1. 2019
  2. Karastoyanova, D., & Stage, L. (2019). Provenance Holde: Bringing Provenance, Reproducibility and Trust to Flexible ScientificWorkflows and Choreographies. In Proceedings of Second Workshop on Security and Privacy-enhanced Business Process Management at BPM 2019 IEEE.
  3. Bhole, A., Falzon, O., Biehl, M., & Azzopardi, G. (2019). A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. In M. Vento, & G. Percannella (Eds.), Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part II (pp. 108-119). (Lecture Notes in Computer Science; Vol. 11679). Cham: Springer. https://doi.org/10.1007/978-3-030-29891-3_10
  4. Bhole, A., Falzon, O., Biehl, M., & Azzopardi, G. (2019). Automatic identification of Holstein cattle using a non-invasive computer vision approach. In M. Vento, & G. Percanella (Eds.), Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part I (Lecture Notes in Computer Science), ( Lecture Notes in Computer Science book series; Vol. 11678). Springer. https://doi.org/10.1007/978-3-030-29888-3_23
  5. Kind, A., & Azzopardi, G. (2019). Computer-Aided Detection System for Diabetic Retinopathy using Retinal Fundus Images. In M. Vento, & G. Percanella (Eds.), International Conference on Computer Analysis of Images and Patterns (Vol. Part 1, pp. 457-468). (Computer Analysis of Images and Patterns; Vol. 11678). Cham: Springer. https://doi.org/10.1007/978-3-030-29888-3_37
  6. 2018
  7. Bhole, A., Biehl, M., & Azzopardi, G. (2018). Automatic identification of Holstein cattle using non-invasive computer vision approach. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
  8. Alsahaf, A., Azzopardi, G., Ducro, B., Veerkamp, R., & Petkov, N. (2018). Predicting slaughter age in pigs using random forest regression. In N. Petkov, N. Strisciuglio, & C. M. Travieso-Gonzalez (Eds.), Applications of Intelligent Systems IOS Press. https://doi.org/10.1093/jas/sky359
  9. Azzopardi, G., Foggia, P., Greco, A., Saggese, A., & Vento, M. (2018). Gender recognition from face images using trainable shape and colour features. In International Conference of Pattern Recognition (pp. 1983-1988). IEEE. https://doi.org/10.1109/ICPR.2018.8545771
  10. Alsahaf, A., Ducro, B., Veerkamp, R., Azzopardi, G., & Petkov, N. (2018). Assigning pigs to uniform target weight groups using machine learning: ProceedingsoftheWorldCongressonGeneticsAppliedtoLivestockProduction,11.112. In World Congress on Genetics Applied to Livestock Production (WCGALP) University of New Zealand.
  11. Buhagiar, J., Strisciuglio, N., Petkov, N., & Azzopardi, G. (2018). Automatic Segmentation of Indoor and Outdoor Scenes from Visual Lifelogging. Paper presented at Applications of Intelligent Systems 2018, Las Palmas de Gran Canaria, Spain.
  12. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2018). Brain-inspired robust delineation operator. In European Conference of Computer Vision (ECCV) 2018: Workshop on Brain-inspired computer vision
  13. Spiteri, M., & Azzopardi, G. (2018). Customer Churn Prediction for a Motor Insurance Company. In ICDIM Proceedings, Berlin IEEE.
  14. Karastoyanova, D., & Pufahl, L. (2018). Enhancing Business Process Flexibility by Flexible Batch Processing. In H. Panetto , C. Debruyne , H. Proper , C. Ardagna , D. Roman , & R. Meersman (Eds.), Enhancing Business Process Flexibility by Flexible Batch Processing (pp. 426-444). ( Lecture Notes in Computer Science book series ; Vol. 11229). Cham: Springer. https://doi.org/10.1007/978-3-030-02610-3_24
  15. Alsahaf, A., Azzopardi, G., & Petkov, N. (2018). Estimation of live muscle scores of pigs with RGB-D images and machine learning. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
  16. Azzopardi, G., & Simanjuntak, F. (2018). Fusion of CNN- and COSFIRE-based features with application to Gender Recognition from Face Images. In Springer series "Advances in Intelligent Systems and Computing"
  17. Bonnici, A., Abela, J., Zammit, N., & Azzopardi, G. (2018). Localisation, Recognition and Expression of Ornaments in Music Scores. In DocEng '18 Proceedings of the ACM Symposium on Document Engineering 2018 [25] ACM Press Digital Library. https://doi.org/10.1145/3209280.3209536
  18. Apap, A., Fernandez Robles, L., & Azzopardi, G. (2018). Retinal Fundus Biometric Analysis using COSFIRE Filters. In Proceedings of the first international APPIS conference, Gran Canaria, Spain (Frontiers of Artificial Intelligence and Applications).
  19. Abadi, F., Ellul, J., & Azzopardi, G. (2018). The Blockchain of Things, Beyond Bitcoin: A Systematic Review. In The 1st International Workshop on Blockchain for the Internet of Things 2018 - 2018 IEEE Blockchain - BIoT IEEE.
  20. Bonnici, A., Bugeja, D., & Azzopardi, G. (2018). Vectorisation of sketches with shadows and shading using COSFIRE filters. In DocEng '18 Proceedings of the ACM Symposium on Document Engineering 2018 [23] New York: ACM Press Digital Library. https://doi.org/10.1145/3209280.3209525
  21. 2017
  22. Rodriguez-Sanchez, A., Chea, D., Azzopardi, G., & Stabinger, S. (2017). A deep learning approach for detecting and correcting highlights in endoscopic images. In Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) IEEE. https://doi.org/10.1109/IPTA.2017.8310082
  23. 2016
  24. Demchenko, Y., Turkmen, F., De Laat, C., Blanchet, C., & Loomis, C. (2016). Cloud based big data infrastructure: Architectural components and automated provisioning. In 2016 International Conference on High Performance Computing and Simulation, HPCS 2016 (pp. 628-636). (2016 International Conference on High Performance Computing and Simulation, HPCS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPCSim.2016.7568394

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