Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.

Deep Learning Technology to Recognize American Sign Language Alphabet.

Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet.

Healthcare transformation journey in the Eastern Region of Saudi Arabia: an overview, challenges and lessons learned.

The Kingdom of Saudi Arabia has embarked on a transformation journey referred to as "Vision 2030", which commenced in June 2016. The healthcare sector is currently going through a radical transformation under this Vision. The new Model of Care shifts the focus of the healthcare sector towards proactive care and wellness, aiming to achieve better health, better care, and better value.