{"product_id":"machine-learning-and-data-science-fundamentals-and-applications-hardback-9781119775614","title":"Machine Learning and Data Science; Fundamentals and Applications (Hardback) 9781119775614","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning and Data Science\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFundamentals and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePrateek Agrawal (Edited by), Agrawal (Author), Charu Gupta (Edited by), Anand Sharma (Edited by), Vishu Madaan (Edited by), Nisheeth Joshi (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119775614, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 8 August 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e272 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eMACHINE LEARNING AND DATA SCIENCE\u003c\/b\u003e \u003cp\u003e\u003cb\u003eWritten and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eMachine 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 that can often be addressed only with innovative machine learning and data science algorithms.  \u003c\/p\u003e\n\u003cp\u003eThese 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eBook Description xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Machine Learning: An Introduction to Reinforcement Learning 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSheikh Amir Fayaz, Dr. S Jahangeer Sidiq, Dr. Majid Zaman and Dr. Muheet Ahmed Butt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Reinforcement Learning Paradigm: Characteristics 11\u003c\/p\u003e \u003cp\u003e1.3 Reinforcement Learning Problem 12\u003c\/p\u003e \u003cp\u003e1.4 Applications of Reinforcement Learning 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Data Analysis Using Machine Learning: An Experimental Study on UFC 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrashant Varshney, Charu Gupta, Palak Girdhar, Anand Mohan, Prateek Agrawal and Vishu Madaan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 23\u003c\/p\u003e \u003cp\u003e2.2 Proposed Methodology 25\u003c\/p\u003e \u003cp\u003e2.3 Experimental Evaluation and Visualization 31\u003c\/p\u003e \u003cp\u003e2.4 Conclusion 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Dawn of Big Data with Hadoop and Machine Learning 47\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBalraj Singh and Harsh Kumar Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 48\u003c\/p\u003e \u003cp\u003e3.2 Big Data 48\u003c\/p\u003e \u003cp\u003e3.3 Machine Learning 53\u003c\/p\u003e \u003cp\u003e3.4 Hadoop 55\u003c\/p\u003e \u003cp\u003e3.5 Studies Representing Applications of Machine Learning Techniques with Hadoop 57\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 61\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Industry 4.0: Smart Manufacturing in Industries -- The Future 67\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDr. K. Bhavana Raj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 COVID-19 Curve Exploration Using Time Series Data for India 75\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eApeksha Rustagi, Divyata, Deepali Virmani, Ashok Kumar, Charu Gupta, Prateek Agrawal and Vishu Madaan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 76\u003c\/p\u003e \u003cp\u003e5.2 Materials Methods 77\u003c\/p\u003e \u003cp\u003e5.3 Concl usion and Future Work 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A Case Study on Cluster Based Application Mapping Method for Power Optimization in 2D NoC 89\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAravindhan Alagarsamy and Sundarakannan Mahilmaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 90\u003c\/p\u003e \u003cp\u003e6.2 Concept Graph Theory and NOC 91\u003c\/p\u003e \u003cp\u003e6.3 Related Work 94\u003c\/p\u003e \u003cp\u003e6.4 Proposed Methodology 97\u003c\/p\u003e \u003cp\u003e6.5 Experimental Results and Discussion 100\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Healthcare Case Study: COVID-19 Detection, Prevention Measures, and Prediction Using Machine Learning \u0026amp; Deep Learning Algorithms 109\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDevesh Kumar Srivastava, Mansi Chouhan and Amit Kumar Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 110\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 111\u003c\/p\u003e \u003cp\u003e7.3 Coronavirus (Covid19) 112\u003c\/p\u003e \u003cp\u003e7.4 Proposed Working Model 118\u003c\/p\u003e \u003cp\u003e7.5 Experimental Evaluation 130\u003c\/p\u003e \u003cp\u003e7.6 Conclusion and Future Work 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Analysis and Impact of Climatic Conditions on COVID-19 Using Machine Learning 135\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrasenjit Das, Shaily Jain, Shankar Shambhu and Chetan Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 136\u003c\/p\u003e \u003cp\u003e8.2 COVID-19 138\u003c\/p\u003e \u003cp\u003e8.3 Experimental Setup 141\u003c\/p\u003e \u003cp\u003e8.4 Proposed Methodology 142\u003c\/p\u003e \u003cp\u003e8.5 Results Discussion 143\u003c\/p\u003e \u003cp\u003e8.6 Conclusion and Future Work 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Application of Hadoop in Data Science 147\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBalraj Singh and Harsh K. Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 148\u003c\/p\u003e \u003cp\u003e9.2 Hadoop Distributed Processing 153\u003c\/p\u003e \u003cp\u003e9.3 Using Hadoop with Data Science 160\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Networking Technologies and Challenges for Green IOT Applications in Urban Climate 169\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSaikat Samanta, Achyuth Sarkar and Aditi Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 170\u003c\/p\u003e \u003cp\u003e10.2 Background 170\u003c\/p\u003e \u003cp\u003e10.3 Green Internet of Things 173\u003c\/p\u003e \u003cp\u003e10.4 Different Energy--Efficient Implementation of Green IOT 177\u003c\/p\u003e \u003cp\u003e10.5 Recycling Principal for Green IOT 178\u003c\/p\u003e \u003cp\u003e10.6 Green IOT Architecture of Urban Climate 179\u003c\/p\u003e \u003cp\u003e10.7 Challenges of Green IOT in Urban Climate 181\u003c\/p\u003e \u003cp\u003e10.8 Discussion \u0026amp; Future Research Directions 181\u003c\/p\u003e \u003cp\u003e10.9 Conclusion 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Analysis of Human Activity Recognition Algorithms Using Trimmed Video Datasets 185\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDisha G. Deotale, Madhushi Verma, P. Suresh, Divya Srivastava, Manish Kumar and Sunil Kumar Jangir\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 186\u003c\/p\u003e \u003cp\u003e11.2 Contributions in the Field of Activity Recognition from Video Sequences 190\u003c\/p\u003e \u003cp\u003e11.3 Conclusion 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Solving Direction Sense Based Reasoning Problems Using Natural Language Processing 215\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVishu Madaan, Komal Sood, Prateek Agrawal, Ashok Kumar, Charu Gupta, Anand Sharma and Awadhesh Kumar Shukla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 216\u003c\/p\u003e \u003cp\u003e12.2 Methodology 217\u003c\/p\u003e \u003cp\u003e12.3 Description of Position 222\u003c\/p\u003e \u003cp\u003e12.4 Results and Discussion 224\u003c\/p\u003e \u003cp\u003e12.5 Graphical User Interface 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Drowsiness Detection Using Digital Image Processing 231\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eG. Ramesh Babu, Chinthagada Naveen Kumar and Maradana Harish\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 231\u003c\/p\u003e \u003cp\u003e13.2 Literature Review 232\u003c\/p\u003e \u003cp\u003e13.3 Proposed System 233\u003c\/p\u003e \u003cp\u003e13.4 The Dataset 234\u003c\/p\u003e \u003cp\u003e13.5 Working Principle 235\u003c\/p\u003e \u003cp\u003e13.6 Convolutional Neural Networks 239\u003c\/p\u003e \u003cp\u003e13.6.1 CNN Design for Decisive State of the Eye 239\u003c\/p\u003e \u003cp\u003e13.7 Performance Evaluation 240\u003c\/p\u003e \u003cp\u003e13.8 Conclusion 242\u003c\/p\u003e \u003cp\u003eReferences 242\u003c\/p\u003e \u003cp\u003eIndex 245\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430938800408,"sku":"9781119775614","price":128.58,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119775614.jpg?v=1784765797","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/machine-learning-and-data-science-fundamentals-and-applications-hardback-9781119775614","provider":"Freshly Printed Books","version":"1.0","type":"link"}