Smart Class Solution Using Machine Learning
Keywords:
Attendance automation, Embedded systems, Face recognition, Machine learning, Smart classroom, SMS notificationAbstract
This paper presents the smart class system, which automates attendance marking using face recognition and machine learning. A dual-camera system captures real-time video and crops faces, generating 128-d embeddings from them. An extremely light-weight K-Nearest Neighbors classifier attains over 95% accuracy in recognition with an inference time of less than 200 ms on Raspberry Pi 5 while being trained on student images. Attendance is divided into configurable periods, data are exported in CSV format, and SMS notifications are sent in real-time. A Flask interface supports dataset management and period configuration. Tested in 153 sessions, the system turned out to be highly reliable and scalable while reducing the administrative workload by over 90%.
References
D. S. Paul Raj, S. Henry Kishore, S. Curle, I. J. Kavitha and A. Fathima, “IoT-based language learning devices for remote English education: Challenges and opportunities,” 2024 International Conference on Emerging Research in Computational Science (ICERCS), Coimbatore, India, 2024, pp. 1-6, doi: https://doi.org/10.1109/ICERCS63125.2024.10894762
A. Kaur and M. Bhatia, “Scientometric analysis of smart learning,” in IEEE Transactions on Engineering Management, vol. 71, pp. 400-413, 2024, doi: https://doi.org/10.1109/TEM.2021.3124977
Hongmei Sziegat, “Digital transformation and innovation in higher education institutions: Status quo and the way forward,” Springer Briefs in Education, pp. 61–92, Jan. 2025, doi: https://doi.org/10.1007/978-981-96-2650-2_5
M. Weiser, “The computer for the 21st Century,” in IEEE Pervasive Computing, vol. 1, no. 1, pp. 19-25, Jan.-March 2002, doi: https://doi.org/10.1109/MPRV.2002.993141
S. K. S. Gupta, Wang-Chien Lee, A. Purakayastha and P. K. Srimani, “An overview of pervasive computing,” in IEEE Personal Communications, vol. 8, no. 4, pp. 8-9, Aug. 2001, doi: https://doi.org/10.1109/MPC.2001.943997
A. Oulefki, H. Kheddar, A. Amira, F. Kurugollu, Y. Himeur, and A. Bounceur, “Innovative AI strategies for enhancing smart building operations through digital twins: A survey,” Energy and Buildings, p. 115567, Mar. 2025, doi: https://doi.org/10.1016/j.enbuild.2025.115567
L. Ting, M. Khan, A. Sharma, and M. D. Ansari, “A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing,” Journal of Intelligent Systems, vol. 31, no. 1, pp. 221–236, Jan. 2022, doi: https://doi.org/10.1515/jisys-2022-0012
M. Khan and M. D. Ansari, “Multi-criteria software quality model selection based on divergence measure and score function,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3179–3188, Jan. 2020, doi: https://doi.org/10.3233/jifs-191153
P. Chinnasamy, A. Albakri, M. Khan, A. Ambeth Raja, A. Kiran, and J. Chinna Babu, “Smart contract-enabled secure sharing of health data for a mobile cloud-based E-health system,” Applied Sciences, vol. 13, no. 6, pp. 3970–3970, Mar. 2023, doi: https://doi.org/10.3390/app1306397
M. Khan and A. Malviya, “Big data approach for sentiment analysis of Twitter data using Hadoop framework and deep learning,” 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1-5, doi: https://doi.org/10.1109/ic-ETITE47903.2020.201
E. Rashid, M. D. Ansari, V. K. Gunjan, and M. Khan, “Enhancement in teaching quality methodology by predicting attendance using machine learning technique,” Studies in Computational Intelligence, pp. 227–235, 2020, doi: https://doi.org/10.1007/978-3-030-38445-6_17
S. N. Ajani, P. Khobragade, P. V. Jadhav, R. A. Mahajan, B. Ganguly, and N. Parati, “Frontiers of computing-evolutionary trends and cutting-edge technologies in computer science and next-generation application,” Journal of Electrical systems, vol. 20, no. 1s, pp. 28-45, 2024, doi: https://doi.org/10.52783/jes.750
S. R. Londhe, R. A. Mahajan, and B. J. Bhoyar, “Overview on methods for mining high utility itemset from transactional database,” International Journal of Scientific Engineering and Research (IJSER), vol. 1, no. 4, Dec. 2013, Available: https://www.ijser.in/archives/v1i4/MDExMzEyMTE=.pdf
S. Dhabarde, R. Mahajan, S. Mishra, S. Chaudhari, S. Manelu, and N S Shelke, “Disease prediction using machine learning algorithms,” International Research Journal of Modernization in Engineering Technology and Science, vol. 4, no. 3, pp. 379-384, Mar. 2022, Available: https://www.irjmets.com/uploadedfiles/paper/issue_3_march_2022/19570/final/fin_irjmets1646661016.pdf
S. B. Rathod, H. Vyawahare, R. A. Mahajan, and S. Ponnusamy, “Blockchain-empowered business realities: Convergence with AI and digital twins,” Advances in Business Information Systems and Analytics Book Series, pp. 53–71, Feb. 2024, doi: https://doi.org/10.4018/979-8-3693-3234-4.ch005
R. A. Mahajan, S. K. Yadav, and S. A. Mahajan, “Development and integration of scrum tree algorithm with k-means data clustering,” International Journal of Engineering and Advanced Technology, vol. 8, no. 6, pp. 4228–4251, Aug. 2019, doi: https://doi.org/10.35940/ijeat.f9026.088619
B. Debnath, R. Dey and S. Roy, “Smart switching system using bluetooth technology,” 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, 2019, pp. 760-763, doi: https://doi.org/10.1109/AICAI.2019.8701298
S. K. Laha et al., “Analysis of mechanical stress and structural deformation on a solar photovoltaic panel through various wind loads,” Microsystem Technologies, vol. 27, no. 9, pp. 3465–3474, Jan. 2021, doi: https://doi.org/10.1007/s00542-020-05142-8
K. Kumar, R. S. Dhar, S. Bhattacharya, and R. Dey, “Performance analysis and development of strain-induced quantum well-based nano-system device technology,” Microsystem Technologies, vol. 27, no. 10, pp. 3703–3710, Jan. 2021, doi: https://doi.org/10.1007/s00542-020-05143-7
M. M. Hassan Sohan, M. M. Khan, I. Nanda, and R. Dey, “Fake product review detection using machine learning,” 2022 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2022, pp. 527-532, doi: https://doi.org/10.1109/AIIoT54504.2022.9817271
R. Dey, A. Pal, A. Biswas, and A. Das, “Creeping and drifting correction of sensor using adaptive method,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 1, pp. 6670-6672, Jan. 2014, Available: https://www.ijareeie.com/upload/2014/january/18K_Creeping.pdf
R. A. Mahajan, R. Dey, and M. Khan, “Enhancing financial predictive modeling with synthetic data using generative approach,” Research Square (Research Square), Mar. 2025, doi: https://doi.org/10.21203/rs.3.rs-6178330/v1
H. R. Das, R. Dey, and S. Bhattacharya, “A review paper on design for microstrip patch antenna,” Topics in Intelligent Computing and Industry Design, vol. 2, no. 2, pp. 166–168, Mar. 2021, doi: https://doi.org/10.26480/etit.02.2020.166.168
