M. Jayalakshmi¹, Lalit Garg^2,*, K. Maharajan³, K. Jayakumar⁴, Kathiravan Srinivasan⁵, Ali Kashif Bashir⁶, K. Ramesh⁷

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2431-2447, 2021, DOI:10.32604/cmc.2021.015352

Abstract In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based… More >

Moeiz Miraoui^a, Sherif El-Etriby^b, Chakib Tadj^c, Abdulbasit Zaid Abid^a

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 299-308, 2018, DOI:10.1080/10798587.2017.1281565

Abstract Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task… More >

Waqar Ali^{1, 2}, Salah Ud Din¹, Abdullah Aman Khan¹, Saifullah Tumrani¹, Xiaochen Wang¹, Jie Shao^{1, 3, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1065-1078, 2020, DOI:10.32604/cmc.2020.010017

Abstract Recommender systems are rapidly transforming the digital world into intelligent information hubs. The valuable context information associated with the users’ prior transactions has played a vital role in determining the user preferences for items or rating prediction. It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades. This paper presents a novel Context Based Rating Prediction (CBRP) model with a unique similarity scoring estimation method. The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential… More >