Machine Learning and Data Science

Prateek Agrawal, PhD, completed his BTech in computer science engineering from Uttar Pradesh Technical University, Lucknow, India and MTech from ABV-IIITM, Gwalior, India. He also received his PhD from IKG-Punjab Technical University, Punjab, India. He has more than ten years of research and teaching experience. He has worked as a post-doctoral researcher at the department of ITEC, University of Klagenfurt, Austria and also serves as an associate professor in the school of computer science engineering, Lovely Professional University, India. He has authored more than 50 research papers in various peer reviewed journals and conferences. He is a life member of IET research society. He is a reviewer and editorial member of many journals of high repute. He also served as technical program committee member of many international conferences. Charu Gupta, PhD, graduated with her BE and MTech in computer science and engineering, with honors. She completed her doctoral degree at the Department of Computer Science, Banasthali Vidyapith, Rajasthan, India. Presently serving as assistant professor at Bhagwan Parshuram Institute of Technology, she has over 10 years of teaching experience. She is a faculty coordinator (Delhi Section) of Free and Open Source Cell (FOSS cell) from the International Centre for Free and Open Source Software (ICFOSS), Govt. of Kerala, India. She is also the faculty co-ordinator of the e-Yantra Lab setup initiative (eLSI) in collaboration with IIT Bombay in the Department of Computer Science and Engineering, BPIT. She also has numerous papers in scientific and scholarly journals to her credit. Vishu Madaan, PhD, received her BTech and MTech degrees in computer science engineering from Lovely Professional University, Phagwara, India and earned her PhD in computer science from IKG-Punjab Technical University, Punjab. She is an assistant professor with Lovely Professional University and has more than eight years of teaching and research experience. She has authored more than 30 research articles in peer-reviewed conferences and journals. She is also a member of IEEE and is a reviewer for many international conferences and technical journals. Anand Sharma, PhD, received his PhD in engineering from MUST, Lakshmangarh, his MTech from ABV-IIITM, Gwalior and BE from RGPV, Bhopal. He has been working with Mody University of Science and Technology, Lakshmangarh for over 10 years and has more than 14 years of experience of teaching and research. He has pioneered research in the areas of information systems, information security, IoT, knowledge management and machine learning. He is the Vice-Chairman of CSI-Lakshmangarh Chapter and Student Branch Coordinator of CSI-MUST, student branch. He has authored or edited five books and has more than 70 papers in scientific and scholarly journals and conferences. He has organized more than 15 conferences, seminars, and workshops and is serving on the editorial boards of several journals as and is on the program committees of several international conferences. Nisheeth Joshi, PhD, is an associate professor at Banasthali Vidyapith, India. He did his PhD in the area of natural language processing and is the recipient of the prestigious ISTE-U.P. Government National Award for Outstanding Work Done in Specified Areas of Engineering and Technology. He has authored several papers in scientific and technical journals and conferences. He has also been a mentor and consultant to various start-ups working in the areas of cognitive computing, natural language processing, speech processing, multimodal information retrieval and machine translation. He was also a consultant to C-DAC Pune where he developed the evaluation methodology for the Mantra Machine Translation System. This Machine Translation System is being used by Rajya Sabha, the Upper House of Parliament of India and Department of Official Languages, with the Government of India. In addition, he has two technology transfers and four copyrights to his credit.

• Preface xiiiBook Description xv1 Machine Learning: An Introduction to Reinforcement Learning 1Sheikh Amir Fayaz, Dr. S Jahangeer Sidiq, Dr. Majid Zaman and Dr. Muheet Ahmed Butt1.1 Introduction 21.2 Reinforcement Learning Paradigm: Characteristics 111.3 Reinforcement Learning Problem 121.4 Applications of Reinforcement Learning 152 Data Analysis Using Machine Learning: An Experimental Study on UFC 23Prashant Varshney, Charu Gupta, Palak Girdhar, Anand Mohan, Prateek Agrawal and Vishu Madaan2.1 Introduction 232.2 Proposed Methodology 252.3 Experimental Evaluation and Visualization 312.4 Conclusion 443 Dawn of Big Data with Hadoop and Machine Learning 47Balraj Singh and Harsh Kumar Verma3.1 Introduction 483.2 Big Data 483.3 Machine Learning 533.4 Hadoop 553.5 Studies Representing Applications of Machine Learning Techniques with Hadoop 573.6 Conclusion 614 Industry 4.0: Smart Manufacturing in Industries -- The Future 67Dr. K. Bhavana Raj4.1 Introduction 675 COVID-19 Curve Exploration Using Time Series Data for India 75Apeksha Rustagi, Divyata, Deepali Virmani, Ashok Kumar, Charu Gupta, Prateek Agrawal and Vishu Madaan5.1 Introduction 765.2 Materials Methods 775.3 Concl usion and Future Work 866 A Case Study on Cluster Based Application Mapping Method for Power Optimization in 2D NoC 89Aravindhan Alagarsamy and Sundarakannan Mahilmaran6.1 Introduction 906.2 Concept Graph Theory and NOC 916.3 Related Work 946.4 Proposed Methodology 976.5 Experimental Results and Discussion 1006.6 Conclusion 1057 Healthcare Case Study: COVID-19 Detection, Prevention Measures, and Prediction Using Machine Learning & Deep Learning Algorithms 109Devesh Kumar Srivastava, Mansi Chouhan and Amit Kumar Sharma7.1 Introduction 1107.2 Literature Review 1117.3 Coronavirus (Covid19) 1127.4 Proposed Working Model 1187.5 Experimental Evaluation 1307.6 Conclusion and Future Work 1328 Analysis and Impact of Climatic Conditions on COVID-19 Using Machine Learning 135Prasenjit Das, Shaily Jain, Shankar Shambhu and Chetan Sharma8.1 Introduction 1368.2 COVID-19 1388.3 Experimental Setup 1418.4 Proposed Methodology 1428.5 Results Discussion 1438.6 Conclusion and Future Work 1439 Application of Hadoop in Data Science 147Balraj Singh and Harsh K. Verma9.1 Introduction 1489.2 Hadoop Distributed Processing 1539.3 Using Hadoop with Data Science 1609.4 Conclusion 16410 Networking Technologies and Challenges for Green IOT Applications in Urban Climate 169Saikat Samanta, Achyuth Sarkar and Aditi Sharma10.1 Introduction 17010.2 Background 17010.3 Green Internet of Things 17310.4 Different Energy--Efficient Implementation of Green IOT 17710.5 Recycling Principal for Green IOT 17810.6 Green IOT Architecture of Urban Climate 17910.7 Challenges of Green IOT in Urban Climate 18110.8 Discussion & Future Research Directions 18110.9 Conclusion 18211 Analysis of Human Activity Recognition Algorithms Using Trimmed Video Datasets 185Disha G. Deotale, Madhushi Verma, P. Suresh, Divya Srivastava, Manish Kumar and Sunil Kumar Jangir11.1 Introduction 18611.2 Contributions in the Field of Activity Recognition from Video Sequences 19011.3 Conclusion 21212 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 Shukla12.1 Introduction 21612.2 Methodology 21712.3 Description of Position 22212.4 Results and Discussion 22412.5 Graphical User Interface 22513 Drowsiness Detection Using Digital Image Processing 231G. Ramesh Babu, Chinthagada Naveen Kumar and Maradana Harish13.1 Introduction 23113.2 Literature Review 23213.3 Proposed System 23313.4 The Dataset 23413.5 Working Principle 23513.6 Convolutional Neural Networks 23913.6.1 CNN Design for Decisive State of the Eye 23913.7 Performance Evaluation 24013.8 Conclusion 242References 242Index 245