Frontiers in Artificial Intelligence | New and Recent Articles

Explainability is particularly challenging in legal prediction, where decisions are expected not only to be accurate but also to be justified under explicit legal norms and open to external scrutiny. Legal reasoning therefore provides one of the most demanding settings for explainable AI because a legal determination often depends on information distributed across the input record, external legal…

The quality of raw materials is fundamental to the reliability and overall performance of final products, serving as the cornerstone of modern manufacturing standards. While traditional inspection methods can be effective, they are frequently time-consuming, labor-intensive, and unsuitable for high-throughput production environments. Recent advances in quantum computing offer significant advantag…

IntroductionRegulated cell death (RCD) pathways influence tumor progression and immune modulation. We previously constructed a signature database mapping 25 RCD forms across seven multi-omic layers and 33 tumor types (CancerRCDShiny). Despite their ability to identify risk populations, translating these signatures into personalized clinical workflows requires a shift from cohort stratification to…

IntroductionThe Air Quality Index (AQI) provides daily information on the quality of outdoor air and increases with rising air emissions. Accurate AQI analysis and prediction are important for understanding and managing air pollution. This study develops an integrated deep learning framework incorporating a fractal approach to analyze and predict AQI.MethodsThe developed framework consists of two…

IntroductionRadial distribution grids require renewable planning methods that jointly account for meteorological generation, WT/PV capacity allocation, and smart-inverter voltage support.MethodsAn ANN-PSO-VoltVAR framework was developed using 2012-2021 NASA POWER irradiance, wind-speed, and temperature data for In Salah, Algeria. Renewable uncertainty was represented by 12 stratified Weibull-Beta…

BackgroundCurrent classification of Acute Myocardial Infarction (AMI) into ST-elevation (STEMI) and non-ST-elevation (NSTEMI) myocardial infarction does not fully reflect the clinical heterogeneity of patients.ObjectivesTo identify clinically meaningful phenotypes of AMI patients using an unsupervised clustering approach and assess their associations with management strategies and long-term outco…

Deep neural networks (DNNs) are known to produce erroneous results under real-world noisy inputs, presenting a major bottleneck to their use in applications where lives, safety, or significant resources are at stake. It has been commonly observed that humans are highly resilient to the noisy inputs that are challenging for DNNs. However, very few efforts have translated this observation into tech…

This study explores the role of ontology in healthcare by surveying numerous research articles to provide a comprehensive overview of its applications, benefits, and challenges. Ontologies, which enable structured representation and integration of complex healthcare knowledge, have been increasingly employed to enhance data interoperability, improve clinical decision making, and support personali…

Rheumatoid arthritis (RA) is a chronic autoimmune disease where early diagnosis is critical for preventing irreversible joint damage. Recent advances in quantum computing have established potential advantages in modelling complex, high-dimensional biomedical data. The main objective of the work is to propose a Hybrid Pretrained Quantum Convolutional Neural Network (HP-QCNN) for automated RA class…

IntroductionIn laparoscopic surgery, which is a minimally invasive procedure, endoscopic cameras are very common in visual guidance. Nevertheless, surgical smoke produced during electrocautery and laser ablation greatly impairs the quality of images produced by decreasing contrast, blurring edges, and distorting color data. This deterioration is experienced with both the visibility of the surgeon…

Online learning platforms contain large question banks, yet learners often lack clear guidance on what to practice next and why a particular item is appropriate. Existing educational recommender systems can optimize learning paths, but their decisions are often difficult for learners to interpret. Large language models (LLMs) can generate natural-language explanations, but unconstrained generatio…

IntroductionFashion pattern generation remains one of the most labor-intensive stages in garment production because it depends heavily on expert manual drafting, iterative revisions, and technical precision. Although existing artificial intelligence (AI)-based fashion systems have primarily focused on garment classification, image synthesis, and trend prediction, limited research has addressed th…

BackgroundDiagnosing infections remains challenging. Clinicians rely on scoring systems and experience, but artificial intelligence (AI) is used to support decision-making by integrating clinical data. However, most AI models focus on predicting adverse outcomes (e.g., ICU admission or sepsis) rather than differentiating between infection types.ObjectiveTo develop predictive models and evaluate t…

As nations face two significant megatrends—demographic aging and digital transformation—there is a need to align technological innovation with overarching sustainability goals. This study examines the application of Artificial Intelligence (AI) to improve intergenerational skill transfer, thereby enabling aging populations to substantially contribute to the enhancement of Environmental, Social, a…

ObjectiveTo develop a multimodal predictive framework that integrates breast magnetic resonance imaging (MRI) and structured clinical features for predicting residual cancer burden (RCB) following neoadjuvant therapy.MethodsThe proposed Breast Cancer Multi-source Multi-scale Model (BCMM) was evaluated on the I-SPY1 cohort (n = 201), incorporating eight structured clinical variables and 2.5D MRI i…

Accurately and in real-time identifying advanced cyber-attacks continues to be a serious challenge for modern Network Intrusion Detection Systems (NIDS), especially in situations of highly imbalanced network traffic load and large-scale network attacks. Signature-based and single-model learning methods are typically ineffecive in capturing the complexity of traffic interactions and are not genera…

IntroductionAnxiety-related stress among college students has become a significant public health concern, affecting student well-being, academic performance, and campus operations. However, the application of digital twins for campus mental health management remains limited. This study proposes a campus-scale digital twin framework to support monitoring, analysis, and management of anxiety-relate…

IntroductionDeep convolutional neural networks learn rich feature representations; however, the final classification head may not fully align with this feature space after end-to-end training, leading to underutilization of discriminative information. To address this limitation, we propose a lightweight post-hoc redundant head mechanism that improves feature-space adaptation without modifying or …

Postpartum depression is a major pregnancy-related mental health issue. Currently, computational intelligence research is increasing rapidly for early detection. This systematic review provides a detailed analysis of previous studies based on datasets, models used in machine learning and deep learning, feature selection methods, preprocessing techniques, and performance metrics. Scholarly works w…