International Journal of Sustainable Development in computer Science Engineering

A Review of IoT and Machine Learning Applications in Healthcare

Dr. Prince Lomg — 2024-07-03

The convergence of IoT and machine learning technologies holds significant promise for revolutionizing healthcare delivery and outcomes. This paper provides a comprehensive review of IoT and machine learning applications in healthcare. It discusses various use cases such as remote patient monitoring, predictive analytics, and personalized medicine. The review highlights the benefits, challenges, and future directions of integrating these technologies in healthcare, emphasizing the need for robust data security and interoperability standards. Key advancements and current research trends are also identified, providing a roadmap for future developments in this rapidly evolving field.

Comparative Analysis of Deep Learning Models for IoT-Enabled Health Monitoring Systems

Dr. Sunita Sharma — 2024-07-03

Deep learning models have shown remarkable potential in enhancing IoT-enabled health monitoring systems by providing accurate and timely analysis of complex health data. This paper presents a comparative analysis of various deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), in the context of health monitoring applications. Using a large dataset of patient health records, we evaluate the performance of these models in terms of accuracy, computational efficiency, and scalability. The results demonstrate the strengths and limitations of each model, offering insights into their suitability for different health monitoring tasks.

IoT-Driven Predictive Maintenance in Healthcare Facilities Using Machine Learning

Prof. Prem Kumar — 2024-07-03

Predictive maintenance (PdM) in healthcare facilities can prevent equipment failures, reduce downtime, and ensure continuous patient care. This paper explores the application of IoT and machine learning for predictive maintenance in healthcare environments. We propose a PdM framework that utilizes IoT sensors to collect real-time data from medical equipment and machine learning algorithms to predict potential failures. A case study conducted in a hospital setting demonstrates the effectiveness of the framework in reducing maintenance costs and improving equipment reliability. The study also discusses the challenges of implementing PdM in healthcare, including data integration and model accuracy.

Enhancing Chronic Disease Management with IoT and Deep Learning: A Case Study on Diabetes

Dr. Santosh Pahuja — 2024-07-03

Managing chronic diseases such as diabetes requires continuous monitoring and timely interventions. This paper investigates the role of IoT and deep learning in enhancing chronic disease management, with a focus on diabetes. We present an IoT-based system that collects real-time health data from wearable devices and uses deep learning algorithms to predict glucose levels and recommend personalized interventions. The system's performance is evaluated through a clinical trial involving diabetic patients, showing significant improvements in glucose control and patient outcomes. The paper also discusses the potential of extending the system to other chronic diseases and the challenges of large-scale deployment.

A Survey of Machine Learning and IoT Integration in Personalized Healthcare

Prof. Ram Nath Makan — 2024-07-03

Personalized healthcare aims to tailor medical treatment to individual characteristics, needs, and preferences. This paper surveys the integration of machine learning and IoT technologies in personalized healthcare. It covers various applications such as personalized treatment plans, real-time health monitoring, and adaptive intervention strategies. The survey highlights recent advancements, key challenges, and future research directions. Emphasis is placed on the potential of machine learning algorithms to analyze large volumes of health data generated by IoT devices, enabling more accurate and personalized healthcare solutions. The paper concludes with recommendations for overcoming current limitations and maximizing the benefits of these technologies in personalized healthcare.