Pub Date : 2025-02-25DOI: 10.1109/ACCESS.2025.3540535
Mehrdad Saif
{"title":"Message From the New Editor-in-Chief of IEEE Access","authors":"Mehrdad Saif","doi":"10.1109/ACCESS.2025.3540535","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3540535","url":null,"abstract":"","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"33682-33682"},"PeriodicalIF":3.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10903664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513027","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}
Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.
{"title":"Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”","authors":"Khaoula Taji;Ali Sohail;Tariq Shahzad;Bilal Shoaib Khan;Muhammad Adnan Khan;Khmaies Ouahada","doi":"10.1109/ACCESS.2025.3542202","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3542202","url":null,"abstract":"Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"31659-31659"},"PeriodicalIF":3.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471839","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}
Pub Date : 2025-02-21DOI: 10.1109/ACCESS.2025.3542259
Muhammad Umar;Saud Altaf;Shafiq Ahmad;Haitham Mahmoud;Adamali Shah Noor Mohamed;Rashid Ayub
Presents corrections to the paper, (Corrections to “Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition With CNN and Improved YOLOv7”).
{"title":"Corrections to “Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition With CNN and Improved YOLOv7”","authors":"Muhammad Umar;Saud Altaf;Shafiq Ahmad;Haitham Mahmoud;Adamali Shah Noor Mohamed;Rashid Ayub","doi":"10.1109/ACCESS.2025.3542259","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3542259","url":null,"abstract":"Presents corrections to the paper, (Corrections to “Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition With CNN and Improved YOLOv7”).","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"31614-31614"},"PeriodicalIF":3.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471111","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}
Pub Date : 2025-02-21DOI: 10.1109/ACCESS.2025.3542937
Zhong Dongbo
{"title":"Retraction Notice: A Novel Radio Frequency Identification Collision Resolution Method Based on Statistical Learning","authors":"Zhong Dongbo","doi":"10.1109/ACCESS.2025.3542937","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3542937","url":null,"abstract":"","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"31658-31658"},"PeriodicalIF":3.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471841","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}
Arrhythmia, a heart rhythm disorder, remains a serious global health problem due to its potential to cause complications such as stroke and heart failure. Early detection and accurate classification of arrhythmia are crucial for appropriate medical intervention. Although deep learning holds promise for automatic arrhythmia detection using ECG signals, several challenges need to be addressed. Previous studies often utilize limited and imbalanced datasets and lack exploration of optimal pre-processing and feature extraction methods. To overcome these limitations, this study proposes the Optimized Cardiac Arrhythmia Detection Network (OCADN), a CNN-based model with hyperparameter optimization and advanced pre-processing techniques such as Discrete Wavelet Transform (DWT) and Z-score normalization. As a comparison to OCADN, this research also develops an arrhythmia detection model using the LSTM algorithm. Experimental results demonstrate that OCADN outperforms LSTM, achieving high accuracy, precision, sensitivity, specificity, and F1-score on both training and test data. The consistent performance of OCADN on both datasets indicates its robustness and potential for clinical implementation. OCADN with hyperparameter tuning exhibits accuracy, precision, sensitivity, specificity, and F1-score of 99.97%, 99.97%, 99.97%, 99.99%, and 99.97%, respectively, on the training data. Meanwhile, the performance on the testing data for accuracy, precision, sensitivity, specificity, and F1-score is 98.87%, 95.23%, 98.09%, 99.65%, and 96.59%, respectively.
{"title":"OCADN: Improving Accuracy in Multi-Class Arrhythmia Detection From ECG Signals With a Hyperparameter-Optimized CNN","authors":"Satria Mandala;Wisnu Jatmiko;Siti Nurmaini;Ardian Rizal;Adiwijaya","doi":"10.1109/ACCESS.2025.3544273","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544273","url":null,"abstract":"Arrhythmia, a heart rhythm disorder, remains a serious global health problem due to its potential to cause complications such as stroke and heart failure. Early detection and accurate classification of arrhythmia are crucial for appropriate medical intervention. Although deep learning holds promise for automatic arrhythmia detection using ECG signals, several challenges need to be addressed. Previous studies often utilize limited and imbalanced datasets and lack exploration of optimal pre-processing and feature extraction methods. To overcome these limitations, this study proposes the Optimized Cardiac Arrhythmia Detection Network (OCADN), a CNN-based model with hyperparameter optimization and advanced pre-processing techniques such as Discrete Wavelet Transform (DWT) and Z-score normalization. As a comparison to OCADN, this research also develops an arrhythmia detection model using the LSTM algorithm. Experimental results demonstrate that OCADN outperforms LSTM, achieving high accuracy, precision, sensitivity, specificity, and F1-score on both training and test data. The consistent performance of OCADN on both datasets indicates its robustness and potential for clinical implementation. OCADN with hyperparameter tuning exhibits accuracy, precision, sensitivity, specificity, and F1-score of 99.97%, 99.97%, 99.97%, 99.99%, and 99.97%, respectively, on the training data. Meanwhile, the performance on the testing data for accuracy, precision, sensitivity, specificity, and F1-score is 98.87%, 95.23%, 98.09%, 99.65%, and 96.59%, respectively.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"34687-34705"},"PeriodicalIF":3.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512845","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}
Pub Date : 2025-02-21DOI: 10.1109/ACCESS.2025.3544244
Kyusoon Kim;Seunghee Oh;Kiwook Bae;Hee-Seok Oh
Optical emission spectroscopy (OES) data is essential for virtual metrology, enabling accurate predictions of wafer performance in plasma etching processes. This approach not only reduces the need for physical measurements of product quality, leading to significant resource savings, but also supports improved decision-making, particularly in process control and quality assurance. To exploit the consecutive nature of OES data, we propose a prediction method based on a functional approach using multivariate functional partial least squares regression, coupled with dimension reduction and a novel outlier detection technique via functional independent component analysis. The proposed approach improves prediction performance by capturing the continuous nature of OES data and effectively extracting the components that describe the data structure. Numerical experiments, including simulation studies and real-world applications of OES data, demonstrate the effectiveness of the proposed method through superior prediction performance, as evidenced by low RMSE and MAE values, particularly in the presence of outliers.
{"title":"Prediction of Wafer Performance: Use of Functional Outlier Detection and Regression","authors":"Kyusoon Kim;Seunghee Oh;Kiwook Bae;Hee-Seok Oh","doi":"10.1109/ACCESS.2025.3544244","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544244","url":null,"abstract":"Optical emission spectroscopy (OES) data is essential for virtual metrology, enabling accurate predictions of wafer performance in plasma etching processes. This approach not only reduces the need for physical measurements of product quality, leading to significant resource savings, but also supports improved decision-making, particularly in process control and quality assurance. To exploit the consecutive nature of OES data, we propose a prediction method based on a functional approach using multivariate functional partial least squares regression, coupled with dimension reduction and a novel outlier detection technique via functional independent component analysis. The proposed approach improves prediction performance by capturing the continuous nature of OES data and effectively extracting the components that describe the data structure. Numerical experiments, including simulation studies and real-world applications of OES data, demonstrate the effectiveness of the proposed method through superior prediction performance, as evidenced by low RMSE and MAE values, particularly in the presence of outliers.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35298-35308"},"PeriodicalIF":3.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10898005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496528","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}
Pub Date : 2025-02-21DOI: 10.1109/ACCESS.2025.3542389
Abdulhadi Shoufan
Just-in-time hands-on experience is essential for learning programming. It not only helps reinforce learned concepts immediately, but also encourages the exploration of new concepts and language constructs. Integrating hands-on activities into traditional programming classrooms, however, presents a fundamental challenge. Coordinating instructor explanations with student coding activities and providing timely feedback to all students before moving forward is impractical for large or medium-sized classes. This paper demonstrates how cognitive and constructive learning principles can transform a classroom into an active learning environment that blends conceptual learning and hands-on experience for an object-oriented programming course. Instead of attending traditional lectures, our students use the class time to participate in Moodle-based activities that feature diverse question types and immediate feedback. They work at their own pace, individually or in small teams, while the instructor guides them and provides support as needed. In a section of 41 students, 88% preferred this approach over lectures. These students scored 14.2% higher on the same exam compared to 111 peers enrolled in other sections that used traditional teaching in the same semester.
{"title":"Rethinking Programming Education: A Lecture-Free Approach","authors":"Abdulhadi Shoufan","doi":"10.1109/ACCESS.2025.3542389","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3542389","url":null,"abstract":"Just-in-time hands-on experience is essential for learning programming. It not only helps reinforce learned concepts immediately, but also encourages the exploration of new concepts and language constructs. Integrating hands-on activities into traditional programming classrooms, however, presents a fundamental challenge. Coordinating instructor explanations with student coding activities and providing timely feedback to all students before moving forward is impractical for large or medium-sized classes. This paper demonstrates how cognitive and constructive learning principles can transform a classroom into an active learning environment that blends conceptual learning and hands-on experience for an object-oriented programming course. Instead of attending traditional lectures, our students use the class time to participate in Moodle-based activities that feature diverse question types and immediate feedback. They work at their own pace, individually or in small teams, while the instructor guides them and provides support as needed. In a section of 41 students, 88% preferred this approach over lectures. These students scored 14.2% higher on the same exam compared to 111 peers enrolled in other sections that used traditional teaching in the same semester.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"34758-34767"},"PeriodicalIF":3.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512856","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}
Pub Date : 2025-02-20DOI: 10.1109/ACCESS.2025.3543838
Janghyeon Lee;Jongyoul Park;Yongkeun Lee
Recent advancements in deep learning for cancer detection have achieved impressive accuracy, yet high computational costs and latency remain significant barriers for practical deployment on resource-constrained devices, such as smartphones and IoT platforms. This study focuses on optimizing MobileNetV1 and MobileNetV2 models to achieve more efficient, real-time cancer type identification. Through optimization strategies including selective layer unfreezing, pruning, and quantization, we demonstrate significant improvements in model size, inference time, and efficiency. For MobileNetV1, model size was reduced from 13.1 MB to 3.23 MB, and inference time was cut from 23 ms to 14 ms, with an F1 score above 0.99. For MobileNetV2, the model size was reduced from 9.41 MB to 2.82 MB, with inference times reduced from 24 ms to 13 ms, while maintaining a high F1 score of 0.98. The efficiency scores for MobileNetV1 and MobileNetV2 were 0.984 and 0.994, respectively, significantly outperforming other state-of-the-art neural networks such as VGG16 (efficiency score: 0.458), ResNet50 (0.418), and DenseNet121 (0.731). These findings demonstrate that with appropriate optimizations, MobileNet models can be deployed on edge devices, achieving high accuracy (above 95%), fast inference times (under one second), and superior efficiency, making them ideal candidates for real-time cancer detection in resource-constrained environments like mobile and IoT devices.
{"title":"Toward Efficient Cancer Detection on Mobile Devices","authors":"Janghyeon Lee;Jongyoul Park;Yongkeun Lee","doi":"10.1109/ACCESS.2025.3543838","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3543838","url":null,"abstract":"Recent advancements in deep learning for cancer detection have achieved impressive accuracy, yet high computational costs and latency remain significant barriers for practical deployment on resource-constrained devices, such as smartphones and IoT platforms. This study focuses on optimizing MobileNetV1 and MobileNetV2 models to achieve more efficient, real-time cancer type identification. Through optimization strategies including selective layer unfreezing, pruning, and quantization, we demonstrate significant improvements in model size, inference time, and efficiency. For MobileNetV1, model size was reduced from 13.1 MB to 3.23 MB, and inference time was cut from 23 ms to 14 ms, with an F1 score above 0.99. For MobileNetV2, the model size was reduced from 9.41 MB to 2.82 MB, with inference times reduced from 24 ms to 13 ms, while maintaining a high F1 score of 0.98. The efficiency scores for MobileNetV1 and MobileNetV2 were 0.984 and 0.994, respectively, significantly outperforming other state-of-the-art neural networks such as VGG16 (efficiency score: 0.458), ResNet50 (0.418), and DenseNet121 (0.731). These findings demonstrate that with appropriate optimizations, MobileNet models can be deployed on edge devices, achieving high accuracy (above 95%), fast inference times (under one second), and superior efficiency, making them ideal candidates for real-time cancer detection in resource-constrained environments like mobile and IoT devices.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"34613-34626"},"PeriodicalIF":3.4,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10896646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489197","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}
This study examines the financial impact of energy costs on electric vehicle charging stations by comparing financial models with fixed and variable energy pricing structures. Through an in-depth financial simulation, key metrics such as Internal Rate of Return, Net Present Value, and payback period were calculated to evaluate the profitability and risk associated with each model. Findings reveal that charging stations with fixed energy costs generally experience a higher Internal Rate of Return at 14.55% and a Net Present Value of 1,711,781, with a payback period of four years. In comparison, stations with variable energy costs exhibit a lower Internal Rate of Return at 7.28% and a Net Present Value of 1,347,781, although the payback period remains consistent at four years. These results demonstrate that fixed energy costs enhance investment predictability and profitability, while variable costs increase exposure to energy price fluctuations, which raises financial risk but may capture greater returns under favorable market conditions. The analysis highlights the critical importance of accounting for energy cost variability in financial planning to maintain sustainable, profitable operations. This research provides essential insights for investors and policymakers to optimize electric vehicle charging infrastructure investments, supporting effective and profitable operations.
{"title":"Evaluating the Financial Dynamics of Electric Vehicle Charging Stations in Thailand: Implications of Energy Cost Variability","authors":"Pathomthat Chiradeja;Chayanut Sottiyaphai;Santipont Ananwattanaporn;and Atthapol Ngaopitakkul","doi":"10.1109/ACCESS.2025.3544079","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544079","url":null,"abstract":"This study examines the financial impact of energy costs on electric vehicle charging stations by comparing financial models with fixed and variable energy pricing structures. Through an in-depth financial simulation, key metrics such as Internal Rate of Return, Net Present Value, and payback period were calculated to evaluate the profitability and risk associated with each model. Findings reveal that charging stations with fixed energy costs generally experience a higher Internal Rate of Return at 14.55% and a Net Present Value of 1,711,781, with a payback period of four years. In comparison, stations with variable energy costs exhibit a lower Internal Rate of Return at 7.28% and a Net Present Value of 1,347,781, although the payback period remains consistent at four years. These results demonstrate that fixed energy costs enhance investment predictability and profitability, while variable costs increase exposure to energy price fluctuations, which raises financial risk but may capture greater returns under favorable market conditions. The analysis highlights the critical importance of accounting for energy cost variability in financial planning to maintain sustainable, profitable operations. This research provides essential insights for investors and policymakers to optimize electric vehicle charging infrastructure investments, supporting effective and profitable operations.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"33985-33994"},"PeriodicalIF":3.4,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10896644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512844","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}
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