Sheetal Sharma^1,2, Kamali Gupta¹, Deepali Gupta¹, Shalli Rani^1,*, Gaurav Dhiman^3,4,5,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2029-2059, 2024, DOI:10.32604/cmes.2023.029997 - 15 December 2023

Abstract The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments. To address… More > Graphic Abstract

Somia Belaidouni^1,2, Moeiz Miraoui^3,4,*, Chakib Tadj¹

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.024056 - 09 May 2022

Abstract A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trial-and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and More >

M. Humayun Kabir¹, Keshav Thapa², Jae-Young Yang², Sung-HyunYang²

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 673-681, 2019, DOI:10.31209/2018.100000035

Abstract Recognition of human activity is a key element for building intelligent and pervasive environments. Inhabitants interact with several objects and devices while performing any activity. Interactive objects and devices convey information that can be essential factors for activity recognition. Using embedded sensors with devices or objects, it is possible to get object-use sequencing data. This approach does not create discomfort to the user than wearable sensors and has no impact or issue in terms of user privacy than image sensors. In this paper, we propose a linear model for activity recognition based on the state-space 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.… More >