Qurrat Ul Ain

Orcid: 0000-0002-6891-9887

Affiliations:
  • Victoria University of Wellington, School of Engineering and Computer Science, Centre for Data Science and Artificial Intelligence, Wellington, New Zealand


According to our database1, Qurrat Ul Ain authored at least 21 papers between 2017 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Genetic Programming for Malignancy Diagnosis From Breast Cancer Histopathological Images: A Feature Learning Approach.
IEEE Trans. Emerg. Top. Comput. Intell., April, 2026

Evolving Ternary Patterns and Discriminative Localisation for Basal Cell Carcinoma Detection.
Proceedings of the Applications of Evolutionary Computation - 29th European Conference, 2026

2025
Genetic Programming with Co-operative Co-evolution for Feature Manipulation in Basal Cell Carcinoma Identification.
Proceedings of the Applications of Evolutionary Computation - 28th European Conference, 2025

2024
Automatically Evolving Interpretable Feature Vectors Using Genetic Programming for an Ensemble Classifier in Skin Cancer Detection.
IEEE Comput. Intell. Mag., August, 2024

Towards Clinically Oriented Feature Detection for Melanoma: A Deep Learning Approach.
Proceedings of the 39th International Conference on Image and Vision Computing New Zealand, 2024

Feature Extraction with Automated Scale Selection in Skin Cancer Image Classification: A Genetic Programming Approach.
Proceedings of the Genetic and Evolutionary Computation Conference, 2024

Evolving Feature Extraction Models for Melanoma Detection: A Co-operative Co-evolution Approach.
Proceedings of the Applications of Evolutionary Computation - 27th European Conference, 2024

Exploring Genetic Programming Models in Computer-Aided Diagnosis of Skin Cancer Images.
Proceedings of the IEEE Congress on Evolutionary Computation, 2024

2023
Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming.
IEEE Trans. Cybern., May, 2023

A New Genetic Programming Representation for Feature Learning in Skin Cancer Detection.
Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming.
Proceedings of the Data Science and Machine Learning, 2023

2022
Genetic programming for automatic skin cancer image classification.
Expert Syst. Appl., 2022

A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2022

2021
Generating Knowledge-Guided Discriminative Features Using Genetic Programming for Melanoma Detection.
IEEE Trans. Emerg. Top. Comput. Intell., 2021

2020
Genetic Programming based Feature Manipulation for Skin Cancer Image Classification.
PhD thesis, 2020

A genetic programming approach to feature construction for ensemble learning in skin cancer detection.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019
Genetic Programming for Multiple Feature Construction in Skin Cancer Image Classification.
Proceedings of the 2019 International Conference on Image and Vision Computing New Zealand, 2019

Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection.
Proceedings of the IEEE Congress on Evolutionary Computation, 2019

2018
Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification.
Proceedings of the PRICAI 2018: Trends in Artificial Intelligence, 2018

A Multi-tree Genetic Programming Representation for Melanoma Detection Using Local and Global Features.
Proceedings of the AI 2018: Advances in Artificial Intelligence, 2018

2017
Genetic programming for skin cancer detection in dermoscopic images.
Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017


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