Dr Hassan Eshkiki

Tiwtor Cyfrifiadureg

Computer Science

h.g.eshkiki@abertawe.ac.uk

015
Llawr Gwaelod
Y Ffowndri Gyfrifiadol
Campws y Bae

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

https://orcid.org/0000-0001-7795-453X

ResearchGate

Trosolwg

Mae Dr Hassan Eshkiki yn ymchwilydd cyfrifiadureg gweithredol sy'n canolbwyntio ar ddysgu peirianyddol, yn enwedig ym meysydd gweledigaeth gyfrifiadurol a dadansoddi delweddau meddygol. Er mwyn ehangu ei feysydd ymchwil, mae'n defnyddio setiau data a metrigau amrywiol yn bennaf. Mae ganddo brofiad mewn geneteg a biowybodeg.

Ar wahân i'w ymchwil, mae ganddo ddiddordeb mawr mewn addysgu ac mae'n gwneud cyfraniad ystyrlon at raglenni sy'n hyrwyddo cynwysoldeb ac yn gwella profiad y myfyriwr. Mae'n chwilio'n gyson am ysgoloriaethau a rhaglenni ariannu sy'n annog prosiectau creadigol sydd â'r nod o feithrin amgylchedd sy'n ffafriol i astudio.

Meysydd Arbenigedd

Dysgu peirianyddol
AI esboniadwy
Rhwydwaith niwral effeithlon
Gweledigaeth Gyfrifiadurol
Gêm ddifrifol

Uchafbwyntiau Gyrfa

Diddordebau Addysgu

Deallusrwydd Artiffisial
Peirianneg Feddalwedd
Algorithmau

Goruchwyliaeth:

Mapping cellular interactivity in networks of human cardiac data- a machine learning approach
PhD
Goruchwyliwr arall: Yr Athro Christopher Georg, Dr Fabio Caraffini

Evolutionary Algorithms and Neural Networks in Personalised Diet Planning: A General Solution (current)
PhD
Goruchwyliwr arall: Dr Fabio Caraffini

Cydweithrediadau

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Kapcia, M., Eshkiki, H., Duell, J., Fan, X., Zhou, S., & Mora, B. (2021). ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 841-845). IEEE.
https://doi.org/10.1109/ictai52525.2021.00134, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58534
Eshkiki, H., Mora, B., & Xie, X. (2024). Fully Connected Networks on a Diet With the Mediterranean Matrix Multiplication. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 634-647.
https://doi.org/10.1109/tnnls.2022.3176197, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60038
Caraffini, F., Eshkiki, H., Mohammadpour, M., Sullo, N., & George, C. (2024). Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks. In Lecture Notes in Computer Science (pp. 104-117). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67383
Enobun, A., Anakwenze, U., Taherkhani, A., Anastassi, Z., Caraffini, F., & Eshkiki, H. (2024). Segmenting Breast Ultrasound Scans Using a Generative Adversarial Network Embedding U-Net. In Lecture Notes in Computer Science (pp. 149-159). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_11, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67384

Holl Ymchwil

Erthyglau Cylchgronau

Eshkiki, H., Mora, B., & Xie, X. (2024). Fully Connected Networks on a Diet With the Mediterranean Matrix Multiplication. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 634-647.
https://doi.org/10.1109/tnnls.2022.3176197, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60038

Cyfraniadau Cynadleddau

Caraffini, F., Eshkiki, H., Mohammadpour, M., Sullo, N., & George, C. (2024). Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks. In Lecture Notes in Computer Science (pp. 104-117). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67383
Enobun, A., Anakwenze, U., Taherkhani, A., Anastassi, Z., Caraffini, F., & Eshkiki, H. (2024). Segmenting Breast Ultrasound Scans Using a Generative Adversarial Network Embedding U-Net. In Lecture Notes in Computer Science (pp. 149-159). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_11, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67384
Kapcia, M., Eshkiki, H., Duell, J., Fan, X., Zhou, S., & Mora, B. (2021). ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 841-845). IEEE.
https://doi.org/10.1109/ictai52525.2021.00134, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58534

Goruchwyliaeth

Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning (cyfredol)

PhD

Goruchwyliwr arall: Dr Fabio Caraffini
Evolutionary Algorithms and Neural Networks in Personalised Diet Planning: A General Solution (cyfredol)

PhD

Goruchwyliwr arall: Dr Fabio Caraffini