Mohamed Alloghani

TL;DR: A systematic review of scholarly articles published between 2015 and 2018 addressing or implementing supervised and unsupervised machine learning techniques in different problem-solving paradigms revealed decision tree, support vector machine, and Naive Bayes algorithms appeared to be the most cited, discussed, and implemented supervised learners.

TL;DR: The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond.

TL;DR: A systematic review of research paper published in the field of homomorphic encryption shows that a majority of research articles discussed the potential use and application of Homomorphic Encryption as a solution to the growing demands of big data and absence of security and privacy mechanisms therein.

TL;DR: The contextual compendium analysis presented in this paper focuses on the Industry 4.0 and healthcare services innovation that relate to it and the integrated of Natural Language Processing model as a calm-system operating in the background to complete a host of the process that improves diagnoses among other service provision and assistance functions.

TL;DR: In this paper, the authors identify the main factors that influence health related users' acceptance to mobile health services technology as a mean for receiving general health services and propose TAM modified by incorporating external variables including perceived security and perceived trust in order to determine the factors that mostly influence the intention to use M-Health services.