Abstract: University records management has grown in importance as a result of the quick growth of big data, artificial intelligence, and other technologies. However, university archives management is prone to data loss, redundancy, and errors. Moreover, the use of scientific management systems and algorithms can effectively improve such problems. To create an effective and secure archive management system and run simulation tests, the study suggests an RFID-based archive management system and uses nested random time slot ALOHA (RS0) and binary tree (BT) anti-collision algorithms to solve the collision problem between tags in the created system. The test results showed that the average query coefficient, recognition efficiency, and communication volume of the proposed algorithm were 1 and 1.2 times, 95% and 90%, 50 Bit and 180 Bit in two scenarios, continuous and uniform, respectively. 0.91% and 3.92%, 24.21% and 31.14% of the system CUP and memory occupation were achieved when the number of clients was 10 and 100, respectively. The average response time of the system was 0.112s and 1.244s when 100 and 1000 users were accessed, respectively. The information extraction accuracy of the system was 94% at 1000 accessed users. This suggests that the approach used in the study can significantly improve the operational effectiveness of the records management system and the accuracy of information extraction, as well as provide technical support for improving the university records management system.

Ying Wang and Ying Mi, “The Application of Anti-Collision Algorithms in University Records Management” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01508113

@article{Wang2024,
title = {The Application of Anti-Collision Algorithms in University Records Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01508113},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01508113},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Ying Wang and Ying Mi}
}