Machine Learning and Knowledge Extraction, Volume 6, Issue 3

Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classif...

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This l...

Operating a powered wheelchair involves significant risks and requires considerable cognitive effort to maintain effective awareness of the surrounding environment. Therefore, people with significant disabilities are at a higher risk, leading to a de...

This research investigates clutch performance in the National Basketball Association (NBA) with a focus on the final minutes of contested games. By employing advanced data science techniques, we aim to identify key factors that enhance winning probab...

Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-making processes to build user trust and promote responsible AI. Hence, a key scientific challenge in XAI is the development of methods that generate transparent...

The study presented in this paper evaluated gene expression profiles from The Cancer Genome Atlas (TCGA). To reduce complexity, we focused on genes in the cGAS–STING pathway, crucial for cytosolic DNA detection and immune response. The study an...

Creutzfeldt–Jakob disease (CJD) is a rare, degenerative, and fatal brain disorder caused by abnormal proteins called prions. This research introduces a novel approach combining AI and graph theory to analyze histopathological microscopic images...

Research into explainable artificial intelligence (XAI) methods has exploded over the past five years. It is essential to synthesize and categorize this research and, for this purpose, multiple systematic reviews on XAI mapped out the landscape of th...

Standard ML relies on ample data, but limited availability poses challenges. Transfer learning offers a solution by leveraging pre-existing knowledge. Yet many methods require access to the model’s internal aspects, limiting applicability to wh...

Achieving carbon neutrality by 2050 requires unprecedented technological, economic, and sociological changes. With time as a scarce resource, it is crucial to base decisions on relevant facts and information to avoid misdirection. This study aims to...