification of Silent-Spreaders

Start

End

Fig. 4.1 Block diagram of the implementation

Modules Importing

ICMR Datasets

Data Visualization

Country wise comparison

End

Regression Algorithms

Data Exploration

Gender & Age Distribution

State-wise Distribution

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Block Diagram The flowchart is model of the system that we are out to be implemented in this paper. The silent spreaders data and active cases data are to be taken.

Hardware and Software Requirements An operating system of Windows 10 with an Intel core of i5 and memory of 2 GB are the software requirements. The program is to be executed in the anaconda navigator tool, using Jupiter notebook with downloaded packages such as TensorFlow, NumPy, Pandas, and datasets of researched, ICMR.

Objective The objective is to design a structured program for prediction of silent spreaders. Silent spreaders are divided into asymptomatic, presymptomatic, and very mild symptomatic. The first is to analyze the collected data in the dataset and train the data.

Arima Arima is a class that contains models which explains a given time series based on the past predicted values. The model contains of terms p, d, q. P is the order of AR terms, Q is the order of MA term, and D is the number of differentiating terms in time series. The approach is to subtract the previous value from the current value depending on the complexity of the series.

Prophet Prophet is open-source software. It is a procedure for predicting compelled data type model which fits a nonlinear trend. It works premier with time series with prehistoric data. It uses nonlinear trends to filter the data approximately.

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Random Forest Random forest is a learning algorithm for classification, other tasks, and regressive to operate by constructing multiple decision trees. It treats each tree as its own part, and each tree is classified as different. The random classifier spits into different models. Each model is given some data, and the maximum count of decision trees is taken as the value. It uses bagging as it main method. In this project, their and om forest is used to find them in imam error ate.

XGBoost Extreme gradient algorithm is a derivation of gradient boosting algorithm. It is supervised learning algorithm. It predicts by taking a variable that estimates simple and weaker models. It has features such as parallelization, distributed computing, out of core computing, and cache optimization. It has the highest execution speed and performance. The regression parameter is added with the threshold parameter which controls the overfitting which is fractioned upon variance multiplied with gemmates hold.

LGBM Light gradient boost machine is a tree-based learning algorithm. It is used to classify the information and train the data. LGBM integrates the tree leaf wise with best fit. It provides with high accuracy and lessens memory storage. It is referred to as fast as light. The practical results in the project provide that light gradient boost machine provides highest accuracy and less error ate.

Existing System The existing system proposed the identification of COVID-19 using multiplex networks. The system has used the concept of neural networks to determine the spreading of COVID-19. The usage of different networking layers has led to a deviation in the graphical presentation of the predicated data which deprived accuracy. The VSP is used in the work for the classification of different countries in distribution. The GC is designed for MCGC. Using a mathematical expression, taking vector points, the rate has been calculated [12].

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Proposed System This work talks about the spreading of COVID-19 for the coming next 15 days through machine learning techniques. The researched data must be analyzed and trained using machine learning algorithms. The proposed system is discrete from the reference method with the method used. Machine learning techniques always have prone to be efficient and accurate. The methods have proven to be giving more accuracy than the previous work. The aim is to identify the increase of COVID-19, through the collected data on all aspects. We can prognosticate the cases that could increase in the days to come with the existing and predicted data.

Working In this project, we have first analyzed the obtained datasets. The ICMR datasets and other researched datasets are analyzed and put into a graphical presentation. The obtained data is analyzed using Arima and prophet. The data obtained is run for MSE, Root MSE and MAE using the machine learning algorithms. The least error rate is too identified when comparing random forests, XGBoost, and LGBM. The dataset taken is up to September 2020. The prediction is done for the next 15 days of time.

Results There is a steep change in the accuracy. There is an approximate accuracy of 80% with this project. The error rates of the technique that we have been used are calculated using different algorithms in Table 4.1. The number of people who possibly would be affected with COVID-19 patients that would increase is also predicted in Fig. 4.2. Here in Table 4.1, we can see the error rates of the prediction of the COVID-19 cases which is obtained from the techniques Arima and Prophet. Table 4.1 Error rates of techniques used Errors

Random forest

LGBM

XGBoost

Mean squared error

1,777,777.79, 588,622.66

1,777,777.79

1,777,777.79, 588,622.66,157,416,419.57

Mean absolute error

441.5, 168.91

Root mean square 1333.33, 767.22 error

441.5 1333.33

441.5, 168.91, 4134.78 1333.33, 767.22, 1254.57

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Fig. 4.2 Prediction of COVID-19 spreaders graph

The algorithms gave out the error rates of the predicted system. The random forest algorithm gives a mean squared error in a set of [1,777,777.79, 588,622.66], mean absolute error in a set of [441.5, 168.91], and RMSE is [1333.33, 767.22]. The light gradient boost machine algorithm gives a mean squared error [1,777,777.79], mean absolute error [441.5], and RMSE is [1333.33, 767.22]. The extreme gradient boost machine algorithm gives a mean squared error in a set of [1,777,777.79, 588,622.66, and 157,416,419.5], mean absolute error in a set of [441.5, 168.91, and 4134.78] and root MSE is [1333.33, 767.22, and 1254.57]. This graph above shows the increase in the COVID-19 cases that are actual based on the datasets taken and the predicted number of raise in next 15 days (Fig. 4.2).

Conclusion The research in this study is mainly focused on prediction of COVID-19 using ML techniques. The research implicates that the chronic disease has affected our lives in a way that cannot be taken back. So, predicting the COVID-19 cases could help in achieving the prevention of the calamities.

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Future Scope The medical sectors could use this estimation for estimating the prerequisites or medical supplies for future patients. This project is also helpful in understating the growth of the virus and always keeps us cautious.

References 1. WHO: Timeline Covid-19. [Online] (2020). Available: https://www.who.Int/news-room/det ail/27-04-2020-who-timeline-covid-19 2. Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, R.: The WHO has declared global emergency: a review on the 2019 novel coronavirus. Int. J. Surg. 76, 71–76 (2020) 3. Zargar, A.J., Singh, N.: Digital watermarking using discrete wavelet techniques with the help of multilevel decomposition technique. Int. J. Comput. Appl. 975, 8887 (2014) 4. Singh, O., Bommagani, G., Ravula, S.R., Gunjan, V.K.: Pattern based gender classification. Int. J. 3(10) (2013) 5. Corman, V.M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D.K., Bleicker, T., Brünink, S., Schneider, J., Schmidt, M.L., Mulders, D.G.: Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance 25(3), 2000045 (2020) 6. Altmann, Gutierrez, B., Kraemer, M.U.G., Colizza, V.: Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study. Lancet 395(10227), 871–877 (2020) 7. Singh, N., Ahuja, N.J.: Bug model based intelligent recommender system with exclusive curriculum sequencing for learner-centric tutoring. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 14(4), 1–25 (2019) 8. Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., Zimmer, T., Thiel, V., Janke, C., Guggemos, W., Seilmaier, M.: Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N. Engl. J. Med. 382(10), 970–971 (2020) 9. Mizumoto, K., Kagaya, K., Zarebski, A., Chowell, G.: Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill. 25(10), 2000180 (2020) 10. Kashyap, A., Gunjan, V.K., Kumar, A., Shaik, F., Rao, A.A.: Computational and clinical approach in lung cancer detection and analysis. Procedia Comput. Sci. 89, 528–533 (2016) 11. Long, Q.X., Liu, B.Z., Deng, H.J., Wu, G.C., Deng, K., Chen, Y.K., Liao, P., Qiu, J.F., Lin, Y., Cai, X.F., Wang, D.Q.: Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat. Med. 1–4 (2020) 12. Singh, N., Ahuja, N.J.: Empirical analysis of explicating the tacit knowledge background, challenges and experimental findings. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 4559–4568 (2019)

Chapter 5

Smart Agricultural Solutions Through Machine Learning K. V. Daya Sagar, Jasti Lalitha Sai, Shaik Sadiq, and Malladi Krishna Prasad

Introduction After looking at the importance of agriculture to the country’s economy, especially India, there are many problems facing farmers to get the expected yield [1–6]. One such issue is poor plant selection. Most Indian farmers are educated. They may not know that the yield depends on the type of soil and its similarity to the earth [7]. India is an agricultural country with a large population. Around 70% of the country’s population depends on agriculture. Extensive farming and crop rotation techniques are essential [8]. It is necessary to choose crops grown according to the soil’s proportions to achieve maximum benefits while maintaining environmental sustainability [9–13]. Soil adaptation refers to a part of the earth’s ability to tolerate crop production sustainably [14–20]. Its assessment provides information on land use issues and opportunities. Therefore, it guides decisions on the efficient use of resources, which is crucial in land use planning and development. An agronomist in the country’s provinces may consider a disease outbreak difficult to distinguish [21]. It’s not unusual to go to the farm to find out what the disease might be different between introducing diseases and plants through photography and machine learning by observing their development. The loss of plants or part of the harvest is attributed to pests and diseases, which result in lower yields and food shortages [22]. Furthermore, knowledge of pest control and disease is limited in various development fields. Some of the main factors are poisonous germs, inadequate disease control, and extreme climate change. Modern methods, such as artificial intelligence and machine learning algorithms, are used to develop the recognition and accuracy. K. V. Daya Sagar (B) · J. L. Sai · S. Sadiq · M. K. Prasad Koneru Lakshmaiah Education Foundation, Deemed to be University, Vaddeswaram, Guntur, Andhra Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_5

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Literature Survey Applying Big Data for Intelligent-Based Agriculture Crop Selection A major problem of food and agriculture has emerged in extreme climate change, and the vast global population increase. Our studies propose using a smart agricultural platform to track environmental factors to evaluate appropriate techniques and crops for the region [14], in French. Our model suggests a moving average and variance in data cleaning to cleanse the data with extreme variations and perform behavioral analysis of farmers’ behavior, such as pesticides and fertilizers; autocorrelation is used to measure regularity and 3D cluster correlation [7]. The research also considers the climate; few of the crops studied in the model were in good soil condition. The method helps a farmer imagine a different crop from his field, which can be used on many crops, the soil’s content, various factors including the environment, rainfall affecting crop yield, and the soil’s influence on the yield. An automated cultivation method can be followed later by artificial intelligence.

Machine Learning Techniques for Classification and Plant Diseases Prediction Type of soil samples by statistical and mathematical methods. These filters and algorithms are developed to discern the colors of the soil. These algorithms are used to evaluate color, texture “Red soil,” “Yellow soil,” “Clay soil.” Classification is carried out using vector machine support machine technology. To make decisions, SVM utilizes only some of the training examples. The consistency of the training data determines the accuracy of the controlled classification. The classification of soil and crops has not yet been coordinated. This project attempts to merge the two techniques of development. The author in [23] defines a classification technique. Firstly, it applies the segmentation algorithm to the environmental data segment. These segments also extract outstanding characteristic classifies that are correlational to the dependent attributes calculated in some segments. This paper uses a decision-making boom, artificial neural remote networks (ANN), and support vector machines (SVMs). The penetration testing is a type of research (CPT). The data compare the form of the sequence by measuring boundary energy using parameterization: The classification technique is based on CPR data. Various types of soil are classified according to different algorithms for soil classification. Figure 5.1 illustrates how soil images are graded. Various analytical methods have been used in agriculture to control disease. A deep learning algorithm focused on convolutional neural networks has taught the technique of crop disease. Different diseases have been found in a cornfield. The most

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Fig. 5.1 Images of soil samples

accurate and efficient methods of solving this problem are HSV [21, 24]. However, the machines have reduced the complexity and the job is thus much easier.

Crop Suitability Detection A farm does not have the desired result with a good fertile soil with an unadopted crop. The crop must be suitable for the region’s field soil and climate conditions to achieve the desired results. The incorrect choice of crops is one of the critical errors made by farmers that reduce their yield. This method is automated with techniques of machine learning. An artificial neural network is fed with soil constituents to achieve an appropriate level of accuracy for the given crop. Table 5.1 shows that the suitability is assessed in degrees of S1, S2, S3, and N [14]. Figure 5.2 shows the suitability of soil detection life cycle steps. The life cycle of soil detection for the required crop has the following stages: (1) image acquisition, (2) image preprocessing, (3) image preprocessing, (4) segmentation, (5) extraction of feature, (6) soil identification.

Image Acquisition Various photographs of soil samples to be labeled are obtained using a color camera and presented to the system as input. Each soil type’s characteristics are collected and stored in a separate database. Table 5.1 Crop suitability categories

Category

Description

S1

Highly suitable conditions

S2

Moderately suitable conditions

S3

Marginally suitable conditions

N

Not suitable for the crop

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Fig. 5.2 Life cycle of detecting suitable soil for crop selection

Image Preprocessing For obtaining the analysis results, the consistency of the image is decisive because it influences the ability to identify features under analysis and the accuracy of subsequent measurements. Error correction methods are implemented to obtain an errorfree picture. In this way, we are getting better and better. In the first one, there are many errors, such as noises and objects such as scratches, lapping tracks, and comet’s tails. The design needs to be updated to improve sharpness and definition. The color filter is used to retain contrast and distinct edges.

Segmentation Once the image is enhanced, then segmentation is performed. A standard recognition algorithm used for the segmentation of images is the k-means clustering algorithm. Partitioning can be applied to various data categories. The most common variable used is the criterion of distance. The center of a cluster is randomly picked, and the pixels are assigned to each cluster point. Cluster center recalculation can be accomplished by utilizing the average of the pixels. Each point belonging to a given dataset is then compared with the closest centroid.

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Feature Extraction After the k-means segment and the image, our next step is to extract features from the data. This technique is one of the essential aspects. In this last step, all the necessary characteristics for soil analysis and crop prediction are completed. To determine the soil types, several diagnostic tools such as texture, color, density, saturation, and hue are extracted. A filter known as Gabor filter is utilized in function extraction. The Gabor filter is an edge detector. They are optimal stimuli for discriminating texture in visual processing. For visualization of useful features of the image, statistical features may be important. Many other statistical attributes, such as entropy, mean, and other statistics, can also be obtained with the Gabor filter. It is critical to identify the key characteristics of colored soil. A peculiar measure called “color moments” is used to distinguish images based on their color characteristics. The corresponding white images are used in image retrieval tasks to match images indexed in the database.

SVM Classification This algorithmic approach is based on the distinctive characteristic analysis by evaluating the predicted error minimization. The empirical approach incorporates the probability of changing the teaching method into the process. The risk estimate is tested and compared using structural analysis to reduce the risk of generalization. The margin of error is analyzed by examining the training errors, and the closest similar patterns are gleaned from it. The model takes advantage of the representation of the kernel polynomial in order to obtain more accurate predictions.

Recognition Plant diseases have a serious impact on yield. An attack on a crop with a disease can reduce the yield to almost nothing. A farmer may not understand all the forms of diseases of the plant/leaf targeted. A serious issue is the innocence of not understanding the disease rather than treating the disease. Modern techniques such as computer training and a deep learning algorithm have been applied to improve the recognition rate and accuracy of the results. A deep learning model is used to classify the disease by coevolutionary neural networks [25]. The model recognizes the disease, as shown in the infected region of the plant.

Crop Yield Prediction from Soil Analysis Five machine learning approaches have been used for data collection, including k-nearest neighbor (KNN), Naïve Bayes, multinomial logistic regression (MLR),

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artificial neural network (ANN), and random forest. It is intended to predict soil analysis crop yield.

Proposed model The proposed model applied the following measures: (1) variety of crops and (2) disease identification.

Crop Selection To identify the crop selection, use a decision tree algorithm. The soil suitability table of each crop is collected and fed to the decision tree classifier to make a model (Fig. 5.3). The decision trees algorithm is used to implement the proposed system. Based on the crop suitability data table, an algorithm provides the model for a decision tree.

Fig. 5.3 Flowchart diagram of the crop selection process

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Disease Recognition A convolutional neural network technique is designed to recognize and extract several features of various diseases with a large amount of data. The neural network model can classify the disease using past data.

Implementation of Proposed System Datasets Used The dataset is made with different images of corn-leafed disease for crop disease detection. A suitable crop selection file is used to feed the decision tree classifier made using a soil suitability table. Every crop has its soil suitability table. The most critical soil parameters are selected to feed the decision tree (Table 5.2). The data collection used is .jpg pictures for plant disease identification. The neural network model is fed with a large amount of marked data. Approximately, 5000 photographs are taken for each disease. Figure 5.4 shows the sample images in the dataset leaf have the disease. Table 5.2 Soil suitability table for corn Land qualities/characteristics

Unit

S1

S2

S3

N

Climate(c) solar radiation

Sunshine Rs/day

> 478

478–355

358–239

239–120

Mean annual Temperature

0C

22–25

20–22

18–20

16–18

Relative humidity

%

70–75

65–70

60–65

< 60

Soil physical properties(s)texture(clay)

%

LC

SCL

SL

S

Workability(w)soil depth

Cm

65–70

50–65

35–50

30–35

Slope

%

0–2

2–4

4–6

6–10

Surface stoniness

None

None to slightly gravel

Slightly stony

Stony

Erosion(s)

None

None to slight

Eroded

Well erode

pH

5.5–7.5

5.2–5.5

7.5

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Fig. 5.4 Sample images have disease of a corn crop

Decision Tree Algorithm Used for Predicting the Crop Based on Soil The decision tree algorithm is an algorithm for supervised learning. Using a decision tree algorithm generates a training model capable of using simple decision-making guidelines interpreted by pre-fed data, that is, training data [22], to predict the class or value of the target variables.

Decision Tree Algorithm Steps Step-1: Begin the tree with the root node S, which contains the entire dataset for a decision. Step-2: Using the attribute selection measure (ASM), the most suitable attribute in the dataset is estimated. Step-3: The root node S is divided into subsets that contain possible values for the estimated attributes. Step-4: The decision tree node that contains the most suitable attribute. Step-5: Using the subsets of the dataset created in step-3, make new decision trees recursively. This process is continuously performed till the final node or the leaf node is reached. This is the stage where the process of further classification cannot be done; i.e., the decision or the result of the class is obtained [8].

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Convolutional Neural Network Algorithm Used for Predicting the Corn Crop Disease Another way to learn in depth is to use artificial neural networks. It may be tracked or unattended. A supervised learning machine model is used [24]. The network consists of three coevolutionary layers, two recurrent layers, and a grading layer. A network of neurons is a human brain–computer model. The pooling layer is present, and the model is a ReLU process. This article focuses on the influence of different combinations on the exactness of image recognition. The accuracy of the picture is significantly enhanced with the inclusion of dropout and ReLU. The random task experiment is appropriate. It also avoids overfitting of the CNNs. The ReLU feature enables the network to learn data from its positions as the dataset is scarce. Models define pictures of maize leaves; then, accuracy is measured.

Convolution For CNN, the most significant step forward is convolution. The convolution calculation for two-dimensional images can be presented by using the sliding window technique to compute the corresponding value. CNNs are complicated by many lists of input feature graphs. For an input x of the convolution layer of ith, it calculates as (5.1), h ic = f (Wi ∗ x)

(5.1)

The convolution operation is expressed as *, W i represents the layer kernel, and f represents the activation function. W i = [W 1i ;W 2i ;:::;W Ki ] are convolution kernels. Each window comprises M rows and N columns, all measuring M by N units.

Activate Function The ReLU function is an unsaturated nonlinear function that enables it to recognize input signals. Saturated nonlinear models have worse performance than unsaturated ones. In this model’s testing, the ReLU activation feature would be used to minimize distortion and improve recognition accuracy.

Pooling The network will become increasingly complex as its layers increase in size. It will decrease the number of parameters virtually on the Web. A pooling operation is carried out to reflect the characteristics of all regions. This paper studies the effects of various pooling procedures on the accuracy of the composite layer.

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Dropout Some debate that it could be a mitigation of the shortage of neural network training data by preventing the same benefits of having fewer samples. Since each input data sample has a different network structure, various models can be modeled based on the inputs. A dropout operation will be added to the model to avoid overfitting and to enhance the model’s generalization to improve the model.

Loss Function The loss function measures the discrepancy between the anticipated outcome and the actual outcome (5.2), E(W ) = −1/n

n  k  xi =1 k=1



yik log p(xi = k) +(1 − yik ) log(1 − p(xi = k))

 (5.2)

W indicates the convolutional and fully connected layer inputs, n indicates the number of training samples, and I index. If ith is an observation of kth class, then yik = 1 and otherwise yik = 0. The probability of input x i belonging to the kth class predicted by the model is P(x i = k). The parameters of w govern the loss function. The goal of network training is to minimize the error function. Our algorithms are stochastic gradient descent, where W is iteratively modified as (5.3), Wk = Wk−1 − α(∂ E(W )/∂ W )

(5.3)

where the parameter that defines the size of the learning phase is the learning rate, here is a symbol of class that is equivalent (5.2). Choosing an appropriate learning rate is vitally important.

Results and Discussion Crop Selection Some of the essential attributes are considered in order to predict the appropriate soil for the plant. As illustrated in Fig. 5.3, the rice prediction parameters are soil pH, soil texture, height, temperature, precipitation, and organic material. Crop suitability following these constituents’ values is predicted. Table 5.3 shows that the assessment of crop yields can be based on all the machine learning techniques under consideration. The highest prediction exactness of KNN and random forest is 88.67 and 94.13%, while the lowest prediction accuracy was

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Table 5.3 Experimental results for ML techniques for crop selection ML Alog.

Accuracy (%)

Precision (%)

Recall (%)

Specificity (%)

F-score

KNN

86.67

78.14

90.92

80.72

0.8405

NB

72.33

70.91

72.69

75

0.7179

MLR

80.24

24.17

96.66

0.0000

0.3866

ANN

76.86

99.94

99.61

99.78

0.9976

RF

94.13

66.55

99.66

100

0.7981

Fig. 5.5 Comparison of ML techniques

achieved at Naïve Bayes of 72.33%. The ANN project of 99.95% obtained the highest value inaccuracy. Figure 5.5 shows all the classifications checked predicted a more than 90% detection rate in Naïve Bayes, offering the maximum false-negative rate and a high false-positive rate for logistics regression. ANN and KNN, respectively, have been given 99.79 and 81.73% higher specificities and a greater f -score of 0.9976 and 0.8405. A comparative overview of the technical summaries is listed in this table. These technologies were evaluated in five parameters: precision, recall, specificity, and FScore. Machine training algorithms gave the best results across all types of statistical models. Figure 5.5 shows comparison of ML techniques.

Plant Disease Recognition Plant disease recognition is achieved through a neural network model for which an epoch of 8 is used. Figure 5.6 shows the corn leaf disease detection using neural network model. In plant disease prediction, an accuracy of 97% is obtained when tested on a test dataset of 1000 images. In this implemented model, Fig. 5.7 shows the training and

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Fig. 5.6 Corn leaf various diseases recognition

validation accuracy and Fig. 5.8 shows the loss of training and validation of the model.

Conclusion Conclusion proposed a web-based tool that helps farmers to predict when to harvest their crops. Farmers may forecast when to harvest their crops with the help of a proposed web-based solution that uses deep convolutional neural networks and decision tree-based recognition techniques. For crop selection, our proposed method performs with 99.9% and 98.99% accuracy in terms of plant disease identification. Seventy-five percent of the total dataset is used for training, while twenty-five percent is utilized for testing, making the classification techniques powerful and dependable and able to handle several samples with accuracy. It was shown that the decommissioning model and recognition accuracy may be enhanced in the evaluation of the training loss utilizing the ReLU model.

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Fig. 5.7 Training and validation accuracy of model

Fig. 5.8 Training and validation loss of model

References 1. Agriculture for Impact Soil Testing: Ag4impact.org, 2019. [Online]. Available: https://ag4imp act.org/sid/ecologicalintensification/precision-agriculture/soil-testing/ (2019) 2. Balakrishna, S., Solanki, V.K., Gunjan, V.K., Thirumaran, M.: A survey on semantic approaches for IoT data integration in smart cities. In: Gunjan, V., Garcia Diaz, V., Cardona, M., Solanki, V., Sunitha, K. (eds.) ICICCT 2019—System Reliability, Quality Control, Safety, Maintenance and Management. ICICCT 2019. Springer, Singapore (2020). https://doi.org/10.1007/978-98113-8461-5_94 3. Daya Sagar, K.V., Sai Durga, P., Kavya, G., Sri Sravya, K., Krishna Veni, K.: Mobile based

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4.

5.

6.

7. 8.

9. 10. 11. 12.

13.

14.

15. 16. 17.

18. 19. 20. 21.

K. V. Daya Sagar et al. home mechanization framework using IoT for smart cities. Int. J. Eng. Technol. 7(2.7), 266–269 (2018) Daya Sagar, K.V., Abbulu, U., Chaitanya Kumar Reddy, K.: Using fuzzy clustering techniques in pharmaceutical industry to find expired medicines. J Adv Res Dyn Control Syst, 10, 02Special Issue, (2018) Daya Sagar, K.V., Shyam Krishna, Ch., Lalith Kumar, G., Surya Teja, P., Charless Babu, G.: A method for finding threated web sites through crime data mining and sentiment analysis, Int. J. Eng. Technol. 7(2.7), 62–65 (2018) Yasaswini, A., DayaSagar, K.V., ShriVishnu, K., HariNandan, V., Prasadara Rao, P.V.R.D.: Automation of an IoT hub using artificial intelligence techniques. Int. J. Eng. Technol. 7(2.7) 25–27 (2018) United Nations Food and Agriculture Organization: A Framework for Land Evaluation. FAO Soil Bulletin 32, Rome (1976) Balakrishna, S., Solanki, V.K., Gunjan, V.K., Thirumaran, M.: Performance analysis of linked stream Big Data processing mechanisms for unifying IoT smart data. In: Gunjan, V., Garcia Diaz, V., Cardona, M., Solanki, V., Sunitha, K. (eds.) ICICCT 2019—System Reliability, Quality Control, Safety, Maintenance and Management. ICICCT 2019. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8461-5_78 Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber. 9(1), 62–66 (1979). https://doi.org/10.1016/j.biosystemseng. Sept 2009 Daya Sagar, K.V., Rupesh Chowdary, M., Mahesh, S.: Smart crop monitoring and farming using Internet of Things with cloud. J. Adv. Res. Dyn. Control Syst. 10(02-Special Issue) (2018) Shirsanth, R., Daya Sagar, K.V.: A review of fine-grained access control techniques. Int. J. Eng. Technol. 7(2.7), 20–24 (2018) Daya Sagar, K.V., Narayana, S., RaghavaRao, K., Bhavya Deepika, G., SaiKiran Reddy, M.: Developing smart kitchen inventory tracking using Internet of Things. J. Adv. Res. Dyn. Control Syst. 10(02-Special Issue) (2018) Daya Sagar, K.V., Kumar, A.P., Ankush, G.S., Harika, T., Saranya, M., Hemanth, D.: Implementation of IoT based Railway Calamity avoidance system using cloud computing technology. Indian J. Sci. Technol. 9(17) (2016), ISSN:0974-6846. https://doi.org/10.17485/ijst/ 2016/v9i17/93020 SuryaNarayana, G., Kolli, K., Ansari, M.D., Gunjan, V.K.: A traditional analysis for efficient data mining with integrated association mining into regression techniques. In: Kumar, A., Mozar, S. (eds.) ICCCE 2020. Lecture Notes in Electrical Engineering, vol. 698. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7961-5_127 Huang, X., Jagota, V., Espinoza-Muoz, E., Flores-Albornoz, J.: Tourist hot spots prediction model based on optimized neural network algorithm. Int. J. Syst. Assur. Eng. Manag. (2021) Gifford, R.C.: Agricultural Mechanization in Development: Guidelines for the Formulation of Strategies, Food and Agriculture Organization of the United Nations, Rome, Italy (1981) Goyal, S.K., Prabha, S.R., Singh, J.P., Rai, Singh, S.N.: Agricultural mechanization for sustainable agricultural and rural development in eastern P.P.—a review. Agron. Sustain. Dev. 2(1), 192–198 (2014) Alfer’ev, D.: Artificial intelligence in agriculture. Agric. Lifestock Technol. poooenika 4(4) (2018) Ben Ayed, R., Hanana, M.: Artificial intelligence to improve the food and agriculture sector. J. Food Quality 2021, 7p, Article ID 5584754 (2021) Kumar, R., Yadav, S., Kumar, M., Kumar, J., Kumar, M.: Artificial intelligence: new technology to improve Indian agriculture. Int. J. Chem. Stud. 8(2), 2999–3005 (2020) Ahmed, S.M., Kovela, B., Gunjan, V.K.: IoT based automatic plant watering system through soil moisture sensing—a technique to support farmers’ cultivation in rural India. In: Gunjan, V., Senatore, S., Kumar, A., Gao, X.Z., Merugu, S. (eds.) Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies. Lecture Notes in Electrical Engineering, vol. 643. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3125-5_28

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22. Singh, J.P., Singh, M.P., Kumar, R., Kumar, P.: Crop selection method to maximize crop yield rate using machine learning technique. Int. J. Eng. Technol. (2015) 23. Hillnhuetter, C., Mahlein, A.-K.: Early detection and localization of sugar beet diseases: new approaches. Gesunde Pfianzen 60(4), 143–149 (2008) 24. Barbedo J.G.A.: Factors that influence the use of comprehensive training in detecting plant diseases. Biosyst. Eng. 172, 84–91 (2018). https://doi.org/10.1016/j.biosystemseng 25. Dhaygude Sanjay, B., Kumbhar Nitin, P.: Detection of leaf plant diseases through image processing. Int. J. Adv. Res. Electron. Electro. Instrum. 2 (2003) 26. Singh, N., Gunjan, V.K., Chaudhary, G., Kaluri, R., Victor, N., Lakshmanna, K.: IoT enabled HELMET to safeguard the health of mine workers. Comput. Commun. 193, 1–9 (2022)

Chapter 6

Low-Cost ECG-Based Heart Monitoring System with Ubidots Platform A. Vamseekrishna, M. Siva Ganga Prasad, P. Gopi Krishna, P. Bhargavi, S. Rohit, and B. Tanmayi

Introduction Heart monitoring system uses to diagnose their health status, the heart generates signals which are used by the heart monitoring system [1]. It detects diagnostic characteristics from the obtained signal, which includes information about heart functionality including repolarization, depolarization and valve movements. The method of producing an electrocardiogram is electrocardiography (ECG or EKG). It is a voltage vs time graph of the electrical operation of the heart made with electrodes connected with the skin [2]. Cardiovascular diseases (CVDs) are the category of cardiac and blood vessel disorders. The primary cause of CVD death in the world is still heart disease. In 2010, CVD was responsible for 29.6% of all deaths worldwide (15,616.1 million deaths) [1]. With the enhancement of the quality of life of people and job burden, the welfare definition of people has evolved dramatically and health is the major concern. Due to less regulation of infectious illnesses with a long course of sickness, high prevalence, concealment and other features, such as coronary disease [3, 4], life and property will be severely affected [5]. The physiological pulse from the body surface is the electrocardiogram. The electrocardiograph is a system used for ECG signal measurement and tracking. Non-invasively mounted electrodes on the surface of the body are used to achieve data on the electrical activity produced by A. Vamseekrishna Department of ECE, Raghu Engineering College, Dakamarri, Visakhapatnam, Andhra Pradesh 531162, India M. Siva Ganga Prasad · P. Bhargavi · S. Rohit · B. Tanmayi Department of ECM, Koneru Lakshmaiah Education Foundation, Vijayawada, Andhra Pradesh 522502, India P. Gopi Krishna (B) Department of IoT, Koneru Lakshamaiah Education Foundation, Guntur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_6

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Fig. 6.1 System architecture of electrocardiogram-based heart monitoring system

the heart [6]. Broadband penetration, on the other hand, is excessive among large segments of the population, particularly in Organization for Economic Co-operation and Development (OECD) countries. The enabling forces of the so-called Internet of Things are the very same trends (IoT), that is, the implementation of applications focused on distributed sensors and actuators that interact [7]. In addition, in longterm monitoring applications, IoT has enabled remote monitoring applications which will dramatically minimize travel, costs and time [8]. Chips and discrete component circuit integration with noise it can provide good results with filtering capabilities, but this more energy has to be consumed from the battery [9]. The measured vitals can also be tracked remotely, allowing medical assistance to be deployed in the event of a medical emergency [10].

System Architecture The system consists of five units of the sensor consisting of electrocardiogram electrodes and a patient’s cable [11], which are connected to the sensors, sensors connected to the cloud through the Wi-Fi module, and finally we can go through the laptop for verifying the patients health Fig. 6.1. The ECG with three electrodes placed on the human body and the three modules and then cloud is connected.

Methodology Electrocardiogram (ECG) Einthoven invented an ECG system in 1901, measuring live electrocardiogram with a string galvanometer and assigning letters P, Q, R, S and T to various deflections

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and forms of electrocardiogram signal (Fig. 6.1). Life science today still produces simple diagnoses [12]. In order to diagnose heart conditions, processing techniques must be accurate in real time. The cardiac cycle’s ECG waveform is clarified by high energetic concentration in the QRS complex and low energy concentration in the T and U waves. In 50–75 you look after electrocardiograms, these two waves (T and U) are usually invisible [2]. Figure 6.2 is an ECG signal with specified waves, cycles and segmentation as an example [2] (Table 6.1). Wave Intervals See Fig. 6.3. The P wave of electrocardiogram reflects atrial contraction. The PR interval is a time measurement that runs from the start of atrial contraction to the start of ventricular contraction (Fig. 6.2). The full depolarization of the ventricles is expressed by the Q R S complex.

Fig. 6.2 Example of electrocardiogram signal with specified waves, intervals and segmentation

Table 6.1 Typical electrocardiogram waves and its action in Fig. 6.2

Waves

Action

P wave

Atria depolarization

Q wave

Activation of anteroseptal region of the ventricular myocardium

R wave

Ventricular myocardium depolarization

S wave

Activation of the ventricles’ posterobasal section

T wave

Rapid ventricular repolarization

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Fig. 6.3 Wave intervals

The full depolarization of the ventricles is represented by the S T section, and the segment elevation or depression can signify heart muscle ischemia (Figs. 6.3, 6.4, 6.5 and 6.6). Fig. 6.4 PR interval

Fig. 6.5 QR interval

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Fig. 6.6 Wave segments

Fig. 6.7 Kutty interval

The full depolarization and repolarization of the ventricles are represented by the Q T interval. A long Kutty interval is linked to ventricular arrhythmias and sudden death, as seen in Fig. 6.7. Components Used 1. 2. 3. 4. 5.

Electrocardiogram ModuleAD8232 [13] Electrocardiogram Electrodes—3pieces [14] Electrocardiogram Electrode Connector −3.5 mm [15] Breadboards Connecting wires.

AD8232 ECG Sensor: In the electrocardiogram and other biopotential calculation applications, the AD8232 is a signal conditioning block that has been optimized. It is intended to isolate, amplify and filter tiny biopotential signals in the presence of noisy environments, like those induced by motion or remote electrode location. This illustrates how an ultralow-power digitizer (ADC) or embedded microcontroller can easily gather the output with this configuration (Fig. 6.8). A two-pole high-pass filters can be introduced by the AD8232 to eliminate motion artefacts thus the electrodes are half-cell potential Fig. 6.8. These filters are closely combined with the amplifiers instrumentation architecture allowing for both significant gain and high-pass filtering in one phase. saving space and expense [12].

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Fig. 6.8 AD8232 ECG sensor

The AD8232 permits an uncommitted operating amplifier to produce a threepole low-pass philtre to eliminate unwanted noise [16]. The frequency cut-off of all filtrations can be selected by the user to suit various kinds of applications. To maximize common-mode exclusion of line frequencies and other unintended interferences in the device, the AD8232 includes an amplifier for monitored lead applications, such as right-leg drive (RLD). The AD8232 has a simple restore feature that shortens the length of high-pass philtres settling tails. After an unforeseen signal shift that routes the amplifier, the AD8232 immediately adjusts to a higher philtre cutoff (such as a leadoff condition). This function causes the AD8232 to rapidly recover, allowing precise measurements to be taken shortly after the electrodes are attached to the subject. The AD8232 is available in 20-lead LFCSP box with dimensions of 4 mm × 4 mm.

Software Implementation The system layer and the equipment application layer are two important components of the programme architecture. The appliance layer is used by the user to drive the hardware and visualize the signal ECG in the device layer. Microcontrollers have code for the embedded production setting. It is used on an Arduino platform. Encoding, which is a language in open-source programming and integrated programming environment IDE for development, develops an API for Android and a laptop application.

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Setting Up Ubidots Ubidots is one among the simplest IoT platform to attach things and visualize data; as you’ll see here, Ubidots have wide no of knowledge visualizing options and it’s an attention grabbing user-friendly interface. Steps for Setting Up Ubidots 1. 2. 3. 4. 5. 6. 7. 8.

9.

10.

11. 12. 13.

Visit ubidots.com. Signup by submitting all the information needed. Go to my account, Click on the devices, create a new device. Choose a blank device, Enter the name for the device then click on this green tick mark to create the device. That’s it the device is successfully created. Click on the newly built device. Click Add Variable, pick the Raw option and then rename this variable with a certain name. That’s it, the function was also developed successfully. Go to “Data”-> “Dashboards” to create a dashboard, click on the “dashboard” icon on the top left of the user interface, hold all default fields and then create them. Choose on add new widget, you’ll be prompted with a bunch of knowledge visualization options, choose one of them, i will be able to use a line chart to plot graph using the uploaded received data from esp32Module. Top on add variables, then select the device and choose the variable, Add a reputation for the graph. Keep the remainder of the choices default. Later create it. Since our device did not interact with Ubidots, it will show No Data Found. Once our device started posting data, we can see the info here itself. By using Arduino IDE, we’ll program the ESP32. So, we should always have the esp32 add on installed in our Arduino IDE. After successful setup of the specified credentials over the esp32 module, and making a necessary reference to Ad8232 and Arduino interfacing with esp32 we will see the specified heart pulses over the Ubidots platform.

Experimental Measurements and Results Ubidots is used as a cloud server to check and manipulate the data from anywhere through Wi-Fi. As we have implemented the hardware in Arduino UNO board. In notebooks and smartphones, this device will work. The electrodes are placed on person the heart monitoring which is seen in Fig. 6.9. As the information is stored via Wi-Fi module in the Ubidots, which is linked to the hardware device. By the stored data, the graph is made so that the patient heart rate is monitored from anywhere through cloud service. The ECG signal is

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Fig. 6.9 Heart rate of the patient

Fig. 6.10 Heart rate of the patient in Ubidots

received in the laptop data by a USB cable attached directly to Arduino Uno by a Wi-Fi module. We stored the ECG signal data in the cloud. The electrocardiogram signal is taken from a woman who is 40+ years old Figs. 6.9 and 6.10.

Cost Analysis See Table 6.2.

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Table 6.2 Cost of the equipments Name of the component

Quantity

Unit price (INR)

Total cost (INR)

Arduino UNO

1

250

250

Connecting wires

10

10

10

AD8232 ECG Sensor

1

750

750

Breadboard

1

50

50

Wi-Fi module

1

100

100

ECG Electrode Connector −3.5 mm

1

50

50

Conclusion This project is to develop and implement an Ubidots-based low-cost ECG and heart monitoring system. The desired ECG signal has been found and checked, and this is for a physician. The heart rate and ECG signal are visualized by our built scheme. We will add irregular ECG identification functionality to this scheme in our future work. The cost of this device is just INR, making it relatively cheap and suitable for developing and underdeveloped countries. For the doctors who are useful and any cardiac patients and in any other developing world, such as Bangladesh, Sri Lanka, this device is an excellent option.

References 1. Al-Busaidi, A.M., Khriji, L.: Digitally filtered ECG signal using low cost microcontroller. In: International Conference on Control, Decision and Information Technologies (CoDIT), pp. 258–263 (2013) 2. Nichols, M., Townsend, N., Scarborough, P., Rayner, M.: Cardiovascular disease in Europe 2014:epidemiological update. Eur. Heart J. 35(42), 2950–2959 (2014) 3. Ueshima, H., Sekikawa, A., Miura, K., Turin, T.C., Takashima, N., Kita, N.Y., Okamura, T.: Cardiovascular disease and risk factors in Asia a selected review. Circulation 118(25):2702– 2709 (2008) 4. Khor, G.L.: Cardiovascular epidemiology in the Asia-Pacific region. Asia Pac. J. Clin. Nutr. 10(2), 76–80 (2001) 5. Liu, B., Shi, G., Zhao, W.: The design of portable ECG health monitoring system. 2017 29th Chinese Control And Decision Conference (CCDC) (2017) 6. Kamble, P., Birajdar, A.: Internet of Things based portable ECG monitoring device for smart healthcare. In: 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (2019) 7. Gunjan, V.K., Reddy, M.J., Shaik, F., Hymavathi, V.: An effective user interface image processing model for classification of Brain MRI to provide prolific healthcare. Helix J 8(3), 2129–2132 (2018) 8. Kamble, P., Birajdar, A.: IoT based portable ECG monitoring device for smart healthcare. In: 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (2019) 9. Shaik A.S., Usha S.: Sensor based garbage disposal system. Int. J. Innov. Technol. Exploring Eng. (IJITEE) 8(4S2), 164–167, ISSN: 2278-307

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10. Satija, U., Ramkumar, B., Sabarimalai Manikandan, M.: Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring. IEEE Internet of Things J. 4(3), 815–823 (2017) 11. Allam, V.K., Madhav, B.T.P., Anilkumar, T., Maloji, S.: A novel reconfigurable bandpass filtering antenna for IoT communication applications. Prog. Electromagnet. Res 96, 13–26 (2019) 12. Shaik A.S., Karsh R.K., Suresh M., Gunjan V.K. LWT-DCT based image hashing for tampering localization via blind geometric correction. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds.) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol. 783. Springer, Singapore (2020) 13. Gao, Y., Soman, V.V., Lombardi, J.P., Rajbhandari, P.P., Dhakal, T.P., Wilson, D., Jin Z.: Heart monitor using flexible capacitive ECG Electrodes. IEEE Trans. Instrum. Meas. (2019) 14. Wannenburg, J., Malekian, R., Hancke, G.P.: Wireless capacitive-based ECG sensing for feature extraction and mobile health monitoring. IEEE Sens. J. 18(14), 6023–6032 (2018) 15. Winter, B.B., Webster, J.G.: Driven-right-leg circuit design IEEE Trans. Biomed. Eng., pp. 62– 66 (1983) 16. Vamseekrishna, A., Madhav, B.T.P.: Defected ground structure switchable notch band antenna for UWB applications. In Smart Computing and Informatics (pp. 139–145). Springer, Singapore (2018)

Chapter 7

Automatic Attendance Management System Using AI and Deep Convolutional Neural Network J. RajaSekhar, Sridevi Sakhamuri, A. Dhruva Teja, and T. Siva Sai Bhargav

Introduction The goals of artificial intelligence are to replicate human intelligence, solve knowledge-intensive tasks, and create an intelligent connection of action. The simulation of human intelligence in machines is programmed to think like humans and behave in their actions. It performs tasks like playing chess, driving a car, and performing a surgical operation. The neural network segment reviews the significant human face acknowledgment strategies that generally apply to frontal faces; favorable circumstances and hindrances of every technique are also given. The techniques considered are eigenfaces (eigenfeatures), neural organizations, dynamic connection design, shrouded Markov model, mathematical element coordinating, and format coordinating.

Literature Survey Smart devices reduce the cost of equipment and allow management to access the data anytime and anywhere. This device is very user-friendly to perform classroom attendance teachers, parents, and students use this application frequently without any conditions, and this paper was proposed by Refik Samet and Muhammed Tanriverdi [1]. The system is implemented using google forms and sheets. The DroidScript application is developed to use speech recognition for attendance. Speech recognition can detect numbers more accurately than detecting. The last three-digit code is used to identify instead of the student name, and this paper was proposed by Vasutan J. RajaSekhar (B) · S. Sakhamuri · A. Dhruva Teja · T. Siva Sai Bhargav Department of ECM, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_7

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Tunbunheng [2]. While facial recognition, if girls maintain veil and the boys consist of beard, it efforts the change in length posture shape and size. It can be solved by facial fiduciary points and done on basics of statistics about the eyes, the nose of girls and boys, and this paper proposed by Mashhood Sajid, Rubab Hussain, Muhammad Usman [3]. The design is divided into three main components, where BLE periodically transmits data caught and recorded by smart applications and finally delivers it to the web service for management. Attendance ratings on particular lectures could be calculated on that bases, attendance rates on a daily or weekly basis could be discovered, and this paper was proposed by Bruno Zoric [4]. The camera is arranged in the classroom, and it will keep on detecting the faces and compare with the dataset and update on excel sheet whether absent or present. If they are absent, then a message sends to their parents, and this paper is proposed by Varadharajan [5]. Here attendance monitoring through RFID at first it scans RFID cards then search for student’s identification for that it compares student’s ID. With database based on this, it will mark whether present or absent and this paper proposed by Sri Madhu [6]. This system can collect real-time fingerprint image signals during the verification process, take the features from this template, and then compare the template with the templates stored in the database. A program is coded in “C” language to implement the algorithms for minutiae extraction, matching processing, and enhancement, and this paper was proposed by Maddu Kamaraju [7]. Here monitoring is done through CCTV inside the classroom, and it will compare with the dataset; based on this, it will update on database whether absent or present and here used algorithms are convolutional neural network (CNN), principal component analysis (PCA) and eigenface value detection and this paper proposed by Muthunagai [8]. This paper reviews several methods in terms of overall system capacity and accuracy. PCA has better performance in the system of attendance management based on facial recognition than the manual attendance system, which is time-consuming; the final result describes that the PCA algorithm is effective in an extensive database. Where convolutional neural network also contributes to the attendance control system which is based on facial recognition by providing a strong classifier, and this paper proposed by Khem Puthea [9]. Angelo G. Menezes et al. proposed a face detection stage using HOG and a CNN with max-margin object detection-based features and achieved accuracy of 91% with iPhone camera and 91.9% with Moto G Camera and 51.2% with Web Camera, respectively [10]. Ravi Kishore et al. proposed novel method of marking attendance using facial recognition. Their proposed method uses small and accurates deeply supervised network for recognition of faces in a wild classroom scenario. A web application is developed for easy inference to the users. All the analytics is performed on Amazon Elastic Compute Cloud (Amazon EC2) Instance [11]. Danijel et al. presented an improved system based on RFID and some results of its use in a real environment. The main improvements have been made to a hardware component RFID reader and to a web-based application that is used by the faculty staff. Besides the usual features of similar systems related to student attendance, the system also includes features that enable integration with external systems, recording of necessary data regarding the work of teachers in class and generating of periodical reports [12].

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The major tasks of this project are detecting, recognizing, updating records in excel, measurement of temperature, managing the dataset of both students and faculty through excel by using GUI, and notifying students and teachers of attendance statistics via email. Face detection helps to envision human faces in digital images. It will detect the face by using the open-CV module. If it does not recognize the face within a period, it will send an alert message to the admin account. If it detects the face, then it will update the record on the excel datasheet. Due to this pandemic time, we additionally added a task as measuring the temperature of a body. This entire data will be managed through an excel sheet by using GUI. This system can also be useful during exam sessions or other teaching activities where attendance is essential. This system eliminates classical student identification, which can interfere with the ongoing teaching process and be stressful for students during examination sessions. An automatic attendance system by facial recognition using machine learning is a smart and organized way for any organization which demands the regular maintenance of the attendance of the employees, workers, or students. This approach can save an organization money, save time, and spare you the frustration of the manual input of attendance, which has been followed for ages. The automatic approach to attendance will increase efficiency by implementing the electronic, integrated time and attendance system, resulting in a profit.

Methodology In the proposed work, we are going to use the facial recognition of the student and monitor the temperature of the student; the workflow is shown in Fig. 7.1.

Facial and Temperature Detection This stage will first detect the image on the particular location of the face in any image/frame by using the bounding box coordinates of the face. It is graphically defined as BBOX and cut, paste the coordinates in various forms, and has latitude and longitude range from −90.0 and 90.0, −180.0 and 180.0, and its area is defined by two longitude and two latitudes. Where in image classification, we assume only one main target in an image, and we focus on how to identify its target category. For this, we usually use a bounding box. MAX30205 does temperature detection and is mainly used to check and measure the human body temperature, and its resolution is 16bits. MAX30205 is a digital thermometer sensor, and it measures from 37° to 39°. Healthy body temperature from 36.1 to 37.5. MAX30205 is accurate to 0.1 °C.

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Fig. 7.1 Flow chart

Facial Recognition It analyzes the facial feature obtained by the process of Euclidian space and compares it with multiple images/frames to identify whether the image matched or not and successfully returns the true/false value at the end of the process. The face embedding vector is mainly used to contrast the images when we map face images to compare Euclidean space, where those distances directly represent face similarities. Where it is useful for security, biometric, and personal security. It has six-layered CNN in Keras & image augmentations to improve model performance.

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Record Management in Excel By using GUI CRUD, operations record management will occur. The main operations of CRUD are: create, read, update, and delete. Also, we have used the openpyxl module for the operations with an excel sheet. After completion of face detection, it will compare with excel sheet data. Based on this, it will go to further steps. If the detected image is matched, it will update the record as a present. In case it does not match, it will alert the message.

Managing Data in the Cloud At first, it has to import the python library as the cloud. Using this, we can access different method calls like client, project, and credentials, where the client is used to bundle the configuration of API requests, and the project is passed when creating a topic. If not passed falls back to inferred from the environment. OAuth2 Credentials are used for the connection. If not passed, it falls back to the default inferred from the environment. There are several methods to upload a file. We can be expecting a file in the payload of a POST or PUT request or have it locally on your file system. Where you can send or post text directly to a text file. Blob uses to save the filename.

Notifying Statistics to Students Using Mail Whenever a particular person is absent, we can send a mail message to that student using a simple mail transfer protocol [SMTP]. We have to secure this entire process by creating a login page, and the admin only uses this page. Because of this, we can secure the data without any proxies. In addition to this, we are detecting the temperature of the body. Face detection using “Haar” cascades and is a machine learning-based approach and where OpenCV contains many pre-trained classifiers for eyes, face, smiles. An LBPH recognizer does recognition. Major python libraries used: OpenCV-python, openpyxl, DateTime, holidays, calendar, Tkinter, smtplib, Pandas, Numpy, Pilow, xlrd, CSV. Local binary pattern (LBP) is a simple yet very efficient texture operator where that marks the pixels of a picture by thresholding the neighborhood of every pixel and thinks about the outcome as a binary number.

Experimental Analysis At first, the admin has got to log in to the page. When the palmy login, the cam can incessantly capture pictures, the pictures, the photographs, and these images

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have got to find and recognized, so compared with the datasheet. Once the image is matched with the datasheet, it’ll update as a present. If it’s not matched, it’ll offer an Associate in Nursing alert message. There is a tendency to add a task for this project as temperature finding wherever it’ll detect a student’s temperature before coming into the category by victimization MAX30205. If the temperature of someone isn’t inside explicit limits, it’ll offer an Associate in Nursing alert message. This entire information has got to transfer to the cloud to access anyplace. Finally, it’ll appraise the scholars of the SMTP protocol victimization wherever it’ll forward the mails to the scholars’ UN agency ever absent from their category. Victimizing this technique will scale back the time for taking group action. In this, face recognition is done by examining multiple images/frames with one another to spot whether the picture is coordinated and comes true/false at the finish of the method. This total comparison is finished by the method of face embedding vector, after mapping face pictures to match Euclidean space wherever those distances directly represent face similarities. CRUD operations square measure produce, read, update, and delete. There square measure few arguments you’ll be able to pass to load workbooks that modify how a program is loaded. The foremost vital one’s square measure the two mathematician operations. Native binary pattern (LBP) could be an easy nonetheless terribly economical texture operator that labels the constituents of an image by thresholding the neighborhood of each pixel and considers the outcome as a binary range. LBPH is one of the best face recognition algorithms. It will represent native options within the pictures. It’s doable to urge nice results (mainly in a controlled environment). It’s strong against monotonic grayscale transformations. It’s provided by the OpenCV library (Open supply laptop Vision Library). Figure 7.2 shows the image recognition and temperature of the student who is attending the class.

Fig. 7.2 Captured image

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Algorithm YOLO [You Only Look Once] 1. To understand the YOLO algorithm, we first have to understand the actual forecast. 2. Here, we predict the image by bounding the box specifying object location. 3. Bounding box has four descriptions such as center of box (bx,by), width (bw), height (bh), value c. 4. Class with max probability is assigned to particular grid cells, which will happen to every grid cell in an image. 5. After completion of this process next step is non-max suppression, which helps eliminate the unnecessary anchor boxes. 6. Process continuously repeats until we are left with all different bounding boxes. 7. Finally, it outputs the required vector and shows the bounding box of a class.

LBPH [Local Binary Pattern] 1. LBPH uses four parameters: radius, neighbors, grid x, and y. 2. Training the algorithm, at first, we have to train the algorithm, and we need to use a dataset with facial images of the people for recognition purposes. 3. Where we mainly need to set an ID for each image based on this, it gives proper output. 4. Images of the same persons must contain the same ID. 5. Using radius and neighbor, it uses the sliding window concept. 6. Using Grid x and Grid y parameters, the image collapses into multiple grids. 7. From the training dataset, each histogram created represents the image. 8. We can use different approaches to compare the histograms like Euclidean distance, chi-square, and absolute value.

HAAR Cascades 1. 2. 3. 4. 5.

It requires a lot of positive and negative images to train the classifiers. We have to pick a pixel location from the image. Crop a sub-image as the center from the source image with the same size. It calculates based on the element-wise product between kernel and sub-image. Insert the resultant value into a new image at the same place where we have picked up the pixel location.

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Result Figure 7.3 shows captured the image frame and extracted that by using bounding box coordinates and recognizing by face embedding vector, and then it will contrast with the data sheet based on datasheet it will recognize. Figure 7.4 represents the student roll no name section of the student, the hour attended, and the date and time of the student attending the class. If the temperature status of the student is normal, they will be allowed to the class; otherwise, it will not allow the student to attend the class, and it is represented in the temperature status as not allowed, and the mail id of the student is used to send the status of the class attended to their mail id. Figures 7.5 and 7.6 represent the status of the number of students present, absent, and not allowed to the class; by seeing this, we can come to an understanding that

Fig. 7.3 Captured image

Fig. 7.4 Data recorded on the server

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Fig. 7.5 Status of students in section 1

Fig. 7.6 Status of students section 2

how many students attended the class in an hour for the particular day, in the same way, we will get the student data for the entire month. Figure 7.7 shows the status sent to the student about the temperature status and present on the date and time, and hour the student attended the class.

Conclusion In this project, by using the local binary pattern [LBPH] technique, we found that the face image can be recognized and detected. Then it will contrast with excel sheet data and send a mail message using the SMTP protocol. By using this project, we can manage or reduce time usage.

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Fig. 7.7 Mail proof to students

References 1. Vamseekrishna, A., Madhav, B.T.P., Anilkumar, T., Reddy, L.S.S.: An IoT controlled octahedron frequency reconfigurable multiband antenna for microwave sensing applications. IEEE Sens. Lett. 3(10), 1–4 (2019) 2. Allam, V.K., Madhav, B.T.P., Anilkumar, T., Maloji, S.: A novel reconfigurable bandpass filtering antenna for IoT communication applications. Prog. Electromagnet. Res. 96, 13–26 (2019) 3. Krishna, A.V., Madhav, B.T.P., Avinash, R., Koukab, A novel h-shaped reconfigurable patch antenna for IoT and wireless applications. Int. J. Innov. Technol. Explor. Eng., 8(7), 1757–1764 (2019) 4. Vamseekrishna, A., Madhav, B.T.P.: A frequency reconfigurable antenna with Bluetooth, Wi-Fi, and WLAN notch band characteristics. Int. J. Eng. Technol. 7(2.7), 127–130 (2018) 5. Allam, V., Madhav, B.T.P.: Defected ground structure switchable notch band antenna for UWB applications. In: Smart Computing and Informatics, pp. 139–145. Springer, Singapore (2018) 6. Krishna, A.V., Madhav, B.T.P.: Planar switchable notch band antenna with DGS for UWB applications. In: Proceedings of 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications, pp. 509–518. Springer, Singapore (2018) 7. Vamseekrishna, A., Madhav, B.T.P., Nagarjuna, Y., LakshmiManasa, S., Mourya, V., Yaswant, Y.: Reconfigurable notch band antenna using pin diodes. J. Adv. Res. Dyn. Control Sys. 2017(14, Special Issue), 1746–1754 (2017) 8. Allam, V., Jyothshnasri, P., Aiswarya, V., Sameer, S.: Smart restaurants. Int. J. Eng. Technol. (UAE), 7(2), 54–57 (2018) 9. Marella, S.T., Karthikeya, K., Myla, S., Sai, M.M., Allam, V.: Detecting fraudulent credit card transactions using outlier detection. Int. J. Sci. Technol. Res. 8(10), 630–637 (2019) 10. Kodali, R.K., Hemadri, R.V.: Attendance management system. In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–5 (2021). https://doi.org/ 10.1109/ICCCI50826.2021.9402659 11. Menezes, A.G., Sá, J.M.D.da.C., Llapa, E., Estombelo-Montesco, C.A.: Automatic attendance management system based on deep one-shot learning. In: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 137–142 (2020). https://doi.org/10. 1109/IWSSIP48289.2020.9145230 12. Miji´c, D., Bjelica, O., Durutovi´c, J., Ljubojevi´c, M.: An improved version of student attendance management system based on RFID. In: 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), pp. 1–5 (2019). https://doi.org/10.1109/INFOTEH.2019. 8717750

Chapter 8

Automatic Vehicle Alert and Accident Detection System Based on Cloud Using IoT Pachipala Yellamma, P. G. Sandeep, R. Revanth Sai, S. Rohith Reddy, and D. Mahesh

Introduction The Internet of things is a fast-growing number of physical devices (which have a unique identifier) interconnected with some devices over the Internet. This rapid growth in physical devices’ usage has been constantly increasing due to their efficiency, user-friendly nature, and simple control, which automatically save time and money [1]. These physical devices combine and transmit information and transfer data via connected devices, exclusive of any human communication [2]. If collision is detected in a humongous transportation vehicle, the less defeat of life due to the faster sending of the victim’s location and information to concerned officials. If IoT is implemented in transportation vehicles for pre-programmed accident detection and transmission of the victim’s location and information to concerned officials, then the loss of life in accidents may gradually decrease [3]. Many studies on smart accident detection systems have been conducted, such as [4], in which the researcher uses a mobile accelerometer to predict the occurrence of an accident. Every car now has a communication device. Reference [5] proposed a design in that investigates ways for an ambulance to arrive at the accident site more quickly. This proposed work is on IoT using Raspberry Pi and is connected with GPS, an Electronic Health Record Reader, and sensors. Initially, people’s emergency contacts are recorded in cloud computing and recovered in an incident of an accident. Amazon Web Services (AWS) is the proposed work of the cloud provider we used (AWS). This paper discusses the Raspberry Pi as a currently under investigation method of connecting victims of accidents in high-risk areas to the emergency contacts provided by the victim via the Internet, resulting in a reduction in the time required to identify accidents and begin treatment on the victims.

P. Yellamma (B) · P. G. Sandeep · R. Revanth Sai · S. Rohith Reddy · D. Mahesh Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur, A.P., India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_8

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The proposed work supports the constant follow-up of the vehicle by means of a GPS sensor and in the event of any accidents [6]. In Raspberry Pi, the specific id of the victim’s electronic record is already saved in text format. The Internet-based Raspberry Pi transmits vehicle location, and a file may provide victims’ with medical and emergency contact information to nearby hospitals in the form of an electronic mail service. Once the mail is received by the authorized personnel, they inform the ambulance and provide them with the accident location. Then, the authorized personnel will get back the essential data from the cloud and process it according to the specific details. Finally, the staff will send an SMS on the incidence of the accident with the hospital address to the emergency contact of the victim.

Literature Review Reference [7] proposed a scheme that detects the accident using a piezoelectric sensor that does not support truly static measurements and also does not provide an exact value when exposed to elevated temperatures [7]. Reference [8] proposed a system that depends on the ARM11 Controller, which is very expensive. Reference [8]. The performance of the ARM microcontroller depends on an implementation, and if the programmer does not fix the bugs properly and compile the program properly, then it takes an exceptionally long time to work sufficiently. Reference [9] produced a system that they used, the Global Mobile System. In some cases, there may be no network in some hilly regions, so in those cases, the system could not make a reasonable effort [9]. Reference [10] proposed a system that contains a vibration sensor that is extremely sensitive to high-frequency noises. This segment ignores comparable existing arrangements, analyses their benefits, and harmed their approach to an existing system for accident detection, reporting, and vehicle navigation is accident detection, reporting, and navigation. Reference [11] proposed using an existing system that uses shock sensors, NFC tags, and GPS to determine the state of the motor vehicle. This method uses the user’s existing data to identify the person. When an accident occurs, the deployment of the airbags activates the shock sensor, and the position of the medium is immediately transmitted to the server via HTTP request. As soon as a request is received, assistance can be dispatched to the location and appropriate medical care can be provided because ethers on have already been identified. By piggybacking on smart phones carried by users, the system performs rich sensing. The accelerometer, microphone, GSM radio, and/or GPS sensors are used by the sensing component to detect potholes, bumps, braking, and honking. Furthermore, the paper addresses a number of energy-efficient challenges, such as arbitrary orientation, honking detection, and localization. GPS-based alert service system for tracking vehicle speed and detecting accidents. It monitors the speed of a vehicle using a microcontroller system, compares it to the previous speed every second, and assumes the occurrence of an accident if the vehicle speed falls below the specified speed. The location of the accident, as well as the time, is obtained from GPS,

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allowing for timely assistance to save valuable human lives. Reference [12] proposed an automatic accident detector and reporting system (AADRS), a cutting-edge and ideologically distinct system aimed at improving road safety [12]. The proposed work’s primary goal is to assist and treat road accident victims [13]. The process behind this system is to automate the alert system by automatically requesting nearby medical assistance in the event of a vehicle collision or accident [14]. The task is completed by the public using the application during the incident, as well as an application linked to the system’s sensors [15]. Both of these organizations Shelton identify accident-prone areas and provide medical assistance as soon as possible. The authors of the paper [16] presented a method for detecting and tracking vehicle accidents using GSM and GPS. The push turns on switches, detects an accident, and tracks the location using GPS to notify the user-defined number via GSM service [17]. The proposed model is specifying the user alerting system in favor of another similar system aimed at providing accident information which is the vehicle accident detection system. This system employs a vibration sensor to detect abnormal vibrations in the vehicle and sends the vehicle’s geographical location via SMS to the enrolled members associated with the person. Platinum created the auto accident application.

Proposed Methodology The proposed model’s main idea is to reduce the time spent gathering all the medical information of the victim and the location of the accident-prone area by automatically intimating emergency contacts given by the victim. This is achieved by the successful connection of Global Positioning System (GPS) and accelerometer sensors to Raspberry Pi 4 models B and C, and the model is associated with Amazon Web Services (AWS). In AWS, we should connect the Raspberry Pi 4 model B to the AWS IoT hub. The proposed work uses the Raspberry Pi 4 model B with the Raspberry Pi an operating system image loaded on a Micro-SD-card. Every input operation is done by keyboard and the mouse is attached to the Raspberry Pi, and all the resulting information is displayed on the monitor connected to the HDMI cable. After the hardware connections, software connections should be started using the connection. We should connect the Raspberry Pi 4 model B to the AWS IoT core, and after a successful connection of the AWS IoT hub, all the data gathered by the Raspberry Pi is broadcast to the AWS IoT hub using MQTT Broker. All the data is received by the AWS IoT hub and is stored in DynamoDB by making a rule in IoT CORE. The related sensors continuously send the measured value when an accident occurs and abnormal values are sent to the AWS IoT. While an abnormal value has been detected in the lambda function, it is sent to SNS and the SNS information is forwarded to the specified emergency contacts. When sending data to SNS, Lambda acts as the manager and always stores the data received from sensors in the AWS IoT subscribes topic in DynamoDB. Every attribute and piece of data is present in JSON format.

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When SNS receives the statistics, it will immediately transmit the location information and the victim’s information to a predefined emergency number. The proposed work contains both hardware and software components. They are explained below. The Raspberry Pi, which is the size of a credit card, takes up very little space in automobiles. A Pi is a uniboard computer that can be associated with abundant devices for many purposes and functions similar to an immense one. There are various models of Raspberry Pi; the model utilized in our proposed work is Raspberry Pi model3B. This model is designed with 1 GB of RAM and a 1.2 GHz quadcore multiprocessor. Global Positioning System (GPS): The global positioning system operates with the assistance of satellites. It is known as the satellite navigation system. Everything that has GPS connects to the satellite for reading the raw data of location with timestamp in all weather conditions. It is done within a fraction of a second. This GPS module receives the satellite raw data and transfers it to/converts it into a human-understandable format. This transformed information is sent to the emergency contacts via electronic mail. The GPS module is associated with the Raspberry Pi utilizing female 2 female jumper wires, and continuous tracking information of the vehicle is sent to the Raspberry Pi, and this information is passed to the cloud. The MPU6050 sensor module incorporates a 6-pivot Motion GPS beacon. It has a 3-axis Gyroscope, a 3-hub accelerometer, a digital motion processor, and a temperature sensor, all in a solitary IC. It can acknowledge contributions from different sensors, like a 3-pivot magnetometer or pressing factor sensor, utilizing its auxiliary I2C transport. In the event that an outer 3-axis magnetometer is associated, it can give a total 9-hub Motion Fusion yield. A microcontroller can speak with this module by utilizing the I2Ccorrespondence convention. Different boundaries can be found by perusing esteems from addresses of specific registers utilizing I2C correspondence. In addition, whirligig and accelerometer perusing along X, Y, and Z tomahawks are accessible in 2’s supplement structure. Gyrator readings are in degrees per second (DPS) units; accelerometer readings are in units. The MPU6050 sensor is combined with the GPS module. The predetermined GPS module starts moving when the sensor notices any difficult situation in the vehicle’s development. At first, the sensor works in consistent manner, but when any sort of unusual development is distinguished, the steady worth changes and an alternate worth will be noticeable. When an unusual value springs up, a mishap happens. Cloud Storage: Cloud storage is a model of computer data storage in which the data looted from devices is saved and stored in the cloud in a pool of resources said to be cloud storage. The physical storage is owned and managed by a hosting company. In this paper, we use Amazon Web Services, called AWS. All the information obtained from the Raspberry Pi is stored in this cloud storage only. It also contains all the listed emergency contacts. AWS IoT Core: AWS IoT Core is a cloud service managed by AWS that allows users to connect IoT devices and interact with various cloud applications and other AWS services in an easy and secure manner. In this, we connect our Raspberry Pi to the AWS IoT core. Using this cloud service, the Raspberry Pi is linked to AWS.

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AWS Lambda: AWS Lambda is a serverless computing platform. This was introduced by the Amazon. It will act as a manager when the data is received. It will process the data relating to the user and sends it to the endpoint. AWS DynamoDB: DynamoDB is an Amazon Web Services data set framework that facilitates data constructions and key prestigious cloud administrations. It gives clients the advantage of auto-scaling in memory reserving, reinforcement and reestablishes alternatives for all their web scale applications. AWS SNS: Amazon Simple Notification Service (Amazon SNS) is a managed service that delivers messages from the sender to the recipients. Using this Amazon service, we send electronic mail to the victim’s emergency contacts. In the event of an accident, every emergency contact given by the victim will get an alert by SNS. Figure 8.1 shows how to connect MPU6050 to Raspberry Pi, Raspberry Pi model contains 4 models contains 40GOIP pins and using female-to-female jumper wires. The proposed system is connecting to theMPU6050 sensor and to the pins in Raspberry Pi. Each bit of the information is broadcasted toward the Raspberry Pi using female-to-female wires. Figure 8.2 shows how to make a connection between the Raspberry Pi and the GPS module. The Raspberry Pi 4 contains 40 GOIP pins. The female-to-female jumper wires are connected together with the GPS module to the Raspberry Pi. All the data is transferred to the Raspberry Pi via female-to-female wires. Figure 8.3 represents the step-by-step workflow. The proposed algorithm is initial. MPU6050 is a sensor which reads the gyro axis in x, y, and z and sends the information to Raspberry Pi. Similarly, the same applies to the GPS module, which is also connected to the Raspberry Pi using female-to-female jumper wires. The GPS module continuously monitors the vehicle’s location and sends the data to the Raspberry Pi. The Raspberry

Fig. 8.1 MPU6050 Interfacing with Raspberry Pi 4

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Fig. 8.2 Interfacing of the Raspberry Pi 4 with GPS module

Pi is connected to the AWS IoT core using the Internet. Each bit of the data is broadcast to the AWS IoT core, and it can read the data in JavaScript Object Notation (JSON) format. Meanwhile, the data is redirected to an AWS rule which was created initially and connected to AWS Lambda. Lambda adds the data and if an abnormal value occurs while monitoring the data, then Lambda opens the AWS SNS module in this component, the accident-prone area. Finally, the medical information sent to the injured party is attached to an e-mail and sent to the emergency contacts initially given by the victim. If no abnormal value is detected, then Lambda forwards all the data to be stored in DynamoDB.

Experimental Results After successful implementation of the proposed system, when an accident occurs, all the emergency contacts would get the following e-mail with the accident-prone are a location and medical information of the victim. The connection is from the Raspberry Pi to the AWS IoT core, transferring data to AWS IoT using the MQTT broker. Data is stored in JSON format. Time, location, detection are transferred. AWS IoT core receives data from the Raspberry Pi 4 and triggers an AWS IoT rule that sends the captured data to lambda. The Dynamo database is tagged along with the NoSQL rule. Data is stored in JSON format; data is represented in the string format and stored in DynamoDB. Electronic mail received by an emergency contact with the location of an accident-prone is at his mail which was triggered by an AWS lambda to AWS SNS. SNS sends the e-mail to the admin e-mail account. The GPS system recognizes the location of the accident area and specifies a red spot in the location. It confirms the accurate spot-accident detected as shown

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Fig. 8.3 Accident detection and sending alert algorithm

in Fig. 8.4. By using the proposed system, it is very easy to identify the accident location.

Conclusion The chance of trailing a life in today’s situation is very high. In this proposed method, real time lets the authoritative people willing to retrieve the location of the accident spot immediately supply medical treatment to the injured party in very little time. This system also deals with various issues, which include security and authentication of information. Proper implementation of this proposed work will preserve the best life-changing technology. The proposed system is to identify and send the location of the accident spot area immediately to all the victims’ emergency contact numbers and send the location to the required local authorities to deploy the resources. We can hope there will be fewer accidents the attack-like.

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Fig. 8.4 Red pin the GPS location of the accident detected

Future Scope This proposed work preserves the best technology ever, and the knowledge is capable of being expanded by introducing new technology built indirectly by the vehicle manufacturing company with some advancements. The proposed work will be useful for a variety of purposes, such as vehicle theft, which can be recognized easily by the GPS sensor present and different security alerts can be given to the proprietor if the vehicle crosses certain characterized speed limits.

References 1. Botta, A., de Donato, W., Persico, V., Pescape, A.: Integration of cloud computing and Internet of Things a survey. Future Generation Computer Syst., pp. 684–700 (2015) 2. Hernandez-Ramos, J.L., Bernabe, J.B., Skarmeta, A.: Army: architecture for a secure and privacy-aware lifecycle of smart objects in the Internet of My Things. IEEE Commun. Mag., pp. 28–36 (2016) 3. Yellamma, P., Anupama, P., Lakshmibhavani, K., Jhansi Siva Priya, U., Kazalalitha, Ch.: Implementation of E-voting system using block chain technology. J. Crit. Rev. 7(6), 865–870 (2020), ISSN-2394-5125 4. Sujatha, N.V.R., Suganya, K.S.: IOT: to enhance automatic accident notification using M2M technologies. Int. J. Sci. Eng. Res. 6(3), 1–4 2015/3 (2015). [8] 5. Sonika, S., Sathiyasekar, D.K., Jaishree, S.: Intelligent accident identification system using GPS, GSM modem. Int. J. Adv. Res. Comput. Commun. Eng. 3(2) (2014) (10)

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6. Tiwari, M., Garg, H., Tiwari, R.K., Gupta, S., Yadav, A.K., Deep, A., Jha, M.: Execution of accident vehicle tracking system and a keen application based following framework. IEEE Trans. Inf. Forensics Secur. (2017) 7. Yellamma, P., Abhinav, B., Jaya Vaishnavi, B., Ushaswini, G., Srinivas, M.: Forecasting techniques for sales prediction. Int. J. Adv. Sci. Technol. 29(6), 3042–3049 (2020) 8. Zargar, A.J., Singh, N.: Digital watermarking using discrete wavelet techniques with the help of multilevel decomposition technique. Int. J. Comput. Appl. 975, 8887 (2014) 9. Yellamma, P., Rajesh, P.S.S., Pradeep, V.V.S.M., Manishankar, Y.B.: Privacy preserving biometric authentication and identification in cloud computing. Int. J. Adv. Sci. Technol. 29(6), 3087–3096 (2020), ISSN: 2005-4238IJAST 10. Singh, N., Ahuja, N.J.: Bug model based intelligent recommender system with exclusive curriculum sequencing for learner-centric tutoring. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 14(4), 1–25 (2019) 11. Meng, J.: Plan of Vehicle situating System Based on ARM Wen Department of Physics and Electronic Information Engineering 12. Singh, O., Bommagani, G., Ravula, S.R., Gunjan, V.K.: Pattern based gender classification. Int. J. 3(10) (2013) 13. Gautam, R., Choudhary, S., Surbhi, Kaur, I., Bhusry, M.: Cloud based automatic accident detection and vehicle management system. In: Second International Conference on Science, Technology and Management, pp 341–352 (2015), ISSN-978-81-931039-6-8 14. Kashyap, A., Gunjan, V.K., Kumar, A., Shaik, F., Rao, A.A.: Computational and clinical approach in lung cancer detection and analysis. Procedia Comput. Sci. 89, 528–533 (2016) 15. Mallidi, S.K.R., Vineela, V.V.: IoT based smart vehicle monitoring system. Int. J. Adv. Res. Comput. Sci. 9, 738–741 (2018) 16. Mounika, J., Charanjit, N., Saitharun, B., Vashista, B.: Accident alert and vehicle tracking system using GPS and GSM. Asian J. Appl. Sci. Technol. 5, 81–89 (2021) 17. Wan, M.: Control cloud-data access privilege and anonymity with fully-anonymous attributebased-encryption. IEEE Trans. Inf. Forensics Secur., pp. 1–10 (2015)

Chapter 9

AEFA-ANN: Artificial Electric Field Algorithm-Based Artificial Neural Networks for Forecasting Crude Oil Prices Sarat Chandra Nayak, Subhranginee Das, Biswajit Sahoo, and B. Satyanarayana

Introduction The crude oil price has a direct impact on the economic situation of a country. The crude oil price depends upon various socio-economic, political factors, international law and global market scenario, therefore its price changes at random. Due to such random movement, the trend it follows is a nonlinear curve. Predicting a point on such a highly nonlinear curve is a difficult job. Nominal variation of crude oil price gives impact to the prices of petroleum products as well as the international economy. The volatility in crude oil price series occurs due to factors such as population density, demand–supply gap, political weathers and international law [1]. Consequently, an efficient prediction instrument is desired for prediction of crude oil price. Assuming linear association of past and current data, several statistical models are recommended in early days for forecasting financial data. However, these methods are not found promising in forecasting crude oil price series. Advances in intelligence techniques such as artificial neural networks (ANNs) have been considered as better substitutes S. C. Nayak (B) CMR College of Engineering & Technology, Hyderabad, India e-mail: [email protected] S. Das Department of computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India e-mail: [email protected] B. Sahoo School of Computer Engineering, KIIT University, Bhubaneswar, India e-mail: [email protected] B. Satyanarayana Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_9

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to statistical methods. Also, successful ANN applications are there in the literature for crude oil price forecasting [2–6]. The parameter fine-tuning (i.e. finding the optimal weight and biases) of ANN structure is a crucial aspect in ANN application that needs human expertise. Usually, gradient-based methods are used to accomplish this, but associated with few drawbacks such as slow convergence may land at local optima. Later, many evolutionary optimization techniques came forward which are inspired from natural phenomena and have been used as better substitutes of gradient-based methods [7]. Evolutionary learning methods such as genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) are more proficient methods in searching the optimal parameters of ANN. Since no single technique is found suitable in solving all sorts of problems, continuous improvements in existing methods have been carried out by researchers through enhancement in an algorithm [8, 9] or hybridization of them [10–13]. Recently, the artificial electric field algorithm (AEFA) has been anticipated as an optimization method inspired by the principle of electrostatic force [14]. AEFA is based on a strong theoretical concept of charged particles, electric field and force of attraction/repulsion between two charged particles in an electric field. The learning capacity, convergence rate and acceleration updates of AEFA have been established in [14] through solving some benchmark optimization problems. This work is an initiative towards investigating the potential of AEFA in finetuning the parameters of an ANN, thus designing a hybrid model called as AEFAANN. It is worth mentioning that, along with the parameters (weight and bias), another crucial factor such as deciding the optimal number of hidden neurons of ANN is also carried out by AEFA. The proposed AEFA-ANN is assessed through forecasting crude oil prices mined from US Department of energy: Energy Information Administration website: http://www.eia.doe.gov/. Data pre-processing, input selection, model design steps are also explained. The article is structured into four parts: Sect. 2 presents short descriptions about methods and materials followed by Sect. 3 which discusses experimental outcomes and Sect. 4 which gives the concluding remarks along with future scope.

ANN A typical ANN architecture is depicted in Fig. 9.1. The first layer links input variables of the given problem. The second layer helps to detain nonlinear associations among variables. The weighted output y j is calculated for each neuron j present in the hidden layer using Eq. 9.1. ( yj = f bj +

n { i=1

) wi j ∗ xi

(9.1)

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Fig. 9.1 Single hidden layer neural network

where xi is the ith input component, wi j represents weight value between ith input neuron and jth hidden neuron and b j represents the bias at node j. The nonlinear activation function is denoted as f . Suppose the hidden layer contains m numbers of nodes, then for the subsequent hidden layers there will be m numbers of inputs. Then for each neuron j of the next hidden layer, input is as in Eq. 9.2. ( yj = f bj +

m {

) wi j ∗ yi

(9.2)

i=1

This signal flows in the forward direction till it reaches the output neurons through each hidden layer. The output yesst is calculated using Eq. 9.3. ⎛ yesst = f ⎝bo +

m {

⎞ v j ∗ y j ⎠,

(9.3)

j=1

where v j is the weight between jth hidden to output neuron, y j represents the calculated weighted sum as Eq. 9.1, and bo is the bias for the output node. N Given a set of training samples S = {xi , yi }i=1 to train the ANN, let yi be the output of ith input sample, and yesst is the computed output of the same ith input, then the error is calculated by using Eq. 9.4. Errori = yi − yesst ,

(9.4)

The error value that is produced by nth training sample at the output of neuron i is defined by Eq. 9.5.

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Errori (n) = yi (n) − yesst (n)

(9.5)

Then the instantaneous error at neuron i is defined by Eq. 9.6. εi (n) =

1 Errori2 (n) 2

(9.6)

Hence, total instantaneous error of the whole network will be expressed as Eq. 9.7 ε(n) =

{

εi (n)

(9.7)

iεC

AEFA-ANN-Based Forecasting AEFA is designed on the principle of Coulomb’s law of electrostatic force [14]. It stimulates the charged particles as agents and measures their strength in terms of their charges. The particles are moveable in the search domain through electrostatic force of attraction/repulsion among them. The charges possessed by the particles are used for interaction, and positions of the charges are considered as the potential solutions for the problem. According to AEFA, the particle having the highest charge is considered as the best individual, and it attracts other particles having inferior charge and moves in the search domain. The mathematical justification of AEFA is illustrated in [14]. Here, we simulate a potential solution of ANN as a charge particle and its fitness function as the quantity of charge associated with that element. The velocity and position of a particle at time instant t are updated as per Eqs. 9.8 and 9.9, respectively. Vid (t + 1) = randi ∗ Vid (t) + accelerationid (t)

(9.8)

X id (t + 1) = X id (t) + Vid (t + 1)

(9.9)

The overall AEFA steps are shown in Fig. 9.2, and the high-level AEFA-ANNbased forecasting is presented by Algorithm 1. Algorithm 1: AEFA-ANN-based forecasting Begin Step 1: Set AEFA and ANN parameters Step 2: Select input data from the crude oil price time series /*Use sliding window method*/ Step 3: Normalization of input signal /*Use sigmoid data normalization method*/

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Step 4: Train ANN model with normalized input data and AEFA Step 5: Test the model with test data and record the error signal for performance analysis End

Experimental Results and Analysis The forecasting ability of AEFA-ANN is tested on four crude oil price datasets (http:// www.eia.doe.gov/). Inputs are carefully chosen (through sliding window) [15], raw data are gone through normalization process (sigmoid normalization) and then fed to the AEFA-ANN. The model is trained through AEFA-based learning and estimates an output. The deviation from actual output is considered as the error generated by the model. To ensure AEFA-ANN performance, four comparative models such as gradient descent ANN (GD-ANN), genetic algorithm-based ANN (GAANN), differential evolution-based ANN (DE-ANN) and PSO-ANN are developed in similar manner. Mean error from twenty experiments is summarized in Table 9.1. The best average errors are shown in bold. For all datasets, AEFA-ANN produced the best average errors. The AEFA-ANN estimated prices against actual prices are plotted in Fig. 9.3.

Conclusions This article presented an AEFA-ANN-based forecast for modelling and efficient prediction of crude oil price movements. AEFA is used to discover the most feasible ANN parameters along with the number of hidden neurons of a single hidden layer ANN, thus crafting an optimal ANN structure on the fly. Four comparative forecasts are developed in a similar manner. To evaluate the proposed and comparative models, experiments are conducted on real crude oil prices datasets considering different forecasting horizons. From exhaustive simulation studies, it is detected that the AEFA-ANN model is more efficient in catching the hidden patterns in the crude oil price series than others. The present study can be stretched with some improvised version of AEFA and adopting other neural models.

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Fig. 9.2 Process of AEFA Table 9.1 Mean error summarized Crude oil Error Model price series statistic GD-ANN Daily series Min Max

Weekly series

Monthly series

Annual series

GA-ANN

DE-ANN

PSO-ANN

AEFA-ANN

8.324e−05

8.6024e−05 7.3036e−06 7.236e−05

7.0522e−06

0.06475

0.03058

0.03622

0.03547

0.03682

Avg.

0.01572

0.01053

0.00975

0.00853

0.00486

Std. Dev

0.00845

0.00828

0.00635

0.00674

0.00328

Min

6.5305e−05 5.5124e−05 3.1276e−06 3.5347e−05 2.4555e−06

Max

0.08325

0.06436

0.05273

0.05832

0.05004

Avg.

0.06583

0.02504

0.03422

0.04835

0.02036

Std. Dev

0.03266

0.05374

0.05465

0.00769

0.00935

Min

4.2743e−03 4.2324e−05 4.2632e−05 4.3163e−04 2.1553e−04

Max

0.06972

0.06754

0.05338

0.05322

0.04307

Avg.

0.03875

0.03650

0.02377

0.01957

0.00836

Std. Dev

0.04235

0.02623

0.01847

0.01752

0.02113

Min

4.4841e−04 4.0834e−04 3.4735e−03 5.2375e−03 3.1455e−04

Max

0.05635

0.03562

0.04005

0.04079

0.02635

Avg.

0.05866

0.04092

0.04365

0.03374

0.03057

Std. Dev

0.01098

0.01145

0.01044

0.01004

0.00632

9 AEFA-ANN: Artificial Electric Field Algorithm-Based Artificial …

(a) Forecasting from daily series

(b) Forecasting from weekly series

(c) Forecasting from monthly series

(d) Forecasting from annual series

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Fig. 9.3 AEFA-ANN estimation vs actual closing prices

References 1. Nelson, Y., Stoner, S., Gemis, G., Nix, H.D.: Results of Delphi VIII Survey of Oil Price Forecasts. Energy Report, California Energy Commission (1994) 2. Mahdiani, M.R., Khamehchi, E.: A modified neural network model for predicting the crude oil price. Intellect. Econ. 10(2), 71–77 (2016) 3. Chiroma, H., Abdul-kareem, S., Shukri Mohd Noor, A., Abubakar, A.I., Sohrabi Safa, N., Shuib, L., Fatihu Hamza, M., Ya’uGital, A., Herawan, T.: A review on artificial intelligence methodologies for the forecasting of crude oil price. Intell. Autom. Soft Comput. 22(3), 449– 462 (2016) 4. Sivaprakash, J., Manu, K.S.: Forecasting crude oil price using artificial neural network model. Asian J. Manag. 12(3), 321–326 (2021) 5. Zargar, A.J., Singh, N.: Digital watermarking using discrete wavelet techniques with the help of multilevel decomposition technique. Int. J. Comput. Appl. 975, 8887 (2014) 6. Karasu, S., et al.: A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy 212,

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118750 (2020) 7. Singh, N., Ahuja, N.J.: Bug model based intelligent recommender system with exclusive curriculum sequencing for learner-centric tutoring. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 14(4), 1–25 (2019) 8. Opara, K., Arabas, J.: Comparison of mutation strategies in differential evolution–a probabilistic perspective. Swarm Evol. Comput. 39, 53–69 (2018) 9. Rashedi, E., Rashedi, E., Nezamabadi-Pour, H.: A comprehensive survey on gravitational search algorithm. Swarm Evol. Comput. 41, 141–158 (2018) 10. Nayak, S.C., Misra, B.B.: A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction. Financ. Innov. 5(1), 1–34 (2019) 11. Singh, O., Bommagani, G., Ravula, S.R., Gunjan, V.K.: Pattern based gender classification. Int. J. 3(10) (2013) 12. Nayak, S.C, Das, S, Ansari, M.D.: TLBO-FLN: teaching learning based optimization of functional link neural networks for stock closing price prediction. Int. J. Sens., Wirel. Commun. Control, Bentham Sci. (2019) 13. Kashyap, A., Gunjan, V.K., Kumar, A., Shaik, F., Rao, A.A.: Computational and clinical approach in lung cancer detection and analysis. Procedia Comput. Sci. 89, 528–533 (2016) 14. Yadav, A.: AEFA: artificial electric field algorithm for global optimization. Swarm Evol. Comput. 48, 93–108 (2019) 15. Nayak, S.C., Misra, B.B., Behera, H.S.: ACFLN: artificial chemical functional link network for prediction of stock market index. Evol. Syst. 10(4), 567–592 (2019)

Chapter 10

A Critical Survey on Machine Learning Paradigms to Forecast Software Defects by Using Testing Parameters Y. Prasanth, T. Satya Sai Vinuthna, P. Komali, K. Kavya, and N. Aneera

Introduction With the growth of the internet and the popularity of computers, today’s computers face significant security challenges, the primary cause of which is the exponential growth of malicious code [1]. Malicious code is computer code that is written with the intent of posing a security risk to a computer or network. Malicious sharing applications, adware Trojans, and viruses are commonly contained in it. On the PC side, there were 14,098,000 malicious programmes intercepted, with an average of 779,000 new malicious programmes intercepted every day. On the PC side, there were 14,098,000 malicious programmes, with an average of 779,000 new malicious programmes intercepted every day [2–4]. McAfee Laboratories uncovered the newest malware in history in the fourth quarter of 2017, with a total of 63.4 million new samples [5–7]. McAfee Laboratories currently reports an average of eight new malware samples per second, up from four new samples per second previously. Malware not only causes users to lose money, but it also places a lot of strain on malicious software anti-killing technologies due to rapid changes. The current technological demand makes it difficult to detect malware until it infects the host [8]. Since most malware cannot talk, detecting malware-infected hosts in network traffic will compensate for this shortcoming with externally hosted command and control (C&C) servers infecting the device. The command-and-control server (C&C server) is the command-and-control centre for malware. It is also where malware collects data. The managed host sends a link request to the C&C server after being infected by malware. The external traffic created by the link is malicious [9, 10]. We can currently classify malware in two ways. The first is to use blacklists to filter malicious domain names, and the second is to use rules to balance malicious external Y. Prasanth (B) · T. Satya Sai Vinuthna · P. Komali · K. Kavya · N. Aneera Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh 522502, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_10

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traffic. Both options have several disadvantages [11]. The blacklist-based filtering scheme can only detect malicious external traffic when it links to a known malicious website, and it doesn’t consider domain name changes. The security practitioner must evaluate each sample one by one, which takes a lot of time and makes it difficult to detect the variant’s malicious external link traffic based on the feature selection theme [12–15].

Theoretical Analysis As part of the technique to detect the malware we have used algorithms like Random Forest, KNN, XGboost, etc., We can also use the feature extraction technique to extract the features from the data set. To detect the malware in the system the preprocessed data set is given to the classifier to each of the algorithms. Based on the accuracy the features are extracted on the ranking of the attributes for the algorithms [16–18]. The features which are extracted are then given to the feature selection tool. Here we have developed a new approach to detect the malware. By classifier training, the original data set classified by using some algorithms in machine learning and gives the malware percent.

Classification Algorithms Characterization calculations in AI utilize input preparing information to foresee the probability that ensuing information will can be categorized as one of the foreordained classes. Perhaps the most well-known employment of arrangement is separating messages into “spam” or “non-spam.” So, an order is a type of “design acknowledgment,” with grouping calculations applied to the preparation information to locate a similar example (comparative words or slants, number successions, and so forth) in future arrangements of information. The issue in perceiving the malware can be seen as a worm grouping issue. To identify the malware here we have utilized a portion of the techniques like KNN, Decision Tree, and XGboost. These calculations go about as a classifier and suspicion of exactness [17–19]. K-Nearest Neighbour (KNN) K-Nearest Neighbor depends on the Supervised Learning procedure and perceived as the least complex based AI calculation. K-NN expects the up to minute information of equities and right now present information and store into the class that is well on the way to the accessible classifications. K-NN calculation stores all the information which is accessible and accepted dependent on uniformity it classifies. This implies when new information is seen then it very well may be handily partitioned into

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a decent suite classification by utilizing K-NN calculation. K-NN calculation can be utilized to arrange however generally it is utilized for the Classification issues. K-NN is a non-parametric calculation, which implies it doesn’t expect on present information. KNN calculation doesn’t gain from preparing set quickly rather it stores the dataset, it follows up on the dataset so it is named an apathetic sprinter calculation [19]. Random Forest Random Forest is in a two-step process, 1. 2. Step-1: Step-2:

Combination of N decision tree Prediction of each tree First select the arbitrary K points(data) among the preparation set. Build the choice trees associated with the recognized information focuses which are subsets. Step-3: Finally pick the number N for choice trees that you like to build. Step-4: Repeat Step 1 and 2. Step-5: For new information focuses, discover the forecasts of every choice tree, and allocate the new information focuses to the class that successes the greater part casts a ballot. XG Boost XG Boost has recently diminishing applied machine learning and Kaggle competitions for structured or tabular data. eXtreme Gradient Boosting is called as XGBoost. The speed and performance of the gradient boosted decision trees are the main implementation of the XGBoost. Reasons for using XGBoost: Speed of the Execution and Performance of the obtained model. It’s a new algorithm and having accuracy as same as a random forest so we compared the accuracy of both the algorithms; so we use XG BOOST but it resulted in low accuracy compared to others.

Methodology To detect the undetermined malware using techniques in machine learning, a flow chart of our approach is shown below. It includes preprocessing of the dataset, promising feature selection, training of the classifier, and detection of advanced malware as shown in Fig. 10.1.

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Fig. 10.1 Architecture of malicious software detection

Accuracy Prediction The ratio of classification accuracy is the number of predictions that are accurate to the total quantity of samples. It works well if they are an equal quantity of samples. Here in our research, the accuracy is predicted using the classifier. The confusion matrix gives us the matrix as the output it tells the overall performance of the model. To be serious, the traditional and pretty good old technology of detecting the malicious code has always very low accuracy and it has got enough capability to detect for new variants. True Positives: Assumption YES output YES, True Negatives: Assumption NO output NO, False Positives: Assumption YES output NO, False Negatives: Assumption NO output YES, Positive True Rate-Sensitivity. It is defined as True Positive Rate determines the number of positive data points that are correctly identified as true, as per the data point which is given as input. Negative True Rate-Specificity: It is defined as False Positive Rate determines the number of false data points that are identified as false, as per the point of data which are given as input.

Features Extraction The main aim of feature extraction is to reduce the number of resources in the input data set. But here the ultimate downside is with the examination of complicated information from the participation of number of variables. A large amount and the memory is required for analyzing the greater number of variables and can cause a classifier algorithm for training samples and an indication of poor to the new samples. It is used for developing methods of the variables with still saying that the data is

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sufficiently inaccurate. The key for effective model construction is done by optimized feature extraction.

Investigationson Data Behaviour After selecting input data we have to use a certain method to calculate the accuracy value. There are various methods like using the KNN model, SVM model, Decision tree, and boost. In our research, we have used the concept of term extracting features to predict the accuracy of the level of the malware which is present in the software.

Preprocessing Every real-time data contains noise such as unwanted words or unwanted attributes or unwanted spaces. As this din might affect our result, we have to preprocess the data to remove this noise. These attributes also increase processing time to remove these unwanted attributes we use classifier remove. Capturing the run time behavior: The run-time activities describe the behaviour file that is executing. Feature Extraction: Feature Extraction module selects run time features from pre-processing data sets. Feature extraction is a common term for various ways of building combinations of the attributes to get around these with sufficient accuracy while describing the data. Classifier Training: To assess the proposed technique, the original data set is classified by using machine learning algorithms and gives the classification of malware files and benign files.

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Flow Chart

START

INPUT AS DATASET PREPROCESSING DATASET

CALCULATING ACCURACY

EXTRACTING FEATURES

FEATURES TO CLASSIFIER

DISPLAYING ACCURACY OF MALWARE

STUDY REPORT

STOP

Table: Accuracy

Algorithms

Accuracy (%)

Extracted features accuracy (%)

Random forest

97

98.2

Naïve Bayes

96.3

96.7

Support vector machine

97.2

97.8

Decision tree

92.6

94.2

J48

98.1

98.4

XGBoost

97.3

97.9

Logistic

87.4

89.5

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Experimental Results and Discussions In our research, we have used two different algorithms to calculate the comparative report. KNN Algorithm Sensitivity: 97.3% Specificity: 95.3% Random Forest Sensitivity: 99.2% Specificity: 96.09% From the above results, we can observe there is quite some difference between the outputs of two models for the same input applications. This is mainly because our second model works well if we want to calculate malware of the software. That is, it only focuses on malware recognition. Whereas our first model works well if we want to calculate the same worm/virus but not efficient. After extracting features the accuracy has improved and hence, we want to prove that the algorithms we used can tell the percent of malware. So, we get accurate results with second as it also considers context. The difference between the results of two models for the same input is because of the different functions of these models. Hence it is preferred to use the second model over the first model.

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Summary A sort of similarity analysis is also conducted using various methodologies and found how malware is detected by this. Usually, the first step is to process the data set that is given as input which is used to recognize the malware in the further process; and the prediction we found in the further step leads to analysis and prediction of each algorithm on the data set. Though the predicted accuracy is not sufficient and on the further step features are extracted based on the variable ranking. The classifier technique to classify the malware.

Conclusion This research mainly focuses on detecting malicious software using machine learning techniques. We have used different models to find the similarity and we have also used some filter-based technique to achieve our goal. Based on the results talked over in Chap. 6 we can conclude that the second model is more accurate or precise compared to the first model. Some applications of this research are the identification of plagiarism in software, it can be used during searching for certain applications, it can be used during the testing phase of software to decrease the testing size, and so on.

Recommendations As we have done two models in which one is effective in detecting the malware and the second model is least as compared to the first one. The second model also detects the malware to some extent. But as an advancement of this research, the model that we recommend is a random forest.

References 1. Zhao, D., Traore, I., Sayed B., et al.: Botnet detection based on traffic behavior analysis and flow intervals. Comput. Secur. 39, 2–16 (2013).Viewat: Publisher Site | Google Scholar 2. Saracino, A., Sgandurra, D., Dini, G., Martinelli, F.: MADAM: effective and efficient behaviorbased android malware detection and prevention. IEEE Trans. Dependable Secure Comput. 15(1), 83–97 (2016). View at: Publisher Site | Google Scholar 3. Yu, Y., Long, J., Cai, Z.: Network intrusion detection through stacking dilated convolutional autoencoders. Secur. Commun. Netw. 2017, 10p (2017), Article ID 4184196. View at: Publisher Site | Google Scholar

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4. Singh, N., Ahuja, N.J., Kumar, A.: A novel architecture for learner-centric curriculum sequencing in adaptive intelligent tutoring system. J. Cases Inf. Technol. (JCIT) 20(3), 1–20 (2018) 5. Souri, A., Hosseini, R.: A state-of-the-art survey of malware detection approaches using data mining techniques. Human-Centric Comput. Inf. Sci. 8(1), 3 (2018).View at: Publisher Site | Google Scholar 6. 360 Internet Security Center: China Internet Security Report for the Third Quarter of 2017 (2017) 7. Mishra, B., Singh, N., Singh, R.: Master-slave group based model for co-ordinator selection, an improvement of bully algorithm. In: 2014 International Conference on Parallel, Distributed and Grid Computing, pp. 457–460. IEEE (2014) 8. Hu, X., Jang, J., Stoecklin M.P. et al.: BAYWATCH: robust beaconing detection to identify infected hosts in large-scale enterprise networks. In: Proceedings of the 201646th Annual IEEE/IFIP International Conference on Dependable Systems andNetworks (DSN), pp. 479– 490, Toulouse, France, June 2016.View at: PublisherSite | Google Scholar 9. Sahu, H., Singh, N.: Software-defined storage. In: Innovations in Software-Defined Networking and Network Functions Virtualization, pp. 268–290. IGI Global (2018) 10. The UNSW-NB15 Data Set: https://www.unsw.adfa.edu.au/unsw-canberracyber/cybersecu rity/ADFA-NB15-Datasets/ 11. Ahmed, M., Laskar, R.H.: Eye detection and localization in a facial image based on partial geometric shape of iris and eyelid under practical scenarios. J. Electron. Imaging 28(3), 18, 033009 (2019) 12. Shaik, A.S., Bhavani, M., Ravi Kiran, K.: Smart pick and drop intimation system of school children. Indian J. Sci. Technol. 10(46), 1–7 (2017). https://doi.org/10.17485/ijst/2017/v10i46/ 117158. ISSN(Print):0974-6846, ISSN(Online):0974-5645 13. Debnath, S., Talukdar, F.A., Islam, M.: Combination of contrast enhanced fuzzy cmeans (CEFCM) clustering and pixel based voxel mapping technique (PBVMT) for three dimensional brain tumour detection. J. Ambient Intell. Humanized Comput. 12(2), 2421–2433 (2021) 14. Shaik, F., Sharma, A.K., Ahmed, S.M., Gunjan, V.K., Naik, C.: An improved model for analysis of diabetic retinopathy related imagery. Indian J. Sci. Technol. 9, 44 (2016) 15. Pal, S., Sillitti, A.: A classification of software defect prediction models. In: 2021 International Conference “Nonlinearity, Information and Robotics” (NIR), 2021, pp. 1–6. https://doi.org/10. 1109/NIR52917.2021.9666110 16. Hoang, T., Khanh Dam, H., Kamei, Y., Lo, D., Ubayashi, N.: DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction. In: Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), Montreal, QC, Canada, 25–31 May 2019, pp. 34–45 17. Gunjan, V.K., Zurada, J.M., Raman, B., Gangadharan, G.R.: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Springer International Publishing (2020) 18. Qiu, S., Lu, L., Cai, Z., Jiang, S.: Cross-project defect prediction via transferable deep learninggenerated and handcrafted features. In: Proceedings of the 31st International Conference on Software Engineering & Knowledge Engineering (SEKE 2019), Lisbon, Portugal, 10–12 July 2019; pp. 1–6. Available online: http://ksiresearch.org/seke/seke19paper/seke19paper_70.pdf. Accessed 17 Dec 2020 19. Ahmed, S.M., Kovela, B., Gunjan, V.K.: IoT based automatic plant watering system through soil moisture sensing—a technique to support farmers’ cultivation in rural India. In: Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies, pp. 259–268. Springer, Singapore (2020)

Chapter 11

Low-Power Comparator-Triggered Method of Multiplication for Deep Neural Networks K. Mariya Priyadarshini, C. Santosh, G. U. S. Aiswarya Likitha, I. B. V. Sai Srikar, and Peram Ramya

Introduction Multipliers play a vital role in today’s era of digital signal process and numerous other applications, in high performance systems like microprocessor, DSP, etc., multipliers and summers became two crucial operators binary in many of the machine learning (ML) and artificial intelligence (AI) applications. Statistics identify greater than 80% programming methods in microprocessor and mainstreams of ML algorithms implement multiplications and summations [1]. These mathematical operators dominate the execution speed. The demand for high speed processors is growing to expand ML and AI applications. Power consumed should be low to meet the standards of customer; this can be met by optimizing the set of operators in the flow of multiplication [2–4]. Hence, the necessity of high speed and low-power multiplier has increased. Designers mainly focus on extraordinary speedy and power competent circuit strategies [5–8]. First we will understand diverse multipliers like Booth multiplier, Wallace tree multiplier, sequential method of multiplication, and combinational method of multiplication. The vital part of almost all sort of fashionable mathematical structures is floating point multipliers. All most all data intent systems, like deep neural networks (D-NNs), go through the majority of their resources and energy take under consideration [9–12]. The error-resilient nature of those applications usually suggests using approximate computing to boost the performance and space of floating point multipliers. Earlier work has shown that using hardware-oriented approximation for computing the fraction (mantissa) product might end with energy reduction of a main system at the expense of a reasonable computational error [13, 14]. This helps us understand the layout of an approximate comparator used inside the floating point multipliers K. Mariya Priyadarshini (B) · C. Santosh · G. U. S. Aiswarya Likitha · I. B. V. Sai Srikar · P. Ramya Koneru Lakashmaiah Education Foundation, Vaddeswaram, AP, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_11

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for doing mantissa product. First, we appear to display the use of actual comparators to improve floating point multiplier power, area, and delay. After that, the planning area of approximate comparators for approximate comparator-enabled multipliers (AxCEM) [15] would appear to be explored. The results of the simulation here show that the proposed design can give us a 66% decrease in power dissipation, another 66% decrease in die area and a 71% delay reduction [16, 17]. The accuracy loss in DNN applications due to the proposed AxCEM may also be smaller than 0.06% [18–20] relative to the state-of-the-art approximate floating point multipliers. Energy consumption is the main issue in the design of VLSI, especially in nanoscale devices, while the continued demand for higher process power for growing applications is increasing. Computing systems are now increasingly firmly calculated in wireless and battery-operated devices such as mobile phones. Research reveals that for many applications, such as data processing and transmission applications, correct computing units are not required. They will tolerate inaccuracy [21]. In several levels of types, approximation strategies are applied to include a trade-off between the desired device parameters such as power consumption, latency, and accuracy, provided by several metrics such as mean error distance (MED), mean square error (MSE), and mean relative error distance (MRED) [22]. Floating point multiplication is the DSPs, multimedia, and it will cause heavy delay and energy consumption in many applications are the key factor. The primary resource-intensive component of floating point multiplications is that the fraction product calculation unit intensifies nearly 80th of the overall device energy [23]. Therefore, various approximate multiplications within the temporal order or functional behaviors have been designed by several researchers to minimize logic complexity or voltage scaling techniques [24–26]. In the creation of estimated floating point multipliers, there has also not been much effort. There are two key steps: (1) adding exponents and (2) multiplying the mantissa. So, we did a 23-bit and 53-bit multiplication of single accuracy and double accuracy (mantissa), respectively. And for a few multipliers, such as booth, Wallace, in order to acquire partial products, a few approximate multiplier blocks are granted [27], and some approximate adders are also proposed to assemble these partial products [28]. In order to minimize its power consumption and silicon footprint when keeping the error at a reasonable range, we decided to use the approximation techniques within the one described. In this, we bring in an approximate multiplier allowed by the comparator, called AxCEM, that will subtract the delay and energy consumption. Using an estimated comparator [29–31], we put forward a fully unique floating multiplier. We used an approximate comparator instead of the mantissa multiplication (23bit single accuracy multiplier and 52-bit double accuracy multiplier in IEEE-754), which decreases the delay and provides an energy-efficient multiplier. The advantage of this paper is that: (1) We studied the use of comparators to improve the power, area, and delay of floating point multipliers as a novel approximation technique. (2) We explore the design space of approximate comparators for designing effective approximate comparator-enabled multipliers (AxCEM) [32] to further enhance the circuit metrics. By performing gate-level logic simplification for CEM, we bring in

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two designs called AxCEM1 and AxCEM2. (3) Lastly, we show that, when used in DNN applications, the proposed designs show less than 0.06 accuracy loss, though showing improved performance compared to the state-of-the-art estimated floating point multipliers [33] (in both training and inference steps).

Floating Point Representation (IEEE754) See Fig. 11.1. For two numbers, the product of X and Y can be realized with three simple operations. Z63 in Fig. 11.2 represents sign value of the product and is easily calculated by EX-oring X63 and Y63 . Summing the exponential terms of X and Y results in exponential bits of final product. 52 bits of mantissa equal to the product of input (i/p) segments. Related Multipliers This section reviews some of the prior research that focuses on approximate and accurate multipliers. In [34], authors proposed circuit designs that permit the implementation of hardware to even with more extensive voltage. A dynamic and fast bit selection scheme to reduce multiplier size is defined in [35]. In [36], the authors designed a versatile multiplication approximation method that reduced the total number of intermediate AND gates. In [37], less-powered micro-level cells are proposed and later linked to create an effective multiplier. Papers [38] used incorrect summers, such as CMA [39] and ICAC [23], to sum up and miss intermediary AND products, otherwise few LSBs. Reference [40] I. Mani et al., correspondingly, suggested R-MAC, C-FPU, C-MUL, and R-MAC. In [41], Jiao, et al. suggested a multiplier that unlocks the openings of calculation reuse and strengthens them by performing 16-BIT

Sign (1bit)

X15 32-BIT

Sign (1 bit)

X31 64-BIT

Sign (1 bit)

X63

Exponent (5 bit)

X14…X10 Exponent (8 bit)

X30…X23 Exponent (11 bit)

X62 ........... 52

Mantissa (10 bit)

X9… ................0 Mantissa ( 23 bit)

X22… ................... 0 Mantissa ( 52 bit)

X51… ............... 0

Fig. 11.1 IEEE 754 standard representation of 16-bit, 32-bit, and 64-bit floating point numbers

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Fig. 11.2 Multiplication of A and B 64-bit floating point bits according to IEEE 754 standard [45]

assumed design. Many previous works are suggested to place on estimated computation on D-NNs manipulating output parameters due to the natural versatility of neural networks (NNs) to failures. Zhang et al. concentrated on improving power usage and improving speed by controlling memory access is not critical [42]. In most D-NNs, multiplication of floating numbers has become a leading block that continues to consume a significant part of the dynamism and production cost of D-NN. Before employing inaccurate procedure of multiplication in D-NNs, a keen work needs to be carried in minimizing power utilization. From, Sarwar et al. proved any imprecise method of multiplication using the idea to share computational blocks can back up power. Neshat pour, et al., explored on iterative C-NN by re-formulating single forward feedback system by cascading micronetworks serially. This allows C-NN to lay off with very few computations.

The Comparator-Triggered Multipliers (CTM) We recommend replacing accurate multiplier with CTM module. A CTM generates output LSB bits (0 to 51) of product bits by considering mantissa X [0–51] and Y [0–51]. After comparing them, the output Z[0–51] will be the lesser value among X and Y. Figure 11.3 displays register transfer logic (RTL) level of 64-bit method of multiplication. A CTM is advised over generic structures of multiplication as it consumes less power and silicon cost. Owing to the fact that the CTM logic requires to perform X < Y no extra logic circuitry is necessary for outputs like X = Y and X > Y. The constraints over which the CTM shows low power and less error over exact multipliers are: (1) When the entire XOR difference between X and Y is noteworthy, (2) Any one of the operand among X and Y is between 1 and 2 or any value closer to them.

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Fig. 11.3 RTL-level diagram of 64-bit CTM

In Fig. 11.4, bit-wise operations performed on 64-bit operands using CTM method of multiplication is shown.

Fig. 11.4 Bit-wise operation of 64-bit float numbers using CTM

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Experimental Results and Analysis Simulations are done to prove the efficiency of proposed multiplier with array and Vedic methods of multiplication. Table 11.1 shows dissipation of power due to switching of gates. Power supply is changed by varying voltage from 3.2 V down to 1 V for performing power analysis on array, Vedic, and comparator-triggered multipliers. For 32-bit CT multiplier, array shows a difference of 26.6nW, Vedic multiplier shows a difference of 61.85nW, and array multiplier shows a difference of 74.3nW with a voltage difference of 2.2 V (3.2–1 V). For 64 bits and 128 bits, no much increase is observed for proposed multiplier when compared with the other two techniques of multiplication. Figures 11.4, 11.5, and 11.6 shows the look up table) (LUT) utilization of multipliers when simulated and synthesized using Xilinx ISE tool. The number of LUTs consumed by array, Vedic, and CT multipliers are 408, 276 m and 196. It can be inferred that approximate multipliers are more efficient than accurate method of multiplication in terms of power and area (with reference to LUT utilization) (Table 11.2). On chip power of CTM is reduced by 22.5% when compared with Vedic multiplier and 40.7% of power is reduced by CTM when compared with array multiplier. An amount of 2.04 W of static power is reduced by proposed method when compared to Vedic multiplier and an amount of 6.52 W is diminished by proposed method when compared with array multiplier.

Conclusion From the results and discussions made in the above session, it can be concluded that approximate method of multiplication yields less dissipation of power in terms of switching power, on chip, and static power. As major portion of power is consumed by multipliers in machine learning (ML) and artificial intelligence (AI)-based application, the CTM helps in high frequency operations with less on-chip power. The number of logic slices utilized by proposed method of multiplication is reduced by 33% when compared with Vedic multiplier and 44.4% when compared with array multiplier. Finally, we would like to conclude that comparator-triggered multiplier can be replaced with any method of approximate and accurate multipliers.

119.6

3.2

99.67

91.5

97.91

109.8

117.4

2.8

89.22

107.06

2.6

3

79.77

84.16

95.7

100.9

71.64

69.3

57.23

53.45

2.2

85.9

2

37.82

49.16

2.4

68.6

83.16

1.6

64.14

1.4

1.8

45.3

58.9

1

1.2

42.8

42.1

39.3

38.3

36.18

34.3

30.8

29.7

24.6

22.9

21.1

16.2

Proposed multiplier

VDD (V)

Vedic multiplier

Power dissipation for 32-bit (nW)

Array multiplier

Voltage

228.4

224.4

209.7

204.4

192.8

182.8

164.1

158.8

131.1

122.5

112.6

86.68

Array multiplier

180.4

177.2

165.6

161.4

152.3

144.3

129.6

125.4

103.5

96.7

88.9

68.4

Vedic multiplier

84.8

83.3

77.9

75.9

71.6

67.9

60.9

59

48.7

45.5

41.8

32.1

Proposed multiplier

Power dissipation for 64-bit (nW)

Table 11.1 Comparison of switching power dissipation of 32-bit, 64-bit, and 128-bit multiplication techniques

251.1

246.7

230.5

224.8

212

201

180.5

174.6

144.2

134.6

123.8

95.3

Array multiplier

209.3

205.61

192.15

187.36

176.73

167.51

150.44

145.53

120.18

112.24

103.23

79.422

Vedic multiplier

94.2

92.6

86.5

84.4

79.6

75.4

67.7

65.5

54.13

50.5

46.5

35.7

Proposed multiplier

Power dissipation for 128-bit (nW)

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Fig. 11.5 LUT utilization for Vedic multiplier

Fig. 11.6 LUT utilization of proposed multiplier

Table 11.2 Summary of power dissipation and LUT utilization

Parameters

Vedic

Array

Proposed

On chip power utilized (W)

99.2

129.6

76.8

Static power dissipation (W)

8.96

13.44

6.92

Slice LUT

276

408

196

Logic slice

128

160

103

LUT as logic

75

90

50

Bounded IOB

320

320

320

References 1. Xu, S., Schafer, B.C.: Exposing approximate computing optimizations at different levels: From behavioral to gate- level. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 25(11), 3077–3088 (2017)

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17. Venkatachalam, S., Lee, H.J., Ko, S.-B.: Power efficient approximate booth multiplier. Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), pp. 1–4, (2018) 18. Alouani, I., Ahangari, H., Ozturk, O., Niar, S.: A novel heterogeneous approximate multiplier for low power and high performance. IEEE Embedded Syst. Lett. 10(2), 45–48 (2018) 19. Garg, B., Patel, S.: Reconfigurable rounding based approximate multiplier for energy efficient multimedia applications. Wirel. Pers. Commun. (2021). https://doi.org/10.1007/s11277-02008051-1 20. Ernest Ravindran, R.S., Priyadarshini, M., Mahesh, K., Vamsi, V.K., Eswar, C., Yasaswi, B.: A novel 24T conventional adder vs low power reconstructable transistor level conventional adder. Int. J. Eng. Adv. Technol. (IJEAT), 8(5), 398–402 (2019), ISSN: 2249-8958 21. Mariya Priyadarshini, K., Ernest Ravindran, R.S., Ratna Bhaskar, P.: A detailed Scrutiny and reasoning on VLSI binary adder circuits and architectures. Int. J. Innov. Technol. Exploring Eng. 8(7), 887–895 (2019) 22. Ernest Ravindran, R.S., Mariya Priyadarshini, K., Thanusha Sai, A., Shiny, P., Sabeena, S.K. Design of finite field multiplier for efficient data encryption. Int. J. Adv. Sci. Technol. 28(20), 42–52 (2019) 23. Mariya Priyadarshini, K., Ernest Ravindran, R.S., Nanda, I.: A novel two level edge activated carry save adder for high speed processors. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 11(4) (2020) 24. Jency, R., Sathish, K.: A survey paper on modern technologies in fixed-width multiplier. In: 4th International Conference on Signal Processing, Communications and Networking (ICSCN2017), Chennai, India, 16–18 March 2017 25. Siva Kumar, M., Inthiyaz, S., Aditya, M., Rupanjani, P., Aravind, B., Mukesh, M., Tulasi, S.K.: Implementation of GDI logic for power efficient SRAM cell with dynamic threshold voltage levels. Int. J. Emerg. Trends Eng. Res. 7(12), 902–906 (2019) 26. Aditya, M., Rao, I.V., Balaji, B., John Philip, B., Ajay Nagendra, N., Krishna, S.V.: A novel lowpower 5th order analog to digital converter for biomedical applications. Int. J. Innov. Technol. Exploring Eng. 8(7), 217–220 (2019) 27. Balaji, B., Aditya, M., Adithya, G., Sai Priyanka, M., Ayyappa Vijay, V.V.S.S.K., Chandu, K.: Implementation of low-power 1-bit hybrid full adder with reduced area. Int. J. Innov. Technol. Exploring Eng. (2019) 28. Havaldar, S., Gurumurthy, K.S.: Design of Vedic IEEE 754 floating point multiplier. In: IEEE International Conference on Recent Trends in Electronics Information Communication Technology, May 20–21, 2016, India 29. Aditya, M., Bhavitha, P., Pgopi, Kiran Babu, P., Pavan, P., Teja Sai Varma, B.: PV variations of pulsed latch circuits. Int. J. Innov. Technol. Exploring Eng. 8(6), 859–862 30. Xi, Y., Tang, X., Li, Z., et al.: Application of digital signal processing tools for the detection of voltage sag/swell. Int. J. Electr. Eng. Educ. 55, 186–209 (2018) 31. Nain, P., Virdi, G.S.: Multiplier-accumulator (MAC) unit. Int. J. Dig. Appl. Contemp. Res. 5(3) (2016). Website: www.ijdacr.com 32. Kiran Kumar, E., Aditya, M., Ernest Ravindran, R.S., Sravani, A., Meenakshi, D., Manoj, R.V.: Design and analysis of carry look-ahead adder with reconfigurable approximation. Int. J. Emerg. Trends Eng. Res. 8(7), 3045–3048 33. Mariya Priyadarshini, K., Ernest Ravindran, R.S., Atindra Chandra Sekhar, M., Sai Kalyan, P.J.V., Rahul, G.: A high-speed precision-controllable approximate 16 bit multiplier. Int. J. Adv. Sci. Technol. 28(20), 31–41 (2019) 34. Neshatpour, K., Behnia, F., Homayoun, H., Sasan, A.: ICNN: an iterative implementation of convolutional neural networks to enable energy and computational complexity aware dynamic approximation. In: Proceedings of the 2018 Design, Automation Test in Europe Conference Exhibition (DATE), Dresden, Germany, 19–23 March 2018; pp. 551–556 35. Jiao, X., Akhlaghi, V., Jiang, Y., Gupta, R.K.: Energy-efficient neural networks using approximate computation reuse. In Proceedings of the 2018 Design, Automation Test in Europe Conference Exhibition (DATE), Dresden, Germany, 19–23 March 2018; pp. 1223–1228

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36. Rathod, R., Ramesh, P., Zele, P.S., Annapurna, K.Y.: Implementation of 32-bit complex floating point multiplier using Vedic multiplier, array multiplier and combined integer and floating point multiplier (CIFM). In: 2020 IEEE International Conference for Innovation in Technology (INOCON), BANGLURU, 2020, pp. 1–5 37. Desai, S., Bhatia, Q.: Design and simulation of pipelined floating-point multiplier using logisim. Int. J. Recent Technol. Eng. (IJRTE), ISSN: 2277-3878, 8(5), (2020) 38. Sarwar, S.S., Venkataramani, S., Raghunathan, A., Roy, K.: Multiplier-less artificial neurons exploiting error resiliency for energy-efficient neural computing. In Proceedings of the 2016 Design, Automation Test in Europe Conference Exhibition (DATE), Dresden, Germany, 14–18 March 2016; pp. 145–150 39. Jaiwal, M.K., Cheung, R.C.: Area-efficient architectures for double precision multiplier on FPGA, with run-time-recon-figurable dual single precision support. Microelectron. J. 44(5), 421–430 (2013) 40. Ansari, M.S., Jiang, H., Cockburn, B.F., Han, J.: Low-power approximate multipliers using encoded partial products and approximate compressors. IEEE J. Emerg. Sel. Top. Circuits Syst. 404–416 (2018) 41. Arulkarthick, V.J., Rathinaswamy, A.: Delay and area efficient approximate multiplier using reverse carry propagate full adder. Microprocess. Microsyst. 74 (2020), ISSN 0141-9331 42. Goswami, S.S.P., Paul, B., Dutt, S., Trivedi, G.: Comparative review of approximate multipliers. In: 2020 30th International Conference Radioelektronika (RADIOELEKTRONIKA), Bratislava, Slovakia, 2020, pp. 1–6. https://doi.org/10.1109/RADIOELEKTRONIKA49387. 2020.9092370

Chapter 12

Assembly Line Implementation for IOT Applications N. Siddaiah, P. Pardhasaradhi, M. Phanigopi, Y. Vasanthi, and Y. Deepika

Introduction With emerging technological devices and enormous shift in Industrial strategies, slight improvements are to be made from time to time and engineers are facing challenges to overcome global competition and are building focus to satisfy market and user needs. In spite of all possible requirements made, industries are still facing similar issues like increase in demand for quality product with much larger quantity in cheaper cost, and time has also taken its place in challenging aspects [1, 2]. This is also effected by market changes and trends from time to time which makes industries mandatory to evolve and shortage of goods storage and protection of goods has its substantial importance. Without introducing different methods and technology advancements/components, a window may open for potential competitors to overtake them in their respective market, thus highlighting the importance of innovation, where innovation can be classified as one of the primary methods for increasing/maintaining market share. Nevertheless, coming up with solutions in the modern age can be difficult, and in some situations expensive [3, 4]. Block diagram of assembly line implementation is shown in Fig. 12.1.

N. Siddaiah (B) · P. Pardhasaradhi · M. Phanigopi · Y. Vasanthi · Y. Deepika Department of Electronics and Communication Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, AP, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_12

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Fig. 12.1 Block diagram of assembly level implementation

Block Diagram

LabVIEW LabVIEW is short for laboratory virtual instrument engineering workbench. It is a user-friendly programming language used by scientists and engineers. LabVIEW is a tool used for graphical display of various real-time operations and provide an idea and makes the project feasible. LabVIEW can be used by association various tools available internally in the software and external tools can also simultaneously run with it. Data from external hardware will be read and processed by predefined programming. The output data can be written on LabVIEW itself.

Hardware IR Sensors An infrared sensor is a device used to measure and detect infrared radiation. It usually works by fact that the temperature in each corner of light is different whenever light ray can get separated. For e.g., red light has maximum temperature. For a human eye, IR is invisible since wavelength of IR is longer than visible light even if both IR and visible are on same spectrum. Anything that emits heat (everything that has a temperature above around five degrees Kelvin) gives off infrared radiation [5, 6]. In this project, IR sensors are used for object detection. The object considered is of black color and background surroundings has been adjusted in required form as if light intensity is not affected. The initial value of sensor is measured by using LabVIEW by associating NI myDAQ. The value of sensor when object detected will be noted similarly and the values are fixed in program. The data will be compared, and each time value change will be noted and considered as number of objects while executing the system.

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Arduino UNO Arduino UNO is a microcontroller board having 14digital pins, 6 are analog pins, a power jack, ICSP header, and reset button. Arduino in this project is used for controlling servo motor arm which is used in replacing object. The servo arm can be viewed as assembly line robotic arm that work with predefined instructions. The program of arm working is written on Arduino software and executed. The rotation angle and speed with delay can be controlled on program code. DC motors are placed that resembles the run platform of assembly line to move all objects on a place [7, 8]. The end of platform consists of sensor which detects object and sends information to process on LabVIEW.

Servo Motors Servo motors are main tool that resembles robotic arm for object replacement. It consists of total 180° and adjusting them provides various ways of its implementation. SG90 servo is used in this project which is portable and cheap, and its lightweight has its advantageous and consume less amount of power. The servo library has to be added additionally on Arduino program. You can use any servo code, hardware or library to control these servos [9, 10]. Servo is available with three pins. Power ground and information controller pin connected to Arduino board.

NI myDAQ NI myDAQ is a data acquisition device which can be interfaced with software instruments that runs by LabView. NI myDAQ makes it easy to interface LabVIEW with real-time analogue and digital signals obtained from sensor measurements and other form of data like ECG, EEG, etc. It is also portable in manipulating various IC signals. In this project, DQA is used to interface IR sensor and LabVIEW. It also interconnects buzzer to LabVIEW. Buzzer involves in digital signal output, whereas sensor gives analog input to LabVIEW through DAQ analog pins for processing [11].

Buzzer Passive buzzer is the one that notifies any operation change on system. It is used for detection notification in this project, whenever an object is detected by sensor the LabVIEW provides signal to buzzer through digital pins available on myDAQ.

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Fig. 12.2 Hardware implementation of assembly line for IOT applications

It can also work with Arduino AC signals. Proper utilization and by adjusting delay in write signal can also generate different tones.

Results and Discussion The program has been executed by starting up program on LabVIEW software, since the while loop condition has been given on program start button is initiated on LabVIEW. Predefined code dumped on Arduino starts up servo arm and object replacement will be done one after another while IR sensor passes information to LabVIEW continuously. Whenever object detected buzzer beeps which gives is our required operation. The system has been termed as data acquisition system since it collects data of object detection and number of objects. This data can be further graphed and plotted based on requirement. Figure 12.2 shows hardware implementation of assembly line for IOT application, and Fig. 12.3 shows the detailed LabVIEW program for assembly line implementation.

Conclusion This entire network is the combination of LabVIEW Arduino and NI myDAQ along with series of sensors for data acquisition and a servo arm for object replacement. The whole flow of process can be visualized on LabVIEW software. This project can also be further extended by using other sensors like temperature and color sensors to check condition of object and the object detection can also be further included.

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Fig. 12.3 LabVIEW program for assembly line implementation

References 1. Betancour, D., Garcia Torres, E.M.: Diseño E Implementación De Un Sistema Scada Para El Proceso Over Head De SelladoEn Omnibus Bb. Universidad PolitecnicaSalesiana (2012) 2. Garcia, E.M.: Diagnóstico de la demanda de consumo de energíaeléctricaenun smart home, enfocadoenel sector residencial de Quito, duranteelaño 2015, Barrió la Kennedy. Caracterización y optimización del consumo de energíaeléctrica. UNiversidad Técnica de Cotopaxi (2016) 3. Francisco, P., Abarca, C., Garcia Torres, E.M.: Estudio De Lamparas Led Para AlumbradoPúblico Y Diseño De Un Sistema Scada Con Control Automático on/Off. Universidad PolitecnicaSalesiana (2013) 4. Lakshmanna, K., Shaik, F., Gunjan, V.K., Singh, N., Kumar, G., Mahammad Shafi, R.: Perimeter degree technique for the reduction of routing congestion during placement in physical design of VLSI circuits. Complexity 2022, Article ID 8658770, 11p (2022). https://doi. org/10.1155/2022/8658770 5. Balakrishna, S., Solanki, V.K., Gunjan, V.K., Thirumaran, M.: Performance analysis of linked stream Big Data processing mechanisms for unifying IoT smart data. In: Gunjan, V., Garcia Diaz, V., Cardona, M., Solanki, V., Sunitha, K. (eds.) ICICCT 2019—System Reliability, Quality Control, Safety, Maintenance and Management. ICICCT 2019. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8461-5_78 6. Siddaiah, N., Rama Koti Reddy, D.V., Prasad, G.R.K., Pakdast, H., Srinivas Babu, P.S.: Optical and dielectric force gradient actuation schemes for excitation of triple coupled micro cantilever sensor in mass sensing applications. ARPN J. Eng. Appl. Sci. (JEAS) 10(8), (2015), ISSN:18196608 7. SuryaNarayana, G., Kolli, K., Ansari, M.D., Gunjan, V.K.: A Traditional analysis for efficient data mining with integrated association mining into regression techniques. In: Kumar, A., Mozar, S. (eds.) ICCCE 2020. Lecture Notes in Electrical Engineering, vol. 698. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7961-5_127 8. Siddaiah, N., Manjusree, B., Aditya, A.L.G.N., Reddy, D.V.R.K.: Design simulation and analysis of u-shaped and rectangular mems based triple coupled cantilevers. J. Sci. Ind. Res. 76(4), 235–238 (2017)

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9. Ahmed, S.M., Kovela, B., Gunjan, V.K.: IoT based automatic plant watering system through soil moisture sensing—a technique to support farmers’ cultivation in rural India. In: Gunjan, V., Senatore, S., Kumar, A., Gao, X.Z., Merugu, S. (eds.) Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies. Lecture Notes in Electrical Engineering, vol. 643. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3125-5_28 10. Agamloh, E.B.: A comparison of direct and indirect measurement of induction motor efficiency. In: Electric Machines and Drives Conference, 2009. IEMDC’09 11. Umans, S.D.: AC induction motor efficiency. In: Electrical Electronics Insulation Conference, 1989. Chicago‘89 EEIC/ICWA Exposition

Chapter 13

Dementia Disease Detection from Psychiatric Disorders Based on Automatic Speech Analysis Merugu Suresh, Abdul Subhani Shaik, and Manir Ahmed

Introduction Dementia disease affects human memory, thinking, and personality [1]. Symptoms of this disease can be seen in human behavior such as communication difficulties, reasoning problems, confusion, irregularities in the language [2, 3]. Thus, speech can be taken as a tool to analyze human behavior. Such speech-based technologies have the potentials to dementia diagnosis with accurate, rapid, and inexpensive monitoring. Such technologies are noninvasive and simple which is mostly preferable by healthcare system. Automatic speech-based dementia detection has another advantage that it does not require specialization personnel to examine. In clinical judgment, appropriate assessment instruments play a vital role to diagnosis of dementia. Pathophysiologic biomarker [4] and video monitoring [5] and [6] are commonly used instruments in clinical criteria. However, a sophisticated assessment instrument is of high interest to continuously monitoring dementia progression. Thus, automatic speech analysis can be used as an assessment instrument to analyze patient action and performance. There are several existing works based on speech analysis [7–9]. Existing works suggest that the patient with dementia often shows symptoms of semantic deterioration. The patient faced difficulty to finding semantic information during his spontaneous speech. Thus, there is a variation in speech characteristics such as pitch level, pitch modulation, word finding pause, slowness [10, 11]. Automatic speech analysis can be used to extract those salient features from the speech of patient. In [2], author identifies features of connected speech which helps to examine longitudinal profiles of impairment in Alzheimer’s disease. References [12–14] used Praat software to analyze the acoustic components of speech. They observed that voiceless M. Suresh (B) · A. S. Shaik · M. Ahmed Department of ECE, CMR College of Engineering & Technology, Hyderabad, Telangana 501401, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Usman and X.-Z. Gao (eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT, Advanced Technologies and Societal Change, https://doi.org/10.1007/978-981-19-4522-9_13

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segments of speech carry important information of variance. The work mentioned is [15–19] demonstrated an artificial intelligence algorithm to find the degree of severity of dementia. Reference [20] utilized some markers to distinguish between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Such markers depict the characteristics of spoken language and treated as speech features such as duration of speech, pause frequency, and many linguistic complexity measures. A SVM-based classifier is proposed to detect dementia-related characteristics in human voice and speech pattern. The classifier is able to distinguish different stage of the dementia disease. None of these methods able to provide expected performance for voice-based dementia assessment systems. This work presents a dementia assessment systems based on automatic speech analysis. For the experiment, voice samples collected from the patients affect by different types of dementia diseases and healthy elderly subjects. Different neuropsychological tests have been performed to find the cognitive status in clinical environment.

Proposed Method The block diagram of the proposed method is shown in Fig. 13.1. The proposed method can be classified into three major blocks: (i) Pre-processing, (ii) speech feature extraction, and (iii) classification. Each of the blocks is explained in the following section.

Pre-processing In pre-processing, data collection, noise removal, and time alignment are taken care. Initially, speech samples are collected at the GVA Institute of Psychology, Hyderabad. Each participant is informed about the research protocol and the recoding of their voice. Participants included both men and women are of in aged 60 years or older. A total of 200 participants gave their voice for four different tasks. Participants are asked to do four tasks based on this research protocol. The tasks are explaining a picture, counting backward, sentence repetition, and semantic fluency. An audiotechnical AT2020 USB Condenser USB Microphone (16 mm diaphragm) is used to recording the vocal task. During recording, the participants are asked to talk keeping Input Speech

Decision on Dementia Preprocessing

Feature extraction

Fig. 13.1 Block diagram of the proposed method

Classification

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Fig. 13.2 Sample of speech waveform

face 15 cm apart from the microphone. The recorded speech signal often corrupted by the noise such as sound of fan, air conditioning system, footsteps, traffic horn, bird’s scream, windows, doors, etc. Figure 13.2 shows the voice, silent, and noise present in the speech signal. After recording, pre-processing is done on recorded voice using WaveSurfer software for training datasets. Voice activity detection, noise cancelation, pre-emphasis, framing, and windowing are well-known automatic preprocessing techniques in speech processing. Processing step tries to find the silence part (where no speech is produce), unvoiced part (where vocal cords is not vibrating, noise), and voiced part (where vocal cords is vibrating periodically). Noise Based on the speech characteristics of four different tasks, the proposed method tries to find the cognitive status of the patients. Different neuropsychological tests are performed to decides the cognitive status such as (i) instrumental activities of daily living scale, Frontal assessment battery [17], five word test [18], neuropsychiatric inventory [19], and mini-mental state examination [20]. Based on the cognitive status check, patients are categorized as affect by dementia or not. Participants are not taken into consideration that has aberrant motor behavior, loss of consciousness, language problem, and history of head trauma.

Feature Extraction The proposed system is speaker independent means it does not bother about who is speaking in front of the microphone. Thus, speech feature should also invariant to changes in the speaker. Initially, voice activity detection algorithm is used to find the silence or voice portion in a speech. This algorithm basically find the energy

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envelop, i.e., intensity of the recorded speech. The energy envelop give the information of presence of voice or silence. Length of continuous voice segment and silent segment are considered as such speech feature. It is expected greater continuity length of voice in healthy elderly participants compared to the participants having dementia. The length of the periodic and aperiodic segments is also considered as speech feature. Periodic and aperiodic information can be extracted from the pitch contour (periodicity) of recorded speech. Based on the information of voice, silence, periodicity, several vocal features can be extracted such as energy, rhythm, pitch, ratio mean of the duration, median of the duration, standard deviation of the duration, ratio standard deviation of the duration, temporal, segment count. From the task explaining a picture and counting backward, following features are extracted. (i) (ii) (iii) (iv) (v)

Length of voice segment Length of silent segment Length of periodic segment Length of aperiodic segment Mean of the duration of above mentioned four segments (i.e., voice, silent, periodic, and aperiodic) (vi) Ratio mean of the duration of above mentioned four segments (vii) Median of the duration of above mentioned four segments (viii) Standard deviation of the duration of above mentioned four segments (ix) Segment count. From repeating a sentence for 10 times, following features are extracted. (i) (ii) (iii) (iv) (v) (vi)

Vocal reaction time Length of silent segment Length of insertions Length of deletions Distance error using first order Distance error using second order.

From semantic fluency task (participants are asked to name of the months and days), the distance between each work is calculated taking first word as a reference. For classification, mel frequency cepstral coefficients (MFCC) feature is extracted from the speech. MFCC algorithm consists of several techniques include windowing the signal, pre-emphasis, DFT spectrum measurement, mel spectrum calculation, DCT, temporal dynamic features. Temporal dynamic is considered as an important step where first order derivative (delta coefficient) and second order derivative (delta–delta coefficient) of cepstral coefficients is calculated. Delta coefficient provides information of speech rate, whereas delta–delta coefficients provide information of acceleration of speech. In this work, delta–delta coefficients are taken into consideration. The MFCC gives 39 dimensional features. All these extracted features are concatenated and then feature normalization technique is performed. Normalization changes all features to the same scale. The min– max normalization technique is adopted in this work. The different dimensional

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features are extracted from the input speech and then feed to the classification stage. These feature dimensions are 39 from MFCC, 18 from semantic fluency, 6 from repeating sentence, 21 from count down and picture description.

Classification Classification is used to capture the pattern present in speech feature. In this work, hidden Markov model (HMM) [21] is used to capture the sequential information of speech. The machine learning techniques [22–25] can also be used as a classifier. The performance of the system is calculated based on the detection error trade-off (DET) curve [26, 27]. False acceptance rate (FAR) and false rejection rate (FRR) are used to plot DET curve. From the plot, equal error rate (EER) is calculated to find the classification accuracy. EER is the point, where FAR and FRR are same.

Experimental Results The performance of extracted feature from four different tasks has been evaluated in this section. These feature sets are namely F1: F2: F3: F4:

Counting backward and explaining a picture Repeating sentence Semantic fluency task MFCC.

The above feature sets have been tested on 200 participants to find the performance of the dementia detection. Performance is tested on individually features as well as combination of the features with MFCC. Table 13.1 listed the performance of the experiments for different features sets. The performance is calculated based of EER value at which the FAR and FRR will be same. The specificity–sensitivity = (1EER/100) is also calculated. From the experiment, it is observed that the repeating sentence task provides poor performance as compare to the others features. This is because vocal reaction time feature carry less information for distinguishing between the participants with dementia than healthy control one. However, MFCC with the semantic fluency features provides the best performance among the others features. The greatest virtue of semantic fluency features is the position calculation of each words uttered in the sentience. Initially, voice activity detection algorithm is used to find the voice and silence segment duration from the sentence. It is observed from the experiment that the distance between voice segments and silence segments carries most relevant information. However, baseline method provides classification accuracy EER of 13 ± 3% in case of Alzheimer’s disease (AD) versus healthy elderly control (HC). Thus, the proposed method provides better accuracy as compared to the baseline method.

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EER (%)

Specificity-sensitivity

F1

16

0.75

F2

28

0.62

F3

14

0.86

F4

15

0.85

F1 + F4

9

0.91

F2 + F4

13

0.87

F3 + F4

7

0.93

Figure 13.3 shows DET curves obtained for each of the feature. It compares for the performance features in terms of FAR and FRR. Form the graph, EER value is calculated taking a point where FAR and FRR value is same. This is measured drawing a line making 45° with X-axis at origin. It can be observed that MFCC with semantic fluency feature outperforms compare to other features. F1. Generally, the vocal task such as semantic fluency or count downward is an appropriate and cognitively challenging task. Utterance of these tasks displayed silent pauses outside the syntactic boundaries by the dementia participants.

Fig. 13.3 DET curves obtained for different feature sets

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The automatic speech-based dementia detection has major advantage that it is an unobtrusive in nature, less stressful to the patients, and more users friendly. Because in open discussion or telephonic discussion, we are able get immediate feedback from the patients.

Conclusion This work proposed automatic dementia detection based on the speech of a patient. Such method is natural, stress less and unobtrusive in nature. Initially, speech sample is taken from the patients based on four different vocal tasks. Different speech analysis was performed such as noise removal, voice and silent segmentation, alignment, and feature extraction. HMM is used as a classifier to give decision whether an individual’s affected by dementia or not. In this work, the proposed method is evaluated on 200 participants and finds an accuracy of 93%. In future, number of participants will be increased with critical patients and classification of different stages of dementia will also be analyzed. Acknowledgements This research work has been carried out in the Research & Development Center, CMR College of Engineering & Technology, Hyderabad, India.

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