H.H. Avilés-Arriaga, L.E. Sucar-Succar, C.E. Mendoza-Durán, L.A. Pineda-Cortés, C. (2011). A Comparison of Dynamic Naïve Bayesian Classifiers and Hidden Markov Models for Gesture Recognition, Journal of Applied Research and Technology, Vol. 9, No. 1, pp. 81102. ISSN: 16656423; eISSN: 24486736.

  1. Batista Nacscimento, H. J., Celestino, J., Teixeira, M. (2025). A Dynamic Bayesian Method for three-dimensional indoor positioning using IEEE 802.11signals. Revista Observatorio de la Economia Latinoamericana, Curitiba, v. 23, n. 1. P- 1-16, 2025. DOI: 10.55905/oelv23n1-174. file:///Users/luis/Downloads/174+OBSERVATORIO+V1.pdf
  2. Tanaka, K., Kudo, M., Kimura, K., Nakamura, A., Long-term Detection System for Six Kinds of Abnormal Behavior of the Elderly Living Alone (2024). https://arxiv.org/pdf/2411.13153
  3. Reza Miry. Time Series Prediction: HMM with TAN and Bayesian Network Observation Structures, Master Thesis, Faculty of Mathematics and Science, Brock University, St. Catharines, Ontario, 2024. https://brocku.scholaris.ca/items/a87a4906-7afa-496c-a174-878638994894
  4. Wang, X., Wang, R., Liu, Y. et al. Impedance Ground Faults Detection and Classification Method for DC Microgrid. J. Electr. Eng. Technol. (2023). https://doi.org/10.1007/s42835-023-01455-6
  5. Shagun Katoch, Varsha Singh, Uma Shanker Tiwary, Indian Sign Language recognition system using SURF with SVM and CNN, Array, 2022, 100141, ISSN 2590-0056,

https://doi.org/10.1016/j.array.2022.100141.

  1. Sucar, L. E. (2021). Dynamic and Temporal Bayesian Networks. In Probabilistic Graphical Models (pp. 181-202). Springer, Cham.
  2. Delgado, A. M. M., Reimer, B. L., Joonbum, L. E. E., Angell, L. S., Seppelt, B. D., Mehler, B. L., & Coughlin, J. F. (2021). U.S. Patent No. 10,902,331. Washington, DC: U.S. Patent and Trademark Office.
  3. XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS - SBrT 2020, 2225 DE NOVEMBRO DE 2020, FLORIANÓPOLIS, SCDynamic Bayesian Approach Applied to LinkAdaptation for 5G Wireless SystemsHitalo J.B. Nascimento, Francisco R. P. Cavalcanti, André L. F. de Almeida and Mateus P. Mota.
  4. Tripathi, A. M., & Baruah, R. D. (2019). Anomaly detection in multivariate time series using fuzzy adaboost and dynamic naive bayesian classifier. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (Vol. 2019-Octob, pp. 19381944). https://doi.org/10.1109/SMC.2019.8914477
  5. Chen, S., Muhammad, W., Lee, J.-H., & Kim, T.-W. (2018). Assessment of Probabilistic Multi-Index Drought Using a Dynamic Naive Bayesian Classifier. WATER RESOURCES MANAGEMENT, 32(13), 43594374. https://doi.org/10.1007/s11269-018-2062-x
  6. Inceoglu, A., Ince, G., Yaslan, Y., & Sariel, S. (2018). Failure Detection Using Proprioceptive, Auditory and Visual Modalities. In Maciejewski, AA and Okamura, A and Bicchi, A and Stachniss, C and Song, DZ and Lee, DH and Chaumette, F and Ding, H and Li, JS and Wen, J and Roberts, J and Masamune, K and Chong, NY and Amato, N and Tsagwarakis, N and Rocco, P and Asfour, T and Chung, WK (Ed.), 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 24912496).
  7. Devanne, M., Berretti, S., Pala, P., Wannous, H., Daoudi, M., & Del Bimbo, A. (2017). Motion segment decomposition of RGB-D sequences for human behavior understanding. Pattern Recognition, 61, 222-233.
  8. Sikder, A. K., Aksu, H., & Uluagac, A. S. (2017). 6thSense: A Context-aware Sensor-based Attack Detector for Smart Devices. In PROCEEDINGS OF THE 26TH USENIX SECURITY SYMPOSIUM (USENIX SECURITY `17) (pp. 397414). (30)
  9. Devanne, M., Berretti, S., Pala, P., Wannous, H., Daoudi, M., & Del Bimbo, A. (2017). Motion segment decomposition of RGB-D sequences for human behavior understanding. PATTERN RECOGNITION, 61(SI), 222233. https://doi.org/10.1016/j.patcog.2016.07.041
  10. Sikder, A. K., Aksu, H., & Uluagac, A. S. (2017). 6thSense: A Context-aware Sensor-based Attack Detector for Smart Devices. PROCEEDINGS OF THE 26TH USENIX SECURITY SYMPOSIUM (USENIX SECURITY `17), 397414. Retrieved from www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/sikder
  11. S. Yang, X. Mao, Y. Chen, and S. Yang, “A multi-agent organization approach for developing social-technical software of autonomous robots,” in Communications in Computer and Information Science, 2016, vol. 623, pp. 2438
  12. A. Angelos, “Data modelling and algorithms for symbiotic assembly operations,” 2016.
  13. Bakas, J., … M. M.-I. J. of, & 2016, undefined. (n.d.). A Comparative Study of Various Classifiers for Character Recognition on Multi-script Databases. Pdfs.Semanticscholar.Org. Retrieved from https://pdfs.semanticscholar.org/f3b9/3f70d265860f3b6085de108b1347a107e2d6.pdf
  14. Muñoz, M., Reimer, B., Lee, J., Mehler, B., & Fridman, L. (2016). Distinguishing patterns in drivers visual attention allocation using Hidden Markov Models. Transportation Research Part F: Traffic Psychology and Behaviour, 43, 90103. https://doi.org/10.1016/j.trf.2016.09.015
  15. Chaney, J., Hugh Owens, E., & Peacock, A. D. (2016). An evidence based approach to determining residential occupancy and its role in demand response management. Energy and Buildings, 125, 254266. https://doi.org/10.1016/j.enbuild.2016.04.060
  16. Slim, S. O., Atia, A., & Mostafa, M.-S. M. (2016). An experimental comparison between seven classification algorithms for activity recognition. Advances in Intelligent Systems and Computing, 407, 3746. https://doi.org/10.1007/978-3-319-26690-9_4
  17. Bakas, J., … M. M.-I. J. of, & 2016, undefined. (n.d.). A Comparative Study of Various Classifiers for Character Recognition on Multi-script Databases. Pdfs.Semanticscholar.Org. Retrieved March 24, 2020, from https://pdfs.semanticscholar.org/f3b9/3f70d265860f3b6085de108b1347a107e2d6.pdf
  18. Jayech, K., Mahjoub, M. A., & Amara, N. E. Ben. (2016). Synchronous Multi-Stream Hidden Markov Model for offline Arabic handwriting recognition without explicit segmentation. Neurocomputing, 214, 958971. https://doi.org/10.1016/j.neucom.2016.07.020
  19. Daleesha M Viswanathan et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1), 2015, 289-293
    Recent Developments in Indian Sign Language Recognition: An Analysis. https://duportal.in/download/265071-recent-developments-in-indian-sign-language-recognition-an-pdf
  20. Chu, T., Chen, R., Liu, K., Liu, J., & Chen, Y. (2015). Contextual Thinking for Inference and Prediction of Daily Activities by Mining Smartphone Data. In PROCEEDINGS OF THE 28TH INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS+ 2015) (pp. 25112517).
  21. Nicholson, A. E. (2015). Dynamic and Temporal Bayesian Networks. In PROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND APPLICATIONS (pp. 161177). https://doi.org/10.1007/978-1-4471-6699-3_9
  22. Nicholson, A. E. (2015). Hidden Markov Models. In PROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND APPLICATIONS (pp. 6382). https://doi.org/10.1007/978-1-4471-6699-3_5
  23. Yu, Z., & Lee, M. (2015). Real-time human action classification using a dynamic neural model. Neural Networks, 69, 2943. https://doi.org/10.1016/j.neunet.2015.04.013
  24. Yang, X., Liu, H., Liu, W., & Zha, Z.-J. (2015). Sparse principle motion component for one-shot gesture recognition. In ACM International Conference Proceeding Series (Vol. 2015-Augus, pp. 329332). https://doi.org/10.1145/2808492.2808520
  25. Wang, M., Chen, W.-Y., & Wu, X. W. (2015). State recognition scheme using feature vector and geometric area ratio techniques. Journal of Applied Research and Technology (Vol. 13). Retrieved from www.jart.ccadet.unam.mx
  26. Gao, R., Wang, S., & Du, R. (2015). Extending dynamic naive bayesian classifier for the time-delay impact analysis of macroeconomic boom index. ICIC Express Letters, Part B: Applications, 6(11), 31273133. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943781229&partnerID=40&md5=3efa4c95b11ac3e004c3692f672efcb3
  27. Nicholson, A. E. (2015). Hidden Markov Models. In PROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND APPLICATIONS (pp. 6382). https://doi.org/10.1007/978-1-4471-6699-3_5
  28. Naeem, M., & Asghar, S. (2014). A Parameter Free BBN Discriminant Function for Optimum Model Complexity versus Goodness of Data Fitting. JOURNAL OF APPLIED RESEARCH AND TECHNOLOGY, 12(4), 734749. https://doi.org/10.1016/S1665-6423(14)70090-2
  29. Escalante, H. J., Guyon, I., Athitsos, V., Jangyodsuk, P., & Wan, J. (2014). Principal motion components for gesture recognition using a single-example 1. arxiv.org. Retrieved from http://www.chalearn.org/
  30. Naeem, M., & Asghar, S. (2014). An Information Theoretic Scoring Function in Belief Network. INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 11(5), 459467.
  31. Duong, Q.-B., Zamai, E., & Tran-Dinh, K.-Q. (2013). Confidence estimation of feedback information for logicdiagnosis. Engineering Applications of Artificial Intelligence, 26(3), 11491161. https://doi.org/10.1016/j.engappai.2012.08.008
  32. Garcia, K. A., Monroy, R., Trejo, L. A., Mex-Perera, C., & Aguirre, E. (2012). Analyzing Log Files for Postmortem Intrusion Detection. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 42(6), 16901704. https://doi.org/10.1109/TSMCC.2012.2217325
  33. Duong, Q. B. (2012). Approche probabiliste pour lestimation dynamique de la confiance accordée à un équipement de production: vers une contribution au diagnostic de services des SED. pdfs.semanticscholar.org. Retrieved from https://pdfs.semanticscholar.org/061b/17197fdc8296710c61595141ca1b46ad239f.pdf
  34. Duong, Q. B., Zamai, E., & Dinh, K. Q. T. (2012). Confidence Estimation of Feedback Information Using Dynamic Bayesian Networks. In 38TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2012) (pp. 37333738).
  35. Cantú, F., & Aldeco, R. (n.d.). Conocimiento y Razonamiento Computacional. amexcomp.mx. Retrieved from http://amexcomp.mx/files/ConocimientoRazonamientoComputacional.pdf#page=52

CITAS DE CO-AUTORES

  1. Hugo Avilés-Arriaga, H., Antonio Gómez Jáuregui, D., & Herrera-Rivas, H. (2014). Robot mirroring View project Rain of Music View project. Retrieved from https://www.researchgate.net/publication/269106024
  2. Vasquez, H., Sucar, L. E., & Escalante, H. J. (2013). Simultaneous segmentation and recognition of gestures for human-machine interaction. In CEUR Workshop Proceedings (Vol. 1088, pp. 2933). Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923860687&partnerID=40&md5=b627207a52fd0b25709f486f13a2cfa9
  3. Chavarria, H. V, Escalante, H. J., & Sucar, L. E. (2013). Simultaneous segmentation and recognition of hand gestures for human-robot interaction. In 2013 16th International Conference on Advanced Robotics, ICAR 2013. https://doi.org/10.1109/ICAR.2013.6766511
  4. Hugo Avilés-Arriaga, H., Antonio Gómez Jáuregui, D., & Herrera-Rivas, H. (2014). Un Esquema 3D para la Descripción Visual de Gestos Dinámicos Visual Gesture Recognition View project Vision-based HCI View project. researchgate.net. Retrieved from https://www.researchgate.net/publication/269106034