Artificial Intelligence Helps Protect Smart Homes against Thieves
| Autor: | Zeydin Pala, Orhan Özkan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
ComputingMethodologies_PATTERNRECOGNITION
Computer science Home automation business.industry Engineering Multidisciplinary Artificial intelligence machine learning classification smart home security thefts Mühendislik Ortak Disiplinler business Computer security computer.software_genre Artificial intelligence machine laerning classification smart home security theft computer GeneralLiterature_MISCELLANEOUS |
| Zdroj: | Volume: 11, Issue: 3 945-952 Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi |
| ISSN: | 1309-8640 2146-4391 |
| Popis: | Interaction with the environments in which humans live is increasing more and more, and Artificial Intelligence (AI) offers significant contributions to this. Although the topic of smart homes has attracted a great deal of attention from researchers, the AI-based application in this area is still in its infancy. In this study, a home security automation system, which is quite simple, but smart and AI-based, is proposed. When the home-dwellers were not at home, the home lighting system tried to be managed with AI at night, as if life was still there. The AI-based smart home physical design was done using Arduino equipment and was tried to be adapted to the real-life environment with software support. As if there was someone at home, a special dataset, which was consisted of nine inputs, one output vector and about 5500 samples was created to turn on/off the home lights in a manner suitable for night life. The home lighting system was successfully managed using an AI-based system that learns nightlife lighting habits. The proposed system performance was tested in support of commonly used machine learning classification algorithms such as Multi-layer perceptron (MLP), Linear support vector machine (L-SVM), Gaussian Naive Bayes (NB), and linear discriminant analysis (LDA). The accuracy values of MLP, L-SVM and NB algorithms were 96.69%, 94.98% and 91.23%, respectively. Our results show that a home with AI could be safer and more secure against theft. |
| Databáze: | OpenAIRE |
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