Transactions on Recent Developments in Artificial Intelligence and Machine Learning

AI-Driven IoT for Early Detection of Epidemics: A Case Study on Influenza Outbreaks

Prof. Imran Malik — 2024-07-04

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.

Smart Rehabilitation: Integrating IoT and Deep Learning for Personalized Therapy

Dr. Harsh Varma — 2024-07-04

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.

IoT-Enabled Predictive Analytics for Chronic Disease Management Using Machine Learning

Dr. Sachine sharma — 2024-07-04

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.

Enhancing Mental Health Care with IoT and Machine Learning: Monitoring and Intervention

Prof. Chinto Sahani — 2024-07-04

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.

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

Prof. Arun Kumar — 2024-07-04

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.

Transfer Learning for Early Diagnosis of Rare Diseases Using Medical Imaging

Manoj Chowdary Vattikuti — 2024-12-12

Diagnosing rare diseases is a challenging task due to the limited availability of labeled medical imaging data and the complexity of disease patterns. This paper proposes a transfer learning approach to address this issue, leveraging pre-trained deep learning models to improve diagnostic accuracy. The methodology involves fine-tuning models on small datasets specific to rare diseases, significantly reducing the need for extensive labeled data. Experiments on rare disease imaging datasets, such as rare cancer types and genetic disorders, demonstrate the effectiveness of transfer learning in achieving high accuracy with minimal training samples. This study highlights the potential of transfer learning to accelerate the early diagnosis of rare diseases, enabling timely interventions and improving patient outcomes.