This work is licensed under the Creative Commons Attribution 4.0 International License.
Jamalian, M., Vahdat-Nejad, H., & Hajiabadi, H. (2022). Investigating the Impact of COVID-19 on Education by Social Network Mining.Search in Google Scholar
Burns, A., Danyluk, P., Kapoyannis, T., & Kendrick, A. (2020). Leading the Pandemic Practicum: One Teacher Education Response to the COVID-19 Crisis. Canadian Network for Innovation in Education, 2.Search in Google Scholar
Wingenbach, T. S. H., Ashwin, C., & Brosnan, M. (2017). Diminished sensitivity and specificity at recognizing facial emotional expressions of varying intensity underlie emotion-specific recognition deficits in autism spectrum disorders. Research in Autism Spectrum Disorders, 34, 52-61.Search in Google Scholar
Ploiz, T., & Fink, G. A. (2016). Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs. Pattern Recognition, 39(12), 2267-2280.Search in Google Scholar
Mollahosseini, A., Chan, D., & Mahoor, M. H. (2015). Going deeper in facial expression recognition using deep neural networks. Computer Science, 1-10.Search in Google Scholar
Valstar, M. F., Mehu, M., Jiang, B., et al. (2012). Meta-Analysis of the First Facial Expression Recognition Challenge. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(4), 966-979.Search in Google Scholar
Timur, S. (2018). Examining Cognitive Structures of Prospective Preschool Teachers Concerning the Subject “Force and Motion.” Educational Sciences: Theory and Practice, 12(4), 3039-3049.Search in Google Scholar
Kim, Y. S., & Um, B. J. (2011). Recommender System Based on Click Stream Data Using Association Rule Mining. Expert Systems with Application, 8(8), 3320-13327.Search in Google Scholar
Butler, D. L. (2014). Collaboration and self-regulation in teachers’ professional development. Teaching and Teacher Education, 20(5), 435-455.Search in Google Scholar
Calabrese, R., & Russo, K. E. (2016). Sbateyl.org: A Virtual Space for Effective Language Training. Procedia - Social and Behavioral Sciences, 228.Search in Google Scholar
Micoulaud-Franchi, J.-A., Quiles, C., Fond, G., Cermolacce, M., & Vion-Dury, J. (2014). The covariation of independent and dependent variables in neurofeedback: A proposal framework to identify cognitive processes and brain activity variables. Consciousness and Cognition, 26.Search in Google Scholar
Guha, M. L., Druin, A., & Fails, J. A. (2013). Cooperative Inquiry revisited: Reflections of the past and guidelines for the future of intergenerational co-design. International Journal of Child-Computer Interaction, 1(1).Search in Google Scholar
Cakula, S., & Sedleniece, M. (2013). Development of a personalized e-learning model using methods of Ontology. Procedia Computer Science, 26, 113-120.Search in Google Scholar
Johnson, W. L., & Shaw, E. (2017). Using agents to overcome deficiencies in Web-based Courseware. In Proceedings of the workshop, Intelligent Educational Systems on the World Wide Web, 8th World Conference of the AIED Society, Kobe, Japan.Search in Google Scholar
Choi, C. S., Aizawa, K., Harashima, H., et al. (2014). Analysis and synthesis of facial image sequences in model-based image coding. IEEE Transactions on Circuits and Systems for Video Technology, 4(3), 257-275.Search in Google Scholar
Wen, Y., Zhang, K., Li, Z., et al. (2016). A Discriminative feature learning approach for deep face recognition. In Computer Vision–ECCV 2016. Springer International Publishing, 499-515.Search in Google Scholar
Russakovsky, O., Deng, J., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252.Search in Google Scholar
Shan, K., Guo, J., You, W., et al. (2017). Automatic facial expression recognition based on a deep convolutional-neural-network structure. In IEEE International Conference on Software Engineering Research, Management and Applications. IEEE, 123-128.Search in Google Scholar
Dornaika, F., Moujahid, A., Raducanu, B., et al. (2013). Facial expression recognition using tracked facial actions: Classifier performance analysis. Engineering Applications of Artificial Intelligence: The International Journal of Intelligent Real-Time Automation, 26(1), 467-477.Search in Google Scholar
Oh, Y.-H., Ngo, A. C. L., See, J., et al. (2015). Monogenic Riesz wavelet representation for micro-expression recognition. In 2015 IEEE International Conference on Digital Signal Processing (DSP 2015), Singapore, 1237-1241.Search in Google Scholar
Chaudilari, S., & Gulati, R. M. (2016). Script identification using Gabor feature and SVM classifier. Procedia Computer Science, 3, 107-112.Search in Google Scholar
Oh, S. K., Yoo, S.-H., Pedrycz, W., et al. (2013). Design of face recognition algorithm using PCA-LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks: Design and its application. Expert Systems with Applications, 40(5), 1451-1466.Search in Google Scholar
Mutelo, R., Woo, W., & Dlay, S. (2018). Discriminant analysis of the two-dimensional Gabor features for face recognition. IET Computer Vision, 2(2), 37-49.Search in Google Scholar
Shu, C., Ding, X., & Fang, C. (2018). Histogram of the Oriented Gradient for Face Recognition. Tsinghua Science and Technology, 16(2), 216-224.Search in Google Scholar
Sagonas, C., Antonakos, E., Tzimiropoulos, G., et al. (2016). 300 Faces In-The-Wild Challenge: database and results. Image & Vision Computing, 47, 3-18.Search in Google Scholar
Han, Y. Z., Cheng, K. Y., Chen, Y. B., et al. (2015). A new classifier for facial expression recognition: Fuzzy buried Markov model. Journal of Computer Science and Technology, 25(3), 641–650.Search in Google Scholar
Kuyuk, H. S., Yildirim, E., Dogan, E., et al. (2014). Clustering Seismic Activities Using Linear and Nonlinear Discriminant Analysis. Journal of Earth Science, 25(1), 140–145.Search in Google Scholar
Lienhart, R., & Wernicke, A. (2010). Localizing and segmenting text in images and videos. IEEE Transactions on Circuits & Systems for Video Technology, 12(4), 256-268.Search in Google Scholar
Ojala, T., Pietikäinen, M., & Mäenpää, T. (2017). Gray scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 24(7), 971-987.Search in Google Scholar
Chang, C.-C., & Lin, C.-J. (2017). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems & Technology, 2(3), 389-396.Search in Google Scholar
Jabid, T., Kabir, M. H., & Chae, O. (2019). Robust facial expression recognition based on local directional pattern. ETRI Journal, 32(5), 784-794.Search in Google Scholar