Machine Learning in Research and Practice: A Multidisciplinary Perspective.
Keywords:
Machine Learning, Deep Learning, Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), Transfer Learning, Computer Vision, AI Applications, Real-Time SystemsSynopsis
This book presents a multidisciplinary exploration of machine learning techniques, frameworks, and applications across diverse real-world domains. Beginning with foundational concepts of supervised, unsupervised, and reinforcement learning, the chapters progressively highlight modern approaches such as natural language processing, deep learning, and transformer-based architectures. Topics include automated medical diagnosis, drug discovery, resume analysis and interview preparation, underwater image classification, and real-time suspicious activity detection. Each contribution emphasizes practical implementation strategies covering dataset preparation, preprocessing, feature extraction, model optimization, and evaluation metrics along with discussions of domain-specific challenges such as data imbalance, interpretability, and ethical considerations. Experimental studies across chapters consistently demonstrate the potential of machine learning to achieve higher accuracy, scalability, and efficiency compared to traditional approaches. Collectively, the book offers insights into emerging research trends and practical methodologies, bridging theory with application in healthcare, security, environmental monitoring, and intelligent automation.
Chapters
Published
September 5, 2025
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Copyright (c) 2025 Genome Publications
