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 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.

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. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.

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. This paper aims to provide an overview of the Model of Care and review its achievements and progress in the Eastern Region. The paper will further discuss the challenges faced and lessons learned through the implementation process. Internal documents were reviewed, and a comprehensive literature search was undertaken in relevant search engines and databases. Some of the successes of the Model of Care implementation include improved data management, collection and visualization, and better patient and community engagement. Nevertheless, there is a sense of urgency to face the many challenges identified in the Saudi Arabian health system over the coming decade. Although the Model of Care focuses on addressing these identified challenges and gaps, there are many difficulties facing its implementation in the country and several lessons learned during the first few years since its launch, which this paper mentions. Hence, there is a need to measure the successes of pathways and the overall impact of the Model of Care on both the healthcare provision as well as improved population health.