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

<p><b>Ashok Kumar, PhD, </b>is an assistant professor at Lovely Professional University, Phagwara, Punjab, India. He has 15+ years of teaching and research experience, filed 3 patents, and published many articles in international journals and conferences. His current areas of research interest include cloud computing, the Internet of Things, and mist computing. <p><b>Megha Bhushan, PhD, </b>is an assistant professor at the School of Computing, DIT University, Dehradun, Uttarakhand, India. She has filed 4 patents and published many research articles in international journals and conferences. Her research interest includes software quality, software reuse, ontologies, artificial intelligence, and expert systems. <p><b>Jose Galindo, PhD, </b>is currently in the Department of Computer Languages and Systems, University of Seville, Spain. He has developed many tools such as FaMa, FaMaDEB, FaMaOVM, TESALIA, and VIVID, and his research interests include recommender systems, software visualization, variability-intensive systems, and software product lines. <p><b>Lalit Garg, PhD, </b>is a Senior Lecturer in the Department of Computer Information Systems, University of Malta, and an honorary lecturer at the University of Liverpool, UK. He has edited four books and published over 110 papers in refereed journals, conferences, and books. He has 12 patents and delivered more than twenty keynote speeches in different countries, and organized/chaired/co-chaired many international conferences. <p><b>Yu-Chen Hu, PhD, </b>is a distinguished professor in the Department of Computer Science and Information Management, Providence University, Taichung City, Taiwan. His research interests include image and signal processing, data compression, information hiding, information security, computer network, and artificial network.

<p><b>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.</b> <p>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. <p><i>Machine Intelligence, Big Data Analytics, and IoT in Image Processing</i> 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. <p><b>Audience</b> <p>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.

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