Includes bibliographical references and index.

Preface xiii Book Description xv 1 Machine Learning: An Introduction to Reinforcement Learning 1Sheikh Amir Fayaz, Dr. S Jahangeer Sidiq, Dr. Majid Zaman and Dr. Muheet Ahmed Butt 1.1 Introduction 2 1.2 Reinforcement Learning Paradigm: Characteristics 11 1.3 Reinforcement Learning Problem 12 1.4 Applications of Reinforcement Learning 15 2 Data Analysis Using Machine Learning: An Experimental Study on UFC 23Prashant Varshney, Charu Gupta, Palak Girdhar, Anand Mohan, Prateek Agrawal and Vishu Madaan 2.1 Introduction 23 2.2 Proposed Methodology 25 2.3 Experimental Evaluation and Visualization 31 2.4 Conclusion 44 3 Dawn of Big Data with Hadoop and Machine Learning 47Balraj Singh and Harsh Kumar Verma 3.1 Introduction 48 3.2 Big Data 48 3.3 Machine Learning 53 3.4 Hadoop 55 3.5 Studies Representing Applications of Machine Learning Techniques with Hadoop 57 3.6 Conclusion 61 4 Industry 4.0: Smart Manufacturing in Industries -- The Future 67Dr. K. Bhavana Raj 4.1 Introduction 67 5 COVID-19 Curve Exploration Using Time Series Data for India 75Apeksha Rustagi, Divyata, Deepali Virmani, Ashok Kumar, Charu Gupta, Prateek Agrawal and Vishu Madaan 5.1 Introduction 76 5.2 Materials Methods 77 5.3 Concl usion and Future Work 86 6 A Case Study on Cluster Based Application Mapping Method for Power Optimization in 2D NoC 89Aravindhan Alagarsamy and Sundarakannan Mahilmaran 6.1 Introduction 90 6.2 Concept Graph Theory and NOC 91 6.3 Related Work 94 6.4 Proposed Methodology 97 6.5 Experimental Results and Discussion 100 6.6 Conclusion 105 7 Healthcare Case Study: COVID-19 Detection, Prevention Measures, and Prediction Using Machine Learning & Deep Learning Algorithms 109Devesh Kumar Srivastava, Mansi Chouhan and Amit Kumar Sharma 7.1 Introduction 110 7.2 Literature Review 111 7.3 Coronavirus (Covid19) 112 7.4 Proposed Working Model 118 7.5 Experimental Evaluation 130 7.6 Conclusion and Future Work 132 8 Analysis and Impact of Climatic Conditions on COVID-19 Using Machine Learning 135Prasenjit Das, Shaily Jain, Shankar Shambhu and Chetan Sharma 8.1 Introduction 136 8.2 COVID-19 138 8.3 Experimental Setup 141 8.4 Proposed Methodology 142 8.5 Results Discussion 143 8.6 Conclusion and Future Work 143 9 Application of Hadoop in Data Science 147Balraj Singh and Harsh K. Verma 9.1 Introduction 148 9.2 Hadoop Distributed Processing 153 9.3 Using Hadoop with Data Science 160 9.4 Conclusion 164 10 Networking Technologies and Challenges for Green IOT Applications in Urban Climate 169Saikat Samanta, Achyuth Sarkar and Aditi Sharma 10.1 Introduction 170 10.2 Background 170 10.3 Green Internet of Things 173 10.4 Different Energy--Efficient Implementation of Green IOT 177 10.5 Recycling Principal for Green IOT 178 10.6 Green IOT Architecture of Urban Climate 179 10.7 Challenges of Green IOT in Urban Climate 181 10.8 Discussion & Future Research Directions 181 10.9 Conclusion 182 11 Analysis of Human Activity Recognition Algorithms Using Trimmed Video Datasets 185Disha G. Deotale, Madhushi Verma, P. Suresh, Divya Srivastava, Manish Kumar and Sunil Kumar Jangir 11.1 Introduction 186 11.2 Contributions in the Field of Activity Recognition from Video Sequences 190 11.3 Conclusion 212 12 Solving Direction Sense Based Reasoning Problems Using Natural Language Processing 215Vishu Madaan, Komal Sood, Prateek Agrawal, Ashok Kumar, Charu Gupta, Anand Sharma and Awadhesh Kumar Shukla 12.1 Introduction 216 12.2 Methodology 217 12.3 Description of Position 222 12.4 Results and Discussion 224 12.5 Graphical User Interface 225 13 Drowsiness Detection Using Digital Image Processing 231G. Ramesh Babu, Chinthagada Naveen Kumar and Maradana Harish 13.1 Introduction 231 13.2 Literature Review 232 13.3 Proposed System 233 13.4 The Dataset 234 13.5 Working Principle 235 13.6 Convolutional Neural Networks 239 13.6.1 CNN Design for Decisive State of the Eye 239 13.7 Performance Evaluation 240 13.8 Conclusion 242 References 242 Index 245

Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges than can often be addressed only with innovative machine learning and data science algorithms.
These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and ultimately, value creation.

College of Engineering and Computer Studies Bachelor of Science in Information Technology

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