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<p>Preface xix</p> <p><b>Part 1: Introduction to Computer Vision 1</b></p> <p><b>1 Artificial Intelligence in Language Learning: Practices and Prospects 3<br /></b><i>Khushboo Kuddus</i></p> <p>1.1 Introduction 4</p> <p>1.2 Evolution of CALL 5</p> <p>1.3 Defining Artificial Intelligence 7</p> <p>1.4 Historical Overview of AI in Education and Language Learning 7</p> <p>1.5 Implication of Artificial Intelligence in Education 8</p> <p>1.5.1 Machine Translation 9</p> <p>1.5.2 Chatbots 9</p> <p>1.5.3 Automatic Speech Recognition Tools 9</p> <p>1.5.4 Autocorrect/Automatic Text Evaluator 11</p> <p>1.5.5 Vocabulary Training Applications 12</p> <p>1.5.6 Google Docs Speech Recognition 12</p> <p>1.5.7 Language MuseTM Activity Palette 13</p> <p>1.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes 13</p> <p>1.6.1 Autonomous Learning 13</p> <p>1.6.2 Produce Smart Content 13</p> <p>1.6.3 Task Automation 13</p> <p>1.6.4 Access to Education for Students with Physical Disabilities 14</p> <p>1.7 Conclusion 14</p> <p>References 15</p> <p><b>2 Real Estate Price Prediction Using Machine Learning Algorithms 19<br /></b><i>Palak Furia and Anand Khandare</i></p> <p>2.1 Introduction 20</p> <p>2.2 Literature Review 20</p> <p>2.3 Proposed Work 21</p> <p>2.3.1 Methodology 21</p> <p>2.3.2 Work Flow 22</p> <p>2.3.3 The Dataset 22</p> <p>2.3.4 Data Handling 23</p> <p>2.3.4.1 Missing Values and Data Cleaning 23</p> <p>2.3.4.2 Feature Engineering 24</p> <p>2.3.4.3 Removing Outliers 25</p> <p>2.4 Algorithms 27</p> <p>2.4.1 Linear Regression 27</p> <p>2.4.2 LASSO Regression 27</p> <p>2.4.3 Decision Tree 28</p> <p>2.4.4 Support Vector Machine 28</p> <p>2.4.5 Random Forest Regressor 28</p> <p>2.4.6 XGBoost 29</p> <p>2.5 Evaluation Metrics 29</p> <p>2.6 Result of Prediction 30</p> <p>References 31</p> <p><b>3 Multi-Criteria–Based Entertainment Recommender System Using Clustering Approach 33<br /></b><i>Chandramouli Das, Abhaya Kumar Sahoo and Chittaranjan Pradhan</i></p> <p>3.1 Introduction 34</p> <p>3.2 Work Related Multi-Criteria Recommender System 35</p> <p>3.3 Working Principle 38</p> <p>3.3.1 Modeling Phase 39</p> <p>3.3.2 Prediction Phase 39</p> <p>3.3.3 Recommendation Phase 40</p> <p>3.3.4 Content-Based Approach 40</p> <p>3.3.5 Collaborative Filtering Approach 41</p> <p>3.3.6 Knowledge-Based Filtering Approach 41</p> <p>3.4 Comparison Among Different Methods 42</p> <p>3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis 42</p> <p>3.4.1.1 Discussion and Result 43</p> <p>3.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally et al. 46</p> <p>3.4.2.1 Dataset and Evaluation Matrix 46</p> <p>3.4.2.2 Training Setting 49</p> <p>3.4.2.3 Result 49</p> <p>3.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng 49</p> <p>3.4.3.1 Evaluation Setting 50</p> <p>3.4.3.2 Experimental Result 50</p> <p>3.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng 51</p> <p>3.4.4.1 Experimental Dataset 51</p> <p>3.4.4.2 Experimental Result 52</p> <p>3.4.5 Multi-Criteria Clustering Approach by Wasid and Ali 53</p> <p>3.4.5.1 Experimental Evaluation 53</p> <p>3.4.5.2 Result and Analysis 53</p> <p>3.5 Advantages of Multi-Criteria Recommender System 54</p> <p>3.5.1 Revenue 57</p> <p>3.5.2 Customer Satisfaction 57</p> <p>3.5.3 Personalization 57</p> <p>3.5.4 Discovery 58</p> <p>3.5.5 Provide Reports 58</p> <p>3.6 Challenges of Multi-Criteria Recommender System 58</p> <p>3.6.1 Cold Start Problem 58</p> <p>3.6.2 Sparsity Problem 59</p> <p>3.6.3 Scalability 59</p> <p>3.6.4 Over Specialization Problem 59</p> <p>3.6.5 Diversity 59</p> <p>3.6.6 Serendipity 59</p> <p>3.6.7 Privacy 60</p> <p>3.6.8 Shilling Attacks 60</p> <p>3.6.9 Gray Sheep 60</p> <p>3.7 Conclusion 60</p> <p>References 61</p> <p><b>4 Adoption of Machine/Deep Learning in Cloud With a Case Study on Discernment of Cervical Cancer<br />65<br /></b><i>Jyothi A. P., S. Usha and Archana H. R.</i></p> <p>4.1 Introduction 66</p> <p>4.2 Background Study 69</p> <p>4.3 Overview of Machine Learning/Deep Learning 72</p> <p>4.4 Connection Between Machine Learning/Deep Learning and Cloud Computing 74</p> <p>4.5 Machine Learning/Deep Learning Algorithm 74</p> <p>4.5.1 Supervised Learning 74</p> <p>4.5.2 Unsupervised Learning 77</p> <p>4.5.3 Reinforcement or Semi-Supervised Learning 77</p> <p>4.5.3.1 Outline of ML Algorithms 77</p> <p>4.6 A Project Implementation on Discernment of Cervical Cancer by Using Machine/Deep Learning in Cloud 93</p> <p>4.6.1 Proposed Work 94</p> <p>4.6.1.1 MRI Dataset 94</p> <p>4.6.1.2 Pre Processing 95</p> <p>4.6.1.3 Feature Extraction 96</p> <p>4.6.2 Design Methodology and Implementation 97</p> <p>4.6.3 Results 100</p> <p>4.7 Applications 101</p> <p>4.7.1 Cognitive Cloud 102</p> <p>4.7.2 Chatbots and Smart Personal Assistants 103</p> <p>4.7.3 IoT Cloud 103</p> <p>4.7.4 Business Intelligence 103</p> <p>4.7.5 AI-as-a-Service 104</p> <p>4.8 Advantages of Adoption of Cloud in Machine Learning/ Deep Learning 104</p> <p>4.9 Conclusion 105</p> <p>References 106</p> <p><b>5 Machine Learning and Internet of Things–Based Models for Healthcare Monitoring 111<br /></b><i>Shruti Kute, Amit Kumar Tyagi, Aswathy S.U. and Shaveta Malik</i></p> <p>5.1 Introduction 112</p> <p>5.2 Literature Survey 113</p> <p>5.3 Interpretable Machine Learning in Healthcare 114</p> <p>5.4 Opportunities in Machine Learning for Healthcare 116</p> <p>5.5 Why Combining IoT and ML? 119</p> <p>5.5.1 ML-IoT Models for Healthcare Monitoring 119</p> <p>5.6 Applications of Machine Learning in Medical and Pharma 121</p> <p>5.7 Challenges and Future Research Direction 122</p> <p>5.8 Conclusion 123</p> <p>References 123</p> <p><b>6 Machine Learning–Based Disease Diagnosis and Prediction for E-Healthcare System 127<br /></b><i>Shruti Suhas Kute, Shreyas Madhav A. V., Shabnam Kumari and Aswathy S. U.</i></p> <p>6.1 Introduction 128</p> <p>6.2 Literature Survey 129</p> <p>6.3 Machine Learning Applications in Biomedical Imaging 132</p> <p>6.4 Brain Tumor Classification Using Machine Learning and IoT 134</p> <p>6.5 Early Detection of Dementia Disease Using Machine Learning and IoT-Based Applications 135</p> <p>6.6 IoT and Machine Learning-Based Diseases Prediction and Diagnosis System for EHRs 137</p> <p>6.7 Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT 140</p> <p>6.8 IoT and Machine Learning–Based System for Medical Data Mining 141</p> <p>6.9 Conclusion and Future Works 143</p> <p>References 144</p> <p><b>Part 2: Introduction to Deep Learning and its Models 149</b></p> <p><b>7 Deep Learning Methods for Data Science 151<br /></b><i>K. Indira, Kusumika Krori Dutta, S. Poornima and Sunny Arokia Swamy Bellary</i></p> <p>7.1 Introduction 152</p> <p>7.2 Convolutional Neural Network 152</p> <p>7.2.1 Architecture 154</p> <p>7.2.2 Implementation of CNN 154</p> <p>7.2.3 Simulation Results 157</p> <p>7.2.4 Merits and Demerits 158</p> <p>7.2.5 Applications 159</p> <p>7.3 Recurrent Neural Network 159</p> <p>7.3.1 Architecture 160</p> <p>7.3.2 Types of Recurrent Neural Networks 161</p> <p>7.3.2.1 Simple Recurrent Neural Networks 161</p> <p>7.3.2.2 Long Short-Term Memory Networks 162</p> <p>7.3.2.3 Gated Recurrent Units (GRUs) 164</p> <p>7.3.3 Merits and Demerits 167</p> <p>7.3.3.1 Merits 167</p> <p>7.3.3.2 Demerits 167</p> <p>7.3.4 Applications 167</p> <p>7.4 Denoising Autoencoder 168</p> <p>7.4.1 Architecture 169</p> <p>7.4.2 Merits and Demerits 169</p> <p>7.4.3 Applications 170</p> <p>7.5 Recursive Neural Network (RCNN) 170</p> <p>7.5.1 Architecture 170</p> <p>7.5.2 Merits and Demerits 172</p> <p>7.5.3 Applications 172</p> <p>7.6 Deep Reinforcement Learning 173</p> <p>7.6.1 Architecture 174</p> <p>7.6.2 Merits and Demerits 174</p> <p>7.6.3 Applications 174</p> <p>7.7 Deep Belief Networks (DBNS) 175</p> <p>7.7.1 Architecture 176</p> <p>7.7.2 Merits and Demerits 176</p> <p>7.7.3 Applications 176</p> <p>7.8 Conclusion 177</p> <p>References 177</p> <p><b>8 A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG 181<br /></b><i>Rupali Gill and Jaiteg Singh</i></p> <p>8.1 Introduction 182</p> <p>8.2 Background and Motivation 183</p> <p>8.2.1 Emotion Model 183</p> <p>8.2.2 Neuromarketing and BCI 184</p> <p>8.2.3 EEG Signal 185</p> <p>8.3 Related Work 185</p> <p>8.3.1 Machine Learning 186</p> <p>8.3.2 Deep Learning 191</p> <p>8.3.2.1 Fast Feed Neural Networks 193</p> <p>8.3.2.2 Recurrent Neural Networks 193</p> <p>8.3.2.3 Convolutional Neural Networks 194</p> <p>8.4 Methodology of Proposed System 195</p> <p>8.4.1 DEAP Dataset 196</p> <p>8.4.2 Analyzing the Dataset 196</p> <p>8.4.3 Long Short-Term Memory 197</p> <p>8.4.4 Experimental Setup 197</p> <p>8.4.5 Data Set Collection 197</p> <p>8.5 Results and Discussions 198</p> <p>8.5.1 LSTM Model Training and Accuracy 198</p> <p>8.6 Conclusion 199</p> <p>References 199</p> <p><b>9 An Extensive Survey of Applications of Advanced Deep Learning Algorithms on Detection of Neurodegenerative Diseases and the Tackling Procedure in Their Treatment Protocol 207<br /></b><i>Vignesh Baalaji S., Vergin Raja Sarobin M., L. Jani Anbarasi, Graceline Jasmine S. and Rukmani P.</i></p> <p>9.1 Introduction 208</p> <p>9.2 Story of Alzheimer’s Disease 208</p> <p>9.3 Datasets 210</p> <p>9.3.1 ADNI 210</p> <p>9.3.2 OASIS 210</p> <p>9.4 Story of Parkinson’s Disease 211</p> <p>9.5 A Review on Learning Algorithms 212</p> <p>9.5.1 Convolutional Neural Network (CNN) 212</p> <p>9.5.2 Restricted Boltzmann Machine 213</p> <p>9.5.3 Siamese Neural Networks 213</p> <p>9.5.4 Residual Network (ResNet) 214</p> <p>9.5.5 U-Net 214</p> <p>9.5.6 LSTM 214</p> <p>9.5.7 Support Vector Machine 215</p> <p>9.6 A Review on Methodologies 215</p> <p>9.6.1 Prediction of Alzheimer’s Disease 215</p> <p>9.6.2 Prediction of Parkinson’s Disease 221</p> <p>9.6.3 Detection of Attacks on Deep Brain Stimulation 223</p> <p>9.7 Results and Discussion 224</p> <p>9.8 Conclusion 224</p> <p>References 227</p> <p><b>10 Emerging Innovations in the Near Future Using Deep Learning Techniques 231<br /></b><i>Akshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi</i></p> <p>10.1 Introduction 232</p> <p>10.2 Related Work 234</p> <p>10.3 Motivation 235</p> <p>10.4 Future With Deep Learning/Emerging Innovations in Near Future With Deep Learning 236</p> <p>10.4.1 Deep Learning for Image Classification and Processing 237</p> <p>10.4.2 Deep Learning for Medical Image Recognition 237</p> <p>10.4.3 Computational Intelligence for Facial Recognition 238</p> <p>10.4.4 Deep Learning for Clinical and Health Informatics 238</p> <p>10.4.5 Fuzzy Logic for Medical Applications 239</p> <p>10.4.6 Other Intelligent-Based Methods for Biomedical and Healthcare 239</p> <p>10.4.7 Other Applications 239</p> <p>10.5 Open Issues and Future Research Directions 244</p> <p>10.5.1 Joint Representation Learning From User and Item Content Information 244</p> <p>10.5.2 Explainable Recommendation With Deep Learning 245</p> <p>10.5.3 Going Deeper for Recommendation 245</p> <p>10.5.4 Machine Reasoning for Recommendation 246</p> <p>10.5.5 Cross Domain Recommendation With Deep Neural Networks 246</p> <p>10.5.6 Deep Multi-Task Learning for Recommendation 247</p> <p>10.5.7 Scalability of Deep Neural Networks for Recommendation 247</p> <p>10.5.8 Urge for a Better and Unified Evaluation 248</p> <p>10.6 Deep Learning: Opportunities and Challenges 249</p> <p>10.7 Argument with Machine Learning and Other Available Techniques 250</p> <p>10.8 Conclusion With Future Work 251</p> <p>Acknowledgement 252</p> <p>References 252</p> <p><b>11 Optimization Techniques in Deep Learning Scenarios: An Empirical Comparison 255<br /></b><i>Ajeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma</i></p> <p>11.1 Introduction 256</p> <p>11.1.1 Background and Related Work 256</p> <p>11.2 Optimization and Role of Optimizer in DL 258</p> <p>11.2.1 Deep Network Architecture 259</p> <p>11.2.2 Proper Initialization 260</p> <p>11.2.3 Representation, Optimization, and Generalization 261</p> <p>11.2.4 Optimization Issues 261</p> <p>11.2.5 Stochastic GD Optimization 262</p> <p>11.2.6 Stochastic Gradient Descent with Momentum 263</p> <p>11.2.7 SGD With Nesterov Momentum 264</p> <p>11.3 Various Optimizers in DL Practitioner Scenario 265</p> <p>11.3.1 AdaGrad Optimizer 265</p> <p>11.3.2 RMSProp 267</p> <p>11.3.3 Adam 267</p> <p>11.3.4 AdaMax 269</p> <p>11.3.5 AMSGrad 269</p> <p>11.4 Recent Optimizers in the Pipeline 270</p> <p>11.4.1 EVE 270</p> <p>11.4.2 RAdam 271</p> <p>11.4.3 MAS (Mixing ADAM and SGD) 271</p> <p>11.4.4 Lottery Ticket Hypothesis 272</p> <p>11.5 Experiment and Results 273</p> <p>11.5.1 Web Resource 273</p> <p>11.5.2 Resource 277</p> <p>11.6 Discussion and Conclusion 278</p> <p>References 279</p> <p><b>Part 3: Introduction to Advanced Analytics 283</b></p> <p><b>12 Big Data Platforms 285<br /></b><i>Sharmila Gaikwad and Jignesh Patil</i></p> <p>12.1 Visualization in Big Data 286</p> <p>12.1.1 Introduction to Big Data 286</p> <p>12.1.2 Techniques of Visualization 287</p> <p>12.1.3 Case Study on Data Visualization 302</p> <p>12.2 Security in Big Data 305</p> <p>12.2.1 Introduction of Data Breach 305</p> <p>12.2.2 Data Security Challenges 306</p> <p>12.2.3 Data Breaches 307</p> <p>12.2.4 Data Security Achieved 307</p> <p>12.2.5 Findings: Case Study of Data Breach 309</p> <p>12.3 Conclusion 309</p> <p>References 309</p> <p><b>13 Smart City Governance Using Big Data Technologies 311<br /></b><i>K. Raghava Rao and D. Sateesh Kumar</i></p> <p>13.1 Objective 312</p> <p>13.2 Introduction 312</p> <p>13.3 Literature Survey 314</p> <p>13.4 Smart Governance Status 314</p> <p>13.4.1 International 314</p> <p>13.4.2 National 316</p> <p>13.5 Methodology and Implementation Approach 318</p> <p>13.5.1 Data Generation 319</p> <p>13.5.2 Data Acquisition 319</p> <p>13.5.3 Data Analytics 319</p> <p>13.6 Outcome of the Smart Governance 322</p> <p>13.7 Conclusion 323</p> <p>References 323</p> <p><b>14 Big Data Analytics With Cloud, Fog, and Edge Computing 325<br /></b><i>Deepti Goyal, Amit Kumar Tyagi and Aswathy S. U.</i></p> <p>14.1 Introduction to Cloud, Fog, and Edge Computing 326</p> <p>14.2 Evolution of Computing Terms and Its Related Works 330</p> <p>14.3 Motivation 332</p> <p>14.4 Importance of Cloud, Fog, and Edge Computing in Various Applications 333</p> <p>14.5 Requirement and Importance of Analytics (General) in Cloud, Fog, and Edge Computing 334</p> <p>14.6 Existing Tools for Making a Reliable Communication and Discussion of a Use Case (with Respect to Cloud, Fog, and Edge Computing) 335</p> <p>14.6.1 CloudSim 335</p> <p>14.6.2 SPECI 336</p> <p>14.6.3 Green Cloud 336</p> <p>14.6.4 OCT (Open Cloud Testbed) 337</p> <p>14.6.5 Open Cirrus 337</p> <p>14.6.6 GroudSim 338</p> <p>14.6.7 Network CloudSim 338</p> <p>14.7 Tools Available for Advanced Analytics (for Big Data Stored in Cloud, Fog, and Edge Computing Environment) 338</p> <p>14.7.1 Microsoft HDInsight 338</p> <p>14.7.2 Skytree 339</p> <p>14.7.3 Splice Machine 339</p> <p>14.7.4 Spark 339</p> <p>14.7.5 Apache SAMOA 339</p> <p>14.7.6 Elastic Search 339</p> <p>14.7.7 R-Programming 339</p> <p>14.8 Importance of Big Data Analytics for Cyber-Security and Privacy for Cloud-IoT Systems 340</p> <p>14.8.1 Risk Management 340</p> <p>14.8.2 Predictive Models 340</p> <p>14.8.3 Secure With Penetration Testing 340</p> <p>14.8.4 Bottom Line 341</p> <p>14.8.5 Others: Internet of Things-Based Intelligent Applications 341</p> <p>14.9 An Use Case with Real World Applications (with Respect to Big Data Analytics) Related to Cloud, Fog, and Edge Computing 341</p> <p>14.10 Issues and Challenges Faced by Big Data Analytics (in Cloud, Fog, and Edge Computing Environments) 342</p> <p>14.10.1 Cloud Issues 343</p> <p>14.11 Opportunities for the Future in Cloud, Fog, and Edge Computing Environments (or Research Gaps) 344</p> <p>14.12 Conclusion 345</p> <p>References 346</p> <p><b>15 Big Data in Healthcare: Applications and Challenges 351<br /></b><i>V. Shyamala Susan, K. Juliana Gnana Selvi and Ir. Bambang Sugiyono Agus Purwono</i></p> <p>15.1 Introduction 352</p> <p>15.1.1 Big Data in Healthcare 352</p> <p>15.1.2 The 5V’s Healthcare Big Data Characteristics 353</p> <p>15.1.2.1 Volume 353</p> <p>15.1.2.2 Velocity 353</p> <p>15.1.2.3 Variety 353</p> <p>15.1.2.4 Veracity 353</p> <p>15.1.2.5 Value 353</p> <p>15.1.3 Various Varieties of Big Data Analytical (BDA) in Healthcare 353</p> <p>15.1.4 Application of Big Data Analytics in Healthcare 354</p> <p>15.1.5 Benefits of Big Data in the Health Industry 355</p> <p>15.2 Analytical Techniques for Big Data in Healthcare 356</p> <p>15.2.1 Platforms and Tools for Healthcare Data 357</p> <p>15.3 Challenges 357</p> <p>15.3.1 Storage Challenges 357</p> <p>15.3.2 Cleaning 358</p> <p>15.3.3 Data Quality 358</p> <p>15.3.4 Data Security 358</p> <p>15.3.5 Missing or Incomplete Data 358</p> <p>15.3.6 Information Sharing 358</p> <p>15.3.7 Overcoming the Big Data Talent and Cost Limitations 359</p> <p>15.3.8 Financial Obstructions 359</p> <p>15.3.9 Volume 359</p> <p>15.3.10 Technology Adoption 360</p> <p>15.4 What is the Eventual Fate of Big Data in Healthcare Services? 360</p> <p>15.5 Conclusion 361</p> <p>References 361</p> <p><b>16 The Fog/Edge Computing: Challenges, Serious Concerns, and the Road Ahead 365<br /></b><i>Varsha. R., Siddharth M. Nair and Amit Kumar Tyagi</i></p> <p>16.1 Introduction 366</p> <p>16.1.1 Organization of the Work 368</p> <p>16.2 Motivation 368</p> <p>16.3 Background 369</p> <p>16.4 Fog and Edge Computing–Based Applications 371</p> <p>16.5 Machine Learning and Internet of Things–Based Cloud, Fog, and Edge Computing Applications 374</p> <p>16.6 Threats Mitigated in Fog and Edge Computing–Based Applications 376</p> <p>16.7 Critical Challenges and Serious Concerns Toward Fog/Edge Computing and Its Applications 378</p> <p>16.8 Possible Countermeasures 381</p> <p>16.9 Opportunities for 21st Century Toward Fog and Edge Computing 383</p> <p>16.9.1 5G and Edge Computing as Vehicles for Transformation of Mobility in Smart Cities 383</p> <p>16.9.2 Artificial Intelligence for Cloud Computing and Edge Computing 384</p> <p>16.10 Conclusion 387</p> <p>References 387</p> <p>Index 391</p>

<p><b>Archana Mire, PhD,</b> is an assistant professor in the Computer Engineering Department, Terna Engineering College, Navi Mumbai, India. She has published many research articles in peer-reviewed journals. <p><b>Shaveta Malik, PhD,</b> is an associate professor in the Computer Engineering Department (NBA accredited), Terna Engineering College, Nerul, India. She has published many research articles in peer-reviewed journals. <p><b>Amit Kumar Tyagi, PhD,</B> is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber-physical systems, and computer vision.

<p><b>The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc.</b> <p>Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. <p>However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. <p>This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. <p><b>Audience</b> <p>Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

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