IEEE Access最新文献

{"title":"A Head-Driven Algorithm for Estimating Upper and Lower Body Motion in Virtual Reality Environments","authors":"Jemin Lee;Jeonghyeon Kim;Youngwon Kim","doi":"10.1109/ACCESS.2025.3565232","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565232","url":null,"abstract":"This study proposes a novel algorithm for effectively estimating partial upper and lower body movements in a Virtual Reality (VR) environment using only head movements and synchronized head rotation axes, without the need for additional hardware. The proposed algorithm calculates the angle between the avatar’s pelvis and the head rotation axis to naturally reproduce the user’s upper body inclination and lower body bending. Notably, it offers the advantage of efficiently utilizing limited computational resources in multiplayer environments. The experiment was conducted in two stages. In the first stage, the objective performance of the algorithm was evaluated by comparing it with ground truth inclination data. In the second stage, participants performed two types of games (e.g., a dodgeball game and a limbo game) to assess their sense of immersion and embodiment. The objective results demonstrated that the proposed algorithm accurately and naturally expressed upper and lower body movements. Additionally, post-experiment surveys indicated that participants reported a high level of immersion and a natural interaction experience. This study presents a cost-effective solution for tracking upper and lower body movements in VR environments without requiring additional hardware, significantly enhancing the immersion of the VR experience. Future research will explore the expansion of the method to include upper body rotation estimation and full-body motion tracking, incorporating user locomotion.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"76627-76637"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Hybrid Tripping Microgrid Protection Strategy With Embedded Hardware Validation","authors":"Pedro Henrique Aquino Barra;Ricardo Augusto Souza Fernandes;Denis Vinicius Coury","doi":"10.1109/ACCESS.2025.3565226","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565226","url":null,"abstract":"This paper proposes an adaptive hybrid-tripping-based protection strategy for microgrids (MGs) that enables a fast and reliable response to faults by leveraging phase voltage and current measurements from relay locations. The protection coordination problem was addressed by optimizing the relay settings for different MG operating scenarios, ensuring proper coordination between the primary and backup relays. Comprehensive performance evaluation using PSCAD simulations demonstrated that the proposed protection scheme operates with 50% of faults cleared within 41.5 ms, while 90% of cases are cleared within 530.8 ms across various fault conditions in both grid-connected and islanded operating conditions. The backup relays exhibited a minimum trip time of 230 ms and a median trip time of 299.6 ms, while the coordination time intervals remained within safe margins (50% of cases maintaining a margin of 246.7 ms), ensuring selectivity. Moreover, real-time hardware-in-the-loop (HIL) tests using TMSF28335 microcontrollers validated the scheme’s practical applicability, showing a strong correlation between simulated and experimental results. The mean difference between the simulated and experimental trip times was 29 ms, with maximum deviations below 7.2% (64 ms) and a minimum deviation of 5 ms. The results confirm the effectiveness of the proposed strategy in reducing tripping times while maintaining coordination, making it a promising solution for both islanded and grid-connected MG operating modes.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"76271-76288"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Training-Free Affordance Labeling and Exploration Using Subspace Projection and Manifold Curvature Over Pre-Trained Deep Networks","authors":"İsmaıl Özçıl;A. Buğra Koku","doi":"10.1109/ACCESS.2025.3565330","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565330","url":null,"abstract":"The advancement in computing power has significantly reduced the training times for deep learning, enabling the rapid development of networks designed for object recognition. However, the exploration of object utility, the object’s affordance, as opposed to object recognition, has received comparatively less attention. Existing object affordance models exhibit shortcomings, including limited robustness across diverse architectures and insufficient performance in complex environments. This work focuses on using pre-trained networks trained on object classification datasets to explore object affordances. While these networks have proven instrumental in transfer learning for classification tasks, the presented approach in this study diverges from conventional object classification methods by labeling affordances without modifying the final layers. Instead, pre-trained networks are employed to learn affordance labels without requiring specialized classification layers. Two approaches are tested: the Subspace Projection Method and the Manifold Curvature Method, which facilitate the determination of affordance labels without such modifications. Both the Subspace Projection Method and the Manifold Curvature Method were evaluated using nine distinct pre-trained networks across two different affordance datasets. The Subspace Projection Method achieved a True Positive Rate of up to 94% and 96% for the best-performing networks on each dataset, while the Manifold Curvature Method attained True Positive Rates exceeding 98% and 99% with its top-performing networks. Furthermore, both methods identify affordance labels that are not marked in the ground truth but are present in various cases. The robustness of the Manifold Curvature Method and the exploration capability of both methods highlight the effectiveness of proposed techniques for affordance labeling.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"82897-82913"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Machine Learning-Based Multi-Stage Framework for Detection of Credit Card Anomalies and Fraud","authors":"Hatoon S. Alsagri","doi":"10.1109/ACCESS.2025.3565612","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565612","url":null,"abstract":"Recently, tremendous growth in e-business has arisen in an increasing number of online transactions. Such widespread adaptation of e-payments has been going along with the increase in deceitful activities, which results in tremendous losses in the financial sector. This led to a novel research paradigm using statistical and auto-data-driven techniques to detect anomalies and fraud. Thus, traditional techniques fail to provide a secure medium for online transactions. Consequently, building a credit card fraud (CCF) detector is essential for secure online operations. Therefore, based on the abovementioned constraints, this paper presents a comprehensive study incorporating heterogeneous machine learning (ML) techniques for CCF detection. The proposed framework utilizes a multi-stage classification system that employs multiple classifiers, i.e., logistic regression, support vector machine (SVM) XGBoost, Random Forest, K-Nearest Neighbors (KNN), and Deep Neural Network (DNN). Furthermore, to accomplish the intensive class imbalance, the proposed technique uses a sampling technique with an internal features selection technique implemented based on voting among different methods. The key finding indicates that the proposed model surpasses the existing DNN simple voting, traditional stacking framework with a fraud recall value of 0.901, a legitimate recall value of 0.995, and a model cost value of 0.421.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"77039-77048"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low Contrast Enhancement Algorithm for Color Image Using Pythagorean Fuzzy Sets With a Fusion of CLAHE and BPDHE Methods","authors":"M. Manivasagan;S. Jagatheswari","doi":"10.1109/ACCESS.2025.3565121","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565121","url":null,"abstract":"In the field of image processing, improving low-contrast images is essential for enhancing the visibility of features that may be hard to see due to slight intensity variations. This issue often arises with images captured in poor lighting or unfavorable conditions, which can impede tasks such as feature extraction, object recognition, and edge detection. This paper presents an algorithm that employs fuzzy techniques to enhance low-contrast images. The approach utilizes a Pythagorean fuzzy set, which is an advanced version of a fuzzy set. The Pythagorean fuzzy set, by integrating membership and non−membership degrees, offers a more precise representation of image uncertainty. The proposed method constructs an increasing function to calculate the degree of non−membership, thereby amplifying the effectiveness of the enhanced image. By integrating this method with contrast limited adaptive histogram equalization, alongside a color restoration technique and brightness-preserving dynamic histogram equalization, the brightness and natural appearance of the original low-contrast image are maintained. We compare our approach to other methods, including conventional histogram equalization, intuitionistic fuzzy generators, interval−valued intuitionistic fuzzy generators, interval−valued intuitionistic fuzzy with contrast limited adaptive histogram equalization, LightNet, and FlightNet. Experimental results indicate that our method surpasses the techniques currently utilized in terms of visibility and color accuracy. Performance evaluation is performed using metrics such as the absolute mean brightness error, contrast improvement index, and structural similarity index.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"84791-84802"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Immersive Virtual Reality in Higher Education: Research Gap and Future Direction—A Scoping Review","authors":"Sunardi;Meyliana;Spits Warnars Harco Leslie Hendric;Yusep Rosmansyah","doi":"10.1109/ACCESS.2025.3565385","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565385","url":null,"abstract":"The rapid advancement of technology in the post-COVID-19 era has positioned immersive learning as a transformative approach to enhance educational experiences. Despite its vast potential, recent research developments reveal persistent challenges and gaps that impede widespread adoption. This study conducts a scoping review using the PRISMA methodology to systematically analyze current literature, identify research gaps, seek the challenge, and propose future research directions. From an initial pool of 414 papers, 75 were selected, comprising 60 research studies and 15 review papers. Notably, 55 studies focus on immersive virtual reality (IVR) purely for educational enhancement in traditional academic settings, while 20 explore the implementation of IVR in education with gaming activities. The analysis indicates a predominance of mixed-methods research within education, computer engineering, and computer science. Most studies are limited by short durations (typically 30 minutes) and small participant groups (under 50), raising concerns about the generalizability of findings. Key themes identified include learning context (21 papers), learning design strategies (ten papers), and immersion elements such as avatars and haptic feedback (six papers). While positive impacts like increased satisfaction, motivation, engagement, knowledge enhancement, and usability are reported, negative effects such as motion sickness (13 papers) and dizziness (11 papers) persist. Crucially, only 11 studies exhibit high statistical power, underscoring the need for more robust research designs. Challenges identified encompass participant limitations, homogeneity, user discomfort, hardware unfamiliarity, and cognitive load—all intricately linked to design strategies. The implications of this review highlight the necessity for future research to focus on long-term studies, optimize user experience, develop cost-effective content creation methods, and integrate gamification into learning design. Addressing these areas is essential for overcoming current barriers and fully realizing the potential of immersive learning in education.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"76308-76321"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate","authors":"Samit Kumar Ghosh;Namareq Widatalla;Ahsan H. Khandoker","doi":"10.1109/ACCESS.2025.3565549","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565549","url":null,"abstract":"Chronic Kidney Disease (CKD) is a progressive condition that requires accurate diagnosis and staging for effective clinical management. Conventional CKD diagnosis relies on estimated Glomerular Filtration Rate (eGFR), a measure of kidney function derived from serum biomarkers such as serum creatinine (SCr) and cystatin C (SCysC). However, eGFR calculations may be inaccurate when applied to diverse patient populations. This study proposes a machine learning (ML) system that integrates regression-based eGFR estimation, metaheuristic optimization using the Grey Wolf Optimizer (GWO), and multi-class classification with various ML models to enhance CKD staging and classification. The model estimates eGFR using three established CKD Epidemiology Collaboration (CKD-EPI) equations incorporating SCr, SCysC, and their combined values. Regression models assess predictive performance, specifically Linear Regression (LR) and Support Vector Regression (SVR). SVR demonstrates superior performance compared to LR for <inline-formula> <tex-math>$text {CKD-EPI}_{text {SCr-SCysC}}$ </tex-math></inline-formula> achieved a root mean squared error (RMSE) of 3.03, a mean absolute percentage error (MAPE) of 2.97%, and a coefficient of determination (<inline-formula> <tex-math>$text {R}^{2}$ </tex-math></inline-formula>) score of 0.97. The application of GWO for hyperparameter tuning has resulted in a 37.3% reduction in root mean square error (RMSE), a 37.4% drop in mean absolute percentage error (MAPE), and a 2.06% improvement in <inline-formula> <tex-math>$text {R}^{2}$ </tex-math></inline-formula> to improve the precision of prediction. Once the model fine-tunes the eGFR estimations, it feeds them into various algorithms for CKD stage classification, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Among these, XGBoost achieves the highest classification accuracy of 97.76%, along with an F1-score of 97.45%, demonstrating its effectiveness in CKD staging. Shapley Additive Explanations (SHAP) provide global and local feature importance insights, enhancing clinical decision-making and model transparency. Future research will validate the model using more extensive and more diverse datasets. Additionally, it will incorporate extra clinical parameters, including biomarkers and genetic data, to enhance the precision of CKD risk prediction. This research enhances AI-driven nephrology by providing a scalable, interpretable, and highly accurate solution for diagnosing and managing CKD.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78057-78072"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring Public Opinions on Renewable Energy and Climate Change Using Structural Equations Modeling","authors":"Ionuţ Nica;Simona-Vasilica Oprea;Adela Bâra","doi":"10.1109/ACCESS.2025.3565676","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565676","url":null,"abstract":"Public attitudes towards energy sources and climate change are increasingly complex. This paper explores Americans’ perceptions of energy sources including renewables, Electric Vehicles (EVs), government policies and climate change. A survey conducted by the Pew Research Center (PRC), comprising over 10,000 responses to 52 questions on energy sources and climate change, is analyzed. In this paper, we propose a data analysis framework that consists of Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), cluster analysis and Principal Component Analysis (PCA) to segment respondents and identify key variables. Furthermore, the Structural Equation Model (SEM) is created to examine relationships between latent and observed variables, using maximum likelihood estimation. The results validated the identified factors, with high loading on key variables indicating strong contributions to attitudes towards energy and climate policies.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"76981-77000"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating a Fuzzy Fitness Function in Genetic Programming to Generate Breast Tissue Segmentation Models","authors":"Ingrid-Aurora Valencia-Hernández;Carlos Alberto Reyes García;Alicia Morales-Reyes;Gabriela del C. Lopez-Armas;José-Antonio Fuentes-Tomás;Efrén Mezura-Montes","doi":"10.1109/ACCESS.2025.3565462","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565462","url":null,"abstract":"Genetic programming (GP) and fuzzy logic are used to automatically segment mammography images. GP allows the evolution of optimized segmentation models, guided by a fuzzy logic-based fitness function that incorporates medical criteria to improve the consistency and accuracy of the segmentation process. Unlike conventional approaches, this function optimizes the segmentation and provides a descriptive representation of the breast tissue, allowing a closer evaluation to that performed by specialists. The proposed method was evaluated in the INbreast and BCDR databases, obtaining a Jaccard index of 0.82 and 0.78, respectively, and a comparative analysis was performed using ROC curves, reaching an AUC of 0.91 in INbreast and 0.89 in BCDR, demonstrating the model’s ability to discriminate between fibroglandular and fat tissue. Its performance was compared with state-of-the-art methods, such as LIBRA, hybrid segmentation with Fuzzy C-Means, and NASGP-Net, showing that integrating fuzzy logic in genetic programming to lead the search allows competitive results with a lower computational burden. These results demonstrate the impact of fuzzy fitness functions in the evolution of segmentation models, highlighting the effectiveness of this approach in improving the segmentation and classification of medical images, in addition to the descriptive capabilities inherent to the fuzzy fitness function.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"80340-80354"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Road Surface Classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN","authors":"Seokchan Kim;Yeong-Jae Kim;Dongwook Lee;Hanmin Lee","doi":"10.1109/ACCESS.2025.3565656","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565656","url":null,"abstract":"Tire-road friction coefficient information is an essential factor in the driving stability and safety of a vehicle. In recent years, there has been a lot of research on using the vibration characteristic of tires to estimate the road surface condition from its features. However, since tire vibration characteristics vary depending on conditions such as tire pressure, load, and driving status, it is still difficult to develop a road surface classification algorithm that is robust to various situations. To overcome this limitation, this paper proposes a road surface classification algorithm using a one-dimensional convolutional neural network (CNN) based on acceleration signals obtained through an intelligent tire sensor attached inside the tire. Moreover, a time series data augmentation method is applied to ensure that the learning network has the robustness to perform well under different tires and driving conditions than that in the training dataset. A road surface classification algorithm is trained using a dataset of accelerations measured on dry asphalt, wet asphalt, and basalt tile roads, and the performance of the trained algorithm is validated through test scenarios considering different tire conditions and vehicle types. Furthermore, the performance of different CNN architectures is compared and the algorithm with the best performance is suggested. The robustness to different tires and driving conditions makes the proposed algorithm practical for estimating road surface conditions in real vehicles.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"76508-76515"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}