Diabetic foot ulcers (DFUs) impose a substantial burden on people with diabetes and healthcare systems. The post-healing “transition phase” remains clinically challenging with limited guideline support. While large language models (LLMs)...
This study investigates consumer perceptions of procedural and distributive fairness/unfairness in service interactions involving AI versus human providers. It also examines how these perceptions are influenced by technology-related discomfort and th...
Background/Objectives: The increasing use of artificial intelligence (AI) chatbots for obtaining health-related information has raised concerns regarding the quality and reliability of the information they provide. This study aimed to compare the qua...
Causality extraction is an important task in natural language processing, yet it remains underexplored in informal Arabic social media text, particularly in dialectal contexts. This study investigates causal-reason extraction from Saudi Arabic tweets...
MDPI, along with all other academic publishers, recognizes the potential benefits and real challenges posed by the advent of Generative AI [GenAI] [...]
Continuous glucose monitoring (CGM) plays a central role in modern diabetes management, yet CGM data are often confined within proprietary manufacturer ecosystems, limiting interoperability and reuse. This paper presents OpenGluco, an open-source and...
In order to evaluate regional sustainability, a comprehensive framework is needed that can integrate a number of economic and environmental variables into a transparent and policy-relevant evaluation approach. The present study presents a data-driven...
Few-shot multimodal sentiment analysis (MSA), constrained by limited annotated data, often suffers from large cross-modal semantic alignment gaps and difficulty in capturing fine-grained sentiment cues, making it challenging to fully exploit the comp...
Smartwatches are commonly used IoT wearable devices, which contain sensors that can support the data management process of smartwatch data. This process is essential for providing data-based insights that can motivate users to achieve their physical...
Public complaints exhibit strong spatiotemporal dependencies, where issues often propagate across categories within short timeframes, yet existing studies largely overlook cross-category recurrence and underutilize embedding representations for struc...
Assembling expert teams under strict skill-coverage and communication-distance constraints is a fundamental challenge in collaborative knowledge work. Existing learning-based approaches excel at probabilistic link prediction but cannot reliably enfor...
Annotated text datasets are increasingly reused as classifier targets, annotation candidates, and inputs to aggregate profiles, yet their labels often circulate without enough information about how they were produced. This article presents a reproduc...
This study presents a Markov chain-based metric called the Reliability Reserve (τ), designed to estimate the time available for operator intervention during the ayran fermentation process. This indicator can be integrated into a digital twin fore...
Chatbots are becoming increasingly significant entry points to digital services and are used in areas including job support, education, healthcare, and customer services. On the other hand, less is known about how chatbots affect people individually,...
Background: Although pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, identifying individuals who may benefit from PrEP and delivering personalized prevention recommendations remain challenging in routine and digital health set...
Glaucoma is a leading cause of irreversible vision loss, and its early diagnosis remains critically important yet challenging. Traditional assessment based on the cup-to-disc ratio is often insufficient at early stages, whereas the retinal vascular n...
In the published publication [...]
The rapid growth of the green bond market has intensified the need for transparent and reliable monitoring systems, particularly in circular-economy environments characterized by complex, multi-stakeholder, and dynamic interactions. However, existing...
Modern inpatient care generates irregular streams of heterogeneous clinical events, yet most predictive models require fixed feature matrices, predefined time windows, or discretization of continuous measurements. We developed CHaRT, a decoder-only a...
This study presents a reproducible evaluation framework for hybrid quantum-classical neural networks (HQCNNs) in healthcare classification, rather than a new architecture. We assess a four-qubit HQCNN combining a compact classical encoder, a two-laye...
Higher-education institutions increasingly use AI-enabled chatbots, personalised communication, recommendation systems, and predictive information services in academic marketing. Adoption of these systems depends not only on technical availability, b...
Reliable, high-quality rainfall data are vital for soil and water management, crop forecasting, and risk assessment. These applications are essential for food security, climate resilience, biodiversity monitoring, and rural livelihoods. Rainfall moni...
Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient,...
The recruitment headhunting process is time-intensive due to manual candidate searches across multiple job platforms, creating inefficiencies in identifying suitable candidates. Current AI-driven recruitment platforms frequently prioritize accuracy o...
Background/Objectives: Discharge letters are essential for continuity of care but are often time-consuming to prepare and variable in quality. Large language models (LLMs) may help standardize and support this process, yet evidence in ophthalmology r...
Ischemic Heart Disease (IHD) is the leading cause of cardiovascular mortality worldwide. Early detection of ischemic changes using electrocardiogram (ECG) signals is vital for timely intervention and enhanced clinical outcomes. However, the diagnosis...
Artificial Intelligence (AI) is rapidly transitioning from a specialized technology to an everyday socio-technical infrastructure, yet public acceptance remains shaped by a trade-off between perceived benefits and risks. This study examines how indiv...
Persons with Disabilities (PWDs) face persistent barriers to healthcare access, welfare services, and timely medical assistance, particularly where hospital information is fragmented across institutions. In Thailand, these challenges are exacerbated...
Smartphone-based augmented reality (AR) exercise systems show promise for supporting physical activity among older adults, yet the effect of presentation-layer information density on motor performance and cognitive workload in this population remains...
Although the sharing of data is an important part of multicenter biomedical AI, direct data sharing is hindered by privacy laws, institutional data silos, and restrained trust and cooperation between institutions. While federated learning offers an o...
Mobile health (mHealth) systems in rural communities require rigorous software engineering methodology and empirical validation of end-user acceptance. A gap exists in applying structured System Development Life Cycle (SDLC) frameworks to community-f...
Power system testing and inspection documents are multimodal and highly structured, making content-level audit labeling challenging due to scattered evidence and cross-component dependencies. We propose EviCal, an evidence-grounded consistency calibr...
This study proposes a hybrid artificial intelligence (AI) framework for graduate subject allocation that enhances fairness, transparency, and operational efficiency in higher education institutions. Traditional subject allocation processes are predom...
Accurate, automated analysis of medical images is indispensable for effective diagnosis and treatment planning, particularly for complex multiclass diseases. This paper presents a system that combines a cascaded dual-stage U-Net with texture-based de...
While social media platforms are primary vectors for misinformation, automated detection systems remain largely confined to English. This paper presents a transferable, three-stage framework for fine-tuning transformer models to detect domain-specifi...
Large language models (LLMs) are increasingly being used in education, yet their correctness alone does not capture the quality, reliability, or pedagogical validity of their problem-solving behavior, especially in mathematics, where multi-step logic...
Selecting the appropriate academic track is a critical decision for students, as misalignment between program requirements and individual cognitive, personality, and competency profiles can significantly impact academic performance, persistence, and...
Obesity is a major public health concern because of its high prevalence and association with cardiometabolic comorbidities. This study compared nine ensemble and meta-ensemble learning models for multiclass obesity-status classification using the Obe...
The volume of daily email traffic continues to grow rapidly, creating challenges in efficiently distinguishing important from irrelevant messages. Beyond spam detection, modern email systems classify messages into categories such as promotions, socia...
Multimodal sentiment analysis (MSA) utilizes complementary information from different modalities to achieve a more thorough and fine-grained understanding of human sentiment. To address the issues of high modality redundancy and insufficient refined...
Emotion detection in social media remains challenging, particularly in polarized public debates where emotional expression is often imbalanced across categories. Addressing this challenge, this study compares the performance of multiple computational...
Governments worldwide are mandating digital tax platforms, yet little is understood about what sustains taxpayer engagement beyond legally compelled minimum use. This study extends the Technology Acceptance Model (TAM) with perceived compulsion and t...
In semi-supervised learning scenarios, the presence of limited labeled data and abundant unlabeled samples poses significant challenges to model robustness and generalization. Although the semi-supervised broad learning system (SSBLS) effectively exp...
Digital repositories have been an important gateway for the dissemination of information regarding objects of art and cultural heritage throughout the World Wide Web, but the vast number of available artifacts, both historical and modern, makes their...
Twitter/x has become a key platform for analyzing public opinion on a large scale; however, traditional centralized approaches raise significant concerns regarding privacy and data governance. To address these challenges, this paper presents an empir...
Email spam and phishing detection is typically evaluated using accuracy-centric metrics under implicitly unconstrained computational settings. However, in practical deployment scenarios—particularly in real-time and resource-constrained environments—...
Blockchain technology has emerged as a foundational paradigm for building decentralized, transparent, and secure systems, particularly in environments that operate without centralized authority. At the core of these systems are consensus mechanisms t...
This paper investigates the problem of cross-lingual named entity recognition (NER), which involves automatically identifying entities such as persons, organizations, locations, and other structured elements in text. High-quality NER typically requir...
Accurate and efficient classification of hematological malignancies from peripheral blood smear (PBS) images remains challenging due to the scarcity of annotated datasets, staining variability, and subtle morphological differences among blood cancer...
Hyperspectral imaging (HSI) provides rich spectral information and serves as a non-destructive technique for forensic stain analysis. Conventional approaches often exhibit degraded performance due to the high dimensionality and spectral redundancy in...
