Hernández Álvarez Myriam Beatriz
Departamento de Informática y Ciencias de la Computación
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Oficina: 207
Ext: 4711
iD orcid.org/0000-0003-4718-0400
Estudios
  1. Ingeniera en Electrónica y Telecomunicaciones en Escuela Politécnica Nacional
  2. Magíster en Ciencias en The Ohio University
  3. Especialista Superior en Dirección de empresas mención mercadeo en Universidad Andina Simón Bolívar
  4. Doctora en Aplicaciones de la Informática en Universitat D'alacant / Universidad de Alicante
Líneas de investigación
  1. DICC-A1-L1 Inteligencia Artificial
  2. DICC-A1-L2 Machine learning
  3. DICC-A2-L1 Computación Aplicada a las Comunicaciones y Seguridades
  4. DICC-A2-L2 Seguridad y Privacidad
  5. DICC-A4-L1 Computación Aplicada a los Sistemas de Información
  6. DICC-A4-L3 Sistemas de Información
Publicaciones
  1. Martinez Santander, C. J., Moreno, H., & Hernandez Alvarez, M. B. (2020). The evolution from traditional to intelligent web security: Systematic literature review. 2020 International Symposium on Networks, Computers and Communications, ISNCC 2020. https://doi.org/10.1109/ISNCC49221.2020.9297240
  2. Torres, E., Granizo, S. L., & Hernandez-Alvarez, M. (2019). Gender and age classification based on human features to detect illicit activity in suspicious sites. Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019, 416–419. https://doi.org/10.1109/CSCI49370.2019.00081
  3. Hernandez-Alvarez, M. (2019). Detection of possible human trafficking in twitter. Proceedings - 2019 International Conference on Information Systems and Software Technologies, ICI2ST 2019, 187–191. https://doi.org/10.1109/ICI2ST.2019.00034
  4. Burbano, D., & Hernandez-Alvarez, M. (2018). Illicit, Hidden Advertisements on Twitter. 2018 5th International Conference on EDemocracy and EGovernment, ICEDEG 2018, 317–321. https://doi.org/10.1109/ICEDEG.2018.8372370
  5. Burbano, D., & Hernandez-Alvarez, M. (2018). Identifying human trafficking patterns online. 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017, 2017-January, 1–6. https://doi.org/10.1109/ETCM.2017.8247461
  6. Silva, J. A. H., & Hernandez-Alvarez, M. (2018). Large scale ransomware detection by cognitive security. 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017, 2017-January, 1–4. https://doi.org/10.1109/ETCM.2017.8247484
  7. Hernández-Alvarez, M., Gomez Soriano, J. M., & Martínez-Barco, P. (2017). Citation function, polarity and influence classification. Natural Language Engineering, 23(4), 561–588. https://doi.org/10.1017/S1351324916000346
  8. Hernández-Alvarez, M., & Gomez, J. M. (2016). Survey about citation context analysis: Tasks, techniques, and resources. Natural Language Engineering, 22(3), 327–349. https://doi.org/10.1017/S1351324915000388
  9. Hernández-Álvarez, M., & Granizo, S. L. (2021). Detection of Human Trafficking Ads in Twitter Using Natural Language Processing and Image Processing. Advances in Intelligent Systems and Computing, 1213 AISC, 77–83. https://doi.org/10.1007/978-3-030-51328-3_12
  10. Torres, E. P., Torres, E. A., Hernández-Álvarez, M., & Yoo, S. G. (2021). Machine Learning Analysis of EEG Measurements of Stock Trading Performance. Advances in Intelligent Systems and Computing, 1213 AISC, 53–60. https://doi.org/10.1007/978-3-030-51328-3_9
  11. Torres P, E. P., Torres H, E., Hernández-Álvarez, M., & Yoo, S. G. (2021). EEG-Based BCI Emotion Recognition Using the Stock-Emotion Dataset. Advances in Intelligent Systems and Computing, 1302, 226–235. https://doi.org/10.1007/978-3-030-63665-4_18
  12. Torres P., E. P., Torres, E. A., Hernández-Álvarez, M., & Yoo, S. G. (2020). EEG-based BCI emotion recognition: A survey. Sensors (Switzerland), 20(18), 1–36. https://doi.org/10.3390/s20185083
  13. Torres P, E. P., Hernández-Álvarez, M., Torres Hernández, E. A., & Yoo, S. G. (2019). Stock Market Data Prediction Using Machine Learning Techniques. Advances in Intelligent Systems and Computing, 918, 539–547. https://doi.org/10.1007/978-3-030-11890-7_52
  14. Yoo, S. G., & Myriam, H.-Á. (2018). Predicting residential electricity consumption using neural networks: A case study. Journal of Physics: Conference Series, 1072(1). https://doi.org/10.1088/1742-6596/1072/1/012005

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