John McCall
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View article: Towards Explainable Metaheuristics: Feature Mining of Search Trajectories through Principal Component Projection
Towards Explainable Metaheuristics: Feature Mining of Search Trajectories through Principal Component Projection Open
While population-based metaheuristics have proven useful for refining and improving explainable AI systems, they are seldom the focus of explanatory approaches themselves. This stems from their inherently stochastic, population-driven sear…
View article: EXTH-19. AN INTERNATIONAL ACCELERATOR PROGRAMME FOR RESEARCHERS DEVELOPING NOVEL THERAPIES FOR BRAIN TUMOURS
EXTH-19. AN INTERNATIONAL ACCELERATOR PROGRAMME FOR RESEARCHERS DEVELOPING NOVEL THERAPIES FOR BRAIN TUMOURS Open
Developing a comprehensive translational programme for brain tumours involves navigating numerous challenges, including robustness of preclinical studies, safety, clinical trial design, understanding and addressing regulatory requirements,…
View article: P26.03.B AN INTERNATIONAL ACCELERATOR PROGRAMME FOR NOVEL BRAIN TUMOUR THERAPIES
P26.03.B AN INTERNATIONAL ACCELERATOR PROGRAMME FOR NOVEL BRAIN TUMOUR THERAPIES Open
BACKGROUND Developing a comprehensive translational programme for brain tumours involves navigating numerous challenges, including robustness of preclinical studies, clinical trial design, safety, understanding and addressing regulatory re…
View article: Mining Potentially Explanatory Patterns via Partial Solutions
Mining Potentially Explanatory Patterns via Partial Solutions Open
We introduce Partial Solutions to improve the explainability of genetic algorithms for combinatorial optimization. Partial Solutions represent beneficial traits found by analyzing a population, and are presented to the user for explainabil…
View article: A Novel Surrogate Model for Variable-Length Encoding and its Application in Optimising Deep Learning Architecture
A Novel Surrogate Model for Variable-Length Encoding and its Application in Optimising Deep Learning Architecture Open
Deep neural networks (DNN) has achieved great successes across multiple domains. In recent years, a number of approaches have emerged on automatically finding the optimal DNN configurations. A technique among these approaches which show gr…
View article: OTHR-06. A NEW INTERNATIONAL, MULTIDISCIPLINARY ACCELERATOR PROGRAMME FOR NOVEL BRAIN TUMOUR THERAPIES
OTHR-06. A NEW INTERNATIONAL, MULTIDISCIPLINARY ACCELERATOR PROGRAMME FOR NOVEL BRAIN TUMOUR THERAPIES Open
BACKGROUND Developing a comprehensive translational programme for brain tumours involves navigating numerous challenges, including robustness of preclinical studies, safety, clinical trial design, regulatory issues, and commercialisation. …
View article: Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems Open
Artificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response, the field of explainable AI (XAI) has emerged to address …
View article: Mining Potentially Explanatory Patterns via Partial Solutions
Mining Potentially Explanatory Patterns via Partial Solutions Open
Genetic Algorithms have established their capability for solving many complex optimization problems. Even as good solutions are produced, the user's understanding of a problem is not necessarily improved, which can lead to a lack of confid…
View article: Exploring Representations for Optimizing Connected Autonomous Vehicle Routes in Multi-Modal Transport Networks Using Evolutionary Algorithms
Exploring Representations for Optimizing Connected Autonomous Vehicle Routes in Multi-Modal Transport Networks Using Evolutionary Algorithms Open
The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, pu…
View article: Introduction to the Special Issue on Explainable AI in Evolutionary Computation
Introduction to the Special Issue on Explainable AI in Evolutionary Computation Open
Explainable Artificial Intelligence (XAI) has recently emerged as one of the most active areas of research in AI. While Evolutionary Computation (EC) is also a very active research area, the intersection between XAI and EC is still rather …
View article: Two-layer Ensemble of Deep Learning Models for Medical Image Segmentation
Two-layer Ensemble of Deep Learning Models for Medical Image Segmentation Open
One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segm…
View article: Perceptions of the Instructional Research Training Environment and Research Self-Efficacy
Perceptions of the Instructional Research Training Environment and Research Self-Efficacy Open
We examined counselor education and supervision (CES) doctoral students’ (n = 117) perceptions of their instructional RTE and research self-efficacy. Students had more positive perceptions of their instructional RTE and higher research sel…
View article: A Weighted Ensemble of Regression Methods for Gross Error Identification Problem
A Weighted Ensemble of Regression Methods for Gross Error Identification Problem Open
In this study, we proposed a new ensemble method to predict the magnitude of gross errors (GEs) on measurement data obtained from the hydrocarbon and stream processing industries. Our proposed model consists of an ensemble of regressors (E…
View article: Towards explainable metaheuristics: Feature extraction from trajectory mining
Towards explainable metaheuristics: Feature extraction from trajectory mining Open
Explaining the decisions made by population‐based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in area…
View article: Explaining a Staff Rostering Genetic Algorithm using Sensitivity Analysis and Trajectory Analysis.
Explaining a Staff Rostering Genetic Algorithm using Sensitivity Analysis and Trajectory Analysis. Open
In the field of Explainable AI, population-based search metaheuristics are of growing interest as they become more widely used in critical applications. The ability to relate key information regarding algorithm behaviour and drivers of sol…
View article: A comparative study of anomaly detection methods for gross error detection problems
A comparative study of anomaly detection methods for gross error detection problems Open
The chemical industry requires highly accurate and reliable measurements to ensure smooth operation and effective monitoring of processing facilities. However, measured data inevitably contains errors from various sources. Traditionally in…
View article: Ensemble Learning based on Classifier Prediction Confidence and Comprehensive Learning Particle Swarm Optimisation for Medical Image Segmentation
Ensemble Learning based on Classifier Prediction Confidence and Comprehensive Learning Particle Swarm Optimisation for Medical Image Segmentation Open
Segmentation, a process of partitioning an image into multiple segments to locate objects and boundaries, is considered one of the most essential medical imaging process. In recent years, Deep Neural Networks (DNN) have achieved many notab…