MACHINE INTELLIGENCE, BIG DATA ANALYTICS, AND IoT IN IMAGE PROCESSING Discusses both theoretical and practical aspects of how to harness advanced technologies to develop practical applications such as drone-based surveillance, smart transportation, healthcare, farming solutions, and robotics used in automation. The concepts of machine intelligence, big data analytics, and the Internet of Things (IoT) continue to improve our lives through various cutting-edge applications such as disease detection in real-time, crop yield prediction, smart parking, and so forth. The transformative effects of these technologies are life-changing because they play an important role in demystifying smart healthcare, plant pathology, and smart city/village planning, design and development. This book presents a cross-disciplinary perspective on the practical applications of machine intelligence, big data analytics, and IoT by compiling cutting-edge research and insights from researchers, academicians, and practitioners worldwide. It identifies and discusses various advanced technologies, such as artificial intelligence, machine learning, IoT, image processing, network security, cloud computing, and sensors, to provide effective solutions to the lifestyle challenges faced by humankind. Machine Intelligence, Big Data Analytics, and IoT in Image Processing is a significant addition to the body of knowledge on practical applications emerging from machine intelligence, big data analytics, and IoT. The chapters deal with specific areas of applications of these technologies. This deliberate choice of covering a diversity of fields was to emphasize the applications of these technologies in almost every contemporary aspect of real life to assist working in different sectors by understanding and exploiting the strategic opportunities offered by these technologies. Audience The book will be of interest to a range of researchers and scientists in artificial intelligence who work on practical applications using machine learning, big data analytics, natural language processing, pattern recognition, and IoT by analyzing images. Software developers, industry specialists, and policymakers in medicine, agriculture, smart cities development, transportation, etc. will find this book exceedingly useful.

Table of Contents:
Preface xv Part I: Demystifying Smart Healthcare 1 1 Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer’s Disease 3 Monika Sethi, Sachin Ahuja and Puneet Bawa 1.1 Introduction 4 1.2 Transfer Learning Techniques 6 1.3 AD Classification Using Conventional Training Methods 9 1.4 AD Classification Using Transfer Learning 12 1.5 Conclusion 16 References 16 2 Medical Image Analysis of Lung Cancer CT Scans Using Deep Learning with Swarm Optimization Techniques 23 Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao 2.1 Introduction 24 2.2 The Major Contributions of the Proposed Model 26 2.3 Related Works 28 2.4 Problem Statement 32 2.5 Proposed Model 33 2.5.1 Swarm Optimization in Lung Cancer Medical Image Analysis 33 2.5.2 Deep Learning with PSO 34 2.5.3 Proposed CNN Architectures 35 2.6 Dataset Description 37 2.7 Results and Discussions 39 2.7.1 Parameters for Performance Evaluation 39 2.8 Conclusion 47 References 48 3 Liver Cancer Classification With Using Gray-Level Co-Occurrence Matrix Using Deep Learning Techniques 51 Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao 3.1 Introduction 52 3.1.1 Liver Roles in Human Body 53 3.1.2 Liver Diseases 53 3.1.3 Types of Liver Tumors 55 3.1.3.1 Benign Tumors 55 3.1.3.2 Malignant Tumors 57 3.1.4 Characteristics of a Medical Imaging Procedure 58 3.1.5 Problems Related to Liver Cancer Classification 60 3.1.6 Purpose of the Systematic Study 61 3.2 Related Works 62 3.3 Proposed Methodology 66 3.3.1 Gaussian Mixture Model 68 3.3.2 Dataset Description 69 3.3.3 Performance Metrics 70 3.3.3.1 Accuracy Measures 70 3.3.3.2 Key Findings 74 3.3.3.3 Key Issues Addressed 75 3.4 Conclusion 77 References 77 4 Transforming the Technologies for Resilient and Digital Future During COVID-19 Pandemic 81 Garima Kohli and Kumar Gourav 4.1 Introduction 82 4.2 Digital Technologies Used 84 4.2.1 Artificial Intelligence 85 4.2.2 Internet of Things 85 4.2.3 Telehealth/Telemedicine 87 4.2.4 Cloud Computing 87 4.2.5 Blockchain 88 4.2.6 5g 89 4.3 Challenges in Transforming Digital Technology 90 4.3.1 Increasing Digitalization 91 4.3.2 Work From Home Culture 91 4.3.3 Workplace Monitoring and Techno Stress 91 4.3.4 Online Fraud 92 4.3.5 Accessing Internet 92 4.3.6 Internet Shutdowns 92 4.3.7 Digital Payments 92 4.3.8 Privacy and Surveillance 93 4.4 Implications for Research 93 4.5 Conclusion 94 References 95 Part II: Plant Pathology 101 5 Plant Pathology Detection Using Deep Learning 103 Sangeeta V., Appala S. Muttipati and Brahmaji Godi 5.1 Introduction 104 5.2 Plant Leaf Disease 105 5.3 Background Knowledge 109 5.4 Architecture of ResNet 512 V 2 111 5.4.1 Working of Residual Network 112 5.5 Methodology 113 5.5.1 Image Resizing 113 5.5.2 Data Augmentation 113 5.5.2.1 Types of Data Augmentation 114 5.5.3 Data Normalization 114 5.5.4 Data Splitting 116 5.6 Result Analysis 116 5.6.1 Data Collection 117 5.6.2 Feature Extractions 117 5.6.3 Plant Leaf Disease Detection 117 5.7 Conclusion 119 References 120 6 Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT 123 N. Marline Joys Kumari, N. Thirupathi Rao and Debnath Bhattacharyya 6.1 Introduction 124 6.1.1 Background of the Problem 127 6.1.1.1 Need of Water Management 127 6.1.1.2 Importance of Precision Agriculture 127 6.1.1.3 Internet of Things 128 6.1.1.4 Application of IoT in Machine Learning and Deep Learning 129 6.2 Related Works 131 6.3 Challenges of IoT in Smart Irrigation 133 6.4 Farmers’ Challenges in the Current Situation 135 6.5 Data Collection in Precision Agriculture 136 6.5.1 Algorithm 136 6.5.1.1 Environmental Consideration on Stage Production of Crop 140 6.5.2 Implementation Measures 141 6.5.2.1 Analysis of Relevant Vectors 141 6.5.2.2 Mean Square Error 141 6.5.2.3 Potential of IoT in Precision Agriculture 141 6.5.3 Architecture of the Proposed Model 143 6.6 Conclusion 147 References 147 7 Machine Learning-Based Hybrid Model for Wheat Yield Prediction 151 Haneet Kour, Vaishali Pandith, Jatinder Manhas and Vinod Sharma 7.1 Introduction 152 7.2 Related Work 153 7.3 Materials and Methods 155 7.3.1 Methodology for the Current Work 155 7.3.1.1 Data Collection for Wheat Crop 155 7.3.1.2 Data Pre-Processing 156 7.3.1.3 Implementation of the Proposed Hybrid Model 157 7.3.2 Techniques Used for Feature Selection 159 7.3.2.1 ReliefF Algorithm 159 7.3.2.2 Genetic Algorithm 161 7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction 162 7.3.3.1 K-Nearest Neighbor 162 7.3.3.2 Artificial Neural Network 163 7.3.3.3 Logistic Regression 164 7.3.3.4 Naïve Bayes 164 7.3.3.5 Support Vector Machine 165 7.3.3.6 Linear Discriminant Analysis 166 7.4 Experimental Result and Analysis 167 7.5 Conclusion 173 Acknowledgment 173 References 174 8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences 177 Abhishek Bhola, Suraj Srivastava, Ajit Noonia, Bhisham Sharma and Sushil Kumar Narang 8.1 Introduction 178 8.2 Types of Wireless Sensor for Smart Agriculture 179 8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture 179 8.4 ml and WSN-Based Techniques for Smart Agriculture 185 8.5 Future Scope in Smart Agriculture 188 8.6 Conclusion 190 References 190 Part III: Smart City and Villages 197 9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics 199 Huma Naz, Sachin Ahuja, Rahul Nijhawan and Neelu Jyothi Ahuja 9.1 Introduction 200 9.1.1 Tasks Involved in Data Pre-Processing 200 9.2 Related Work 202 9.3 Experimental Setup and Methodology 205 9.3.1 Methodology 205 9.3.2 Application of Various Data Pre-Processing Tasks on Datasets 206 9.3.3 Applied Techniques 207 9.3.3.1 Decision Tree 207 9.3.3.2 Naive Bayes 207 9.3.3.3 Artificial Neural Network 208 9.3.4 Proposed Work 208 9.3.4.1 PIMA Diabetes Dataset (PID) 208 9.3.5 Cleveland Heart Disease Dataset 211 9.3.6 Framingham Heart Study 215 9.3.7 Diabetic Dataset 217 9.4 Experimental Result and Discussion 220 9.5 Conclusion and Future Work 222 References 222 10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications 225 Anurag Sinha, N. K. Singh, Ayushman Srivastava, Sagorika Sen and Samarth Sinha 10.1 Introduction 226 10.2 Background 228 10.2.1 History of Cloud Computing 228 10.2.1.1 Software-as-a-Service Model 230 10.2.1.2 Infrastructure-as-a-Service Model 230 10.2.1.3 Platform-as-a-Service Model 232 10.2.2 Types of Cloud Computing 232 10.2.3 Cloud Service Model 232 10.2.4 Characteristics of Cloud Computing 234 10.2.5 Advantages of Cloud Computing 234 10.2.6 Challenges in Cloud Computing 235 10.2.7 Cloud Security 236 10.2.7.1 Foundation Security 236 10.2.7.2 SaaS and PaaS Host Security 237 10.2.7.3 Virtual Server Security 237 10.2.7.4 Foundation Security: The Application Level 238 10.2.7.5 Supplier Data and Its Security 238 10.2.7.6 Need of Security in Cloud 239 10.2.8 Cloud Computing Applications 239 10.3 Literature Review 241 10.4 Cloud Computing Challenges and Its Solution 242 10.4.1 Solution and Practices for Cloud Challenges 246 10.5 Cloud Computing Security Issues and Its Preventive Measures 248 10.5.1 General Security Threats in Cloud 249 10.5.2 Preventive Measures 254 10.6 Cloud Data Protection and Security Using Steganography 258 10.6.1 Types of Steganography 259 10.6.2 Data Steganography in Cloud Environment 260 10.6.3 Pixel Value Differencing Method 261 10.7 Related Study 263 10.8 Conclusion 263 References 264 11 Internet of Drone Things: A New Age Invention 269 Prachi Dahiya 11.1 Introduction 269 11.2 Unmanned Aerial Vehicles 271 11.2.1 UAV Features and Working 274 11.2.2 IoDT Architecture 275 11.3 Application Areas 280 11.3.1 Other Application Areas 284 11.4 IoDT Attacks 285 11.4.1 Counter Measures 291 11.5 Fusion of IoDT With Other Technologies 296 11.6 Recent Advancements in IoDT 299 11.7 Conclusion 302 References 303 12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction 305 Mukul Joshi, Gayatri Valluri, Jyoti Rawat and Kriti 12.1 Introduction 305 12.2 Literature Review 307 12.3 System Architecture 309 12.3.1 Model Development Phase 309 12.3.2 Development Environment Phase 311 12.4 Methodology 312 12.4.1 Image Pre-Processing Phase 312 12.4.2 Model Building Phase 313 12.5 Implementation and Results 314 12.5.1 Performance 314 12.5.2 Confusion Matrix 318 12.6 Conclusion and Future Scope 318 References 319 13 Recent Advances in Intelligent Transportation Systems in India: Analysis, Applications, Challenges, and Future Work 323 Elamurugan Balasundaram, Cailassame Nedunchezhian, Mathiazhagan Arumugam and Vinoth Asaikannu 13.1 Introduction 324 13.2 A Primer on ITS 325 13.3 The ITS Stages 326 13.4 Functions of ITS 327 13.5 ITS Advantages 328 13.6 ITS Applications 329 13.7 ITS Across the World 331 13.8 India’s Status of ITS 333 13.9 Suggestions for Improving India’s ITS Position 334 13.10 Conclusion 335 References 335 14 Evolutionary Approaches in Navigation Systems for Road Transportation System 341 Noopur Tyagi, Jaiteg Singh and Saravjeet Singh 14.1 Introduction 342 14.1.1 Navigation System 343 14.1.2 Genetic Algorithm 347 14.1.3 Differential Evolution 348 14.2 Related Studies 349 14.2.1 Related Studies of Evolutionary Algorithms 351 14.3 Navigation Based on Evolutionary Algorithm 352 14.3.1 Operators and Terms Used in Evolutionary Algorithms 353 14.3.2 Operator and Terms Used in Evolutionary Algorithm 357 14.4 Meta-Heuristic Algorithms for Navigation 359 14.4.1 Drawbacks of DE 362 14.5 Conclusion 362 References 363 15 IoT-Based Smart Parking System for Indian Smart Cities 369 E. Fantin Irudaya Raj, M. Appadurai, M. Chithamabara Thanu and E. Francy Irudaya Rani 15.1 Introduction 370 15.2 Indian Smart Cities Mission 371 15.3 Vehicle Parking and Its Requirements in a Smart City Configuration 373 15.4 Technologies Incorporated in a Vehicle Parking System in Smart Cities 375 15.5 Sensors for Vehicle Parking System 383 15.5.1 Active Sensors 384 15.5.2 Passive Sensors 386 15.6 IoT-Based Vehicle Parking System for Indian Smart Cities 387 15.6.1 Guidance to the Customers Through Smart Devices 389 15.6.2 Smart Parking Reservation System 391 15.7 Advantages of IoT-Based Vehicle Parking System 392 15.8 Conclusion 392 References 393 16 Security of Smart Home Solution Based on Secure Piggybacked Key Exchange Mechanism 399 Jatin Arora and Saravjeet Singh 16.1 Introduction 400 16.2 IoT Challenges 404 16.3 IoT Vulnerabilities 405 16.4 Layer-Wise Threats in IoT Architecture 406 16.4.1 Sensing Layer Security Issues 407 16.4.2 Network Layer Security Issues 408 16.4.3 Middleware Layer Security Issues 409 16.4.4 Gateways Security Issues 410 16.4.5 Application Layer Security Issues 411 16.5 Attack Prevention Techniques 411 16.5.1 IoT Authentication 412 16.5.2 Session Establishment 413 16.6 Conclusion 414 References 414 17 Machine Learning Models in Prediction of Strength Parameters of FRP-Wrapped RC Beams 419 Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor and Ashok Kumar 17.1 Introduction 420 17.1.1 Defining Fiber-Reinforced Polymer 421 17.1.2 Types of FRP Composites 422 17.1.2.1 Carbon Fiber–Reinforced Polymer 422 17.1.2.2 Glass Fiber 423 17.1.2.3 Aramid Fiber 424 17.1.2.4 Basalt Fiber 424 17.2 Strengthening of RC Beams With FRP Systems 425 17.2.1 FRP-to-Concrete Bond 426 17.2.2 Flexural Strengthening of Beams With FRP Composite 427 17.2.3 Shear Strengthening of Beams With FRP Composite 427 17.3 Machine Learning Models 428 17.3.1 Prediction of Bond Strength 430 17.3.2 Estimation of Flexural Strength 434 17.3.3 Estimation of Shear Strength 434 17.4 Conclusion 441 References 441 18 Prediction of Indoor Air Quality Using Artificial Intelligence 447 Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar, Aman Kumar and Harish Chandra Arora 18.1 Introduction 448 18.2 Indoor Air Quality Parameters 450 18.2.1 Physical Parameters 453 18.2.1.1 Humidity 453 18.2.1.2 Air Changes (Ventilation) 454 18.2.1.3 Air Velocity 454 18.2.1.4 Temperature 454 18.2.2 Particulate Matter 455 18.2.3 Chemical Parameters 456 18.2.3.1 Carbon Dioxide 456 18.2.3.2 Carbon Monoxide 456 18.2.3.3 Nitrogen Dioxide 456 18.2.3.4 Sulphur Dioxide 457 18.2.3.5 Ozone 457 18.2.3.6 Gaseous Ammonia 458 18.2.3.7 Volatile Organic Compounds 458 18.2.4 Biological Parameters 459 18.3 AI in Indoor Air Quality Prediction 459 18.4 Conclusion 464 References 465 Index 471