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How Machine Learning is Innovating Today's World
A Concise Technical Guide

Arindam Dey (Edited by), A Dey (Author), Sukanta Nayak (Edited by), Ranjan Kumar (Edited by), Sachi Nandan Mohanty (Edited by)

9781394214112, Wiley

Hardback, published 1 July 2024

480 pages
25.4 x 17.8 x 2.9 cm, 1.202 kg

Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques.

Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. How Machine Learning is Innovating Today's World is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries.

It provides a comprehensive understanding of the practical applications of ML techniques. The wide range of topics include:

  • An analysis of various tokenization techniques and the sequence-to-sequence model in natural language processing
  • explores the evaluation of English language readability using ML models
  • a detailed study of text analysis for information retrieval through natural language processing
  • the application of reinforcement learning approaches to supply chain management
  • the performance analysis of converting algorithms to source code using natural language processing in Java
  • presents an alternate approach to solving differential equations utilizing artificial neural networks with optimization techniques
  • a comparative study of different techniques of text-to-SQL query conversion
  • the classification of livestock diseases using ML algorithms
  • ML in image enhancement techniques
  • the efficient leader selection for inter-cluster flying ad-hoc networks
  • a comprehensive survey of applications powered by GPT-3 and DALL-E
  • recommender systems' domain of application
  • reviews mood detection, emoji generation, and classification using tokenization and CNN
  • variations of the exam scheduling problem using graph coloring
  • the intersection of software engineering and machine learning applications
  • explores ML strategies for indeterminate information systems in complex bipolar neutrosophic environments
  • ML applications in healthcare, in battery management systems, and the rise of AI-generated news videos
  • how to enhance resource management in precision farming through AI-based irrigation optimization.

Audience

The book will be extremely useful to professionals, post-graduate research scholars, policymakers, corporate managers, and anyone with technical interests looking to understand how machine learning and artificial intelligence can benefit their work.

Preface xvii

Part 1: Natural Language Processing (NLP) Applications 1

1 A Comprehensive Analysis of Various Tokenization Techniques and Sequence-to-Sequence Model in Natural Language Processing 3
Kuldeep Vayadande, Ashutosh M. Kulkarni, Gitanjali Bhimrao Yadav, R. Kumar and Aparna R. Sawant

2 A Review on Text Analysis Using NLP 13
Kuldeep Vayadande, Preeti A. Bailke, Lokesh Sheshrao Khedekar, R. Kumar and Varsha R. Dange

3 Text Generation & Classification in NLP: A Review 25
Kuldeep Vayadande, Dattatray Raghunath Kale, Jagannath Nalavade, R. Kumar and Hanmant D. Magar

4 Book Genre Prediction Using NLP: A Review 37
Kuldeep Vayadande, Preeti Bailke, Ashutosh M. Kulkarni, R. Kumar and Ajit B. Patil

5 Mood Detection Using Tokenization: A Review 47
Kuldeep Vayadande, Preeti A. Bailke, Lokesh Sheshrao Khedekar, R. Kumar and Varsha R. Dange

6 Converting Pseudo Code to Code: A Review 57
Kuldeep Vayadande, Preeti A. Bailke, Anita Bapu Dombale, Varsha R. Dange and Ashutosh M. Kulkarni

Part 2: Machine Learning Applications in Specific Domains 69

7 Evaluating the Readability of English Language Using Machine Learning Models 71
Shiplu Das, Abhishikta Bhattacharjee, Gargi Chakraborty and Debarun Joardar

8 Machine Learning in Maximizing Cotton Yield with Special Reference to Fertilizer Selection 89
G. Hannah Grace and Nivetha Martin

9 Machine Learning Approaches to Catalysis 101
Sachidananda Nayak and Selvakumar Karuthapandi

10 Classification of Livestock Diseases Using Machine Learning Algorithms 127
G. Hannah Grace, Nivetha Martin, I. Pradeepa and N. Angel

11 Image Enhancement Techniques to Modify an Image with Machine Learning Application 139
Shiplu Das, Sohini Sen, Debarun Joardar and Gargi Chakraborty

12 Software Engineering in Machine Learning Applications: A Comprehensive Study 159
Kuldeep Vayadande, Komal Sunil Munde, Amol A. Bhosle, Aparna R. Sawant and Ashutosh M. Kulkarni

13 Machine Learning Applications in Battery Management System 173
Ponnaganti Chandana and Ameet Chavan

14 ML Applications in Healthcare 201
Farooq Shaik, Rajesh Yelchurri, Noman Aasif Gudur and Jatindra Kumar Dash

15 Enhancing Resource Management in Precision Farming through AI-Based Irrigation Optimization 221
Salina Adinarayana, Matha Govinda Raju, Durga Prasad Srirangam, Devee Siva Prasad, Munaganuri Ravi Kumar and Sai babu veesam

16 An In-Depth Review on Machine Learning Infusion in an Agricultural Production System 253
Sarthak Dash, Sugyanta Priyadarshini and Sukanya Priyadarshini

Part 3: Artificial Intelligence and Optimization Techniques 271

17 Reinforcement Learning Approach in Supply Chain Management: A Review 273
Rajkanwar Singh, Pratik Mandal and Sukanta Nayak

18 Alternate Approach to Solve Differential Equations Using Artificial Neural Network with Optimization Technique 303
Ramanan R., Sukanta Nayak and Arun Kumar Gupta

19 GPT-3- and DALL-E-Powered Applications: A Complete Survey 329
Kuldeep Vayadande, Chaitanya B. Pednekar, Priya Anup Khune, Vinay Sudhir Prabhavalkar and Varsha R. Dange

20 New Variation of Exam Scheduling Problem Using Graph Coloring 343
Angshu Kumar Sinha, Soumyadip Laha, Debarghya Adhikari, Anjan Koner and Neha Deora

Part 4: Emerging Topics in Machine Learning 353

21 A Comparative Study of Different Techniques of Text-to-SQL Query Converter 355
Kuldeep Vayadande, Preeti A. Bailke, Vikas Janu Nandeshwar, R. Kumar and Varsha R. Dange

22 Trust-Based Leader Election in Flying Ad-Hoc Network 367
Joydeep Kundu, Sahabul Alam and Sukanta Oraw

23 A Survey on Domain of Application of Recommender System 375
Sudipto Dhar

24 New Approach on M/M/c/K Queueing Models via Single Valued Linguistic Neutrosophic Numbers and Perceptionization Using a Non-Linear Programming Technique 383
Antony Crispin Sweety C. and Vennila B.

25 The Rise of AI-Generated News Videos: A Detailed Review 423
Kuldeep Vayadande, Mustansir Bohri, Mohit Chawala, Ashutosh M. Kulkarni and Asif Mursal

References 449

Index 453

Subject Areas: Computer science [UY]
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