Computers, Volume 14, Issue 8

The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes,...

Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lea...

This study proposes an image processing framework based on bionic principles to optimize 3D visual perception in virtual reality (VR) systems. By simulating the physiological mechanisms of the human visual system, the framework significantly enhances...

Soulslike games are renowned for their challenging gameplay and distinctive design. To examine player reception of this genre, 993,932 user reviews of 21 Soulslike video games were collected from the Steam platform, of which 418,483 were tagged as En...

With the growing demand for multilingual financial information, cross-lingual financial sentiment recognition faces significant challenges, including semantic misalignment, ambiguous sentiment expression, and insufficient transferability. To address...

Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task,...

The agricultural sector plays a vital role in achieving the second Sustainable Development Goal: “Zero Hunger”. To ensure food security, agriculture must remain resilient and productive. In Indonesia, a major rice-producing country, the c...

In recent years, the increasing adoption of High-Performance Computing (HPC) clusters in scientific research and engineering has exposed challenges such as resource imbalance, node idleness, and overload, which hinder scheduling efficiency. Accurate...

Accurately distinguishing hemiplegic gait from healthy gait is significant for alleviating clinicians’ diagnostic workloads and enhancing rehabilitation efficiency. The center of pressure (CoP) trajectory extracted from pressure sensor arrays c...

The aim of this study is a comparative evaluation of the effectiveness of YOLO and RT-DETR family models for the automatic recognition and localization of meniscus tears in knee joint MRI images. The experiments were conducted on a proprietary annota...

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings th...

Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine le...

Cardiovascular Disease (CVD) remains the number one cause of morbidity and mortality, accounting for 17.9 million deaths every year. Precise and early diagnosis is therefore critical to the betterment of the patient’s outcomes and the many burd...

Predictive maintenance (PdM) represents a significant evolution in maintenance strategies. However, challenges such as system integration complexity, data quality, and data availability are intricately intertwined, collectively impacting the successf...

This study presents a simulation-driven methodology for the design and optimization of a lightweight drone frame. Starting with a CAD model developed in SolidWorks, finite element analysis (FEA) and computational fluid dynamics (CFD) which are used t...

The development of reliable visual inference models is often constrained by the burdensome and time-consuming processes involved in collecting and annotating high-quality datasets. This challenge becomes more acute in domains where key phenomena are...

With the rise in cloud computing and virtualisation, secure and efficient VPN solutions are essential for network connectivity. We present a systematic performance comparison of OpenVPN (v2.6.12) and WireGuard (v1.0.20210914) across Azure and VMware...

Missing data imputation is a critical preprocessing task that directly impacts the quality and reliability of data-driven analyses, yet many existing methods treat numerical and categorical data separately and lack the integration of advanced techniq...

As cyber threats such as phishing and ransomware continue to escalate, healthcare systems are facing significant challenges in protecting sensitive data and ensuring operational continuity. This study explores how email communication practices influe...

The increasing use of Arabic and Urdu on social media platforms, particularly Twitter, has created a growing need for robust Named Entity Recognition (NER) systems capable of handling noisy, informal, and code-mixed content. However, both languages r...

As an introductory core course in computer science and related fields, “Fundamentals of Programming” has always faced many challenges in stimulating students’ interest in learning and cultivating their practical coding abilities. Th...

The rapid expansion of the Metaverse presents complex security challenges, particularly in verifying virtual objects and avatars within immersive environments. Conventional authentication methods, such as passwords and biometrics, often prove inadequ...

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have b...

This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO₂ emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navig...

Industries 4.0 and 5.0 are based on technological advances, notably large language models (LLMs), which are making a significant contribution to the transition to smart factories. Although considerable research has explored this phenomenon, the liter...

Deep Federated Learning (DFL) revolutionizes machine learning (ML) by enabling collaborative model training across diverse, decentralized data sources without direct data sharing, emphasizing user privacy and data sovereignty. Despite its potential,...

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications...

Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversari...

In distance learning environments, learner engagement directly impacts attention, motivation, and academic performance. Signs of fatigue, negative affect, or critical remarks can warn of growing disengagement and potential dropout. However, most exis...

Lighting Systems (LSs) are a key component of modern cities. Across the years, thousands of articles have been published on this topic; nevertheless, a map of the state of the art of the extant literature is lacking. The present review reports on an...

In manufacturing environments, the proper use of Personal Protective Equipment (PPE) is essential to prevent workplace accidents. Despite this need, existing PPE monitoring methods remain largely manual and suffer from limited coverage, significant e...

Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine lear...

This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation....

The rapid evolution of deep learning (DL) has fundamentally transformed the paradigm for detecting special components in agricultural products, addressing critical challenges in food safety, quality control, and precision agriculture. This comprehens...

Power system infrastructure is exposed to a range of threats, including both naturally occurring events and intentional attacks. Traditional vulnerability assessment models, typically based on the N-1 criterion, do not account for the intentionality...

Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant...

This article investigates the revolutionary potential of AI-powered virtual assistants in augmented reality (AR) and virtual reality (VR) environments, concentrating primarily on their impact on special needs schooling. We investigate the complex cha...

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to t...

The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In respon...

Diffusion tensor imaging (DTI) metrics provide insights into neural pathways, which can be pivotal in differentiating congenital and acquired hearing loss to support diagnosis, especially for those diagnosed late. In this study, we analyzed DTI param...

In the recommendation system (RS) literature, a distinction exists between studies dedicated to fully operational (known users/items) and cold-start (new users/items) RSs. The warm-up phase—the transition between the two—is not widely res...

The escalating complexity of cyberattacks necessitates advanced strategies for their detection and mitigation. This study presents a hybrid model that integrates Graph Neural Networks (GNNs) with Long Short-Term Memory (LSTM) networks to reconstruct...

Integrating blockchain into healthcare devices offers the potential for improved data control but faces significant usability and acceptance challenges. This study addresses this gap by evaluating CipherPal, an improved blockchain-enabled Smart Fidge...

This overview examines recent advancements in EEG-based biometric identification, focusing on integrating emotional recognition to enhance the robustness and accuracy of biometric systems. By leveraging the unique physiological properties of EEG sign...

Age-related ocular conditions like macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are leading causes of irreversible vision loss globally. Optical coherence tomography (OCT) provides essential non-invasive visualization of retina...

The increasing complexity of cybersecurity risks highlights the critical need for novel teaching techniques that provide students with the necessary skills and information. Traditional on-premises laboratory setups frequently lack the scalability, fl...

This paper looks at and describes the potential of using artificial intelligence in smart environments. Various environments such as houses and residential and commercial buildings are becoming smarter through the use of various technologies, i.e., v...

Dark web traffic classification is an important research direction in cybersecurity; however, traditional classification methods have many limitations. Although deep learning architectures like CNN and LSTM, as well as multi-structural fusion framewo...