Improved Cuckoo Search Optimization And Transductive Support Vector Machine Algorithm For E-Learning Recommendation System
DOI:
https://doi.org/10.56294/sctconf2024.1118Keywords:
E-learning, recommendation system, clustering, Improved Cuckoo Search Optimization (ICSO), Transductive Support Vector Machine (TSVM)Abstract
Introduction: Growing numbers of students opt for self-learning via the Internet, an established e-learning approach, as a result of the popularity and advancement of data search technology. A challenge for e-learning has constantly been the ability to learn different knowledge items methodically and effectively in a certain topic because the majority of the learning material on the network is dispersed. Still, the existing system has issue with higher error rate and computational complexity.
Methods: To overcome this problem, Improved Cuckoo Search Optimization (ICSO) andTransudative Support Vector Machine (TSVM) algorithm were introduced. The main steps of this research are such as pre-processing, clustering, optimization and e- learning recommendation.
Results: Initially, the pre-processing is performed utilizing K-Means Clustering (KMC) which is focused to deal with noise rates effectively. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster data where data space’s dense objectregions are examined to divide low-density areas. In the improved DBSCAN method, density reachability and density connectedness are used. Then, ICSO algorithm is applied to fine tune the parameters using best fitness values.
Conclusion: Finally, the classification of recommendation system is done by using TSVM algorithm which more precise outcomes for the specified datasets. According to the findings, the recommended ICSO-TSVM approach excels the existing ones regards to higher accuracy, recall, precision, mean absolute error (MAE), and also time difficulty
References
1. Shi D, Wang T, Xing H, Xu H. A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowledge-Based Systems. 195, pp. 105618. https://doi.org/10.1016/j.knosys.2020.105618
2. Chen W, Niu Z, Zhao X, Li Y. A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web. 17, pp. 271-84. https://doi.org/10.1007/s11280-012-0187-z
3. Halawa MS, Hamed EM, Shehab ME. Personalized E-learning recommendation model based on psychological type and learning style models. In2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 578-584. https://doi.org/10.1109/IntelCIS.2015.7397281
4. Furtado F, Singh A. Movie recommendation system using machine learning. International journal of research in industrial engineering. 9(1), pp. 84-98. https://doi.org/10.22105/riej.2020.226178.1128
5. Choudhury SS, Mohanty SN, Jagadev AK. Multimodal trust based recommender system with machine learning approaches for movie recommendation. International Journal of Information Technology. 13. Pp. 475-82. https://doi.org/10.1007/s41870-020-00553-2
6. Kiran R, Kumar P, Bhasker B. DNNRec: A novel deep learning based hybrid recommender system. Expert Systems with Applications.144, pp. 113054. https://doi.org/10.1016/j.eswa.2019.113054
7. Hussain M, Zhu W, Zhang W, Abidi SM. Student Engagement Predictions in an e‐Learning System and Their Impact on Student Course Assessment Scores. Computational intelligence and neuroscience. 2018(1), pp. 6347186. https://doi.org/10.1155/2018/6347186
8. Moubayed A, Injadat M, Nassif AB, Lutfiyya H, Shami A. E-learning: Challenges and research opportunities using machine learning & data analytics. IEEE Access, 6, pp. 39117-38. https://doi.org/10.1109/ACCESS.2018.2851790
9. Wang Z, Yu X, Feng N, Wang Z. An improved collaborative movie recommendation system using computational intelligence. Journal of Visual Languages & Computing. 25(6), pp. 667-75. https://doi.org/10.1016/j.jvlc.2014.09.011
10. Guo G, Zhang J, Yorke-Smith N. Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowledge-Based Systems. 74, pp. 14-27. https://doi.org/10.1016/j.knosys.2014.10.016
11. Li J, Ye Z. Course recommendations in online education based on collaborative filtering recommendation algorithm. Complexity. 2020(1), pp. 6619249. https://doi.org/10.1155/2020/6619249
12. Intayoad W, Kamyod C, Temdee P. Reinforcement learning based on contextual bandits for personalized online learning recommendation systems. Wireless Personal Communications. 115(4), pp. 2917-32. https://doi.org/10.1007/s11277-020-07199-0
13. Aziz MA, Hassanien AE. Modified cuckoo search algorithm with rough sets for feature selection. Neural Computing and Applications. 29, pp. 925-34. https://doi.org/10.1007/s00521-016-2473-7
14. Bhaskaran S, Marappan R. Design and analysis of an efficient machine learning based hybrid recommendation system with enhanced density-based spatial clustering for digital e-learning applications. Complex & Intelligent Systems. 9(4), pp. 3517-33. https://doi.org/10.1007/s40747-021-00509-4
15. Mohamad IB, Usman D. Research article standardization and its effects on k-means clustering algorithm. Res J Appl Sci Eng Technol. 6(17), pp. 3299-303. https://doi.org/10.19026/rjaset.6.3638
16. Bagunaid W, Chilamkurti N, Veeraraghavan P. Aisar: Artificial intelligence-based student assessment and recommendation system for e-learning in big data. Sustainability. 14(17), pp. 10551. https://doi.org/10.3390/su141710551
17. Huang L, Ding S, Yu S, Wang J, Lu K. Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Applied Mathematical Modelling. 40(5-6), pp. 3860-75. https://doi.org/10.1016/j.apm.2015.10.052
18. Kamoona AM, Patra JC, Stojcevski A. An enhanced cuckoo search algorithm for solving optimization problems. In2018 IEEE congress on evolutionary computation (CEC), pp. 1-6. https://doi.org/10.1109/CEC.2018.8477784
19. Chen H, Yu Y, Jia Y, Gu B. Incremental learning for transductive support vector machine. Pattern Recognition. 133, pp. 108982. https://doi.org/10.1016/j.patcog.2022.108982
Published
Issue
Section
License
Copyright (c) 2024 D.Poornima, D. Karthika (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
