2026 August - 38 articles
Anyone who has watched a distant scene shimmer above hot pavement has seen atmospheric turbulence destroy image detail. In long-range imaging, turbulence produces spatially varying blur, geometric warping, scintillation, and temporal instability. Rec...
Medical treatment decision making is a complex process that involves integrating multivariate time-oriented data from multiple sources and is often influenced by factors such as patient load. In this study, we propose the Recurring Target Prediction...
The development of Motor Imagery (MI) Brain–Computer Interfaces (BCIs) is systematically constrained by low signal-to-noise ratios (SNRs), signal non-stationarity, and acute data scarcity. While complex Blind Source Separation (BSS) methods opt...
In the era of intelligence, accurate remaining useful life (RUL) prediction is essential to ensure the reliable operation of smart equipment, particularly for rolling bearings—critical components that are highly susceptible to degradation in ro...
Small Language Models (SLMs) enable efficient deployment, but their limited parameter count constrains factual knowledge, particularly in low-resource languages like Kazakh. Integrating live web search can address this limitation, though its effectiv...
With the growing use of digital twins and 3D city models, 3D point cloud data have become increasingly important. Such data, however, may contain privacy-sensitive objects, including people and vehicles, which poses challenges for public release and...
Multimodal emotion recognition in conversation (MERC) aims to identify the emotions in each utterance by modeling textual, acoustic, and visual evidence. Compared with unimodal emotion recognition in conversation (ERC), MERC can leverage complementar...
Relational databases power high-stakes decisions in lending, healthcare, and justice, yet SQL lacks native constructs for causal and counterfactual reasoning. Prior SQL-based causal systems address parts of this gap but do not unify treatment-effect...
Student retention analytics often treats withdrawal as a single outcome, although students may attribute dropout intention to personal, socioeconomic, and academic pressures simultaneously. We benchmarked nine supervised classifiers for identifying a...
This article presents a systematic, evidence-weighted review of Deep Learning (DL) in farming, with a primary emphasis on the agricultural production stage and on four operational domains: precision crop management, precision livestock farming, soil...
Backpropagation is less a single algorithm than a pipeline of choices: how the error signal is propagated, how the weight gradient is assembled, and how the update is applied. This paper revisits three consecutive steps and proposes small, mathematic...
Cardiovascular diseases remain the leading global cause of mortality, necessitating continuous monitoring solutions that extend beyond clinical settings. This paper proposes a real-time, end-to-end Edge-IoT framework for cardiovascular risk assessmen...
Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) pose a significant global healthcare burden due to challenges in early diagnosis. This study investigates the clinical generalizability of machine le...
Infrastructure project reports contain rich narrative evidence on delay causes, yet transforming such unstructured text into reliable causal knowledge remains challenging because reports mix confirmed events with hypothetical, conditional, or localiz...
The growing capabilities of artificial intelligence (AI) have not translated straightforwardly into organisational value. A persistent disconnect—the “last-mile problem”—arises from structural gaps between idealised AI tasks a...
Automatic sentiment analysis requires models that can effectively handle texts of varying levels of complexity. Monolithic methods use the same algorithm uniformly, without taking into account the intrinsic complexity of the input text. In response t...
Understanding how clinical pathways evolve over time is essential for characterizing care processes. It also helps identify potential shifts in diagnostic and organizational practices. This study provides a descriptive analysis of patient trajectorie...
ViTs have achieved excellent performance in image recognition tasks, but their large parameter counts and high computational complexity limit their deployment on resource-constrained devices. Most existing ViT pruning methods adopt class-agnostic pru...
In recent years, there has been an increase in the amount of fake news in the media, which is why fact-checking systems are gaining popularity, particularly those that use natural language processing (NLP) to quickly identify and flag fake news. One...
Accurate blood glucose prediction is essential for glycemic management in people with diabetes, but the size of many high-performing models complicates execution on resource-constrained artificial pancreas controllers. We propose GluKDnet, a lightwei...
English-teaching agents use external knowledge retrieval to update instructional content, broaden domain coverage, and personalize support beyond standalone large language models (LLMs). However, open sources may introduce harmful, biased, or mislead...
Large language models have become routine participants in everyday cognition. Their role has widened from retrieval and text generation to helping users define problems, organize arguments, make judgments, and interpret themselves. Yet their cognitiv...
Node classification is a central graph data mining task, yet repeated message passing can over-smooth representations and degrade frozen graph neural network (GNN) predictions after deployment. We present SETTA (Spectral-Energy Test-Time Adaptation),...
This paper presents a novel approach to speech-to-speech (STS) translation for low-resource Turkic languages. Today, STS has progressed rapidly for high-resource languages; the Turkic family remains significantly underrepresented. Consequently, it is...
The deployment of natural-language-to-SQL (NL-to-SQL) systems in primary healthcare requires more than accurate query generation: it also requires governed data access, robustness to local terminology, and reliable handling of ambiguous user requests...
Record linkage is a fundamental step in ensuring the quality of data by detecting duplicate records within different databases. Nevertheless, dealing with big, imbalanced databases and ensuring data confidentiality is still difficult in terms of perf...
Phishing attacks exploit human vulnerabilities through social engineering techniques; therefore, a multidimensional framework integrating personality and cognition is crucial for tailoring personalized defenses against phishing threats. To prevent ph...
By considering the real-life research project Pain-RELife, which focuses attention on big data management and analytics tools over patients suffering from chronic pain, located in the Region Lombardy of North Italy, this paper provides a relevant jou...
Many organizations increasingly adopt personalization techniques to enhance user satisfaction. However, current systems generally cannot automatically infer and interpret individual personality traits (PTs), although these traits are key drivers of u...
Driven by non-stationary factors in real-world sensor-driven applications, time series streams from energy meters, traffic detectors, weather stations, and industrial monitors often exhibit complex patterns composed of long-term trends and multi-scal...
The rapid proliferation of Internet of Things (IoT) technologies has transformed the modern home into a complex cyber–physical ecosystem encompassing hundreds of millions of connected devices globally. Smart homes support automation, energy man...
Frozen speech-to-LLM systems train an adapter between a frozen audio encoder and a text LLM. We test whether adapters preserve sentence stress beyond transcripts and whether the LLM uses it. In a five-seed Qwen3-8B/WavLM baseline, transcript alignmen...
Existing e-commerce recommender systems, like graph-based retrieval and user behavior-driven Collaborative Filtering, usually take either textual or visual features for item recommendations to users. Traditional hierarchical large language models (HL...
There are a wide range of possible applications for the collection and analysis of statistical data in sports, although their potential has not yet been fully exploited. This study focuses on the areas in which cognitive computing systems can offer a...
Multimorbidity risk identification in elderly health consultation scenarios is challenging because chronic disease history and medication exposure may interact through complex causal pathways. Existing LLM-, RAG-, and graph-based retrieval methods of...
Missing labels are common in multi-label learning and can bias both label-correlation estimation and classifier induction. Existing missing-label methods often recover incomplete supervision mainly through global label correlations. However, correlat...
Safe human-to-humanoid motion imitation is crucial for shared environments, where direct motion retargeting may induce self-collision or human–humanoid collision due to embodiment mismatch, kinematic limits, perception uncertainty, and human pr...
This work presents a CUDA-accelerated methodology for training multiple neural networks in parallel using population-based metaheuristics. The goal is to obtain fast and accurate short-term energy-forecasting models for time-sensitive building-manage...
