The effectiveness of wearable exoskeletons critically depends on assistance patterns, yet their determination is challenging because physiological responses vary across individuals and gait conditions. Human-in-the-loop (HIL) optimization can persona...
Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-cons...
This study presents an AI-based, human-in-the-loop decision support system designed for large-scale institutional query routing and response generation. The proposed system combines semantic text classification with large language model-based respons...
Improvements in safety in the air and in space can be achieved through better ergonomics, better work environment, and other efforts of the traditional avionic psychology that directly affect human behaviors and performance. There is also a significa...
The reciprocal relationship between large language models (LLMs) and knowledge graphs (KGs) opens new opportunities for knowledge discovery from scientific literature; however, most approaches address only one direction of this interaction. In comple...
Objective clinical analyses are required to evaluate balance control performance. To this outcome, it is relevant to study experimental protocols and to develop devices that can provide reliable information about the ability of a subject to maintain...
This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are r...
In the human-in-loop (HIL) guidance mode, a pilot quickly identifies and flexibly locks on to a target through a real-time image signal transmitted by the aircraft. Then, the line-of-sight (LOS) angle error in the viewing field is tracked and compens...
Wearable robots are emerging as a viable and effective solution for assisting and enabling people who suffer from balance and mobility disorders. Virtual prototyping is a powerful tool to design robots, preventing the costly iterative physical protot...
Adversarial manipulation of perception is usually scored per frame by an adversarial success rate (ASR). For a remotely supervised drone this is insufficient: a manipulation is dangerous when neither the modeled remote-human path nor the onboard auto...
Autism diagnosis remains a critical healthcare challenge, with current assessments contributing to average diagnostic ages of 5 and extending to 8 in underserved populations. With the FDA approval of CanvasDx in 2021, the paradigm of human-in-the-loo...
In dynamic and extreme space environments, current multi-robot systems inevitably encounter failures during autonomous task execution. Addressing these failures requires human-in-the-loop collaboration with astronauts, who first conduct fault analysi...
Background/Objectives: Population pharmacokinetic (PopPK) modeling in NONMEM requires iterative, expertise-dependent workflows. Naïve zero-shot prompting of general-purpose large language models (LLMs) typically produces NONMEM code that fails t...
Human vitamin K epoxide reductase (hVKORC1) enzymatic activity requires an initial activation by a specific redox protein, a less studied step in the hVKORC1 vital cycle. Significant steric conditions must be met by enzymes, being that to adapt their...
The pharmaceutical industry’s pursuit of enhanced product quality, regulatory compliance, and operational efficiency has catalyzed the integration of Artificial Intelligence (AI) into Ongoing Process Verification (OPV) frameworks. This comprehe...
Objective: This paper seeks to provide an effective and automated method for the creation and updating of building information modeling compliance rules using the integration of human-in-the-loop collaboration with advanced natural language processin...
Exoskeletons offer promising solutions for enhancing human mobility; however, personalizing assistance parameters to optimize physiological outcomes remains challenging. Human-in-the-loop (HIL) optimization has emerged as an effective strategy for ta...
Industry 5.0 promotes sustainable, resilient, and human-centric manufacturing. Many factories struggle to produce energy-aware schedules that balance throughput, energy, and time-of-use (TOU) tariffs. Classical methods (heuristics and optimization) h...
Large language models (LLMs) show promise for grading open-ended assessments but still exhibit inconsistent accuracy, systematic biases, and limited reliability across assignments. To address these concerns, we introduce SURE (Selective Uncertainty-b...
Large language models can produce fluent requirements refinements and planning artifacts while still leaving information unresolved for implementation, testing, or planning commitment. This paper presents ReqPlan-Eval, an evidence-aware human-in-the-...
Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-ba...
Agentic Process Automation (APA) extends Robotic Process Automation by delegating workflow construction and runtime decisions to Large Language Model (LLM) agents. In regulated business processes, this autonomy creates a specific safety risk: an LLM...
This paper presents a criteria-governed, human-in-the-loop information system architecture integrating formative AI-assisted learning with preliminary assessment of spoken and written responses. It supports two modes: a retrieval-augmented assistant...
While emerging technology for self-driving automation in vehicles progresses rapidly, the transition to an era of roads full of fully connected and automated vehicles (CAVs) may take longer than expected. Until then, it is inevitable that CAVs should...
The non-transparency of artificial intelligence (AI) systems, particularly in deep learning (DL), poses significant challenges to their comprehensibility and trustworthiness. This study aims to enhance the explainability of DL models through visual a...
Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial in...
Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to wide...
Early-stage architectural design is still dominated by hand-drawn 2D floor-plan sketches, and converting these sketches into object-based Building Information Modeling (BIM) models remains a manual bottleneck that delays downstream simulation, costin...
Large language models are increasingly used to generate engineering code, but executable output does not establish correctness. This study examines an expert supervised workflow used to develop Python implementations for two analytical geotechnical p...
Large Language Models (LLMs) and Multimodal Models (LMMs) are significantly influencing scientific research, including archaeology—a discipline dealing with uniquely complex, multimodal data. This comprehensive review systematically examines re...
The public release of ChatGPT on 30 November 2022, constituted an inflection point in the history of artificial intelligence applied to medicine. Within oncology, a specialty defined by the need to synthesize complex, heterogeneous, and rapidly evolv...
Process industries, including chemical, petrochemical, energy, and wastewater treatment systems, operate under high-dimensional, nonlinear, dynamic, and safety-critical conditions. Data-driven process monitoring and fault detection and diagnosis (FDD...
Artificial intelligence (AI)-driven fraud detection systems in FinTech ecosystems increasingly face a governance tension between high predictive accuracy and limited regulatory transparency, a gap that existing reviews have not addressed through an i...
Industry 4.0 and 5.0 technologies have made industrial environments data-rich, yet a persistent cognitive gap remains: operators face substantial difficulty interpreting and acting on this data in unstructured, time-critical situations. This paper pr...
Intrusion detection systems (IDSs) must operate under severe class imbalance, evolving attack behavior, and the need for calibrated decisions that integrate smoothly with security operations. We propose a human-in-the-loop IDS that combines a convolu...
Smart-city infrastructure is increasingly operated through cyber–physical systems that combine embedded sensing, edge computing, communication networks, automation platforms, and digital-twin environments. The emerging industrial metaverse exte...
Market-surveillance authorities face a growing volume of digital marketing content spanning video, social-media, influencer, and AI-generated or synthetic media, whose compliance with EU consumer and digital law cannot be assessed manually at scale....
This paper presents a first-of-its-kind evaluation of integrating Large Language Models (LLMs) within a Human-In-The-Loop (HITL) framework for risk analysis in machinery functional safety, adhering to ISO 12100. The methodology systematically address...
Open-ended Student Evaluations of Teaching (SET) provide rich information about students’ learning experiences and instructional needs, yet their large-scale analysis remains difficult to conduct through manual coding alone. This study proposes...
Generative artificial intelligence (AI) shifts educational focus from access to information toward judging machine-generated claims and regulating the division of epistemic work. This conceptual article proposes the SAM model—self-regulation (S...
Groundwater depletion threatens global freshwater resources, necessitating urgent water management and policies to meet current and future needs. However, existing data-intensive approaches to assessments do not fully account for the complex human, c...
This paper presents a broad study on the evaluation of explanatory capabilities of machine learning models, with a focus on Decision Trees, Random Forest, and XGBoost using a pancreatic cancer data set. We use Human-in-the-Loop-related techniques and...
In this study, a structured and methodological evaluation approach for eXplainable Artificial Intelligence (XAI) methods in medical image classification is proposed and implemented using LIME and SHAP explanations for chest X-ray interpretations. The...
Safety alignment of large language models faces a feedback dilemma: human judgment is reliable but costly to scale; automated judgment is cheap but less trustworthy. Existing methods commit to one source per run, so misjudged responses are never corr...
Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approache...
According to the World Bank, a key factor to poverty reduction and improving prosperity is financial inclusion. Financial service providers (FSPs) offering financially-inclusive solutions need to understand how to approach the underserved successfull...
According to the World Health Organization (WHO) and the World Bank, malnutrition is one of the most serious but least-addressed development challenges in the world. Malnutrition refers to the malfunction or imbalance of nutrition, which could be inf...
More than 80% of young people (11–17 years) do not meet recommended levels of physical activity, while excessive sedentary smartphone use increases rapidly, highlighting the need for accessible tools that promote active and kinesthetic learning...
This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of dis...
Thematic analysis of large-scale political discourse remains a challenge due to semantic complexity and overlapping policy areas and changing diplomatic vocabulary. Although large language models (LLMs) offer promise for scalable thematic classificat...
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