2025 September - 44 articles
The automated detection of deceptive language is a crucial challenge in computational linguistics. This study provides a rigorous comparative analysis of three tiers of machine learning models for detecting instructed deception: traditional machine l...
Non-Intrusive Load Monitoring (NILM) aims to disaggregate a household’s total aggregated power consumption into appliance-level usage, enabling intelligent energy management without the need for intrusive metering. While deep learning has impro...
Remote Access Trojans (RATs) pose a serious cybersecurity risk due to their stealthy control over compromised systems. This study presents a detection framework that integrates host, network, and newly engineered behavioral features to enhance the id...
Large language-model chatbots such as ChatGPT and DeepSeek are quickly gaining traction as an easy, first-stop tool for trip planning because they offer instant, conversational advice that once required sifting through multiple websites or guidebooks...
Cotton is the most important natural fiber crop worldwide, and its automated harvesting is essential for improving production efficiency and economic benefits. However, cotton boll detection faces challenges such as small target size, fine-grained ca...
Background: Electrocardiogram (ECG) signals are crucial for cardiovascular diagnosis, but their analysis face challenges from noise contamination, compression difficulties due to their non-stationary nature, and the inherent complexity of its morphol...
The Transformer architecture has been the foundational cornerstone of the recent AI revolution, serving as the backbone of Large Language Models, which have demonstrated impressive language understanding and reasoning capabilities. When pretrained on...
The rapid proliferation of Large Language Models (LLMs) has democratized access to advanced generative capabilities while raising urgent concerns about misuse in sensitive security domains. Border security, in particular, represents a high-risk envir...
Traditional Reinforcement Learning (RL) struggles in dynamic decision-making due to data dependence, limited generalization, and imbalanced subjective/objective factors. This paper proposes an intelligent model combining the Model-Agnostic Meta-Learn...
Background: Over the past two decades, high-frequency oscillations (HFOs) between 80 and 500 Hz have emerged as valuable biomarkers for delineating and tracking epileptogenic brain networks. However, inspecting HFO events in lengthy EEG recordings re...
In the field of modern computer vision, pedestrian detection technology holds significant importance in applications such as intelligent surveillance, autonomous driving, and robot navigation. However, single-modal images struggle to achieve high-pre...
We introduce a code-only approach for modeling web page layouts directly from their source code (HTML and CSS only), bypassing rendering. Our method employs a neural architecture with specialized encoders for style rules, CSS selectors, and HTML attr...
Artificial Intelligence (AI) is rapidly transforming medical education by enabling adaptive tutoring, interactive simulation, diagnostic enhancement, and competency-based assessment. This narrative review explores how AI has influenced learning proce...
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval to improve factual consistency and reduce hallucinations. Despite growing interest, its use in healthcare remains fragmented. This...
This article presents a systematic review of optimization methods applied to enhance the performance of photovoltaic (PV) systems, with a focus on critical challenges such as system design and spatial layout, maximum power point tracking (MPPT), ener...
The geriatric syndrome known as frailty is characterized by diminished physiological reserves and heightened susceptibility to unfavorable health consequences. As the world’s population ages, it is crucial to detect frailty early and accurately...
Background: Critical Incident Reporting Systems (CIRS) play a key role in improving patient safety but facess limitations due to the unstructured nature of narrative data. Systematic analysis of such data to identify latent risk patterns remains chal...
This study proposes Dual-Stream Former, a novel architecture that integrates a Video Swin Transformer and Conformer designed to address the challenges of visual speech recognition (VSR). The model captures spatiotemporal dependencies, achieving a sta...
The National Football League draft is one of the most important events in the creation of a successful franchise in professional American football. Selecting players as part of the draft process, however, is difficult, as a multitude of factors affec...
Background: Exploration of the physical environment is an indispensable precursor to information acquisition and knowledge consolidation for living organisms. Yet, current artificial intelligence models lack these autonomy capabilities during trainin...
Multi-label arrhythmia classification from 12-lead ECG signals is a tricky problem, including spatiotemporal feature extraction, feature fusion, and class imbalance. To address these issues, a multi-scale temporal–dynamic graph convolutional wi...
Background: Artificial Intelligence (AI) techniques have been extensively deployed in sensor-based Human Activity Recognition (HAR) systems. Recent advances in deep learning, especially Convolutional Neural Networks (CNNs), have advanced HAR by enabl...
The integration of Artificial Intelligence (AI) technologies in students’ lives necessitates the systematic incorporation of foundational AI literacy into educational curricula. Students are challenged to develop conceptual understanding of com...
Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual re...
Long Short-Term Memory (LSTM) networks have revolutionized the field of deep learning, particularly in applications that require the modeling of sequential data. Originally designed to overcome the limitations of traditional recurrent neural networks...
Objectives: The persistent evolution of software vulnerabilities—spanning novel zero-day exploits to logic-level flaws—continues to challenge conventional cybersecurity mechanisms. Static rule-based scanners and opaque deep learning model...
To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and red...
Lately, deep learning methods have greatly improved the accuracy of brain-tumor segmentation, yet slice-wise inconsistencies still limit reliable use in clinical practice. While volume-aware 3D convolutional networks achieve high accuracy, their memo...
Emotion recognition capabilities in multimodal AI systems are crucial for developing culturally responsive educational technologies yet remain underexplored for Arabic language contexts, where culturally appropriate learning tools are critically need...
This study evaluates the effect of incorporating behavioral variables, sleep quality (SQ) and stress level (SL), into neural network models for predicting weight loss. An artificial neural network (ANN) was trained using data from 100 adults aged 18...
The coupling of quantum computing with multi-agent reinforcement learning (MARL) provides an exciting direction to tackle intricate decision-making tasks in high-dimensional spaces. This work introduces a new quantum-inspired multi-agent reinforcemen...
Accurate imputation of missing data in air quality monitoring is essential for reliable environmental assessment and modeling. This study compares two imputation methods, namely Random Forest (RF) and Bidirectional Recurrent Imputation for Time Serie...
Precise classification of unsound wheat grains is essential for crop yields and food security, yet most existing approaches rely on vision-only models that demand large labeled datasets, which is often impractical in real-world, data-scarce settings....
Prompt engineering (PE) has emerged as a transformative paradigm in software engineering (SE), leveraging large language models (LLMs) to support a wide range of SE tasks, including code generation, bug detection, and software traceability. This stud...
This study introduces a promising authentication framework utilizing brain–computer interface (BCI) technology to enhance both security protocols and user experience. A key strength of this approach lies in its reliance on objective, physiologi...
Computer-aided systems can assist doctors in detecting cancer at an early stage using medical image analysis. In estrogen receptor immunohistochemistry (ER-IHC)-stained whole-slide images, automated cell identification and segmentation are helpful in...
Evacuation in public places under emergency situations represents a significant area of management research. With the rapid development of the railway industry, the evacuation of railway stations has gradually attracted attention. This article employ...
Stroke is a major global cause of death and disability and necessitates both quick diagnosis and treatment within narrow windows of opportunity. CT scanning is still the first-line imaging in the acute phase, but correct interpretation may not always...
Open-vocabulary object detection (OVOD) aims to localize and recognize objects in images by leveraging category-specific textual inputs, including both known and novel categories. While existing methods excel in general scenarios, their performance s...
Uterine fibroids are one of the leading health concerns for women worldwide, affecting up to 80% of women by the age of 50. While recent advancements have improved the diagnosis and treatment of fibroids, the current standard of care still faces impo...
Camouflaged object detection (COD) represents one of the most challenging tasks in computer vision, requiring sophisticated approaches to accurately extract objects that seamlessly blend within visually similar backgrounds. While contemporary techniq...
Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-...
Mass shootings are forms of loosely defined violent crimes typically involving four or more casualties by firearm and have become increasingly more frequent, and organized and speedy responses from police are necessary to mitigate harm and neutralize...
Digital livestock farming faces a critical deployment challenge: bridging the gap between cutting-edge AI algorithms and practical implementation in resource-constrained agricultural environments. While deep learning models demonstrate exceptional ac...
