Nayak S How Machine Learning is Innovating Today's World Technical Guide 2024 | 56.22 MB
477 Pages

Title: How Machine Learning is Innovating Today's World: A Concise Technical Guide
Author: Arindam Dey (Editor), Sukanta Nayak (Editor), Ranjan Kumar (Editor), Sachi Nandan Mohanty (Editor)

Description:
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.

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