International Journal of Sustainable Devlopment in field of IT

Smart Healthcare: Leveraging IoT and Deep Learning for Real-Time Disease Detection

Prof. Arun sharma — 2024-07-03

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.

IoT and Machine Learning in Precision Medicine: A New Paradigm for Treatment Personalization

Prof. Mamta Rani — 2024-07-03

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.

The Future of Telemedicine: IoT and Machine Learning for Enhanced Patient-Doctor Interactions

Dr. Rahul garg — 2024-07-03

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.

Reducing Hospital Readmissions with IoT and Predictive Analytics: A Machine Learning Approach

Prof. shyam sunder — 2024-07-03

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.

IoT and Deep Learning for Elderly Care: Monitoring and Predictive Health Management

Prof. Mangat Singh — 2024-07-03

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.

Natural Language Processing for Automated Legal Document Analysis and Contract Review

Manoj Chowdary Vattikuti — 2024-12-12

The legal domain generates vast amounts of text, making manual document analysis and contract review time-intensive and error-prone. This paper introduces a Natural Language Processing (NLP) framework for automating legal document analysis and contract review. By employing transformer-based models like BERT and GPT, the system identifies critical clauses, detects anomalies, and provides risk assessments. The approach is validated on a dataset of legal contracts, demonstrating high accuracy in clause extraction and anomaly detection. The study emphasizes the potential of NLP to enhance efficiency and accuracy in legal workflows, reducing the burden on legal professionals and enabling faster decision-making.

AI-Enhanced Virtual Assistants for Healthcare Support and Patient Monitoring

Prof. Kishan Suraj — 2024-12-12

Virtual assistants powered by AI can significantly enhance healthcare delivery by providing continuous patient support and monitoring. This paper introduces an AI-enhanced virtual assistant designed to assist healthcare professionals and patients in managing chronic conditions. The system integrates natural language processing and machine learning to provide personalized health advice, medication reminders, and real-time monitoring of patient vitals. Evaluations on patient datasets demonstrate improved adherence to treatment plans and better health outcomes. This research highlights the role of AI in supporting both patients and healthcare providers in managing long-term health conditions.