2026 April - 34 articles
With the scaling down of integrated circuit dimensions and the increasing complexity of transistor structures, the role of etching in manufacturing has become increasingly critical. We propose an etching simulation approach based on a video generatio...
Background: Accurate mortality prediction remains a major challenge in public health due to the complex interactions among demographic, socioeconomic, behavioral, and medical factors. This problem is particularly relevant for identifying high-risk gr...
The need for Arabic Aspect-Based Sentiment Analysis (ABSA) has grown steadily alongside the expansion of digital content, while the linguistic complexity of Modern Standard Arabic and its diverse dialects introduces significant challenges. However, p...
Large language models (LLMs) are increasingly used in applied intelligent systems, but mid-sized models still lag on mathematical reasoning, partly because reliable step-level supervision is scarce. Many existing remedies rely on costly human annotat...
This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorith...
The evolution toward 6G communications requires integrating Tiny Machine Learning (TinyML) principles to enable intelligent, energy-efficient, and adaptable signal processing at the network edge. However, current receiver architectures face a fundame...
Diagnosis of colorectal cancer (CRC) primarily relies on histopathological examination of hematoxylin and eosin-stained tissue sections; however, manual interpretation is time-consuming, subjective, and increasingly impractical given the rapid growth...
Large Language Models (LLMs) have become foundational to modern Artificial Intelligence (AI), enabling advanced reasoning, multimodal understanding, and scalable human-AI interaction across diverse domains. This survey provides a comprehensive review...
Reliable change detection in indoor environments remains a challenge for autonomous robotic systems using 3D LiDAR. Existing methods often require manual annotation, computationally intensive architectures, or focus on outdoor scenes. This paper pres...
Type 1 diabetes (T1D) is an autoimmune disease characterized by T-cell-mediated destruction of pancreatic β-cells. Antigen-specific peptide immunotherapy represents a promising strategy to restore immune tolerance. Reliable identification of rel...
Ontology engineering plays a critical role in clinical decision support systems for Parkinson’s Disease (PD) monitoring and alerting. While Large Language Models (LLMs) have shown promise in knowledge modeling tasks, their effectiveness in auto...
This study addresses the challenge of high signal-to-noise ratios in financial sentiment analysis by introducing a hybrid, multi-stage AI framework. We combine the high-throughput capabilities of FinBERT with the deep contextual reasoning of Google G...
This paper presents a novel approach for the early detection of cattle diseases. We present a uniquely integrated image classification-based project for real-time cattle disease diagnosis that combines image classification models to identify diseases...
This research utilizes machine learning (ML)-based malicious node detection techniques to effectively incorporate security and trustworthiness into fifth-generation (5G) and Vehicular Ad hoc Network (VANET) systems, in contrast to traditional methods...
Vision–language models (VLMs), such as Contrastive Language–Image Pretraining (CLIP), are increasingly deployed in real-world applications, including content moderation, misinformation detection, and fraud analysis, making their robustnes...
The increasing integration of generative artificial intelligence (GenAI) into scientific research raises the question of whether such systems can be evaluated not only through external benchmarks but also through structured analysis of their own meta...
In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to...
Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison with human perception. One subtle ability is to judge whether an image looks like a given object...
Benchmark-driven evaluation helps distinguish between planning quality and interface reliability when large language models are utilized for embodied reasoning in simulation. Our submission to the Embodied Agent Interface Challenge (EAI) is evaluated...
The process of manually scoring drawings for the Goodenough–Harris Draw-a-Person (DAP) test is time-consuming and labor-intensive. It is also prone to inconsistencies due to subjective interpretation. Keeping these drawbacks in mind, this study...
Nearly 2 million stillbirths occur globally each year. These outcomes are often driven by disparities in healthcare access, especially in low- and middle-income countries, where limited resources and shortages of trained medical personnel further inc...
Most state-of-the-art machine learning techniques revolve around the optimization of loss functions, making the choice of an objective critical to model performance and reliability. Although recent reviews discuss loss functions in specific domains o...
Federated learning (FL) enables collaborative model training across distributed devices and robotic systems while preserving data privacy, making it well-suited for swarm robotics and edge-device-powered intelligence. However, FL remains vulnerable t...
Autonomous navigation is essential for orchard transportation robots to support automated operations and precision orchard management. However, in trellis orchards, dense vegetation and complex canopy structures often degrade the stability of GNSS-ba...
Accurate and early identification of ocular diseases is essential to prevent vision impairment and enable timely medical intervention. In routine clinical practice, ophthalmologists rely on a structured diagnostic workflow that incorporates multiple...
With the intensification of global agricultural labor shortage and scaled development of facility agriculture, autonomous precision harvesting robots for unstructured greenhouse environments have become an urgent need. For cluster-picking crops such...
The tourism industry faces increasing pressure for agile, personalized services, yet travel agencies struggle with fragmented knowledge scattered across isolated systems and legacy formats. While Large Language Models (LLMs) are widely applied in cus...
Non-Intrusive Load Monitoring (NILM) represents a powerful approach for energy disaggregation, which enables detailed insights into energy consumption patterns without requiring extensive sensor deployment. While significant advances have been achiev...
Background: Artificial Intelligence (AI) and Generative AI (GenAI) are increasingly integrated into educational and professional settings, offering personalized learning, productivity gains, and enhanced engagement. However, excessive reliance may co...
The use of virtual environments to collect the experience required by deep reinforcement learning models is accelerating the deployment of these algorithms in industrial environments. However, once the experience-gathering problem is solved, it is ne...
This paper presents a comparative analysis of advanced AI-based techniques for human face inpainting using semantic masks that fully occlude targeted facial components. The primary objective is to evaluate the ability of image inpainting methods to a...
Cross-view geo-localization tries to find the matching place in large satellite or aerial pictures from photos taken at ground level, which is useful for applications like self-driving cars, flying drones, and adding virtual objects to real city scen...
This paper introduces Historical State Reconstruction, a novel framework for low-latency financial decision-making using Large Language Models. While agentic systems have demonstrated potential in synthesizing complex financial narratives, they typic...
Affected section: Institutional Review Board Statement [...]
