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Original Article

To Design Department Management System with WhatsApp Integration by using MEAN Stack

Gulnaj Sayyad1 Ganesh More2 Avishkar Kumbhar3 Rohan Jantre4 Rushikesh Gaikwad5
1 Professor, SRCOE, Department of Computer Engineering, Pune, Maharashtra, India. 2 3 4 5 Student, SRCOE, Department of Computer Engineering, Pune, Maharashtra, India.

Published Online: May-August 2026

Pages: 20-28

References

1. Sharma R., Singh P., Verma A. (2021). Web-Based Attendance Management System Using Cloud Technology. International Journal of Computer
Applications, 179(7), 1–5. https://doi.org/10.5120/ijca2021916944
2. Verma A.K., Kumar S. (2018). Design and Implementation of Student Attendance Management System Using Web Technology. International
Journal of Advanced Research in Computer Science, 9(2). https://doi.org/10.26483/ijarcs.v9i2.5851
3. Patel H., Shah K. (2020). Secure Authentication System Using JSON Web Tokens. International Journal of Engineering Research & Technology
(IJERT), 9(5). https://www.ijert.org/research/secure-authentication-system-using-json-web-tokens-IJERTV9IS050123.pdf
4. Gupta D., Mehta R. (2019). Web-Based Notification System Using Messaging APIs. International Journal of Computer Applications, 182(44).
https://doi.org/10.5120/ijca2019918592
5. Singh P., Sharma A. (2021). Performance Analysis of Web Applications Using Load Testing Tools. International Journal of Engineering Research
& Technology (IJERT), 10(7). https://www.ijert.org/performance-analysis-of-web-applications-using-load-testing-tools
6. Fielding R.T. (2000). Architectural Styles and the Design of Network-Based Software Architectures. Doctoral Dissertation, University of
California. https://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm
7. Tilkov S., Vinoski S. (2010). Node.js: Using JavaScript to Build High-Performance Network Programs. IEEE Internet Computing, 14(6), 80–83.
https://doi.org/10.1109/MIC.2010.145
8. Neelam Labhade-Kumar, Mangala S Biradar, Ashvini Narayan Pawale,"Reinforcement Learning-Based Deep FEFM for Blockchain Consensus
Mechanism Optimization with Non-Linear Analysis"Journal of Computational Analysis and Applications, Vol. 33 No. 05 (2024)
9. Neelam Labhade-Kumar “Shot Boundary Detection Using Artificial Neural Network”, Advances in Signal and Data Processing. Lecture Notes
in Electrical Engineering, Springer, Vol 703. PP-44-55 Jan-2021
10. Neelam Labhade-Kumar Optimizing Cluster Head Selection in Wireless Sensor Networks Using Mathematical Modeling and Statistical Analysis
of The Hybrid Energy-Efficient Distributed (HEED) Algorithm, Communications on Applied Nonlinear Analysis, ISSN: 1074-133XVol 31 No.
6s (2024), PP-602-617 August 2024
11. Neelam Labhade-Kumar “Experimental Design of Electricity Theft Detection and Alert System Using Arduino Assisted Controller and Smart
Sensors"7th International Conference on Inventive Computation Technologies, IEEE Xplore Part Number : CFP24F70-ART ; ISBN : 979-8-
3503-5929-9, 2024,PP-1961-1968
12. Dr.Neelam Labhade-Kumar “Novel Management Trends Using IOT in Indian Automotive Spares Manufacturing Industries”, Journal of
Pharmaceutical Negative Results , Vol. 13 ISSUE 09,PP 4887-4899, Nov-2022
13. Dr.Neelam Labhade-Kumar “Adaptive Hybrid Bird Swarm Optimization Based Efficient Transmission In WSN”, Journal of Pharmaceutical
Negative Results, Vol. 14 ISSUE 02,PP-480-484, Jan-2023,
14. Neelam Labhade-Kumar “Combining Hand-crafted Features and Deep Learning for Automatic Classification of Lung Cancer on CT Scans”,
Journal of Artificial Intelligence and Technology, 2023
15. Neelam Labhade-Kumar “Enhancing Crop Yield Prediction in Precision Agriculture through Sustainable Big Data Analytics and Deep Learning
Techniques”, Carpathian Journal of Food Science and Technology,2023, Special Issue, 1-18
16. Neelam Labhade-Kumar “Accident prevention and management system in urban VANET for improving slippery roads ride after rain” Journal of
environmental protection and ecology, ISSN:1311-5065 Issue 2 volume 25,PP 586–599,2024
17. Prof. Dr. Neelam Labhade-Kumar, An image processing method for kidney stone segmentation in CT scan images based on CNN-regularized
extreme learning machine approach, Hybrid and Advanced Technologies, PP- 217-222,202

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