EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 477 (2024) E3S Web Conf., 477 (2024) 00074 References
Open Access
Issue
E3S Web Conf.
Volume 477, 2024
International Conference on Smart Technologies and Applied Research (STAR'2023)
Article Number 00074
Number of page(s) 17
DOI https://doi.org/10.1051/e3sconf/202447700074
Published online 16 January 2024
  1. Samara S. AL-Hadithy a, Dr.Ghadah K. AL-Khafaji b, Dr. Mohammed, M. Siddeq. “Adaptive 1-d polynomial coding of c621 base for image compression.” Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12.13 (2021): 5720-5731. [Google Scholar]
  2. AHMED, Shams Forruque, ALAM, Md Sakib Bin, HASSAN, Maruf, et al. “Deep learning modelling techniques: current progress, applications, advantages, and challenges.” Artificial Intelligence Review (2023): 1-97. [Google Scholar]
  3. Al-Azazi, Fatima Ahmed, and Mossa Ghurab. “ANN-LSTM: A deep learning model for early student performance prediction in MOOC.” heliyon 9.4 (2023): e15382. [CrossRef] [PubMed] [Google Scholar]
  4. AL-DUJAILI, Abdullah, HUANG, Alex, HEMBERG, Erik, et al. “Adversarial deep learning for robust detection of binary encoded malware.” IEEE Security and Privacy Workshops (SPW) (2018): 76-82. [Google Scholar]
  5. ALRUWAIS, Nuha. “Deep FM-Based Predictive Model for Student Dropout in Online Classes.” IEEE Access (2023): 2-4. [Google Scholar]
  6. Altaf S, Soomro W, Rawi MIM. “Student Performance Prediction using Multi-Layers Artificial Neural Networks: A Case Study on Educational Data Mining.” Proceedings of the 2019 3rd International Conference on Information System and Data Mining 2-3 (2019). [Google Scholar]
  7. Altaf S, Soomro W, Rawi MIM. “Altaf S, Soomro W, Rawi MIM.Student Performance Prediction using Multi-Layers Artificial Neural Networks: A Case Study on Educational Data Mining.” Proceedings of the 2019 3rd International Conference on Information System and Data Mining. (2019). [Google Scholar]
  8. Al-Tameemi G, Xue J, Ajit S. “A Deep Neural Network-Based Prediction Model for Students’ Academic Performance.” 14th International Conference on Developments in eSystems Engineering (DeSE) (2021): 364–369. [Google Scholar]
  9. ALZUBAIDI, Laith, ZHANG, Jinglan, HUMAIDI, Amjad J., et al. “ Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.” J Big Data 8 (2021): 1-74. [Google Scholar]
  10. Andrea Seveso, Andrea Campagner, Davide Ciucci, Federico Cabitza. “Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings.” BMC Medical Informatics and Decision Making 20.5. (2020): 1-14. [Google Scholar]
  11. ASIF, Raheela, MERCERON, Agathe, ALI, Syed Abbas, et al. “Analyzing undergraduate students' performance using educational data mining.” Computers & education 113 (2017): 177-194. [CrossRef] [Google Scholar]
  12. Awuku, Bright, Ying Huang, Nita Yodo. “Predicting Natural Gas Pipeline Failures Caused by Natural Forces: An Artificial Intelligence Classification Approach.” Applied Sciences 13.7 (2023): 4322. [CrossRef] [Google Scholar]
  13. Bhutto EngrS, Siddiqui IF, Arain QA, Anwar M. “Predicting Students’ Academic Performance Through Supervised Machine Learning.” International Conference on Information Science and Communication Technology (ICIS (2020). [Google Scholar]
  14. BHUTTO, Engr Sana, SIDDIQUI, Isma Farah, ARAIN, Qasim Ali, et al. “Predicting students’ academic performance through supervised machine learning.” In : 2020 International Conference on Information Science and Communication Technology (ICISCT) (2020): 1-6. [Google Scholar]
  15. Cetintas S, Si L, Xin YP, Tzur R. “Probabilistic latent class models for predicting student performance.” Proceedings of the 22nd ACM international conference on Conference on information & knowledge management ACM Pres (2013). [Google Scholar]
  16. Cheng Ma, Baofeng Yao,Fang Ge,Yurong Pan,Youqiang Guo. “ Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning.” Proceedings of the 31st ACM International Conference on Information & Knowledg (2022). [Google Scholar]
  17. MA, Cheng, YAO, Baofeng, GE, Fang, et al. Improving prediction of student performance based on multiple feature selection approaches. Proceedings of the 2017 1st International Conference on E-Education, E-Business and E-Technology. 36-41.( 2017). [Google Scholar]
  18. CRUZ-JESUS, Frederico, CASTELLI, Mauro, OLIVEIRA, Tiago, et al. “Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country.” Heliyon 6.6 (2020): 2-4. [Google Scholar]
  19. Deuja R, Karna R, Kusatha R. “Data-Driven Predictive Analysis of Student Performance In College Using Neural Networks.” 3rd International Conference on Computing ,Communication and Security (ICCCS) IEEE (2018): 77-81. [Google Scholar]
  20. FERNANDES, Eduardo, HOLANDA, Maristela, VICTORINO, Marcio, et al. “Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil.” Journal of business research 94 (2019): 335-343. [CrossRef] [Google Scholar]
  21. Gnat, Sebastian. “Impact of Categorical Variables Encoding on Property Mass Valuation.” Procedia Computer Science 192 (2021): 3542-3550. [CrossRef] [Google Scholar]
  22. Gupta S, Agarwal J. “Machine Learning Approaches for Student Performance Prediction.” 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (2022): 1-6. [Google Scholar]
  23. HANCOCK, John T, KHOSHGOFTAAR, Taghi M. “Survey on categorical data for neural networks.” Journal of Big Data 7.1 (2020): 1-41. [Google Scholar]
  24. Hassan H, Ahmad NB, Anuar S. “Improved students’ performance prediction for multi-class imbalanced problems using hybrid and ensemble approach in educational data mining.” IOP Publishing Ltd (2020). [Google Scholar]
  25. HAYES, William M. et WEDELL, Douglas H. “Testing models of context-dependent outcome encoding in reinforcement learning.” Cognition 230 (2023): 105280. [CrossRef] [PubMed] [Google Scholar]
  26. Hernández-Blanco A, Herrera-Flores B, Tomás D, Navarro-Colorado B. “A Systematic Review of Deep Learning Approaches to Educational Data Mining.” Complexity (2019): 1-22. [Google Scholar]
  27. Jawthari, Moohanad, Veronika Stoffova. “EFFECT OF ENCODING CATEGORICAL DATA ON STUDENT’S ACADEMIC PERFORMANCE USING DATA MINING METHODS.” ictejournal (2020): 521–526. [Google Scholar]
  28. K Karthikeyan, Kavipriya Palaniappan. “On Improving Student Performance Prediction in Education Systems using Enhanced Data Mining Techniques.” The International Journal of Computer Science and Software Engineering (IJCSSE) (2017). [Google Scholar]
  29. KASHYAP, Shristy, THAWARE, Tavisha, SAHU, Shubham Raj, et al. “ Multi-Crop Leaf Disease Detection using Deep Learning Methods.” IEEE 19th India Council International Conference (INDICON) (2022): 1-6. [Google Scholar]
  30. Kehinde AJ, Adeniyi AE, Ogundokun RO. “Prediction of Students’ Performance with Artificial Neural Network Using Demographic Traits.” Recent Innovations in Computing (n.d.): 613–624. [Google Scholar]
  31. Kim B-H, Vizitei E, Ganapathi V. “GritNet: Student Performance Prediction with Deep Learning.” (2018): 1-5. [Google Scholar]
  32. Kim, Yoonsang, Young-Ku Choi, Sherry Emery. “Logistic Regression With Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.” The American Statistician 67.3 (2013): 171–182. [CrossRef] [Google Scholar]
  33. Kosaraju, Nishoak, Sainath Reddy Sankepally, and K. Mallikharjuna Rao. “Categorical Data: Need, Encoding, Selection of Encoding Method and Its Emergence in Machine Learning Models A Practical Review Study on Heart Disease Prediction Dataset Using Pearson Correlation.” Proceedings of International Conference on Data Science and Applications: ICDSA 1 (2022): 369-382. [Google Scholar]
  34. KRAEVOY, Vladislav et SHEFFER, Alla. “Mean-value geometry encoding.” International Journal of Shape Modeling 12.01 (2006): 29-46. [CrossRef] [Google Scholar]
  35. LI, Shuping, LIU, Taotang. “Performance prediction for higher education students using deep learning.” Complexity 2021 (2021): 1-10. [Google Scholar]
  36. López Zambrano, Javier, Juan Alfonso Lara Torralbo, Cristóbal Romero Morales. “Early prediction of student learning performance through data mining: A systematic review.” Psicothema (2021): 7. [Google Scholar]
  37. Slimani K, Ruichek Y, Messoussi R. Compound facial emotional expression recognition using CNN deep features. Engineering Letters. 30, 4 (2022). [Google Scholar]
  38. MALLIKHARJUNA RAO, K SAIKRISHNA, Ghanta, SUPRIYA, Kundrapu. “Data preprocessing techniques: emergence and selection towards machine learning models-a practical review using HPA dataset.” Multimedia Tools and Applications (2023): 1-20. [Google Scholar]
  39. Mathew, Tittu Varghese, et al. “Adaptive importance sampling based neural network framework for reliability and sensitivity prediction for variable stiffness composite laminates with hybrid uncertainties.” Composite Structures 245 (2020): 112344. [CrossRef] [Google Scholar]
  40. MATHEW, Tittu Varghese, PRAJITH, P., RUIZ, Rafael O., et al. “Adaptive importance sampling based neural network framework for reliability and sensitivity prediction for variable stiffness composite laminates with hybrid uncertainties.” Composite Structures 245 (2020): 112344. [CrossRef] [Google Scholar]
  41. Minn, Sein. “BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance prediction. [ed.].2012.12218. 2020.” arXiv preprint (2020): arXiv:2012.12218. [Google Scholar]
  42. MOHAMMADI, Majid, ATASHIN, Amir Ahooye, TAMBURRI, Damian A. “From ℓ1 subgradient to projection: A compact neural network for ℓ1-regularized logistic regression.” Neurocomputing 526 (2023): 30-38. [CrossRef] [Google Scholar]
  43. Mondal A, Mukherjee J. “An Approach to Predict a Student’s Academic Performance using Recurrent Neural Network (RNN).” IJCA (2018): 1-5. [Google Scholar]
  44. Mondal, A., & Mukherjee, J. (2018). An Approach to predict a student’s academic performance using Recurrent Neural Network (RNN). Int. J. Comput. Appl, 181(6), 1-5. [Google Scholar]
  45. Moohanad Jawthari, Veronika Stoffova. “EFFECT OF ENCODING CATEGORICAL DATA ON STUDENT’S ACADEMIC PERFORMANCE USING DATA MINING METHODS.” ICTE Journal (2020): 521–526. [Google Scholar]
  46. Namoun, Abdallah, Abdullah Alshanqiti. “Predicting student performance using data mining and learning analytics techniques: A systematic literature review” Applied Sciences 11.1 (2020): 237. [CrossRef] [Google Scholar]
  47. PODUVAL, Murali, GHOSE, Avik, MANCHANDA, Sanjeev, et al. “Artificial intelligence and machine learning: a new disruptive force in orthopaedics.” Indian Journal of Orthopaedics 54 (2020): 109-122. [CrossRef] [PubMed] [Google Scholar]
  48. POLIKOFF, Morgan S., PORTER, Andrew C., et SMITHSON, John. “How well aligned are state assessments of student achievement with state content standards?” American Educational Research Journal 48.4 (2011): 965-995. [CrossRef] [Google Scholar]
  49. POUYANFAR, Samira, SADIQ, Saad, YAN, Yilin, et al. “A survey on deep learning: Algorithms, techniques, and applications.” ACM Computing Surveys (CSUR) 51.5 (2018): 1-36. [Google Scholar]
  50. Qiang Liu, Feng Yu, Shu Wu, Liang Wang. “A Convolutional Click Prediction Model.” Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (2015): 1743–1746. [Google Scholar]
  51. Raga RC, Raga JD. “Early Prediction of Student Performance in Blended Learning Courses Using Deep Neural Networks.” International Symposium on Educational Technology (ISET) (2019): 39–43. [Google Scholar]
  52. Rodríguez-Hernández CF, Musso M, Kyndt E, Cascallar E. “Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation.” Computers and Education: Artificial Intelligence (2021). [Google Scholar]
  53. SÁIZ-MANZANARES, María Consuelo, RODRÍGUEZ-DÍEZ, Juan José, DÍEZ-PASTOR, José Francisco, et al. “Monitoring of student learning in learning management systems: An application of educational data mining techniques.” Applied Sciences 11.6 (2021): 2677. [CrossRef] [Google Scholar]
  54. SARKER, Iqbal H. “Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions.” SN Computer Science 2.6 (2021): 420. [CrossRef] [PubMed] [Google Scholar]
  55. Sarker, Iqbal H. “Machine learning: Algorithms, real-world applications and research directions.” SN computer science 2.3 (2021): 160. [CrossRef] [PubMed] [Google Scholar]
  56. SEVESO, Andrea, CAMPAGNER, Andrea, CIUCCI, Davide, et al. “Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings.” BMC Medical Informatics and Decision Making 20.5 (2020): 1-14. [Google Scholar]
  57. SHARMA, Pooja, SINGH, Surendra Pratap, IQBAL, Hafiz MN, et al. “Omics approaches in bioremediation of environmental contaminants: An integrated approach for environmental safety and sustainability.” Environmental Research 211 (2022): 113102. [CrossRef] [PubMed] [Google Scholar]
  58. Wei H, Li H, Xia M. “Predicting student performance in interactive online question pools using mouse interaction features.” Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (2020): 645–654. [Google Scholar]
  59. Wentong Liu, Wei Xu, Xiaoqing Zhan, Wei Liu, Wenqing Cheng. “Student Performance Prediction by LMS Data and Classroom Videos.” 15th International Conference on Computer Science & Education (ICCSE) (2020): 535–539. [Google Scholar]
  60. Y.-Wei Chu, S. Hosseinalipour, E. Tenorio, L. Cruz, K. Douglas, A. Lan, C. Brinton ?Ghurab M. “ANN-LSTM: A deep learning model for early student performance prediction in MOOC.” heliyon (2023): e15382. [Google Scholar]
  61. ZEESHAN, Zeshan Ali, JAWAD, Muhammad Zakira. “Research on Chinese-Urdu machine translation based on deep learning.” Journal of Autonomous Intelligence 3.2 (2020): 34-44. [Google Scholar]
  62. ZHANG, Yifan, THORBURN, Peter J. “Handling missing data in near real-time environmental monitoring: A system and a review of selected methods.” Future Generation Computer Systems 128 (2022): 63-72. [CrossRef] [Google Scholar]
  63. ZHOU, Jiandong, ZHANG, Qingpeng, LI, Xiang. “Fuzzy factorization machine.” Information Sciences 546 (2021): 1135-1147. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.