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@article{189038,
        author = {Dr M J Chandrashekhar and Balaji C A and Dhanush K More and Govardhana Naidu C P and Swastik Shetty},
        title = {Smart Classroom Automation and Attendance Control System Using Raspberry Pi},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7855-7860},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189038},
        abstract = {This project focuses on the design and implementa- tion of a Smart Classroom Automation and Attendance Monitor- ing System that integrates Internet of Things (IoT) and Artificial Intelligence (AI) technologies using a Raspberry Pi. The main goal is to promote energy conservation and modernize classroom management. The Raspberry Pi serves as the central processing unit. Environmental automation is achieved using a DHT11 sensor to monitor temperature and humidity, automatically adjusting fan speed via Pulse Width Modulation (PWM), and an LDR (Light Dependent Resistor) sensor to detect ambient light, automatically switching ON/OFF the classroom lights. The system also includes an intelligent attendance monitoring system utilizing facial recognition technology. A Pi Camera Module captures real- time facial images, which are processed using OpenCV and the face recognition Python library. Upon successful recognition, the student’s details are automatically recorded in a local or cloud- based database. This integration provides a fully automated, real- time monitoring solution that is accurate, scalable, and cost- effective.},
        keywords = {Smart Classroom Automation, Attendance Mon- itoring System, Raspberry Pi ,IoT (Internet of Things), AI (Artificial Intelligence), Facial Recognition Technology, Energy Conservation, DHT11 sensor, LDR sensor,OpenCV, Pulse Width Modulation (PWM), Pi Camera Module.},
        month = {December},
        }