https://journals.threws.com/index.php/TRDAIML/issue/feedTransactions on Recent Developments in Artificial Intelligence and Machine Learning2024-07-04T06:31:21+00:00Open 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/259AI-Driven IoT for Early Detection of Epidemics: A Case Study on Influenza Outbreaks2024-07-04T06:06:36+00:00Prof. Imran Malikmalik@gmail.com<p>Early detection of epidemics is crucial for effective public health response and mitigation. This paper explores the use of AI-driven IoT systems for the early detection of influenza outbreaks. We present a framework that integrates IoT devices to collect real-time health data from populations and machine learning algorithms to identify patterns indicative of an outbreak. The system's performance is evaluated using historical influenza data, demonstrating its ability to detect outbreaks earlier than traditional methods. The paper discusses the implications for public health policy and the potential for applying this approach to other infectious diseases.</p> <p> </p>2024-07-04T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIML/article/view/260Smart Rehabilitation: Integrating IoT and Deep Learning for Personalized Therapy2024-07-04T06:10:44+00:00Dr. Harsh Varmavarma@gmail.com<p>Rehabilitation therapy is essential for patients recovering from injuries or surgeries, but traditional approaches often lack personalization. This paper presents a smart rehabilitation system that uses IoT devices and deep learning to provide personalized therapy plans. IoT sensors monitor patient movements and progress, while deep learning algorithms analyze the data to adapt therapy exercises in real-time. A clinical trial involving post-stroke patients shows that the system enhances recovery outcomes and patient engagement. The paper also addresses the technical challenges and future prospects of smart rehabilitation technologies.</p> <p> </p> <p> </p>2024-07-04T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIML/article/view/261 IoT-Enabled Predictive Analytics for Chronic Disease Management Using Machine Learning2024-07-04T06:14:37+00:00Dr. Sachine sharmasharma@gmail.com<p>Effective management of chronic diseases requires continuous monitoring and timely interventions. This paper investigates the use of IoT-enabled predictive analytics for chronic disease management, focusing on conditions such as diabetes and hypertension. We develop a predictive model using machine learning algorithms to analyze data from IoT devices, predicting disease exacerbations and suggesting proactive measures. The system's efficacy is validated through a longitudinal study, demonstrating improved disease management and patient adherence to treatment plans. The paper also discusses the integration of predictive analytics into existing healthcare systems and the potential for scaling this approach.</p> <p> </p>2024-07-04T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIML/article/view/262Enhancing Mental Health Care with IoT and Machine Learning: Monitoring and Intervention2024-07-04T06:17:29+00:00Prof. Chinto Sahanisahani@gmail.com<p>Mental health care can benefit significantly from continuous monitoring and timely interventions. This paper explores the application of IoT and machine learning in enhancing mental health care. We propose a system that uses IoT devices to monitor physiological and behavioral indicators of mental health and machine learning algorithms to analyze this data for early detection of mental health issues such as depression and anxiety. The system also provides personalized intervention recommendations. A pilot study with participants suffering from mental health conditions shows promising results in improving mental health outcomes. The paper also addresses privacy concerns and the ethical use of data in mental health care.</p> <p> </p> <p> </p>2024-07-04T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIML/article/view/264IoT and Deep Learning for Elderly Care: Monitoring and Predictive Health Management2024-07-04T06:24:26+00:00Prof. Arun Kumararun23@gmail.com<p>The aging population presents unique healthcare challenges, including the need for continuous monitoring and early intervention. This paper explores the application of IoT and deep learning for elderly care, focusing on monitoring and predictive health management. We present an IoT-based system that collects data from wearable sensors and home environment sensors, using deep learning algorithms to predict health issues such as falls, heart problems, and cognitive decline. A field study conducted in a senior living community demonstrates the system's ability to provide timely alerts and improve the overall quality of care. The paper also addresses the ethical considerations and data privacy concerns associated with elderly care technologies.</p> <p> </p>2024-07-04T00:00:00+00:00Copyright (c) 2024