Accepted Manuscript version of an article presenting the design and development of a smart garment called MyWear that continuously monitors and collects physiological data. It can analyze muscle activity, stress levels, and heart rate variations and send all the data to the cloud. With a in-built alert system, it can notify the associated medical officials if necessary. The authors also propose a deep neural network model that classifies heartbeat data into abnormalities with 96.9% accuracy and 97.3% precision.

Article introducing a Blockchain-based medical data-sharing framework (called GlobeChain) to overcome the technical challenges to handle outbreak records. The challenges that might arise due to the proposed Blockchain-based framework are also presented as a future direction that grabs the proposal's effectiveness. This is the accepted manuscript version of the article.

Article providing a brief review about various approaches of continuous glucose measurement with noninvasive manner. This article covers the state-of-the-art glucose measurement methods and its control mechanism. The study of various consumer products have also been discussed along with the open challenges. This is the Accepted Manuscript version.

Accepted Manuscript version of an article that presents thoughts on paradigm-shift next generation cryptosystems to overcome the vulnerabilities of the omnipresent conventional cryptosystems.

This article investigates the e-learning experiences of deaf students, focusing on the college of the Technical and Vocational Training Corporation (TVTC) in the Kingdom of Saudi Arabia (KSA). Particularly, it studies the challenges and concerns faced by deaf students during the sudden shift to online learning. Results report problems with internet access, inadequate support, and inaccessibility of content from learning systems, among other issues. The authors argue that institutions should consider a procedure to create more accessible technology that is adaptable during the pandemic to serve individuals with diverse needs.

Accepted Manuscript version of an article introducing the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of Agriculture Cyber-Physical System (ACPS). An accuracy of 99.24% is achieved by the proposed plant disease prediction framework.