Paper Contents
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological innovation, driving advancements across diverse fields such as healthcare, finance, education, and industry. AI aims to develop systems that replicate human intelligence, while ML empowers these systems to learn from data and improve performance over time. This journal, Studies in Artificial Intelligence and Machine Learning, provides an in-depth exploration of fundamental principles, including knowledge representation, reasoning, and decision-making, alongside core ML methods such as supervised, unsupervised, and reinforcement learning. A special focus is placed on deep learning architectures, including multi-layer perceptrons, convolutional neural networks, recurrent neural networks, and transformers, which power breakthroughs in natural language processing, computer vision, and generative AI. Ethical and social issues, including bias, privacy, and responsible AI, are critically discussed to emphasize sustainable and fair applications. Furthermore, the journal examines emerging trends such as explainable AI, quantum machine learning, and edge intelligence, highlighting their potential for shaping the future of intelligent systems. By blending theory, applications, and real-world case studies, this journal serves as a valuable resource for students, educators, and researchers, inspiring innovation and promoting responsible use of AI and ML technologies
Copyright
Copyright © 2025 Santhiya A.B . This is an open access article distributed under the Creative Commons Attribution License.
