Information Fusion

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.

Cross-user variability remains a fundamental challenge in wearable Human Activity Recognition (HAR), as differences in body morphology and movement styles cause significant distribution shifts across users. Existing approaches typically focus on feature-level alignment while overlooking the biomechanical invariants—shared anatomical constraints governing human movement that persist across individuals. Moreover, current Graph Neural Network (GNN)-based HAR methods rely on single-type edge relationships (e.g., physical adjacency) with static edge features, limiting their capacity to capture the diverse and dynamic biomechanical dependencies essential for cross-user generalization. We propose Edge-Enhanced Graph-Based Adversarial Domain Generalization (EEG-ADG), a novel framework that explicitly encodes multi-relational anatomical knowledge into graph-based representation learning with probabilistic edge modeling. Unlike prior work that treats sensor relationships as fixed and homogeneous, EEG-ADG constructs a unified graph capturing three complementary biomechanical priors: Interconnected Units, Analogous Units, and Lateral Units. To handle the inherent uncertainty in these relationships across users, we introduce a Variational Edge Feature Extractor that learns probabilistic representations of inter-sensor correlations—a capability absent in deterministic edge-based GNNs. An adaptive edge attention mechanism further modulates relationship importance based on activity context, while adversarial training on graph-structured representations disentangles activity-relevant features from user-specific biases. Comprehensive experiments on the OPPORTUNITY and DSADS benchmarks demonstrate that EEG-ADG achieves state-of-the-art cross-user generalization performance, outperforming existing domain generalization methods. Our findings highlight the value of integrating biomechanical prior knowledge with probabilistic graph learning for robust, generalizable HAR systems.