Smart Attendance Management System: Design and Implementation

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | 02 | Page :
    By

    Prathamesh Mane,

  • Chaitanya Kale,

  • Suraj Masal,

  • A. C. Pise,

  1. Student, Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
  2. Student, Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
  3. Student, Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
  4. Assistant Professor, Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India

Abstract

The Smart Attendance Management System (SAMS) is an all-in-one, role-specific digital platform created to transform how educational institutions manage both attendance and academic content. Designed with modern technological standards, SAMS simplifies and streamlines the tracking of student attendance while also facilitating effective course administration. The system supports a wide range of features such as automatic attendance recording, detailed reports for individual subjects, and consolidated overviews of overall class attendance. In addition to attendance monitoring, SAMS incorporates a powerful course content management component, enabling faculty to efficiently share, update, and organize academic materials. It is structured to offer distinct access levels based on user roles—Superadmins, Admins, Teachers, and Students—ensuring that each stakeholder interacts with relevant tools and information appropriate to their responsibilities. A major highlight of SAMS is its integration of real-time data analysis, which allows for immediate insights and quick decision-making. It also prioritizes the security and confidentiality of user data through stringent data protection measures. The system’s scalable architecture and user-friendly interface make it suitable for institutions of various sizes. By combining advanced features with best practices in educational technology, SAMS effectively meets the evolving requirements of modern academic environments.

Keywords: SAMS, Biometric systems, Internet of Things (IoT), RFID technology, Database

How to cite this article:
Prathamesh Mane, Chaitanya Kale, Suraj Masal, A. C. Pise. Smart Attendance Management System: Design and Implementation. Journal of Web Engineering & Technology. 2025; 12(02):-.
How to cite this URL:
Prathamesh Mane, Chaitanya Kale, Suraj Masal, A. C. Pise. Smart Attendance Management System: Design and Implementation. Journal of Web Engineering & Technology. 2025; 12(02):-. Available from: https://journals.stmjournals.com/jowet/article=2025/view=230668


References

  1. Mufron A, Wei Z. Applying Biometric Technology in School Attendance and Security Management. Al-Hijr: Journal of Adulearn World. 2024 Jun 1;3(2).
  2. Moshayedi AJ, Roy AS, Liao L, Lan H, Gheisari M, Abbasi A, Bamakan SM. Automation attendance systems approaches: a practical review. BOHR Int. J. Internet Things Artif. Intell. Mach. Learn. 2021;1:23-31.
  3. Makinde AI, Adeleye SA, Oronti AO, Jimoh IT. Revolutionizing education. Artificial Intelligence for Wireless Communication Systems: Technology and Applications. 2024 Oct 16;103.
  4.  Nale D, Pise A. Web Based–Application for Result Analysis. InInternational Conference on Emerging Trends in Artificial Intelligence, Data Science and Signal Processing 2025 (pp. 1-12). Springer, Cham.
  5. Kumar MA, Sekhar YR. Android based health care monitoring system. In2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) 2015 Mar 19 (pp. 1-5). IEEE.
  6. Maske Y, Jagadale AB, Mulani AO, Pise AC. Development of BIOBOT system to assist COVID patient and caretakers. European Journal of Molecular & Clinical Medicine. 2023;10(01):2023.
  7. Maske Y, Jagadale MA, Mulani AO, Pise A. Implementation of BIOBOT System for COVID Patient and Caretakers Assistant Using IOT. International Journal of Information Technology and. 2021:30-43.
  8. Kong X, Fan B, Nie W, Ding Y. Design on mobile health service system based on Android platform. In2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) 2016 Oct 3 (pp. 1683-1687). IEEE.
  9. Godase MV, Mulani A, Ghodak MR, Birajadar MG, Takale MS, Kolte M. A MapReduce and Kalman Filter based Secure IIoT Environment in Hadoop.
  10. Gadade B, Mulani AO, Harale AD. IoT Based Smart School Bus and Student Tracking System. Volume 19, June 2024. https://www.researchgate.net/profile/Altaf- Mulani- 2/publication/384246234_IoT_Based_Smart_School_Bus_and_Student_Tracking_Syste m/links/66f0dd1a6b101f6fa4fe4b4c/IoT-Based-Smart-School-Bus-and-Student- Tracking-System.pdf
  11. Dhanawadel A, Mulani AO, Pise AC. IOT based Smart farming using Agri BOT. https://www.researchgate.net/profile/Altaf-Mulani- 2/publication/384246245_IOT_based_Smart_farming_using_Agri_BOT/links/66f0e0b5f c6cc46489704321/IOT-based-Smart-farming-using-Agri-BOT.pdf .
  12. Mulani A, Mane PB. DWT based robust invisible watermarking. Scholars’ Press; 2016.
  13. Hasan KM, Newaz SS, Ahsan MS. Design and development of an aircraft type portable drone for surveillance and disaster management. International Journal of Intelligent Unmanned Systems. 2018 Jul 2;6(3):147-59.
  14. Swami SS, Mulani AO. An efficient FPGA implementation of discrete wavelet transform for image compression. In2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) 2017 Aug 1 (pp. 3385- 3389). IEEE.
  15. Mane PB, Mulani AO. High speed area efficient FPGA implementation of AES algorithm. International Journal of Reconfigurable and Embedded Systems. 2018 Nov;7(3):157-65.
  16. Mulani AO, Mane PB. Area efficient high speed FPGA based invisible watermarking for image authentication. Indian journal of Science and Technology. 2016 Oct;9(39):1-6.
  17. Kashid MM, Karande KJ, Mulani AO. IoT-based environmental parameter monitoring using machine learning approach. InProceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, Volume 1 2022 Nov 1 (pp. 43-51). Singapore: Springer Nature Singapore.

Ahead of Print Subscription Review Article
Volume 12
02
Received 16/04/2025
Accepted 22/05/2025
Published 07/11/2025
Publication Time 205 Days


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