14 November 2026
Cellphone return rates remain low despite increasing awareness of electronic waste and established recycling programs. Existing reverse logistics strategies rely on static rules that fail to adapt to sequential decision-making under uncertainty, resu...
14 November 2026
Background: With the continued integration of machine learning (ML) systems into distributed big-data environments, there is now an urgent need for explainability in order to achieve transparency, accountability, and trustworthiness in large intellig...
Post-default debt collection requires agencies to allocate delinquent cases across collectors under eligibility and workload constraints, yet most predictive research concentrates on credit scoring rather than on the assignment decision itself. This...
Personalized pronunciation-exercise design requires context-sensitive content, longitudinal adaptation, and professional oversight. This study presents Speak4All, a cloud-deployed human-in-the-loop (HITL) generative AI framework integrating structure...
Total impulse characterizes the overall propulsive capability of a solid rocket motor. The cost and resources required for ground static firing tests limit data availability, while temperature-dependent thrust variations complicate modeling with few...
Cross-subject motor-imagery electroencephalography (MI-EEG) classification remains challenging due to substantial inter-subject variability and non-stationarity, which cause task-relevant spectral and spatial patterns to vary across users and reduce...
SwarmPNM combines an adaptive second-moment estimate and positive–negative gradient momentum with attraction towards the lowest-loss weights stored along a single training trajectory. The attraction is inspired by particle swarm optimization, b...
We study learning-augmented combinatorial optimization in the absence of externally supplied labels. We propose CLIO (Contrastive Learning with Iterative Optimization), a search-guided graph neural framework for the maximum independent set (MIS) prob...
This study examines the key ethical and professional challenges associated with the application of generative artificial intelligence (GenAI) in journalism, their relationship with public attitudes, and the potential role of GenAI as an auxiliary too...
Machine learning (ML) can guide measurement allocation, but predictive accuracy does not establish operational value. We evaluate whether a bounded ML allocator improves industrial monitoring when a frozen variance detector retains alarm authority. T...
Using an artificial neural network (ANN) multilayer perceptron (MLP) analysis, this study examined predictors of AI users’ behavioral intention to buy products recommended by ChatGPT-enabled virtual influencers. The findings from an ANN MLP ana...
Non-negative matrix factorization (NMF) is a well-established parts-based representation learning paradigm for high-dimensional data. However, existing NMF methods often struggle to capture the intrinsic structure of data and lack sufficient robustne...
Selecting hyperparameters for training convolutional neural networks (CNNs) and determining the amount of training image data required for reliable generalization remain challenging and often time-consuming tasks, typically relying on manual trial-an...
Weakly supervised video anomaly detection (WSVAD) tries to find abnormal events in untrimmed videos, while only video-level labels are given. However, many existing methods still have two main problems. First, abnormal events may last for different l...
Digital government initiatives have become a central feature of contemporary governance, reshaping institutional environments and influencing firm behavior across industries, including the global healthcare sector. The study empirically examines the...
News headlines mainly aim to summarize an event in a news article in a few words. Media outlets may use them either to summarize objectively or to attract attention. For the latter, they may employ stylistic devices such as sarcasm, irony, and exagge...
Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) often rely heavily on subject-specific feature selection to achieve high classification accuracy. However, this introduces substantial computational overhead and calibration friction tha...
Knowledge graphs are foundational to structured knowledge representation and drive the evolution of artificial intelligence from data-driven to knowledge-enhanced paradigms. Hyper-relational knowledge graphs employ hyperedge structures to overcome th...
In biomedicine, answers must be complete, current and verifiable, yet a general-purpose large language model (LLM) can give confident but wrong or outdated answers with no sources to check. We present NeuroRAG, a multi-agent retrieval-augmented syste...
This study presents the design and technical validation of AI Edu, an educational platform combining course delivery, retrieval-augmented generation (RAG), progress records, and interactive virtual laboratories. The architecture separates the portal,...
Watermarking chest radiographs requires limiting changes within lung fields while retaining payload recovery. We evaluated a map-assisted, host-image-independent transform-domain framework using three paired carrier-selection strategies: Global, refe...
Squash is among the least analytically instrumented racket sports, despite consistently producing world-class athletes and gaining global recognition through its inclusion in the 2028 Olympic program. Automated match analysis is complicated by the ba...
In semantic recognition based on surface electromyography (sEMG), low signal-to-noise ratios and physiological differences across subjects limit the ability of unimodal sEMG features to fully represent speech semantics, constraining recognition accur...
We audit the object–evidence identity and stage denominators of a frozen LiDAR–camera detector with learned post-processing heads. Motorcycle and bicycle annotations are withheld during eight-class adaptation, although both classes occur...
Multimodal medical artificial intelligence (AI) aggregates complementary evidence from imaging, omics, pathology, and clinical records, yet hospital deployment routinely confronts structurally incomplete inputs. This review synthesizes 77 algorithmic...
Adversarial robustness is an important consideration for the deployment of artificial intelligence-based intrusion detection systems (IDSs). Existing studies have developed various defense strategies and commonly evaluate them using individual perfor...
Near midair collisions (NMACs) remain a complex aviation safety challenge shaped by operational conditions and human factors. Aviation Safety Reporting System (ASRS) narratives provide useful information about communication, visual acquisition, workl...
Camera traps are a key tool for non-invasive wildlife monitoring, yet models trained on existing benchmarks often generalize poorly to new regions due to domain shift, class imbalance, and natural variability in illumination, season, and background....
Local explanation rankings can depend on the classifier, masking operator and computational budget. This study compares LIME, KernelSHAP and Ablation or Occlusion in two controlled cases: Breast Cancer Wisconsin Diagnostic with a random forest and mu...
Wearable systems promise real-time detection of workers’ acute physiological stress in underground mining and construction, yet whether existing detectors are trained on the stressors that injure and kill workers has not been examined. This sim...
(1) Background: Generative and, increasingly, agentic artificial intelligence (AI) are reshaping how firms create, deliver, and capture value. Yet business model innovation (BMI) research often assumes that humans retain design authority while digita...
Multivariate time-series anomaly detection (MTSAD) is challenging because abnormal events may appear as fine-grained deviations, local temporal distortions, or global semantic shifts. Existing methods often learn a unified representation or rely on o...
Multimodal analysis of human behavior has advanced rapidly, yet emotion recognition (ER), personality trait assessment (PTA), and ambivalence/hesitancy recognition (AHR) are usually reviewed separately. This review jointly examines these related but...
Cross-age face recognition remains a challenge as people’s facial appearance changes significantly over time, reducing the reliability of conventional recognition systems. The proposed framework introduces a confidence-driven age-aware matching...
Tokenization affects the efficiency and cost of large language models (LLMs), especially for dialectal Arabic. To the best of our knowledge, this is the first study to evaluate LLM tokenization for the Kuwaiti Arabic dialect. We evaluated twelve LLM...
Object-detection benchmark scores are often transferred across settings without restating the evaluation target that gives a metric its meaning. This focused interpretive review examines how that target changes along a scale-centered path from regula...
Synthetic transit trip-record generation can support activity-based and agent-based travel demand modeling by providing disaggregate records with realistic spatial, temporal, and operational characteristics. Traditional population synthesis methods s...
Learning in higher education is shaped not only by the amount of practice students complete but also by the temporal allocation of practice opportunities. This study presents and retrospectively evaluates a continuous-time framework that connects lea...
Lithology identification supports reservoir characterization and well-log interpretation. Random sample splits can overestimate performance because adjacent samples from the same well are correlated, while logging-channel differences may be underused...
Historical digital personas are a promising means of interactive and personalized access to cultural heritage; however, their practical deployment requires simultaneous control over factual accuracy, source provenance, persona consistency, and multim...
Pseudo-label-based deep clustering has emerged as a compelling paradigm for unsupervised visual learning, yet two fundamental limitations impede its effectiveness: (1) pseudo-label noise, where unreliable hard assignments generated in early training...
The rapid growth of visual data in imaging and sensing motivates compression before full electronic processing. This work investigates a hybrid D
Human gait is a complex motor process regulated by the nervous system, where even subtle alterations in stride symmetry, stance duration or joint coordination directly reflect underlying motor and neurological impairments. Consequently, gait analysis...
The evolution of public opinion on social media is marked by elevated diffusion efficiency, heightened interactivity, and considerable influence. Failure to effectively control the dissemination of negative public opinion or the polarization of group...
Image regression models may exhibit substantial output variation under small input perturbations, which can reduce their reliability in applications requiring continuous predictions. This study investigates whether adaptive aggregation of noise-pertu...
Cultural heritage interpretation requires communicating relationships among physical locations, historical narratives, and symbolic meanings while accommodating diverse visitor information needs. Conversational artificial intelligence (AI) enables fl...
Training sequential recommenders for interaction-disjoint target domains is challenging: no target interaction data are available for training, model selection, or calibration. Semantic item representations support transfer across non-overlapping cat...
Normalization is a core concept in Relational Database Management Systems (RDBMS) education, but assessing student’s normalization solutions is tedious and hard to scale. Current automated methods produce schemas out of structured inputs which...
Physics-informed neural networks (PINNs) remain difficult to train for hyperbolic conservation laws. Artificial viscosity is a common remedy, and several methods reduce its coefficient during training. We examine adaptive moment estimation (Adam) fol...
Reliable manual annotations for Vietnamese logistics calls are scarce, and telephone channel degradation compounds errors in business-critical entities. Existing adaptation methods typically optimize aggregate transcription accuracy, use uniform toke...
