Nazik Alturki¹, Raed Alharthi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Reemah M. Alhebshi⁴, Shtwai Alsubai⁵, Ali Kashif Bashir^6,7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3387-3415, 2024, DOI:10.32604/cmes.2023.044700

Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets as “OFF” or “ON” based… More >

Daniel Chioran^*, Honoriu Valean

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1939-1952, 2022, DOI:10.32604/iasc.2022.020649

Abstract In today’s world, more than ever before, we are fascinated and drawn towards smart autonomous devices that make our lives safer and more comfortable. Devices that aid in reducing our energy consumption are also highly appreciated but often quite expensive to buy. This context is favorable for developing an autonomous smart home automation system (SHAS) with energy-saving potential and low price, making it widely accessible. This paper presents the design and prototype implementation of such a low-cost micro-controller based autonomous SHAS that learns the resident’s work schedule and integrates a wide array of sensors and actuators to automatically control the… More >