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Daniel, B. K. (2019). Big data and data science: a critical review of issues for educational research. British Journal of Educational Technology, 50(1).Search in Google Scholar
Kim, M., Man, K. L., & Helil, N. (2019). Advanced internet of things and big data technology for smart human-care services. Journal of Sensors, 2019, 1-3.Search in Google Scholar
Spanjol, J., & Noble, C. H. (2023). From the editors: engaging with generative artificial intelligence technologies in innovation management research—some answers and more questions. Journal of Product Innovation Management, 40(4), 383-390.Search in Google Scholar
Grover, V., & Lyytinen, K. (2023). Innovative theory in the digital age: clarifying our positions: Journal of Information Technology, 38(1), 79-82.Search in Google Scholar
Onifade, S. T., Gyamfi, B. A., Haouas, I., & Bekun, F. V. (2021). Re-examining the roles of economic globalization and natural resources consequences on environmental degradation in e7 economies: are human capital and urbanization essential components? Resources Policy, 74, 102435-.Search in Google Scholar
Corsi, M., D’Ippoliti, C., & Zacchia, G. (2019). Diversity of backgrounds and ideas: the case of research evaluation in economics. Research Policy, 48(9), 103820.Search in Google Scholar
Daniele, B. (2017). Accuracy of environmental monitoring in china: exploring the influence of institutional, political and ideological factors. Sustainability, 9((3)).Search in Google Scholar
Xia, T., & Ahmad, M. T. (2022). Method of ideological and political teaching resources in universities based on school-enterprise cooperation mode. Mathematical Problems in Engineering, 2022.Search in Google Scholar
Gao, H. W. (2021). Innovation and development of ideological and political education in colleges and universities in the network era. International Journal of Electrical Engineering Education, 002072092110134.Search in Google Scholar
Li, Y. (2020). Analysis on the effective combination of ideological and political education and mental health education for college students n. Basic & clinical pharmacology & toxicology, (S1), 127.Search in Google Scholar
Zhang, X., & Li, Z. (2020). A study on the effect of ideological and political education curriculum on college students neurasthenia intervention. Basic & clinical pharmacology & toxicology, (S1), 127.Search in Google Scholar
Yuan, X. (2022). Construction of moral education evaluation model based on quality cultivation of college students. Scientific Programming, 2022, 1-11.Search in Google Scholar
Valor, C., Antonetti, P., & Merino, A. (2020). The relationship between moral competences and sustainable consumption among higher education students. Journal of Cleaner Production, 248, 119161.Search in Google Scholar
Rong, M. (2017). An innovation teaching of moral education in colleges and universities under the perspective of project teaching. Boletin Tecnico/Technical Bulletin, 55(17), 410-413.Search in Google Scholar
Wu, W. (2017). Development and study on ideological and political education management system based on big data. Revista de la Facultad de Ingenieria, 32(14), 847-853.Search in Google Scholar
Di, W., Yun, L., & Jia, C. (2017). Research on the innovation and development model of ideological and political education in we-media environment based on big data. Revista de la Facultad de Ingenieria, 32(16), 351-357.Search in Google Scholar
Su, R. (2017). Evaluation-model-based research on the combination of internet platforms with the ideological and political education in colleges and universities. Revista de la Facultad de Ingenieria, 32(13), 681-686.Search in Google Scholar
Ren, L. (2017). A research on the timeliness of college student ideological and political education in the internet era based on an interest shift model. Revista de la Facultad de Ingenieria, 32(13), 630-635.Search in Google Scholar
Jiang, J. (2017). Research on development of internet ideological and political education platform for university students based on asp.net. Revista de la Facultad de Ingenieria, 32(15), 227-233.Search in Google Scholar
Zhao, J., & Wang, J. (2017). A design of college ideological and political education management system based on data mining technology. Revista de la Facultad de Ingenieria, 32(14), 73-78.Search in Google Scholar
Na, J., Jeon, K., & Bo Lee, W. (2018). Toxic gas release modeling for real-time analysis using variational autoencoder with convolutional neural networks. Chemical Engineering ence, 68-78.Search in Google Scholar
Lee, Y. S., & Chen, J. (2022). Augmenting deviation of faults from the normal using fault assistant gaussian mixture prior variational autoencoder. Journal of the Taiwan Institute of Chemical Engineers, (130-), 130.Search in Google Scholar
Parisotto, S., Leone, N., C.-B., S., & Launaro, A. (2022). Unsupervised clustering of roman potsherds via variational autoencoders. Journal of Archaeological Science, 142.Search in Google Scholar
Bo, S., & Xuan, W. (2022). A hidden feature label propagation method based on deep convolution variational autoencoder for fault diagnosis. Measurement Science & Technology, (5), 33.Search in Google Scholar
Garambois, P. A., Larnier, K., Monnier, J., Finaud-Guyot, P., & Calmant, S. (2019). Variational inference of effective channel and ungauged anabranching river discharge from multi-satellite water heights of different spatial sparsity. Journal of Hydrology, 581, 124409.Search in Google Scholar
Han, J., Chen, X., Zhang, Y., Hou, W., & Hu, Z. (2022). Demvsnet: denoising and depth inference for unstructured multi-view stereo on noised images. IET Computer Vision.Search in Google Scholar
Sebastián Bejos, Feliciano-Avelino, I., José Francisco Martínez-Trinidad, & Carrasco-Ochoa, J. A. (2020). Improved fast partitional clustering algorithm for text clustering. Journal of Intelligent and Fuzzy Systems, 39(2), 2137-2145.Search in Google Scholar
Vidyadhari, C., Sandhya, N., & Premchand, P. (2019). Particle grey wolf optimizer (pgwo) algorithm and semantic word processing for automatic text clustering. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 27(2).Search in Google Scholar
Qiang, J., Li, Y., Yuan, Y., & Wu, X. (2017). Short text clustering based on pitman-yor process mixture model. Applied Intelligence, 48(3), 1-11.Search in Google Scholar