Abstract
Usually, home automation is constructed to control the appliances even when the owner is not at home. Our system is designed in such a way that the implementation should be cost-effective and powerful using the Internet of Things. This system can be controlled via our developed Android-based mobile application. Using a camera inside home, an automatic identification of intruder as a part of intelligence access control system. Once the intruder gets detected by infrared sensor and our systems will compare the intruder face with existing data set, then it will match the face, if matched occur system will consider the face otherwise, an alert notification will be sent to owner android device. Then the owner can switch on the appliances (light and fans) using the mobile application [4]. This paper combines machine learning and image processing which are powerful modern technologies. This paper basically deals with the integration of all three criteria: Android application, machine learning, and home automation.
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Das, H., Meher, P. (2024). Android Application-Based Security Surveillance Implementing Machine Learning. In: Borah, M.D., Laiphrakpam, D.S., Auluck, N., Balas, V.E. (eds) Big Data, Machine Learning, and Applications. BigDML 2021. Lecture Notes in Electrical Engineering, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-99-3481-2_1
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DOI: https://doi.org/10.1007/978-981-99-3481-2_1
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