Optimization of university management based on reptile search algorithm combined with short-duration memory neural network
Due to the lack of accuracy and difficulty to meet the actual requirements of the poor students' financial assistance in the management of smart colleges and universities. Therefore, a precise funding model for poor students is constructed on the basis of improved reptile search algorithm and s...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000309 |
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| author | Qinquan Sun Jing Su |
| author_facet | Qinquan Sun Jing Su |
| author_sort | Qinquan Sun |
| collection | DOAJ |
| description | Due to the lack of accuracy and difficulty to meet the actual requirements of the poor students' financial assistance in the management of smart colleges and universities. Therefore, a precise funding model for poor students is constructed on the basis of improved reptile search algorithm and short-term memory neural network. The performance of the algorithm is evaluated by test function, rank sum test and combinatorial model validation. In test function 5, the algorithm is 2.90E+01±6.04E-03, which is lower than the comparison algorithm, and begins to converge after about 10 iterations, and the convergence speed is significantly higher than that of the comparison algorithm. In the rank sum test, the experimental results of the comparison algorithm on most test functions are less than 5 %. In the combined model verification, the fitness result of the maximum convergence times was 0.2203 %, and the classification accuracy reached 98.7 %, which was better than the comparison model. The precise funding model of poor students proposed in this study has important application value in the management of smart colleges and universities, which can effectively improve the accuracy of poor students' funding and meet the actual needs. At the same time, the high accuracy and fast convergence of the model provide a new idea and method for smart university management. |
| format | Article |
| id | doaj-art-b20e4e1ef113417b937f4ec9a7710638 |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-b20e4e1ef113417b937f4ec9a77106382024-12-19T11:03:01ZengElsevierSystems and Soft Computing2772-94192024-12-016200101Optimization of university management based on reptile search algorithm combined with short-duration memory neural networkQinquan Sun0Jing Su1School of Marxism, Shandong University of Technology, Zibo 255000, China; Corresponding author.School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, ChinaDue to the lack of accuracy and difficulty to meet the actual requirements of the poor students' financial assistance in the management of smart colleges and universities. Therefore, a precise funding model for poor students is constructed on the basis of improved reptile search algorithm and short-term memory neural network. The performance of the algorithm is evaluated by test function, rank sum test and combinatorial model validation. In test function 5, the algorithm is 2.90E+01±6.04E-03, which is lower than the comparison algorithm, and begins to converge after about 10 iterations, and the convergence speed is significantly higher than that of the comparison algorithm. In the rank sum test, the experimental results of the comparison algorithm on most test functions are less than 5 %. In the combined model verification, the fitness result of the maximum convergence times was 0.2203 %, and the classification accuracy reached 98.7 %, which was better than the comparison model. The precise funding model of poor students proposed in this study has important application value in the management of smart colleges and universities, which can effectively improve the accuracy of poor students' funding and meet the actual needs. At the same time, the high accuracy and fast convergence of the model provide a new idea and method for smart university management.http://www.sciencedirect.com/science/article/pii/S2772941924000309Smart universityLERA-LSTMAccuracyPoor studentsTest function |
| spellingShingle | Qinquan Sun Jing Su Optimization of university management based on reptile search algorithm combined with short-duration memory neural network Systems and Soft Computing Smart university LERA-LSTM Accuracy Poor students Test function |
| title | Optimization of university management based on reptile search algorithm combined with short-duration memory neural network |
| title_full | Optimization of university management based on reptile search algorithm combined with short-duration memory neural network |
| title_fullStr | Optimization of university management based on reptile search algorithm combined with short-duration memory neural network |
| title_full_unstemmed | Optimization of university management based on reptile search algorithm combined with short-duration memory neural network |
| title_short | Optimization of university management based on reptile search algorithm combined with short-duration memory neural network |
| title_sort | optimization of university management based on reptile search algorithm combined with short duration memory neural network |
| topic | Smart university LERA-LSTM Accuracy Poor students Test function |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000309 |
| work_keys_str_mv | AT qinquansun optimizationofuniversitymanagementbasedonreptilesearchalgorithmcombinedwithshortdurationmemoryneuralnetwork AT jingsu optimizationofuniversitymanagementbasedonreptilesearchalgorithmcombinedwithshortdurationmemoryneuralnetwork |