Trosolwg
Mae Ben Mora yn Athro Cyswllt yn yr Adran Gyfrifiadureg ym Mhrifysgol Abertawe.
Dr Benjamin Mora
Athro Cyswllt
Computer Science
335
Trydydd Llawr
Y Ffowndri Gyfrifiadol
Campws y Bae
Trydydd Llawr
Y Ffowndri Gyfrifiadol
Campws y Bae
Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig
Dolenni Ymchwil
Cyhoeddiadau
Uchafbwyntiau Ymchwil
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Major, L., Clare, A., Daykin, J., Mora, B., & Zarges, C. (n.d.) Heuristics for the Run-length Encoded Burrows-Wheeler Transform Alphabet Ordering Problem. Journal of HeuristicsSU Repository: https://cronfa.swan.ac.uk/Record/cronfa67539
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Duckworth, E., Hole, A., Deshmukh, A., Chaturvedi, P., Chilakapati, M., Mora, B., & Roy, D. (2022). Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer. Analytical Chemistry, 94(40), 13642-13646.
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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.
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Nagoor, O., Whittle, J., Deng, J., Mora, B., & Jones, M. (2020). Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 2815-2819). IEEE.
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Nagoor, O., Whittle, J., Deng, J., Mora, B., Jones, M., & Whittle, J. (2021). MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM). In 2020 25th International Conference on Pattern Recognition (ICPR)IEEE.
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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.
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Nagoor, O., Whittle, J., Deng, J., Mora, B., & Jones, M. (2022). Sampling strategies for learning-based 3D medical image compression. Machine Learning with Applications, 8, 100273
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Major, L., Clare, A., Daykin, J., Mora, B., Gamboa, L., & Zarges, C. (2020). Evaluation of a Permutation-Based Evolutionary Framework for Lyndon Factorizations. In Parallel Problem Solving from Nature – PPSN XVI (pp. 390-403). Springer International Publishing.
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Mora, B., Maciejewski, R., Chen, M., Ebert, D., & Mora, B. (2009). Visualization and Computer Graphics on Isotropically Emissive Volumetric Displays. IEEE Transactions on Visualization and Computer Graphics, 15(2), 221
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Tsitiridis, A., Mora, B., Richardson, M., & Mora, B. (2013). Hierarchical Object Recognition Model of Increased Invariance. Engineering Applications of Neural Networks -202).
Holl Ymchwil
Erthyglau Cylchgronau
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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.
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Duckworth, E., Hole, A., Deshmukh, A., Chaturvedi, P., Chilakapati, M., Mora, B., & Roy, D. (2022). Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer. Analytical Chemistry, 94(40), 13642-13646.
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Nagoor, O., Whittle, J., Deng, J., Mora, B., & Jones, M. (2022). Sampling strategies for learning-based 3D medical image compression. Machine Learning with Applications, 8, 100273
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Doidge, I., Jones, M., & Mora, B. (2012). Mixing Monte Carlo and progressive rendering for improved global illumination. The Visual Computer, 28(6-8), 603-612.
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Clarke, L., Chen, M., Mora, B., & Mora, B. (2011). Automatic Generation of 3D Caricatures Based on Artistic Deformation Styles. IEEE Transactions on Visualization and Computer Graphics, 17(6), 808-821.
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Mora, B. (2011). Naive ray-tracing. ACM Transactions on Graphics, 30(5), 1
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Mora, B. (2011). Naive ray-tracing: A divide-and-conquer approach. ACM Transactions on Graphics, 30(5), 117
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Mora, B., Maciejewski, R., Chen, M., Ebert, D., & Mora, B. (2009). Visualization and Computer Graphics on Isotropically Emissive Volumetric Displays. IEEE Transactions on Visualization and Computer Graphics, 15(2), 221
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Aidong, L., Ebert, D., Qiao, W., Kraus, M., & Mora, B. (2007). Volume illustration using wang cubes. ACM Transactions on Graphics, 26(2), 11
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Mora, B. & Ebert, D. (2005). Low-complexity maximum intensity projection. ACM Transactions on Graphics, 24(4), 1392
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Mora, B. & Evert, D. (2004). Instant Volumetric Understanding with Order-Independent Volume Rendering. Computer Graphics Forum, 23(3), 489
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Major, L., Clare, A., Daykin, J., Mora, B., & Zarges, C. (n.d.) Heuristics for the Run-length Encoded Burrows-Wheeler Transform Alphabet Ordering Problem. Journal of HeuristicsSU Repository: https://cronfa.swan.ac.uk/Record/cronfa67539
Penodau Llyfrau
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Tsitiridis, A., Mora, B., Richardson, M., & Mora, B. (2013). Hierarchical Object Recognition Model of Increased Invariance. Engineering Applications of Neural Networks -202).
Cyfraniadau Cynadleddau
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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.
-
Nagoor, O., Whittle, J., Deng, J., Mora, B., Jones, M., & Whittle, J. (2021). MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM). In 2020 25th International Conference on Pattern Recognition (ICPR)IEEE.
-
Nagoor, O., Whittle, J., Deng, J., Mora, B., & Jones, M. (2020). Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 2815-2819). IEEE.
-
Major, L., Clare, A., Daykin, J., Mora, B., Gamboa, L., & Zarges, C. (2020). Evaluation of a Permutation-Based Evolutionary Framework for Lyndon Factorizations. In Parallel Problem Solving from Nature – PPSN XVI (pp. 390-403). Springer International Publishing.
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Ryan, A. & Mora, B. (2014). Variations on Image Reconstruction:Weighted Back Projection and Fourier Expectation Maximization. In Rita Borgo and Wen Tang (Ed.), Computer Graphics and Visual ComputingThe Eurographics Association.
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Kammaje, R. & Mora, B. (2008). Row tracing using hierarchical occlusion maps. (pp. 27IEEE.
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Barthe, L., Mora, B., Dodgson, N., Sabin, M., & Mora, B. (2002). Triquadratic reconstruction for interactive modelling of potential fields. (pp. 145
Goruchwyliaeth
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Early Pancreatic Cancer Detection using Medical Imaging«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /» and Spectroscopy (cyfredol)
PhDGoruchwyliwr arall: Dr Deb Roy -
New Algorithms For Machine Learning And Explainable Artificial Intelligence (dyfarnwyd 2023)
PhDGoruchwyliwr arall: Prof Xianghua Xie -
Enhancing the designability and functional efficacy of the anti-body using deep learning methods. (cyfredol)
MScGoruchwyliwr arall: Dr Yuanbo Wu
Modiwlau a Addysgir
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CSC337 Data Visualisation
Data Visualization is concerned with the automatic or semi-automatic generation of digital images that depict data in a meaningful way(s). It is a relatively new field of computer science that is rapidly evolving and expanding. It is also very application oriented, i.e., real tools are built in order to help scientists from other disciplines.
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CSCM37 Data Visualisation
Data Visualisation is concerned with the automatic or semi-automatic generation of digital images that depict data in a meaningful way(s). It is a relatively new field of computer science that is rapidly evolving and expanding. It is also very application oriented, i.e., real tools are built in order to help scientists from other disciplines.
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CSCM437 Data Visualisation & Visual Analytics
Data Visualisation is concerned with the automatic or semi-automatic generation of digital images that depict data in a meaningful way(s). It is a relatively new field of computer science that is rapidly evolving and expanding. It is also very application oriented, i.e., real tools are built in order to help scientists from other disciplines.
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CSCM98 Operating Systems and Architectures
This module gives an overview of current and future processor architectures, operating systems and basic concurrency problems. It intends to teach most details of the developing environment that must be taken into consideration when developing efficient software