Monarca Pintle, G. I., Cibrian Robles, F. L., Chávez González, E. L., & Tentori Espinosa, M. E. (2022). Using a small dataset to classify strength-interactions with an elastic display: a case study for the screening of autism spectrum disorder. International Journal of Machine Learning and Cybernetics. doi: 10.1007/s13042-022-01554-2. (ID: 28411)

Peña Morales, D., Chernykh, A., Dorronsoro, B., & Ruiz, P. (2022). A novel multi-objective optimization approach to guarantee equality of service and energy efficiency in a heterogeneous bus fleet system. ENGINEERING OPTIMIZATION. doi: 10.1080/0305215X.2022.2055007. (ID: 28213)

García Jacas, C. R., Pinacho Castellanos, S. A., García González, L. A., & Brizuela Rodríguez, C. A. (2022). Do deep learning models make a difference in the identification of antimicrobial peptides?. BRIEFINGS IN BIOINFORMATICS, 16. doi: 10.1093/bib/bbac094. (ID: 27757)

Álvarez Rebollar, J. L., Cravioto Lagos, J., Marín Nevarez, J. N., Solís Villarreal, E., & Urrutia Galicia, J. (2022). Representing point sets on the plane as permutations. INFORMATION PROCESSING LETTERS, 175, 196228. doi: 10.1016/j.ipl.2021.106228. (ID: 29730) ^(E)

Ibarra Vazquez, G., Olague Caballero, G., Chan Ley, M. A., Puente Montejano, C. A., & Soubervielle Montalvo, C. (2022). Brain programming is immune to adversarial attacks: Towards accurate and robust image classification using symbolic learning. Swarm and Evolutionary Computation, 71(101059), 17. doi: 10.1016/j.swevo.2022.101059. (ID: 28104)

Gabrielli, G., Ferreira, I., Dalchiele, P., Chernykh, A., & Nesmachnow , S. (2022). A machine learning approach for detecting traffic incidents from video cameras. In Nesmachnow, S., Hernández Callejo, L. (Eds.), Smart Cities. Book series: Communications in Computer and Information Science (CCIS, vol 1555) (pp. 162-177). Springer. (ID: 30288)

Maylin , R., Marrero Ponce, Y., Rodríguez, H., Agüero-Chapin,, G., Antunes, A., Aguilera-Mendoza, L., & Martinez-Rios, F. (2022). A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials. Antibiotics, 11, 401. doi: 10.3390/antibiotics11030401. (ID: 28199)

Ruchay, A., Kober, V., Dorofeev, K., Kolpakov, V., Dzhulamanov, K., Kalschikov, V., & Guo, H. (2022). Comparative analysis of machine learning algorithms for predicting live weight of Hereford cows. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 195, 6. doi: 10.1016/j.compag.2022.106837. (ID: 28133)

Soubervielle Montalvo, C., Perez Cham, O. E., Puente Montejano, C. A., González Galván, E. J., Olague Caballero, G., Aguirre Salado, C. A., Cuevas Tello, J. C., & Ontanon Garcia, L. J. (2022). Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for a Real-Time Video Tracking. Sensors, 22(1280), 1-26. doi: 10.3390/s22031280. (ID: 27583)

Talamantes Alvarez, A., & Chávez González, E. L. (2022). Instance-based learning using the half-space proximal graph. Pattern Recognition Letters, 156, 88-95. doi: 10.1016/j.patrec.2022.01.025. (ID: 28313)