2026 May - 31 articles
This research introduces a quantum machine learning (QML) approach for predicting heart disease (HD). The method combines preprocessing of data with quantum feature map (QFM) and quantum classification techniques. In the method, clinical data of HD a...
The Industrial Internet of Things (IIoT) is central to modern industrial automation, yet its growing connectivity exposes critical systems to evolving cyber threats. Traditional intrusion detection methods struggle in IIoT environments due to class i...
Measuring the impact of citizen-science projects is hard because inputs are heterogeneous, mostly categorical, and sparse. We present Alquimics, a compact supervised neural network trained on one-hot project descriptors to predict impacts across five...
Robust exercise recognition is essential for robot-assisted lower-limb rehabilitation, where misclassifications of sensor-derived movements can degrade therapy execution and supervision. This study proposes a novel hybrid weighted stacking ensemble t...
Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitati...
Artificial intelligence (AI) is transforming how organizations develop human potential, offering scalable and data-driven support for coaching and capability building. This study proposes and validates a conceptual framework for integrating AI into o...
Evaluating large language models (LLMs) critically depends on human judgment. This article reviews and develops a conceptual framework for human-centered LLM evaluation, synthesizing research across evaluation methodology, psychometrics, cognitive sc...
In the context of rapid technological evolution and increasing market uncertainty, technoparks have emerged as critical ecosystems for bridging scientific research and high-tech industrial production; however, their effectiveness is often constrained...
Machine vision plays a pivotal role in precision engineering for high-precision measurement that relies on high-resolution images. The highly reflective nature of metal surfaces and the need for high-quality images pose significant challenges in imag...
Generating multiple text sequences and refining them through feedback is essential for improving the quality of outputs in many NLP tasks. While Large Language Models can leverage iterative feedback during inference, smaller models often lack this ca...
Large language models (LLMs) have demonstrated remarkable capabilities in learning complex tasks purely from sequential data. To explore whether such models can internalize strategic world representations, We investigate whether generative transforme...
Remaining Useful Life (RUL) forecasting models are essential to enable predictive maintenance strategies. However, selecting the most appropriate model based solely on conventional accuracy metrics may be insufficient for practical decision making, w...
At present, there are no easily understood explainable artificial intelligence (AI) methods for discrete token inputs, like text. Most explainable AI techniques do not extend well to token sequences, where both local and global features matter, becau...
Artificial intelligence (AI), particularly large language models, is increasingly shaping how people access and engage with news. Guided by a multi-level digital divide framework, this exploratory study examines patterns of AI use for news consumptio...
Background and Objectives: Post-hepatectomy liver failure (PHLF) remains the leading cause of mortality following hepatic resection, with reported incidence rates ranging from 1.2% to 32%. Traditional scoring systems such as the Child–Pugh scor...
In recent years, with the advancement of production technology in the manufacturing industry, the scheduling problems that rely on modeling in real-world scenarios have gradually evolved into complex process flows. Aiming at the limited problem model...
Optical coherence tomography plays a critical role in diagnosing retinal diseases, yet automated deep learning classification is hindered by severe class imbalance in which rare pathologies are underrepresented and frequently misclassified, a limitat...
Machine learning has a wide range of applications, including classification, which categorizes elements based on their characteristics. This paper addresses the challenge of optimizing attribute weighting while assessing two crossover operators on di...
This paper presents an innovative methodology detecting out-of-distribution individuals based on a multimodal contrastive learning approach. The system combines voice and facial image data by projecting them into a shared representation in the embedd...
Technical documents differ from general text corpora in ways that complicate retrieval-augmented generation (RAG). Evidence for a single answer is often distributed across numbered clauses, tables, figures, captions, and ordered procedures rather tha...
Artificial intelligence techniques have made notable progress in supporting learning processes, with increasing adoption across educational contexts. However, despite the increasing work on AI-assisted techniques, explicit and implicit learning mecha...
Galaxy morphology combines measurable structural properties with subjective visual interpretation, limiting strictly hard-label classifications. This study proposes a framework designed to compare physically derived and human-based galaxy classificat...
Higher technical and professional education is increasingly discussed in relation to workforce readiness, innovation, and societal inclusion. In Italy, the PNRR-funded Rome Technopole operates as a multi-institutional ecosystem in which universities,...
This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped...
Semantic segmentation of high-resolution remote-sensing images is critical for urban planning, land-cover mapping, and ecological monitoring. However, existing methods face limitations in handling complex land-cover types, multi-scale objects, and mo...
The integration of artificial intelligence (AI) into the diagnosis and prognosis of heart diseases is transforming cardiovascular and cardiac healthcare, improving predictive accuracy, and personalizing treatment plans. This review presents a novel c...
In the past 20 years, Artificial Intelligence (AI) has made several advancements. Because of AI’s ability to process large datasets better than humans, it is thought to have a promising future in many fields. Despite their advantages, AI and sp...
Building reliable vehicle detection models for intelligent transportation systems calls for large, well-annotated datasets—yet gathering and labelling real traffic data remains both costly and labour-intensive. This paper introduces Traffic Syn...
Large Language Models (LLMs) are increasingly deployed across professional and societal domains, introducing security, privacy, and governance challenges beyond traditional software vulnerabilities. Despite extensive research on individual risk categ...
Multilingual learning is key in natural language processing, but is challenged by the transfer–interference trade-off, where positive transfer benefits certain languages, while negative interference affects others. Prior methods, including ling...
Objectives: To address the difficulty of data sharing under privacy constraints and the performance degradation of conventional federated models caused by pronounced inter-client data heterogeneity in rolling bearing remaining useful life prediction,...
