The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

Deep learning methods have achieved a lot of success in various applications involving
converting wearable sensor data to actionable health insights. A common application areas …

Objective: Lesion detection with positron emission tomography (PET) imaging is critical for
tumor staging, treatment planning, and advancing novel therapies to improve patient …

Portable electronic systems allow the analysis and monitoring of continuous time signals,
such as human activity, integrating deep learning techniques with cloud computing, causing …

Abstract Knowledge distillation as a broad class of methods has led to the development of
lightweight and memory efficient models, using a pre-trained model with a large capacity …

Smart wearable devices detection and recording of people's everyday activities is critical for
health monitoring, helping persons with disabilities, and providing care for the elderly. Most …

Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health
status of patients and elderly people remotely. Through specific time intervals, the …

Wearable sensor data analysis with persistence features generated by topological data
analysis (TDA) has achieved great success in various applications, and however, it suffers …

Converting wearable sensor data to actionable health insights has witnessed large interest
in recent years. Deep learning methods have been utilized in and have achieved a lot of …

This study addresses the problem of inadequate datasets in Time-Slotted Channel Hopping
(TSCH) protocol in Wireless Sensor Networks (WSN) by introducing a viable machine …