Preface xix -- Part 1: Introduction to Computer Vision 1 -- 1 Artificial Intelligence in Language Learning: Practices and Prospects 3 Khushboo Kuddus -- 1.1 Introduction 4 -- 1.2 Evolution of CALL 5 -- 1.3 Defining Artificial Intelligence 7 -- 1.4 Historical Overview of AI in Education and Language Learning 7 -- 1.5 Implication of Artificial Intelligence in Education 8 -- 1.5.1 Machine Translation 9 -- 1.5.2 Chatbots 9 -- 1.5.3 Automatic Speech Recognition Tools 9 -- 1.5.4 Autocorrect/Automatic Text Evaluator 11 -- 1.5.5 Vocabulary Training Applications 12 -- 1.5.6 Google Docs Speech Recognition 12 -- 1.5.7 Language MuseTM Activity Palette 13 -- 1.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes 13 -- 1.6.1 Autonomous Learning 13 -- 1.6.2 Produce Smart Content 13 -- 1.6.3 Task Automation 13 -- 1.6.4 Access to Education for Students with Physical Disabilities 14 -- 1.7 Conclusion 14 -- References 15 -- 2 Real Estate Price Prediction Using Machine Learning Algorithms 19 Palak Furia and Anand Khandare -- 2.1 Introduction 20 -- 2.2 Literature Review 20 -- 2.3 Proposed Work 21 -- 2.3.1 Methodology 21 -- 2.3.2 Work Flow 22 -- 2.3.3 The Dataset 22 -- 2.3.4 Data Handling 23 -- 2.3.4.1 Missing Values and Data Cleaning 23 -- 2.3.4.2 Feature Engineering 24 -- 2.3.4.3 Removing Outliers 25 -- 2.4 Algorithms 27 -- 2.4.1 Linear Regression 27 -- 2.4.2 LASSO Regression 27 -- 2.4.3 Decision Tree 28 -- 2.4.4 Support Vector Machine 28 -- 2.4.5 Random Forest Regressor 28 -- 2.4.6 XGBoost 29 -- 2.5 Evaluation Metrics 29 -- 2.6 Result of Prediction 30 -- References 31 -- 3 Multi-Criteria–Based Entertainment Recommender System Using Clustering Approach 33 Chandramouli Das, Abhaya Kumar Sahoo and Chittaranjan Pradhan -- 3.1 Introduction 34 -- 3.2 Work Related Multi-Criteria Recommender System 35 -- 3.3 Working Principle 38 -- 3.3.1 Modeling Phase 39 -- 3.3.2 Prediction Phase 39 -- 3.3.3 Recommendation Phase 40 -- 3.3.4 Content-Based Approach 40 -- 3.3.5 Collaborative Filtering Approach 41 -- 3.3.6 Knowledge-Based Filtering Approach 41 -- 3.4 Comparison Among Different Methods 42 -- 3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis 42 -- 3.4.1.1 Discussion and Result 43 -- 3.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally et al. 46 -- 3.4.2.1 Dataset and Evaluation Matrix 46 -- 3.4.2.2 Training Setting 49 -- 3.4.2.3 Result 49 -- 3.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng 49 -- 3.4.3.1 Evaluation Setting 50 -- 3.4.3.2 Experimental Result 50 -- 3.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng 51 -- 3.4.4.1 Experimental Dataset 51 -- 3.4.4.2 Experimental Result 52 -- 3.4.5 Multi-Criteria Clustering Approach by Wasid and Ali 53 -- 3.4.5.1 Experimental Evaluation 53 -- 3.4.5.2 Result and Analysis 53 -- 3.5 Advantages of Multi-Criteria Recommender System 54 -- 3.5.1 Revenue 57 -- 3.5.2 Customer Satisfaction 57 -- 3.5.3 Personalization 57 -- 3.5.4 Discovery 58 -- 3.5.5 Provide Reports 58 -- 3.6 Challenges of Multi-Criteria Recommender System 58 -- 3.6.1 Cold Start Problem 58 -- 3.6.2 Sparsity Problem 59 -- 3.6.3 Scalability 59 -- 3.6.4 Over Specialization Problem 59 -- 3.6.5 Diversity 59 -- 3.6.6 Serendipity 59 -- 3.6.7 Privacy 60 -- 3.6.8 Shilling Attacks 60 -- 3.6.9 Gray Sheep 60 -- 3.7 Conclusion 60 -- References 61 -- 4 Adoption of Machine/Deep Learning in Cloud With a Case Study on Discernment of Cervical Cancer 65 Jyothi A. P., S. Usha and Archana H. R. -- 4.1 Introduction 66 -- 4.2 Background Study 69 -- 4.3 Overview of Machine Learning/Deep Learning 72 -- 4.4 Connection Between Machine Learning/Deep Learning and Cloud Computing 74 -- 4.5 Machine Learning/Deep Learning Algorithm 74 -- 4.5.1 Supervised Learning 74 -- 4.5.2 Unsupervised Learning 77 -- 4.5.3 Reinforcement or Semi-Supervised Learning 77 -- 4.5.3.1 Outline of ML Algorithms 77 -- 4.6 A Project Implementation on Discernment of Cervical Cancer by Using Machine/Deep Learning in Cloud 93 -- 4.6.1 Proposed Work 94 -- 4.6.1.1 MRI Dataset 94 -- 4.6.1.2 Pre Processing 95 -- 4.6.1.3 Feature Extraction 96 -- 4.6.2 Design Methodology and Implementation 97 -- 4.6.3 Results 100 -- 4.7 Applications 101 -- 4.7.1 Cognitive Cloud 102 -- 4.7.2 Chatbots and Smart Personal Assistants 103 -- 4.7.3 IoT Cloud 103 -- 4.7.4 Business Intelligence 103 -- 4.7.5 AI-as-a-Service 104 -- 4.8 Advantages of Adoption of Cloud in Machine Learning/ Deep Learning 104 -- 4.9 Conclusion 105 -- References 106 -- 5 Machine Learning and Internet of Things–Based Models for Healthcare Monitoring 111 Shruti Kute, Amit Kumar Tyagi, Aswathy S.U. and Shaveta Malik -- 5.1 Introduction 112 -- 5.2 Literature Survey 113 -- 5.3 Interpretable Machine Learning in Healthcare 114 -- 5.4 Opportunities in Machine Learning for Healthcare 116 -- 5.5 Why Combining IoT and ML? 119 -- 5.5.1 ML-IoT Models for Healthcare Monitoring 119 -- 5.6 Applications of Machine Learning in Medical and Pharma 121 -- 5.7 Challenges and Future Research Direction 122 -- 5.8 Conclusion 123 -- References 123 -- 6 Machine Learning–Based Disease Diagnosis and Prediction for E-Healthcare System 127 Shruti Suhas Kute, Shreyas Madhav A. V., Shabnam Kumari and Aswathy S. U. -- 6.1 Introduction 128 -- 6.2 Literature Survey 129 -- 6.3 Machine Learning Applications in Biomedical Imaging 132 -- 6.4 Brain Tumor Classification Using Machine Learning and IoT 134 -- 6.5 Early Detection of Dementia Disease Using Machine Learning and IoT-Based Applications 135 -- 6.6 IoT and Machine Learning-Based Diseases Prediction and Diagnosis System for EHRs 137 -- 6.7 Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT 140 -- 6.8 IoT and Machine Learning–Based System for Medical Data Mining 141 -- 6.9 Conclusion and Future Works 143 -- References 144 -- Part 2: Introduction to Deep Learning and its Models 149 -- 7 Deep Learning Methods for Data Science 151 K. Indira, Kusumika Krori Dutta, S. Poornima and Sunny Arokia Swamy Bellary -- 7.1 Introduction 152 -- 7.2 Convolutional Neural Network 152 -- 7.2.1 Architecture 154 -- 7.2.2 Implementation of CNN 154 -- 7.2.3 Simulation Results 157 -- 7.2.4 Merits and Demerits 158 -- 7.2.5 Applications 159 -- 7.3 Recurrent Neural Network 159 -- 7.3.1 Architecture 160 -- 7.3.2 Types of Recurrent Neural Networks 161 -- 7.3.2.1 Simple Recurrent Neural Networks 161 -- 7.3.2.2 Long Short-Term Memory Networks 162 -- 7.3.2.3 Gated Recurrent Units (GRUs) 164 -- 7.3.3 Merits and Demerits 167 -- 7.3.3.1 Merits 167 -- 7.3.3.2 Demerits 167 -- 7.3.4 Applications 167 -- 7.4 Denoising Autoencoder 168 -- 7.4.1 Architecture 169 -- 7.4.2 Merits and Demerits 169 -- 7.4.3 Applications 170 -- 7.5 Recursive Neural Network (RCNN) 170 -- 7.5.1 Architecture 170 -- 7.5.2 Merits and Demerits 172 -- 7.5.3 Applications 172 -- 7.6 Deep Reinforcement Learning 173 -- 7.6.1 Architecture 174 -- 7.6.2 Merits and Demerits 174 -- 7.6.3 Applications 174 -- 7.7 Deep Belief Networks (DBNS) 175 -- 7.7.1 Architecture 176 -- 7.7.2 Merits and Demerits 176 -- 7.7.3 Applications 176 -- 7.8 Conclusion 177 -- References 177 -- 8 A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG 181 Rupali Gill and Jaiteg Singh -- 8.1 Introduction 182 -- 8.2 Background and Motivation 183 -- 8.2.1 Emotion Model 183 -- 8.2.2 Neuromarketing and BCI 184 -- 8.2.3 EEG Signal 185 -- 8.3 Related Work 185 -- 8.3.1 Machine Learning 186 -- 8.3.2 Deep Learning 191 -- 8.3.2.1 Fast Feed Neural Networks 193 -- 8.3.2.2 Recurrent Neural Networks 193 -- 8.3.2.3 Convolutional Neural Networks 194 -- 8.4 Methodology of Proposed System 195 -- 8.4.1 DEAP Dataset 196 -- 8.4.2 Analyzing the Dataset 196 -- 8.4.3 Long Short-Term Memory 197 -- 8.4.4 Experimental Setup 197 -- 8.4.5 Data Set Collection 197 -- 8.5 Results and Discussions 198 -- 8.5.1 LSTM Model Training and Accuracy 198 -- 8.6 Conclusion 199 -- References 199 -- 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 Vignesh Baalaji S., Vergin Raja Sarobin M., L. Jani Anbarasi, Graceline Jasmine S. and Rukmani P.

-- 9.1 Introduction 208 -- 9.2 Story of Alzheimer’s Disease 208 -- 9.3 Datasets 210 -- 9.3.1 ADNI 210 -- 9.3.2 OASIS 210 -- 9.4 Story of Parkinson’s Disease 211 -- 9.5 A Review on Learning Algorithms 212 -- 9.5.1 Convolutional Neural Network (CNN) 212 -- 9.5.2 Restricted Boltzmann Machine 213 -- 9.5.3 Siamese Neural Networks 213 -- 9.5.4 Residual Network (ResNet) 214 -- 9.5.5 U-Net 214 -- 9.5.6 LSTM 214 -- 9.5.7 Support Vector Machine 215 -- 9.6 A Review on Methodologies 215 -- 9.6.1 Prediction of Alzheimer’s Disease 215 -- 9.6.2 Prediction of Parkinson’s Disease 221 -- 9.6.3 Detection of Attacks on Deep Brain Stimulation 223 -- 9.7 Results and Discussion 224 -- 9.8 Conclusion 224 -- References 227 -- 10 Emerging Innovations in ...