IOT – ML BASED SMART ATTENDANCE SYSTEM WITH REPORT GENERATION

Authors

  • Dr.D. Shanthi Professor, HOD, CSE(AI&ML), Vignan’s Institute of Management and Technology for Women, Hyderabad, India, Author
  • Mulpuru V N V CH Sree Samhitha B.Tech 3rd year Student, CSE(AI&ML), Vignan’s Institute of Management and Technology for Women, Hyderabad, India, Author
  • Kothamasu Sathwika B.Tech 3rd year Student, CSE(AI&ML), Vignan’s Institute of Management and Technology for Women, Hyderabad, India, Author
  • Battula Akhila B.Tech 3rd year Student, CSE(AI&ML), Vignan’s Institute of Management and Technology for Women, Hyderabad, India, Author
  • Kolipaka Keerthana B.Tech 3rd year Student, CSE(AI&ML), Vignan’s Institute of Management and Technology for Women, Hyderabad, India, Author

Keywords:

Biometric, IoT integration, Proxy Prevention, Real-Time Monitoring, Early Detection, Transparency, ML Integration, Attendance Management

Abstract

With rapid developments in educational technology, intelligent and trusted attendance systems beyond conventional methods are greatly needed. Manual and simple biometric systems fail to deal with multi-subject monitoring, proxy beating, real-time monitoring, and early pattern identification. The suggested project introduces a futuristic hybrid system that integrates biometric verification and smart recognition for accuracy, transparency, and student responsibility. It safely captures and reports attendance, trends, early intervention alerts, and has a balanced, rule-based leave management interface. Minimizes manual entry and maximizes automation, the system provides a flexible, efficient, and future-proof solution for education administration today.

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References

Muhammad Shahrul Zaim Ahmad, Mohd Asyraf Zulkifli, and Amirul Akmal Mohd Khir. "Development of Automated Attendance System Using Pretrained Deep Learning Models." IEEE Access, 12, 2024:45562–45572.

2. Babu, Suryavamshi. (2025). App Based Digital Audiometer with Different Frequencies. Journal of Information Systems Engineering and Management. 10. 1050-1057. 10.52783/jisem.v10i51s.10615.

3. Thejovathi, M., K. Jayasri, K. Munni, B. Pooja, B. Madhuri, and S. Meghana Priya. "Skinguard-Ai FOR Preliminary Diagnosis OF Dermatological Manifestations." Metallurgical and Materials Engineering (2025): 912-916.

R. Alam, Priya Yadav, and Ravi Kishore. "Predictive Model for Student Absenteeism Using ML." Machine Learning in Education, 6(2), 2024:78–85.

5. Srilatha, Mrs. A., R. Usha Rani, Reethu Yadav, Ruchitha Reddy, Laxmi Sathwika, and N. Bhargav Krishna. 2025. “Learn Rights: A Gamified Ai-Powered Platform For Legal Literacy And Children’s Rights Awareness In India”. Metallurgical and Materials Engineering, May, 592-98. https://metall-mater-eng.com/index.php/home/article/view/1611.

6. A. Rahman and T. Zia. "ML-Powered Attendance Alerts System." Journal of Educational Technology Development and Exchange, 16(2), 2023:98–106.

7. Jia Rou Lee, Muhammad Azmi Ayub, and Nor Ashidi Mat Isa. "Face and Facial Expressions Recognition System for Blind People." Computers Helping People with Special Needs, 13768, 2023:350–360.

8. D. Saraswat, M. Kumar, R. Jain, and P. Agarwal. "Anti-spoofing-enabled Contactless Attendance Monitoring System." Journal of Intelligent & Fuzzy Systems, 45(2), 2023:1457–1465.

9. A. Kulkarni, S. R. Patil, M. D. Ambekar, and T. N. Desai. "Smart Campus: IoT and Face Recognition-based Student Tracking." International Journal of Interactive Multimedia and Artificial Intelligence, 7(7), 2023:55–63.

10. B. Kiran, R. Reddy, and K. V. Kumar. "A Review on Face Detection Techniques for Attendance."

11. Journal of Applied Computer Science, 11(4), 2023:121–128.

12. A. Rahman and T. Zia. "ML-Powered Attendance Alerts System." Journal of Educational Technology Development and Exchange, 16(2), 2023:98–106.

13. Geetha, M. D. . ., Haritha, M., Pavani, B. ., Srivalli, C. ., Chervitha, P., & Ishrath, S. . (2025). Eco Earn: E-Waste Facility Locator. Metallurgical and Materials Engineering, 767–773. Retrieved from https://metall-mater-eng.com/index.php/home/article/view/1632

14. V. Raj and K. Batra. "Facial Recognition Enabled IoT System in Academia." International Journal of Computer Applications, 182(24), 2024:12–16.

15. S. Mukherjee. "Intelligent Attendance Analysis Using AI in Education." Education and Information Technologies, 29(1), 2024:117–128.

16. R. Alam, Priya Yadav, and Ravi Kishore. "Predictive Model for Student Absenteeism Using ML."

17. Machine Learning in Education, 6(2), 2024:78–85.

18. D Shanthi, Smart Healthcare for Pregnant Women in Rural Areas, Medical Imaging and Health Informatics, Wiley Publishers,ch-17, pg.no:317-334, 2022, https://doi.org/10.1002/9781119819165.ch17

19. Shanthi, R. K. Mohanty and G. Narsimha, "Application of machine learning reliability data sets", Proc. 2nd Int. Conf. Intell. Comput. Control Syst. (ICICCS), pp. 1472-1474, 2018.

20. D Shanthi, N Swapna, Ajmeera Kiran and A Anoosha, "Ensemble Approach Of GPACOTPSOAnd SNN For Predicting

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Published

2025-06-01

Issue

Vol. 10 No. 6 (2025): June

Section

Articles

How to Cite

IOT – ML BASED SMART ATTENDANCE SYSTEM WITH REPORT GENERATION. (2025). International Journal of Multidisciplinary Engineering In Current Research, 10(6), 430-438. https://ijmec.com/index.php/multidisciplinary/article/view/820