336 citations · 764 across the 37 of their papers we have counts for
40 papers
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…
Deep Reinforcement Learning for Digital Twin-Oriented Complex Networked Systems
Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
The Digital Twin Oriented Complex Networked System (DT-CNS) aims to build and extend a Complex Networked System (CNS) model with progressively increasing dynamics complexity toward…
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…
Uncertainty Quantification Using Ensemble Learning and Monte Carlo Sampling for Performance Prediction and Monitoring in Cell Culture Processes
Thanh Tung Khuat, Robert Bassett, Ellen Otte +1
Biopharmaceutical products, particularly monoclonal antibodies (mAbs), have gained prominence in the pharmaceutical market due to their high specificity and efficacy. As these prod…
Transfer Learning and the Early Estimation of Single-Photon Source Quality using Machine Learning Methods
David Jacob Kedziora, Anna Musiał, Wojciech Rudno-Rudziński +1
The use of single-photon sources (SPSs) is central to numerous systems and devices proposed amidst a modern surge in quantum technology. However, manufacturing schemes remain imper…
Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunities
Thanh Tung Khuat, Robert Bassett, Ellen Otte +2
While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support f…
Network Disruption via Continuous Batch Removal: The Case of Sicilian Mafia
Mingshan Jia, Pasquale De Meo, Bogdan Gabrys +1
Network disruption is pivotal in understanding the robustness and vulnerability of complex networks, which is instrumental in devising strategies for infrastructure protection, epi…
Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems
Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
Building models of Complex Networked Systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling n…
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules
Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent rea…
Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality
David Jacob Kedziora, Anna Musiał, Wojciech Rudno-Rudziński +1
Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental v…
Review and Assessment of Digital Twin--Oriented Social Network Simulators
Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real sy…
A Network Science perspective of Graph Convolutional Networks: A survey
Mingshan Jia, Bogdan Gabrys, Katarzyna Musial
The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on…
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…
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial +1
The automated machine learning (AutoML) process can require searching through complex configuration spaces of not only machine learning (ML) components and their hyperparameters bu…
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…
Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey
Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they appro…
Automated Deep Learning: Neural Architecture Search Is Not the End
Xuanyi Dong, David Jacob Kedziora, Katarzyna Musial +1
Deep learning (DL) has proven to be a highly effective approach for developing models in diverse contexts, including visual perception, speech recognition, and machine translation.…
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…
Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems
Patryk Grelewicz, Thanh Tung Khuat, Jacek Czeczot +3
In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control indust…
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys
Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisatio…
AutoWeka4MCPS-AVATAR: Accelerating Automated Machine Learning Pipeline Composition and Optimisation
Tien-Dung Nguyen, Bogdan Gabrys, Katarzyna Musial
Automated machine learning pipeline (ML) composition and optimisation aim at automating the process of finding the most promising ML pipelines within allocated resources (i.e., tim…
Measuring Quadrangle Formation in Complex Networks
Mingshan Jia, Bogdan Gabrys, Katarzyna Musial
The classic clustering coefficient and the lately proposed closure coefficient quantify the formation of triangles from two different perspectives, with the focal node at the centr…
An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data
Thanh Tung Khuat, Bogdan Gabrys
General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms…