A Machine Learning Framework with Fuzzy Logic for Improved Smart Home Management and Safety
Keywords:
Machine Learning (ML), Smart Home Management (SHM), Fuzzy logic with Bi-Lateral Support Vector Machine model (FL-BLSVM), Fuzzy logic with Bi-Lateral Support Vector Machine model (FL-BLSVM)Adaptive Median Filter (AMF), Kernel Principal Component Analysis (KPCA)Abstract
A Machine Learning (ML) framework is a concept or research article that suggests a framework using ML methods to improve the smart home. The terms "Smart Home Management" (SHM) and "safety" refer to the use of technology and automation to control and monitor various aspects of a home to enhance convenience, efficiency, and security. In this research, the SHM entails managing and improving several components of a house, including energy consumption, security features, and appliance automation. In this study, we propose a fuzzy logic with a bilateral support vector machine (FL-BLSVM) technique to increase the intelligence and effectiveness of smart home systems. In this instance, the ML technique improves the FL-BLSVM classification effectiveness. To evaluate the effectiveness of the recommended strategy, three actual data sources were examined, each of which included 10 devices from a smart home firm. The Adaptive Median Filter (AMF) filter eliminates the noisy data from raw data samples. An analysis known as a Kernel Principal Component Analysis (KPCA) is used to separate the attributes from the segmented data. Accuracy, precision, recall, and F1 score are some of the assessment criteria for classification tasks, according to the research's performance. Smart homes may operate more adaptable, effectively, and securely by using the recommended approach FL-BLSVM.
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