2025 October - 101 articles
To address the challenge that MOSFET temperature in motor controllers is influenced by multiple factors, exhibits strong temporal dependence, and involves complex feature interactions, this study proposes a temperature prediction model that integrate...
Object detection has a vital impact on the analysis and interpretation of visual scenes. It is widely utilized in various fields, including healthcare, autonomous driving, and vehicle surveillance. However, complex scenes containing small, occluded,...
Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that com...
Background: Heart disease continues to be one of the foremost causes of mortality worldwide, emphasizing the urgent need for reliable and early diagnostic tools. Accurate prediction methods can support timely interventions and improve patient outcome...
In the digitally saturated hospitality environment, research on digital transformation remains dominated by macro-level adoption trends and user-generated content, while the potential of micro-level web-behavioural data remains largely untapped. Rece...
This paper presents HE-DMDeception, a novel adversarial attack network that integrates human visual deception with deep model deception to enhance the security of 3D object detection. Existing patch-based and camouflage methods can mislead deep learn...
Knowledge graphs (KGs) represent a promising approach for detecting and correcting errors in automated audio and video transcripts. Yet the lack of accessible tools leaves human reviewers with limited support, as KG construction from media data often...
This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few...
This paper presents an automatic system for the classification of musical instruments from audio recordings. The project leverages deep learning (DL) techniques to achieve its objective, exploring three different classification approaches based on di...
In many developing regions, recruitment still relies heavily on traditional methods that often ignore the importance of aligning a candidate’s personality with the job role. This mismatch can lead to poor performance, dissatisfaction, and high...
With the widespread adoption of virtual fitting technology in e-commerce and fashion, optimizing user experience through interface design has become increasingly critical. However, research on the usability of virtual fitting interaction interfaces r...
Non-IID is one of the key challenges in federated learning. Data heterogeneity may lead to slower convergence, reduced accuracy, and more training rounds. To address the common Non-IID data distribution problem in federated learning, we propose a com...
In evaluating classroom teaching quality, students’ attention assessment is a critical indicator in education management, as it holds significant practical value for improving teaching methods and instructional quality. Electroencephalogram (EE...
The aim of this study is to examine the role and use of augmented reality in engineering education by examining the existing literature. A total of 235 studies from Scopus and Web of Science published during 2011–2025 were examined. The study f...
The recent advancements in blockchain technology have also expanded its applications to smart agricultural fields, leading to increased research and studies in areas such as supply chain traceability systems and insurance systems. Policies and reward...
This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle...
With the development of generative artificial intelligence (AI) technology, collaboration between humans and AI is expected to improve productivity, efficiency, and safety in various industries. This study presents and empirically analyzes the factor...
Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or c...
In increasingly turbulent environments, organizations must go beyond generic robustness and develop Requisite Resilience, the capacity to align internal variety with environmental variety to sustain core functions during crises. This study situates R...
The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, an...
In order to solve the problem of inconsistent data distribution in machine learning, domain adaptation based on feature representation methods extracts features from the source domain, and transfers them to the target domain for classification. The e...
Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived fr...
This study focuses on the evolution of the Internet Water Army on social media, identifying a novel form known as artificial-intelligence-generated-content-enhanced social bots (AESBs), and compares their structural influence with traditional social...
This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering ra...
Despite the remarkable progress of deep learning in image segmentation, models often struggle with generalization across diverse datasets. This study explores novel input augmentation techniques and ensemble strategies to improve image segmentation p...
The aim of the present paper is to further develop a theory on the flow of linguistic variables making a sentence, namely, the transformation of (a) characters into words; (b) words into word intervals; and (c) word intervals into sentences. The rela...
Universities face growing pressure to deliver personalized learning that prepares students with adaptable, future-ready competencies. Traditional static curricula are often unable to meet these demands. This paper introduces a novel framework based o...
The massive volumes of data and the intensification of digital transformation are reshaping recruitment practices within organizations, particularly for specialized information technology (IT) profiles. However, existing studies have often remained c...
As a key technology in multimodal information processing, infrared and visible image fusion holds significant application value in fields such as military reconnaissance, intelligent security, and autonomous driving. To address the limitations of exi...
Large Language Models (LLMs) encounter significant challenges when applied in specialized domains that require precise and localized information. This problem is particularly critical in regulatory sectors, such as the animal health sector in Brazil,...
Considering the new technologies, trends, and geopolitical challenges brought about by the globalization of the Internet, research on international rules in cyberspace holds theoretical urgency, practical significance, and provides guidance for real-...
Nowadays, the rapid expansion of Internet of Things (IoT) systems has introduced significant challenges related to system management, especially in cybersecurity and resource efficiency areas. This systematic review investigates how AI/ML techniques...
This study examines the potential of satellite internet technologies to enhance crisis management in urban evacuation scenarios in Italy, with a specific focus on the Starlink system as a case study. In emergency situations, traditional mobile and Wi...
Surface material recognition is a key component in robotic perception and physical interaction, particularly when leveraging both tactile and visual sensory inputs. In this work, we propose Surformer v1, a transformer-based architecture designed for...
In multi-agent systems (MAS), autonomy is a fundamental characteristic that enables agents to operate independently and adaptively within complex environments. However, such characteristics may cause the system to fall into undesirable situations. On...
In order to enhance leakage detection accuracy in water distribution networks (WDNs) while reducing sensor deployment costs, an intelligent algorithm for the optimal deployment of water network monitoring sensors based on the automatic labelling and...
Nonintrusive load monitoring (NILM) relies on high-resolution sensor data to disaggregate total building energy into end-use load components, for example HVAC, ventilation, and appliances. On the ADRENALIN corpus, simple NaN handling with forward fil...
This paper addresses the critical challenge of deploying a minimum number of wireless charging pads (WCPs) in obstacle-rich, large-scale Wireless Rechargeable Sensor Networks (WRSNs) to sustain drone operations. We assume a single base station, stati...
In recent years, most conventional emotion recognition approaches have concentrated primarily on facial cues, often overlooking complementary sources of information such as body posture and contextual background. This limitation reduces their effecti...
The reliability of health monitoring technologies has become increasingly critical as Ambient Intelligence (AmI) becomes integrated into healthcare. However, a significant gap remains in systematically evaluating reliability, particularly in resource...
To address the limitation of receptive fields caused by the use of local convolutions in current point cloud object detection methods, this paper proposes a LiDAR point cloud object detection algorithm that integrates global features. The proposed me...
As global economic integration deepens, import and export trade plays an increasingly vital role in China’s economy. To enhance regulatory efficiency and achieve scientific, transparent credit supervision, this study proposes a trade credit ris...
Video games have become one of the most influential digital entertainment platforms. They offer advertisers new opportunities through in-game placements. This study examines the relationship between the socio-demographic characteristics of gamers (ge...
Poor code locality in virtual memory systems significantly contributes to page faults, leading to degraded system performance. Although many solutions aim to minimize page faults, most rely on clustering techniques that do not quantify the approximat...
Identifying and ranking influential nodes is central to tasks such as targeted immunization, misinformation containment, and resilient design. Structural entropy (SE) offers a principled, community-aware scoring rule, yet the one-shot (static) use of...
In this work, we propose SCRATCH-AI, a tool which relies on interpretable machine learning (ML) methods (namely, Bayesian networks and decision trees) to classify honey samples into wound healing categories. Classification explores the impact of bota...
Multi-variant execution (MVX) is an active defense technique that can detect unknown attacks by comparing the outputs of redundant program variants. Despite notable progress in MVX techniques in recent years, current approaches for recovery of abnorm...
Russia’s full-scale invasion of Ukraine in late February 2022 triggered a series of dramatic events with both humanitarian and informational repercussions. In this context, social media became saturated with rhetorical strategies and narrative...
In the construction industry, occupational health and safety plays a critical role in preventing occupational accidents and increasing productivity. In recent years, computer vision and artificial intelligence-based systems have made significant cont...
The widespread adoption of deep learning (DL) models in neuroscience research has introduced a fundamental epistemological paradox: while these models demonstrate remarkable performance in pattern recognition and prediction tasks, their inherent opac...
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