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<p>Preface xv</p> <p><b>1 Emergence of Advanced Computational Intelligence Coupled with Smart Environment 1</b><br /><i>Risha Rani and Tirtha Deb</i></p> <p>1.1 Introduction 2</p> <p>1.2 Background Works 3</p> <p>1.3 Integrated Smart Environment 4</p> <p>1.4 Proposed Models for Smart Intelligent Environment 5</p> <p>1.5 IoT Architecture 16</p> <p>1.6 Smart Environment and Advanced Computational Intelligence 23</p> <p>1.7 Advanced Computational Intelligences: Possible Uses in Smart Environment 24</p> <p>1.8 Conclusion 26</p> <p><b>2 Machine Learning-Enabled Integrated Information Platform for Educational Universities 29</b><br /><i>Sai Smurti Sahu, Rishav Kumar, Soumya Sahoo, Balwant Kumar and Padmabati Mohanta</i></p> <p>2.1 Introduction 30</p> <p>2.2 Cloud-Based Web Application for University 30</p> <p>2.3 Integrated Information Platform of Indian Universities Using Machine Learning 36</p> <p>2.4 Applications Used to Designed This Web Platform 37</p> <p>2.5 Analysis Result 38</p> <p><b>3 False Data Injection Attack Detection Using Machine Learning in Industrial Internet of Things 49</b><br /><i>Hafizunisa, Prerna Rai and Damini Sinha</i></p> <p>3.1 Introduction 50</p> <p>3.2 Literature Review 54</p> <p>3.3 Technical Methodology 56</p> <p>3.4 Proposed Model for Detecting False Data and its Correction 59</p> <p>3.5 Complexity Analysis of Proposed Model 63</p> <p>3.6 Advantages of the Model 64</p> <p>3.7 Future Scope and Limitations of the Proposed Model 65</p> <p>3.8 Conclusion 65</p> <p><b>4 Fake News Detection: Restricting Spreading of Misinformation Using Machine Learning 69</b><br /><i>Shubham Choudhary and Pratyush Mishra</i></p> <p>4.1 Introduction 70</p> <p>4.2 Scope of False News Detection 73</p> <p>4.3 Main Highlights of the Analysis 73</p> <p>4.4 A Novel Model for False News Detection 76</p> <p>4.5 Literature Review 78</p> <p>4.6 Results and Analysis 80</p> <p>4.7 Conclusion 81</p> <p><b>5 Adaptability, Flexibility, and Accessibility Through Telemedicine 85</b><br /><i>Dipti Verma, Somyajyoti Talukdar and Kumari Alankrita Sharma</i></p> <p>5.1 Introduction 86</p> <p>5.2 Related Works 89</p> <p>5.3 Proposed Model for Remote Health Monitoring System 93</p> <p>5.3.1 Microcontroller and Sensor 95</p> <p>5.4 Benefits of the Proposed Model 96</p> <p>5.5 Constraints of the Proposed Model 98</p> <p>5.6 Conclusion 101</p> <p>5.7 Future Works 102</p> <p><b>6 Crop Prediction by Implementing Machine Learning in an IoT-Based System 107</b><br /><i>Vivian Rawade and Shubham Sahoo</i></p> <p>6.1 Introduction 108</p> <p>6.2 Literature Review 110</p> <p>6.3 Proposed Model for Crop Prediction 112</p> <p>6.4 Results and Analysis 123</p> <p>6.5 Challenges Faced 125</p> <p>6.6 Advantages of the Proposed Model 127</p> <p>6.7 Disadvantages of the Proposed Model 127</p> <p>6.8 Conclusion 128</p> <p><b>7 Relevance of Smart Management of Road Traffic System Using Advanced Intelligence 131</b><br /><i>Koustab Chowdhury and Rishabh Kapoor</i></p> <p>7.1 Introduction 132</p> <p>7.2 Related Works 135</p> <p>7.3 Proposed Model of Traffic Management System 139</p> <p>7.4 Role of AI in Traffic Management 146</p> <p>7.5 Conclusion and Future Works 148</p> <p><b>8 Visualization of Textual Corpora Using Social Network Analysis 151</b><br /><i>Indu Rodda and Durga Bhavani S.</i></p> <p>8.1 Introduction 152</p> <p>8.2 Related Literature 154</p> <p>8.3 Proposed Method 156</p> <p>8.4 Implementation and Results 163</p> <p>8.5 Conclusion and Future Work 169</p> <p><b>9 Autonomous Intelligent Vehicles: Impact, Current Market, Future Trends, Challenges, and Limitations 173</b><br /><i>Kamalanathan Shanmugam, Muhammad Ehsan Rana and Felix Ting Yu Hong</i></p> <p>9.1 Introduction 174</p> <p>9.2 The Global Impact of the AV Industry 176</p> <p>9.3 Role of Machine Learning in Autonomous Vehicles 177</p> <p>9.4 Significance of the AV Industry in Various Sectors 179</p> <p>9.5 Current Market and Future Trends in AV Industry 184</p> <p>9.6 Challenges and Limitations 189</p> <p>9.7 Conclusion 192</p> <p><b>10 Role of Smart and Predictive Healthcare in Modern Society 195</b><br /><i>Muhammad Ehsan Rana and Manoj Jayabalan</i></p> <p>10.1 Introduction 196</p> <p>10.2 Healthcare System 197</p> <p>10.3 Role of Predictive Analytics in Healthcare 198</p> <p>10.4 Application of IoT in Healthcare 199</p> <p>10.5 IoT Based Healthcare Management Framework 200</p> <p>10.6 Future Recommendations for Research 210</p> <p>10.7 Conclusion 211</p> <p><b>11 An Analytical Study on Depression Detection Using Machine Learning 215</b><br /><i>Angelia Melani Adrian and Junaidy Budi Sanger</i></p> <p>11.1 Introduction 216</p> <p>11.2 Literature Survey 217</p> <p>11.3 Proposed System 220</p> <p>11.4 Challenges of Machine Learning in Depression Detection 225</p> <p>11.5 Conclusion and Future Work 226</p> <p><b>12 Revolutionizing Healthcare: Empowering Faster Treatment with IoT-Powered Smart Healthcare 229</b><br /><i>Prerna Kumari, Rupali Agarwal and Shruti Kumari</i></p> <p>12.1 Introduction 230</p> <p>12.2 Scope/Motivation 233</p> <p>12.3 Literature Survey 234</p> <p>12.4 Smart Technology 235</p> <p>12.5 Methods and Materials 236</p> <p>12.6 Result 245</p> <p>12.7 Conclusion 248</p> <p><b>13 Machine Learning Algorithms for Initial Diagnosis of Parkinson’s Disease 251</b><br /><i>Udayan Das, Manish Jena and Manish Roy</i></p> <p>13.1 Overview of Parkinson’s Disease 251</p> <p>13.2 Scope 254</p> <p>13.3 Related Works 255</p> <p>13.4 Comparative Analysis of Parkinson’s Disease 260</p> <p>13.5 Pros and Cons Using ML Algorithms 267</p> <p>13.6 Conclusion and Future Works 271</p> <p>13.7 Bibliography 271</p> <p><b>14 Towards a Sustainable Future: Harnessing the Power of Computational Intelligence to Track Climate Change 275</b><br /><i>Satyam Sinha, Shreyash Kumar Agnihotri and Oshmita Sarkar</i></p> <p>14.1 Introduction 276</p> <p>14.2 Artificial Intelligence and Climate Change Adaptation 277</p> <p>14.3 Related Works 278</p> <p>14.4 Comparative Analysis of Technological Frameworks to Handle Climate Crisis 280</p> <p>14.5 Future Scope of Climatic Crisis Handling with AI 299</p> <p>14.6 Conclusion 300</p> <p><b>15 Impact of Computational Intelligence and Modeling in Tackling Weather Fluctuation 305</b><br /><i>Rohan Karn, Aniket Rouniyar, Ranjit Kumar Das and Amit Gupta</i></p> <p>15.1 Introduction 306</p> <p>15.2 Objective 308</p> <p>15.3 Causes of Climate Crisis 309</p> <p>15.4 Significance of AI and Modeling on Climate Crisis 311</p> <p>15.5 Plastic Waste Detection Model 319</p> <p>15.6 Forest Fire Prediction Models Using AI 325</p> <p>15.7 Results 329</p> <p>15.8 Conclusion 331</p> <p>References 332</p> <p>Index 335</p>

<p><b>Hrudaya Kumar Tripathy, PhD,</b> is an associate professor in the School of Computer Engineering, KIIT Deemed to be University, He has more than 20 years of teaching experience and his research interests include neural networks, pattern recognition, software engineering, machine learning, and big data. He has published several books and research papers in various journals and conferences. Tripathy received the 2013 Young IT Professional Award from the Computer Society of India. <p><b>Sushruta Mishra, PhD,</b> is an associate professor in the School of Computer Engineering, KIIT Deemed to be University, Odisha, India. He obtained his doctorate in 2017 and his research interests include image processing, machine learning, the Internet of Things, and cognitive computing. He has published 130+ research articles in international journals and conferences. <p><b>Minakhi Rout, PhD,</b> is an associate professor in the School of Computer Engineering, KIIT Deemed to be University, Odisha, India. She obtained her PhD in 2015 and her research interests focus on computational finance, data mining, and machine learning. Rout has published 50+ research papers in international journals and conferences. <p><b>S. Balamurugan, PhD,</b> is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents. <p><b>Samaresh Mishra, PhD,</b> is the director of student affairs at KIIT Deemed to be University. He obtained a PhD in computer science from Utkal University. His research areas focus on software testing, machine learning, and cloud computing. He has published 30+ academic papers.

<p><b>This book covers a wide range of advanced techniques and approaches for designing and implementing computationally intelligent methods in different application domains which is of great use to not only researchers but also academicians and industry experts.</b> <p>Optimized Computational Intelligence (OCI) is a new, cutting-edge, and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, biologically-inspired computation, software engineering, AI, cybernetics, cognitive science, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. OCI aims to apply modern computationally intelligent methods to generate optimum outcomes in various application domains. This book presents the latest technologies-driven material to explore optimized various computational intelligence domains. <ul><li>includes real-life case studies highlighting different advanced technologies in computational intelligence;</li> <li>provides a unique compendium of current and emerging hybrid intelligence paradigms for advanced informatics;</li> <li>reflects the diversity, complexity, and depth and breadth of this critical bio-inspired domain;</li> <li>offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in dealing with cognitive informatics challenges;</li> <li>presents a variety of intelligent and optimized techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional data analytics research in intelligent decision-making system dynamics;</li> <li>includes architectural models and applications-based augmented solutions for optimized computational intelligence.</li></ul> <p><b>Audience</b> <p>The book will interest a range of engineers and researchers in information technology, computer science, and artificial intelligence working in the interdisciplinary field of computational intelligence.

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