2022
Andrés L. Suárez-Cetrulo, David Quintana, Alejandro Cervantes, A survey on machine learning for recurring concept drifting data streams, Expert Systems with Applications,2022,118934,ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118934.
de-la-Fuente-Valentín, L., Verdú, E., Padilla-Zea, N., Villalonga, C., Blanco Valencia, X. P., & Baldiris Navarro, S. M. (2022). Semiautomatic Grading of Short Texts for Open Answers in Higher Education. In Communications in Computer and Information Science. https://doi.org/10.1007/978-3-030-96060-5_4
Khattak, M. I., Saleem, N., Nawaz, A., Almani, A. A., Umer, F., & Verdú, E. (2022). ERBM-SE: Extended Restricted Boltzmann Machine for Multi-Objective Single-Channel Speech Enhancement. International Journal of Interactive Multimedia and Artificial Intelligence, 7(4), 185–195. https://doi.org/10.9781/ijimai.2022.03.002
Arfelis, S., Rainer, J., & González-Pérez, D. M. (2022). Architecture Development to Incorporate Industry 4.0 Solutions to Plastics Management: Circular Economy. In Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-3-030-90033-5_14
Gupta, N., Khosravy, M., Patel, N., Dey, N., & Crespo, R. G. (2022). Lightweight Computational Intelligence for IoT Health Monitoring of Off-Road Vehicles: Enhanced Selection Log-Scaled Mutation GA Structured ANN. IEEE Transactions on Industrial Informatics, 18(1), 611–619. https://doi.org/10.1109/TII.2021.3072045
Adame-Carrillo, D., Gaset, J., & Román-Roy, N. (2022). The second-order problem for k-presymplectic Lagrangian field theories: application to the Einstein–Palatini model. Revista de La Real Academia de Ciencias Exactas, Fisicas y Naturales – Serie A: Matematicas, 116(1). https://doi.org/10.1007/s13398-021-01136-x
Lamo, P., Perales, M., & De-La-fuente-valentín, L. (2022). Case of Study in Online Course of Computer Engineering during COVID-19 Pandemic. Electronics (Switzerland), 11(4). https://doi.org/10.3390/electronics11040578
Verdu, E., Nieto, Y. V., & Saleem, N. (2022). Call for Special Issue Papers: Cloud Computing and Big Data for Cognitive IoT. Big Data, 10(1), 83–84. https://doi.org/10.1089/big.2021.29048.cfp2
Jahnavi, Y., Elango, P., Raja, S. P., Parra Fuente, J., & Verdú, E. (2022). A new algorithm for time series prediction using machine learning models. Evolutionary Intelligence. https://doi.org/10.1007/s12065-022-00710-5
Hsu, C.-H., Marin, C. E. M., Crespo, R. G., & El-Sayed, H. F. M. (2022). Guest Editorial Introduction to the Special Section on Social Computing and Social Internet of Things. IEEE Transactions on Network Science and Engineering, 9(3), 947–949. https://doi.org/10.1109/TNSE.2022.3167460
Gerlache, H. A.-M., Ger, P. M., & Valentín, L. F. (2022). Towards the Grade’s Prediction. A Study of Different Machine Learning Approaches to Predict Grades from Student Interaction Data. International Journal of Interactive Multimedia and Artificial Intelligence, 7(4), 196–204. https://doi.org/10.9781/ijimai.2021.11.007
Mateusz Ciok, K., Pascual Espada, J., & González Crespo, R. (2022). Flex-request: Library to make remote changes in the communication of IoT devices. Expert Systems. https://doi.org/10.1111/exsy.12994
Hidalgo, Á. C., Ger, P. M., & Valentín, L. D. L. F. (2022). Using Meta-Learning to predict student performance in virtual learning environments. Applied Intelligence, 52(3), 3352–3365. https://doi.org/10.1007/s10489-021-02613-x
Kadry, S., Rajinikanth, V., Taniar, D., Damaševičius, R., & Valencia, X. P. B. (2022). Automated segmentation of leukocyte from hematological images—a study using various CNN schemes. Journal of Supercomputing, 78(5), 6974–6994. https://doi.org/10.1007/s11227-021-04125-4
Cordero, A., Garrido, N., Torregrosa, J. R., & Triguero-Navarro, P. (2022). Symmetry in the Multidimensional Dynamical Analysis of Iterative Methods with Memory. Symmetry, 14(3). https://doi.org/10.3390/sym14030442
Moysi, A., Argyros, M., Argyros, I. K., Magreñán, Á. A., Sarría, Í., & González, D. (2022). Local convergence comparison between frozen Kurchatov and Schmidt–Schwetlick–Kurchatov solvers with applications. Journal of Computational and Applied Mathematics, 404. https://doi.org/10.1016/j.cam.2021.113392
Chicharro, F. I., Cordero, A., Garrido, N., & Torregrosa, J. R. (2022). On the effect of the multidimensional weight functions on the stability of iterative processes. Journal of Computational and Applied Mathematics, 405. https://doi.org/10.1016/j.cam.2020.113052
Saleem, N., Gao, J., Irfan, M., Verdu, E., & Fuente, J. P. (2022). E2E-V2SResNet: Deep residual convolutional neural networks for end-to-end video driven speech synthesis. Image and Vision Computing, 119. https://doi.org/10.1016/j.imavis.2022.104389
Kadry, S., Rajinikanth, V., González Crespo, R., & Verdú, E. (2022). Automated detection of age-related macular degeneration using a pre-trained deep-learning scheme. Journal of Supercomputing, 78(5), 7321–7340. https://doi.org/10.1007/s11227-021-04181-w
Khattak, M. I., Saleem, N., Gao, J., Verdu, E., & Fuente, J. P. (2022). Regularized sparse features for noisy speech enhancement using deep neural networks. Computers and Electrical Engineering, 100. https://doi.org/10.1016/j.compeleceng.2022.107887
de León, M., Gaset, J., & Lainz, M. (2022). Inverse problem and equivalent contact systems. Journal of Geometry and Physics, 176. https://doi.org/10.1016/j.geomphys.2022.104500