31 citations · 66 across the 21 of their papers we have counts for
17 papers · 1 filter
Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review
Johnny Peng, Thanh Tung Khuat, Katarzyna Musial +1
Data is crucial for machine learning (ML) applications, yet acquiring large datasets can be costly and time-consuming, especially in complex, resource-intensive fields like biophar…
Hyperbox Mixture Regression for Process Performance Prediction in Antibody Production
Ali Nik-Khorasani, Thanh Tung Khuat, Bogdan Gabrys
This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall sh…
The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry
Alexander Scriven, David Jacob Kedziora, Katarzyna Musial +1
With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake. Whilst some sciences have robust and well-established proces…
hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms
Thanh Tung Khuat, Bogdan Gabrys
Hyperbox-based machine learning algorithms are an important and popular branch of machine learning in the construction of classifiers using fuzzy sets and logic theory and neural n…
The Roles and Modes of Human Interactions with Automated Machine Learning Systems
Thanh Tung Khuat, David Jacob Kedziora, Bogdan Gabrys
As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the `how' and `why' of human-computer…
Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning
Tien-Dung Nguyen, David Jacob Kedziora, Katarzyna Musial +1
Machine learning (ML) pipeline composition and optimisation have been studied to seek multi-stage ML models, i.e. preprocessor-inclusive, that are both valid and well-performing. T…