Kuniko Paxton
Scholar

Kuniko Paxton

Google Scholar ID: h8Gvp8UAAAAJ
University of Hull
AI FairnessExplainabilityPrune LearningFederated Learning
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Citations & Impact
All-time
Citations
1
 
H-index
1
 
i10-index
0
 
Publications
5
 
Co-authors
5
list available
Contact
No contact links provided.
Publications
8 items
Exploring the Impact of Skin Color on Skin Lesion Segmentation
2026
Cited
0
EcoFair: Trustworthy and Energy-Aware Routing for Privacy-Preserving Vertically Partitioned Medical Inference
2026
Cited
0
HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification
2025
Cited
0
Skewness-Guided Pruning of Multimodal Swin Transformers for Federated Skin Lesion Classification on Edge Devices
2025
Cited
0
Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting
2025
Cited
0
Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning
2025
Cited
0
Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML
2025
Cited
0
Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression
2025
Cited
0
Resume (English only)
Co-authors
5 total
Koorosh Aslansefat
Koorosh Aslansefat
Assistant Professor of Computer Science, University of Hull
Professor Dhaval Thakker
Professor Dhaval Thakker
Professor in AI & IoT, University of Hull
Yiannis Papadopoulos
Yiannis Papadopoulos
Professor of Computer Science, University of Hull
Amila Akagic
Amila Akagic
Associate Professor, University of Sarajevo, ORCID: 0000-0002-4795-5424
Medina Kapo
Medina Kapo
Teaching Assistant & Research Assistant, Faculty of Electrical Engineering, University of Sarajevo