Today’s distribution networks face challenging voltage stability problems from the increasing deployment of electric vehicles (EVs) and solar photovoltaic (PV) systems. The current methods of voltage stability assessment rely on computation-int...
Federated learning (FL) leaves training records with participating clients, but restrictions on the visibility and retention of model updates can limit later verification, robustness checks, and deletion. This review examines these dependencies acros...
The increasing use of Large Language Models (LLMs) as synthetic respondents, experimental participants, or comparators to human samples and populations raises a critical statistical question: under what conditions can LLM-generated observations suppo...
Accurately predicting the progression of Alzheimer’s disease is crucial for physicians, who need not only to identify the most likely short-term diagnosis but also to understand how the condition will evolve over time and how reliable the predi...
Physiological signals have attracted growing interest in biometric recognition because they contain individual-specific characteristics that can support person identification. However, Electrodermal Activity (EDA) remains less investigated than other...
Artificial Intelligence (AI) is increasingly transforming the development and analysis of advanced materials and engineering systems by enabling data-driven prediction, materials discovery, simulation acceleration, and optimization. However, the rapi...
Financial sustainability is a growing concern for Higher Education Institutions (HEIs). Many institutions face structural financial pressures due to reduced public funding, volatile enrolment, and rising costs. Although institutions collect increasin...
Rhythmic syntax forms the temporal structural framework essential for musical meaning and emotion. While crucial for music perception, how rhythmic syntax is processed without direct attentional focus remains unknown. Using EEG, we examined this proc...
Generative artificial intelligence (GenAI) has become widespread through user-friendly interfaces that require minimal technical skill, yet rural communities and small and medium-sized enterprises (SMEs) still face a capability gap that low barriers...
Large language models such as GPT-4, Med-PaLM 2, and BioMedLM are being increasingly applied to clinical decision support tasks including diagnostic reasoning, clinical documentation, medical question answering, and patient-facing health communicatio...
The increasing integration of artificial intelligence (AI) into everyday communication has complicated the conceptual boundaries of empathy by enabling systems to generate responses that appear emotionally attentive, supportive, and context-sensitive...
E-commerce search engines often fall short in delivering accurate and fair results due to their reliance on keyword matching and bias towards larger companies. This paper introduces a novel search engine architecture that integrates advanced AI techn...
Citizens’ social media posts can provide rapid, complementary situational information during emerging wildfires, alongside conventional sensing and official reporting. For Greek, no annotated wildfire corpus or end-to-end pipeline existed, and...
Artificial Intelligence (AI)-enabled systems are now increasingly deployed across safety-critical, mission-critical, and socio-technical domains. Existing engineering disciplines offer mature approaches to human and software assurance; however, AI-en...
In recent years, many studies have focused on the robustness of federated learning (FL) systems against Byzantine attacks. Prior work has shown that integrating stochastic optimization algorithms with momentum can effectively enhance the robustness,...
Residue-soil mixing and reduced spectral sensitivity complicate summer maize leaf area index (LAI) estimation from unmanned aerial vehicle multispectral imagery. We compared full-plot and canopy domains, paired differences, neighborhood statistics, a...
Although machine learning-based forecasting models often function as opaque black boxes, explainability methods provide the essential tools required to foster trust in deep learning systems, optimize computational resources, and enhance model accurac...
Warfarin dosing is complicated by wide inter-patient variability driven by genetic, demographic, and pharmacological factors. Although machine learning approaches to dose prediction have been widely studied, published evaluations are frequently affec...
This paper addresses dynamic vehicle routing with online requests, where insertion heuristics follow fixed rules ignoring the effect of each insertion on later requests. It proposes a two-stage attention-based insertion policy: a first stage selects...
Predefined thematic taxonomies can obscure how misinformation narratives emerge, overlap, and change over time. One way to address this limitation is by developing bottom-up data-driven approaches that identify narratives directly from data. We prese...
Generative artificial intelligence has changed the conditions under which users encounter and evaluate machine-produced communication. In systems based on large language models, conversational output is no longer merely functional; it can be fluent,...
Hemorrhage is the leading cause of preventable death in trauma, yet traditional vital signs can fail to detect early blood loss due to physiological compensatory mechanisms. Objective, continuous monitoring of hemorrhage progression remains an unmet...
Colonoscopy is a crucial method for colorectal cancer screening and the early detection of precancerous polyps; however, accurate polyp segmentation is challenged by factors such as indistinct boundaries, minute or flat structures, and regions of hig...
Chaos-based image ciphers may exhibit favorable statistical metrics while retaining a recoverable algebraic structure. We analyze the modified one-time-pad image cryptosystem of Stoycheva et al., which combines static, Chua-system-derived, and neural...
Cloud defenders increasingly want to anticipate an attacker’s next move, not merely detect the last one. We study attack-path forecasting in multi-cloud environments: given the partial trace of an in-progress campaign, predict the next ATT&...
Large Language Models (LLMs) are increasingly being used to support the structured extraction of methodological evidence from scientific literature and to build reviews and surveys. However, their use for scientific evidence extraction raises methodo...
Stereoscopic vision relies on the ability of the visual system to estimate binocular disparity across the visual field. In computational models, stereo matching can be represented through multiple candidate disparity hypotheses defined over retinotop...
Class imbalance is common in biomedical machine learning, yet imbalance-correction strategies are typically evaluated using predictive performance rather than their effects on biological interpretation. We evaluated six resampling methods: Random Ove...
This paper presents a novel algorithm, the Drug Pair-based Fuzzy Decision Tree (DP-FDT), designed to improve classification performance on imbalanced drug combination datasets. Unlike traditional decision trees, the DP-FDT integrates drug combination...
AI-generated text detectors often achieve high in-domain accuracy but remain sensitive to changes in domain, generator, and writing style. This limitation partly arises because conventional detectors rely predominantly on latent language-model repres...
Multi-stream graph convolutional networks improve skeleton action recognition by combining representations, but each branch adds inference cost. We evaluate one fixed stream-removal operating point in a CTR-GCN pipeline using a paired non-inferiority...
Engagement with generative artificial intelligence (GenAI) involves practical literacy as well as learning, professional, sociotechnical, and configuration concerns. This mixed-methods study examined whether these responses were differentiated among...
Self-refinement can improve generated code without additional training, but without execution feedback, revision can also turn already correct code into incorrect code. We study how self-refinement protocol design affects code correctness using six l...
Diabetic foot ulcer (DFU) classification increasingly relies on learning frameworks that exploit medical image representations without centralizing raw data. This study presents FedQDFU, a feature-based federated quantum learning benchmark evaluating...
Accurately recognizing maize leaf diseases requires analyzing visual information expressed across multiple spatial scales. This includes the leaf’s overall appearance, intermediate-scale texture structures, and localized lesion patterns. This s...
Learned control is commonly evaluated at the policy output, although a deployment layer may transform that output before execution. We study this post-inference semantic boundary for offline multi-agent reinforcement learning policies that produce th...
The rapid adoption of generative Artificial Intelligence systems has massively expanded the use of Large Language Models (LLMs) even in complex engineering domains, where formal accuracy and logical-mathematical consistency are essential requirements...
Learning-enabled control requires training to produce competent policies consistently when success requires every robot to finish. This study evaluates masked entity-attention multi-agent proximal policy optimization (MAPPO) for simulated landmark co...
Agentic Artificial Intelligence (Agentic AI) has emerged as a new paradigm that extends conventional artificial intelligence by integrating autonomous reasoning, planning, memory, tool use, and adaptive decision-making into goal-directed systems. Alt...
Agentic social networks expose autonomous agents to large volumes of potentially malicious content, including prompt injection, social engineering, and unsafe execution requests. Large Language Model (LLM) judges can detect such content, but applying...
In post-training compression of Transformer models, the compression ratio or pruning rate is often predetermined and does not directly take into account the memory capabilities of the target hardware. This work introduces a cache-aware post-training...
Configure-Price-Quote (CPQ) systems are central to enterprise B2B sales execution, yet most deployments remain rule-based and deterministic, unable to learn from deal outcomes. This paper presents CognitiveCPQ, combining a Heterogeneous Constraint Gr...
This paper studies a hybrid anti-jamming architecture using a Stackelberg game as its theoretical formulation in which a Soft Actor-Critic (SAC) agent selects a robustness radius and a power-control coefficient, while model-based power allocation and...
Traditional group decision-making methods are typically employed with a limited number of experts and alternatives. Nevertheless, in real-world scenarios, such as those involving social networks or e-democracy in municipalities or companies, these me...
Multimodal time-series anomaly detection incorporates textual data regarding external events to provide contextual information for numerical fluctuations; this facilitates the interpretation of sudden fluctuations in complex systems—such as tho...
Automating the evaluation of technical documents, such as laboratory reports, requires balancing accurate multimodal information extraction with efficient downstream processing. This paper proposes a vision–language model (VLM) processing pipel...
The demand for more accurate Convolutional Neural Networks (CNNs) has led to increasingly deep and computationally expensive models. These architectures face limiting deployment and resource constraints when implemented. Static CNN models treat all i...
Deep learning has achieved strong predictive performance in facial emotion recognition (FER), but interpreting the internal representations underlying model decisions remains challenging. In this study, we propose a cross-representation concept-based...
Artificial intelligence (AI) is increasingly used for tool-wear prediction in computer numerical control (CNC) machining, yet a high average accuracy can conceal systematic failure in the safety-critical high-wear region, where under-prediction makes...
Quantum computers endanger the security of digital signatures as they are generated today, e.g., by cryptographic algorithms like RSA, DSA, and ECDSA, whose security is based on algebraic problems. They have to be replaced by quantum-computer-resista...
