https://journals.threws.com/index.php/TRDAIML/issue/feed 2025-03-04T22:59:33+00:00 Open Journal Systems <p>Transactions on Recent Developments in Artificial Intelligence and Machine Learning</p> <p><strong>Scope:</strong></p> <p>The "Transactions on Recent Developments in Artificial Intelligence and Machine Learning" is an international peer-reviewed journal dedicated to providing a platform for the dissemination of cutting-edge research, innovations, and developments in the fields of artificial intelligence (AI) and machine learning (ML). The journal aims to foster collaboration, share knowledge, and advance the frontiers of AI and ML research by offering a comprehensive scope that encompasses various aspects of these dynamic and rapidly evolving fields.</p> <p><strong>1. Research Articles:</strong> The journal invites high-quality research articles covering a wide range of topics in AI and ML. This includes but is not limited to:</p> <ul> <li>Advanced machine learning algorithms and techniques</li> <li>Deep learning models and applications</li> <li>Natural language processing and understanding</li> <li>Computer vision and image recognition</li> <li>Reinforcement learning and robotics</li> <li>AI ethics, explainability, and fairness</li> <li>AI and ML in healthcare, finance, and other domains</li> <li>AI-driven data analytics and decision support systems</li> <li>AI for autonomous systems, including self-driving cars and drones</li> </ul> <p><strong>2. Review Articles:</strong> The journal publishes comprehensive review articles that provide in-depth analyses of specific AI and ML subfields, summarizing recent developments, challenges, and future directions.</p> <p><strong>3. Case Studies and Applications:</strong> We welcome case studies and practical applications of AI and ML in real-world scenarios, highlighting their impact on industries, businesses, and society.</p> <p><strong>4. Ethical and Regulatory Considerations:</strong> We encourage submissions addressing the ethical and regulatory aspects of AI and ML, including discussions on bias, privacy, transparency, and responsible AI development and deployment.</p> <p><strong>5. Interdisciplinary Research:</strong> The journal promotes interdisciplinary research by accepting contributions that bridge the gap between AI/ML and other domains such as healthcare, finance, environmental science, and more.</p> <p><strong>6. Emerging Technologies:</strong> We provide a platform for exploring emerging AI and ML technologies, including quantum machine learning, federated learning, and AI at the edge.</p> <p><strong>7. Reproducibility and Open Source:</strong> We support open-source initiatives and encourage authors to provide code and data to enhance the reproducibility of their research.</p> <p><strong>8. Surveys and Trends:</strong> The journal includes surveys and trend articles that offer an overview of the current state of AI and ML research and their implications.</p> <p><strong>9. Educational Resources:</strong> We publish educational resources, including tutorials, to help researchers, students, and practitioners stay updated with the latest advancements in AI and ML.</p> <p>The "Transactions on Recent Developments in Artificial Intelligence and Machine Learning" is committed to promoting interdisciplinary collaboration, fostering a global research community, and advancing knowledge in AI and ML. We invite researchers, scientists, professionals, and academics to contribute their original work and share their insights in order to drive innovation and shape the future of AI and machine learning.</p> <p><strong><span style="font-size: 0.875rem;">Transaction on Recent Development in Artificial Intelligence and Machine Learning</span></strong></p> <p><strong> <span style="font-size: 0.875rem;">is a double-blind peer-reviewed journal indexed in several databases like Google Scholar, Wos, Dooj, EI </span></strong></p> <div id="journalDescription"> <p> </p> <p>JCR Impact Factor: 4.71 (2019)</p> <p>JCR Impact Factor: 5.62 (2020)</p> <p>JCR Impact Factor: 5.99 (2021)</p> <p>JCR Impact Factor: 6.11 (2022)</p> <p>JCR Impact Factor: Under Evaluation (2023)</p> <div align="left"><strong>Peer Review Policy</strong></div> <div align="left"><br />All submitted manuscripts are subject to initial appraisal by the Editors. If found suitable for further consideration, papers are subject to peer review by independent, anonymous expert referees, under the guidance of a team of expert Associate Editors. All peer-review is double-blind and submissions can be made online via the Submission Portal.</div> <p><br /><strong>Publishing Ethics</strong></p> <div align="left">The Journal adheres to the highest standards of publishing ethics, with rigorous processes in place to ensure this is achieved. We are member of the Committee of Publications Ethics (COPE) and utilizes CrossCheck for all Journals. </div> </div> https://journals.threws.com/index.php/TRDAIML/article/view/383 2025-03-04T22:59:33+00:00 Dr. Prakash singh singh@gmail.com

<p>With the increasing reliance on AI-driven applications, concerns over data privacy and security have become paramount. Federated learning (FL) offers a decentralized machine learning approach that enables collaborative model training without directly sharing sensitive data. This paper explores the fundamentals of FL, its advantages over traditional centralized AI models, and its application in privacy-sensitive domains such as healthcare, finance, and IoT. We analyze the performance and security trade-offs of FL, discussing challenges such as communication overhead, adversarial attacks, and model poisoning. Our findings suggest that FL provides a promising pathway toward secure and privacy-preserving AI development.</p>

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