Kazarlis, S., Petridis, V., & Fragkou, P. (2005). Solving university timetabling problems using advanced genetic algorithms. GAs, 2(7), 8-12.
 McCollum, B. (2006, August). A perspective on bridging the gap between theory and practice in university timetabling. International conference on the practice and theory of automated timetabling (pp. 3-23). Springer, Berlin, Heidelberg.
 Aladag, C. H., & Hocaoglu, G. (2007). A tabu search algorithm to solve a course timetabling problem. Hacettepe journal of mathematics and statistics, 36(1), 53-64.
 Carter, M. W. (2000, August). A comprehensive course timetabling and student scheduling system at the University of Waterloo. International conference on the practice and theory of automated timetabling (pp. 64-82). Springer, Berlin, Heidelberg.
 Kheiri, A., & Keedwell, E. (2017). A hidden markov model approach to the problem of heuristic selection in hyper-heuristics with a case study in high school timetabling problems. Evolutionary computation, 25(3), 473-501.
 Liu, Y., Zhou, S., Chen, Q. (2011). Discriminatory deep faith networks for visual data classification. Pattern recognition, 44(10-11), 2287-2296.
 Burke, E. K., & Petrovic, S. (2002). Recent research directions in automated timetabling. European journal of operational research, 140(2), 266-280.
 Burke, E., Jackson, K., Kingston, J. H., & Weare, R. (1997). Automated university timetabling: The state of the art. The computer journal, 40(9), 565-571.
 Carter, M. W., & Laporte, G. (1995, August). Recent developments in practical examination timetabling. International conference on the practice and theory of automated timetabling (pp. 1-21). Springer, Berlin, Heidelberg.
 Corr, P. H., McCollum, B., McGreevy, M. A. J., & McMullan, P. (2006). A new neural network based construction heuristic for the examination timetabling problem. In Parallel problem solving from nature-PPSN IX (pp. 392-401). Springer, Berlin, Heidelberg.
 Glover, F. (1987). Tabu search methods in artificial intelligence and operations research. ORSA artificial intelligence, 1(2). ci.nii.ac.jp/naid/10026173744
 Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
 Kheiri, A., Özcan, E., Lewis, R., & Thompson, J. (2016). A sequence-based selection hyper-heuristic: A case study in nurse rostering. Proceedings of the 11th international conference on practice and theory of automated timetabling (pp. 503-505). Udine, Italy.
 Lee, M., Pham, H. & Zhang, X. (1999). Methodology for priority with application to software development process. European journal of operational research, 118(2), 375-389.
 Lee, H., Ekanadham, C., & Ng, A. (2007). Sparse deep belief net model for visual area V2. Advances in neural information processing systems, 20, 873-880.
 Lai, X., Hao, J. K., Glover, F., & Lü, Z. (2018). A two-phase tabu-evolutionary algorithm for the 0–1 multidimensional knapsack problem. Information sciences, 436, 282-301.
 Keyvanrad, M. A., & Homayounpour, M. M. (2014). A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet). https://arxiv.org/abs/1408.3264
 Salakhutdinov, R., & Hinton, G. (2009, April). Deep boltzmann machines. Artificial intelligence and statistics (pp. 448-455). http://proceedings.mlr.press/v5/salakhutdinov09a/salakhutdinov09a.pdf
 Dener, M., & Calp, M. H. (2018). Solving the exam scheduling problems in central exams with genetic algorithms. https://arxiv.org/abs/1902.01360
 Zhang, H., Huang, T., Liu, S., Yin, H., Li, J., Yang, H., & Xia, Y. (2020). A learning style classification approach based on deep belief network for large-scale online education. Journal of cloud computing, 9(1), 9-26.
 Sanchis-Font, R., Castro-Bleda, M. J., Gonzalez, J. A., Pla, F., & Hurtado, L. F. (2020). Cross-Domain Polarity Models to Evaluate User eXperience in E-learning. Neural processing letters
 AlHadid, I., Kaabneh, K., Tarawneh, H., & Alhroob, A. (2020). Investigation of simulated annealing components to solve the university course timetabling problem. Italian journal of pure and applied mathematics, 44, 282-290. https://www.researchgate.net/profile/Huan_Nan_Shi/publication/343609279_Schur_convexity_of_the_dual_form_of_complete_symmetric_function_involving_exponent_parameter/links/5f33e6d192851cd302ef63fa/Schur-convexity-of-the-dual-form-of-complete-symmetric-function-involving-exponent-parameter.pdf#page=306
 Hossain, S. I., Akhand, M. A. H., Shuvo, M. I. R., Siddique, N., & Adeli, H. (2019). Optimization of university course scheduling problem using particle swarm optimization with selective search. Expert systems with applications, 127, 9-24.
 Chen, T., & Xu, C. (2015). Solving a timetabling problem with an artificial bee colony algorithm. World transactions on engineering and technology education, 13(3), 438-442. http://www.wiete.com.au/journals/WTE&TE/Pages/Vol.13,%20No.3%20(2015)/41-Chen-T.pdf
 Nugroho, M. A., & Hermawan, G. (2018). Solving University course timetabling problem using memetic algorithms and rule-based approaches. MS&E, 407(1), 012012.
 Bolaji, A.L., Khader, A.T., Al-Betar, M.A., & Awadallah, M.A. (2014). University course timetabling using hybridized artificial bee colony with hill climbing optimizer. Journal of computational science, 5(5), 809-818.
 Rizk Y., Hajj, N., Mitri, N., & Awad, M. (2019). Deep belief networks and cortical algorithms: A comparative study for supervised classification. Applied computing and informatics, 15(2), 81-93.
 Fukushima, K. (1988). A hierarchical neural network capable of visual pattern recognition. Neural newt. 1(2) 119–130.
 Felder, R. M., Soloman, B. A. (1996). Index of learning styles questionnaire. Retrieved 18 September, 2020 from http://www.engr.ncsu.edu/learningstyles/ilsweb.html