International Journal of Sustainable Devlopment in field of IT
https://journals.threws.com/index.php/IT
<div id="journalDescription"> <p><strong>International Journal of Sustainable Development in Information Technology </strong></p> <p><strong>About the Journal:</strong> The International Journal of Sustainable Development in Information Technology is a peer-reviewed, open-access journal dedicated to advancing the field of sustainable development within the realm of information technology. It serves as a platform for scholars, researchers, practitioners, and policymakers to exchange knowledge and insights on sustainable practices, innovations, and advancements in the IT sector.</p> <p><strong>Scope:</strong> IJSUST-IT is committed to promoting sustainable development in the field of information technology by addressing a wide range of interdisciplinary topics and challenges. The journal's scope encompasses but is not limited to the following areas:</p> <p><strong>1. Green IT and Eco-Friendly Technologies:</strong></p> <ul> <li>Sustainable design and development of IT infrastructure</li> <li>Energy-efficient computing and data centers</li> <li>Eco-friendly hardware and software solutions</li> <li>Renewable energy integration in IT systems</li> </ul> <p><strong>2. Sustainable Software Development:</strong></p> <ul> <li>Sustainable Coding practices and Methodologies</li> <li>Reducing software carbon footprint</li> <li>Sustainable software architecture and design</li> <li>Energy-efficient algorithms and data processing</li> </ul> <p><strong>3. IT for Environmental Monitoring and Management:</strong></p> <ul> <li>IoT and sensor technologies for environmental monitoring</li> <li>Data analytics for sustainable resource management</li> <li>IT solutions for climate change mitigation and adaptation</li> <li>Sustainable IT applications in agriculture and forestry</li> </ul> <p><strong>4. E-Waste Management and Recycling:</strong></p> <ul> <li>Strategies for sustainable disposal and recycling of electronic waste</li> <li>Circular economy approaches in the IT industry</li> <li>Reuse and refurbishment of IT equipment</li> </ul> <p><strong>5. Sustainable Information Systems and Data Security:</strong></p> <ul> <li>Sustainable information management practices</li> <li>Privacy and security in sustainable IT systems</li> <li>Data ethics and sustainability in data processing</li> <li>Blockchain technology for sustainable data security</li> </ul> <p><strong>6. IT for Social and Economic Development:</strong></p> <ul> <li>IT solutions for inclusive and equitable access to technology</li> <li>Digital literacy and education for sustainable development</li> <li>IT-enabled social entrepreneurship and community development</li> <li>Sustainable IT policies and regulations</li> </ul> <p><strong>7. Case Studies and Best Practices:</strong></p> <ul> <li>Real-world case studies on sustainable IT implementations</li> <li>Success stories and best practices in sustainable IT projects</li> <li>Lessons learned and recommendations for sustainable IT initiatives</li> </ul> <p><strong>8. Interdisciplinary Approaches:</strong></p> <ul> <li>Collaborative research at the intersection of IT and sustainability</li> <li>Multidisciplinary studies in environmental informatics and green computing</li> <li>Cross-disciplinary research in IT applications for sustainable development</li> </ul> <p>IJSUST-IT invites original research articles, reviews, and contributions that offer novel insights and practical solutions for sustainable development in the IT field. The journal encourages cross-disciplinary collaboration, data-driven research, and ethical considerations to advance the sustainability agenda in information technology.</p> <p>We welcome contributions from scholars, researchers, industry experts, and policymakers who are committed to the vision of a more sustainable and environmentally responsible information technology sector. IJSUST-IT aims to be a leading source of knowledge, fostering innovation and sustainability in IT for a better, greener future.</p> <p>IJSDIT is a double-blind peer-reviewed journal indexed in several databases like google scholar, Wos, Dooj, EI</p> <p> </p> <p>JCR Impact Factor: 4.63 (2019)</p> <p>JCR Impact Factor: 5.64 (2020)</p> <p>JCR Impact Factor: 5.98 (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>The research worlden-USInternational Journal of Sustainable Devlopment in field of ITSmart Healthcare: Leveraging IoT and Deep Learning for Real-Time Disease Detection
https://journals.threws.com/index.php/IT/article/view/254
<p>The integration of IoT and deep learning has the potential to revolutionize real-time disease detection in healthcare. This paper explores a smart healthcare system that utilizes IoT sensors to continuously monitor patient vitals and deep learning algorithms to detect early signs of diseases such as sepsis and cardiac events. We present the design and implementation of the system, along with a pilot study conducted in a clinical setting. The results show that the system can provide timely alerts to healthcare providers, potentially saving lives by enabling prompt medical intervention. The paper also discusses the challenges of data accuracy, real-time processing, and system scalability.</p> <p> </p>Prof. Arun sharma
Copyright (c) 2024
2024-07-032024-07-031616IoT and Machine Learning in Precision Medicine: A New Paradigm for Treatment Personalization
https://journals.threws.com/index.php/IT/article/view/255
<p>Precision medicine aims to customize healthcare treatments based on individual genetic, environmental, and lifestyle factors. This paper investigates the role of IoT and machine learning in advancing precision medicine. We propose a framework that integrates IoT devices to collect comprehensive health data and machine learning models to analyze this data for personalized treatment recommendations. The study includes a case study on oncology, demonstrating how the framework can optimize chemotherapy regimens based on patient-specific data. Our findings highlight the potential of IoT and machine learning to enhance treatment effectiveness and reduce adverse effects in precision medicine.</p> <p> </p>Prof. Mamta Rani
Copyright (c) 2024
2024-07-032024-07-031616The Future of Telemedicine: IoT and Machine Learning for Enhanced Patient-Doctor Interactions
https://journals.threws.com/index.php/IT/article/view/256
<p>Telemedicine has emerged as a crucial component of modern healthcare, particularly in remote and underserved areas. This paper explores the future of telemedicine by integrating IoT and machine learning to enhance patient-doctor interactions. We present a telemedicine platform that uses IoT devices to monitor patients' health metrics in real-time and machine learning algorithms to analyze the data for remote diagnosis and treatment recommendations. The platform's effectiveness is evaluated through a series of clinical trials, showing improved patient outcomes and satisfaction. The paper also discusses the technical and regulatory challenges of implementing such a system on a large scale.</p> <p> </p>Dr. Rahul garg
Copyright (c) 2024
2024-07-032024-07-031616Reducing Hospital Readmissions with IoT and Predictive Analytics: A Machine Learning Approach
https://journals.threws.com/index.php/IT/article/view/257
<p>Hospital readmissions pose significant challenges to healthcare systems, often indicating suboptimal patient care. This paper proposes a machine learning approach to reduce hospital readmissions by leveraging IoT devices and predictive analytics. We develop a predictive model that analyzes data from IoT-enabled patient monitoring systems to identify patients at high risk of readmission. The model's predictions are used to tailor post-discharge care plans, aiming to prevent readmissions. A retrospective analysis of hospital data shows that our approach can significantly reduce readmission rates, improving patient outcomes and reducing healthcare costs. The paper also discusses the implementation challenges and future research directions.</p> <p> </p>Prof. shyam sunder
Copyright (c) 2024
2024-07-032024-07-031616IoT and Deep Learning for Elderly Care: Monitoring and Predictive Health Management
https://journals.threws.com/index.php/IT/article/view/258
<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>Prof. Mangat Singh
Copyright (c) 2024
2024-07-032024-07-031616