Mechraoui, F., Martins, P., Caldeira, F. (2024).
OpenStack: a virtualisation overview.
International Journal of Information Technology and Management (IJITM), 23(1), 1-12.
http://dx.doi.org/10.1504/ijitm.2024.10061679
Abbasi, M., Bernardo, M. V., Váz, P., Silva, J., Martins, P. (2024).
Optimizing Database Performance in Complex Event Processing through Indexing Strategies.
Data, 9(8), 93.
https://doi.org/10.3390/data9080093
Abbasi, M., Bernardo, M. V., Váz, P., Silva, J., Martins, P. (2024).
Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches.
Information, 15(8), 429.
https://doi.org/10.3390/info15080429
Abbasi, M., Bernardo, M. V., Váz, P., Silva, J., Martins, P. (2024).
Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study.
Information, 15(9), 574.
https://doi.org/10.3390/info15090574
Abbasi, M., Váz, P., Silva, J., Martins, P. (2024).
Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting.
Technologies, 12(11), 216.
https://doi.org/10.3390/technologies12110216
Silva, J., Vaz, P., Martins, P., Ferreira, L. (2023).
Reliability Estimation Using EM Algorithm with Censored Data: A Case Study on Centrifugal Pumps in an Oil Refinery.
Applied Sciences, 13(13):7736.
https://doi.org/10.3390/app13137736
Sá, F., Martins, P., Abbasi, M. (2021).
Portuguese Local E-government: A Study on the Most Adopted Databases in 2019.
Journal of e-Government Studies and Best Practices, 2021:254977.
DOI: 10.5171/2021.254977
Gomes, A., Santos, C., Wanzeller, C., Martins, P. (2021).
Database Encryption for Balance Between Performance and Security.
Journal of Information Assurance & Cyber security, 2021.
DOI: 10.5171/2021.614511
Caldeira, M., Martins, P., Costa, R., Furtado, P. (2020).
Image Classification Benchmark (ICB).
Expert Systems with Applications, 142(2020), 112998.
https://doi.org/10.1016/j.eswa.2019.112998
Furtado, P., Caldeira, M., Martins, P. (2020).
Human Visual System vs Convolution Neural Networks in food recognition task: An empirical comparison.
Computer Vision and Image Understanding, 191 (2020), 102878.
http://dx.doi.org/10.1016/j.cviu.2019.102878
