Pub Date : 2025-12-31DOI: 10.1109/ACCESS.2025.3650056
Satirtha Paul Shyam;Shaikh Anowarul Fattah;Mohammad Saquib
Medical image segmentation often involves inherent uncertainty due to inter observer variability. In this case, a single deterministic mask obtained by conventional segmentation networks, such as U-Net, cannot capture the distribution of plausible expert annotations, risking missed clinically relevant variants. In order to enable uncertainty quantification and reflect inter expert variability, probabilistic models like Probabilistic U-Net are used, which perform aleatoric or ambiguous segmentation where a latent space is sampled to generate multiple segmentation masks. However, the common use of a conditioned unimodal posterior in these models fails to represent true multimodality, leading to mode bias and limited diversity. To address these limitations, a multi-level Probabilistic U-Net augmented with normalizing flows is proposed to enhance the expressiveness of the latent distribution. The multi-level design induces multiple latent distributions in separate levels of U-Net, enabling more diverse sampling, while the flow module transforms the posterior to add data required modes and expand representational capacity, thereby enriching the expressiveness of the distributions. The proposed flow incorporated multi-level network enables a more flexible and powerful distribution, thereby enhancing the model’s ability to generate high fidelity segmentation masks. Extensive experiments on some publicly available datasets with multiple expert annotations per image demonstrate that the proposed model reduces generalized energy distance (GED), preserves clinically meaningful diversity and sharpens boundary fidelity, with latent grid analyses indicating fuller mode coverage and fewer artifacts. Collectively, these results indicate that the proposed framework advances accuracy, robustness, and clinical reliability for aleatoric, uncertainty aware medical image segmentation.
{"title":"A Multi-Level Probabilistic Deep Learning Network Augmented With Normalizing Flow for Ambiguous Medical Image Segmentation","authors":"Satirtha Paul Shyam;Shaikh Anowarul Fattah;Mohammad Saquib","doi":"10.1109/ACCESS.2025.3650056","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3650056","url":null,"abstract":"Medical image segmentation often involves inherent uncertainty due to inter observer variability. In this case, a single deterministic mask obtained by conventional segmentation networks, such as U-Net, cannot capture the distribution of plausible expert annotations, risking missed clinically relevant variants. In order to enable uncertainty quantification and reflect inter expert variability, probabilistic models like Probabilistic U-Net are used, which perform aleatoric or ambiguous segmentation where a latent space is sampled to generate multiple segmentation masks. However, the common use of a conditioned unimodal posterior in these models fails to represent true multimodality, leading to mode bias and limited diversity. To address these limitations, a multi-level Probabilistic U-Net augmented with normalizing flows is proposed to enhance the expressiveness of the latent distribution. The multi-level design induces multiple latent distributions in separate levels of U-Net, enabling more diverse sampling, while the flow module transforms the posterior to add data required modes and expand representational capacity, thereby enriching the expressiveness of the distributions. The proposed flow incorporated multi-level network enables a more flexible and powerful distribution, thereby enhancing the model’s ability to generate high fidelity segmentation masks. Extensive experiments on some publicly available datasets with multiple expert annotations per image demonstrate that the proposed model reduces generalized energy distance (GED), preserves clinically meaningful diversity and sharpens boundary fidelity, with latent grid analyses indicating fuller mode coverage and fewer artifacts. Collectively, these results indicate that the proposed framework advances accuracy, robustness, and clinical reliability for aleatoric, uncertainty aware medical image segmentation.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"4063-4079"},"PeriodicalIF":3.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11321095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929343","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 : 2025-12-31DOI: 10.1109/ACCESS.2025.3649779
Chien-Wei Hu;Yi-Hsuan Liao;Hewijin Christine Jiau
Enhancing programming skills is essential for developers to keep pace with technological advancements and to maintain effective participation in software development practices. Game-based programming platforms have been widely adopted to promote learner engagement and skill acquisition. However, without structured guidance, programmers may adopt ineffective strategies, leading to stagnation and wasted effort. This paper investigates the programming skills developed through ELOP, a competitive game-based training platform that has accumulated longitudinal programming data from hundreds of users. A mixed-methods analysis reveals that while ELOP fosters iterative strategy refinement, key skills such as writing well-documented code and refactoring maintainable programs remain difficult for many learners to become proficient. To address these challenges, we present GPST (Game-based Programming Skill Trainer), an extended platform that augments ELOP with instructional features including automated code smell detection, comment quality guidance, and targeted training materials. GPST aims to support learners in developing clean, readable, and maintainable code while preserving the motivational benefits of game-based learning. Preliminary evaluation results from a small-scale pilot study (n=5) demonstrate the feasibility of GPST and suggest positive learning outcomes, while indicating directions for larger future deployments.
{"title":"From Strategy to Structure: Guiding Code Quality With GPST in Game-Based Programming Environments","authors":"Chien-Wei Hu;Yi-Hsuan Liao;Hewijin Christine Jiau","doi":"10.1109/ACCESS.2025.3649779","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649779","url":null,"abstract":"Enhancing programming skills is essential for developers to keep pace with technological advancements and to maintain effective participation in software development practices. Game-based programming platforms have been widely adopted to promote learner engagement and skill acquisition. However, without structured guidance, programmers may adopt ineffective strategies, leading to stagnation and wasted effort. This paper investigates the programming skills developed through ELOP, a competitive game-based training platform that has accumulated longitudinal programming data from hundreds of users. A mixed-methods analysis reveals that while ELOP fosters iterative strategy refinement, key skills such as writing well-documented code and refactoring maintainable programs remain difficult for many learners to become proficient. To address these challenges, we present GPST (Game-based Programming Skill Trainer), an extended platform that augments ELOP with instructional features including automated code smell detection, comment quality guidance, and targeted training materials. GPST aims to support learners in developing clean, readable, and maintainable code while preserving the motivational benefits of game-based learning. Preliminary evaluation results from a small-scale pilot study (n=5) demonstrate the feasibility of GPST and suggest positive learning outcomes, while indicating directions for larger future deployments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"4152-4161"},"PeriodicalIF":3.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11320304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929298","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 : 2025-12-31DOI: 10.1109/ACCESS.2025.3649777
Dohoon Kim
With the advent of the NewSpace era, space-based systems are facing complex, multi-vector-based cyber threats. Accordingly, a lifecycle-oriented approach to internalizing security becomes essential., and the concept of a Space Risk Management Framework (S-RMF), similar to the existing Risk Management Framework (RMF) system in the defense field, is required for space cybersecurity. Based on Model-Based Security Engineering (MBSE), this study references MITRE ATT&CK and Security and Privacy Architecture Through Threat Assessment (SPARTA) and formalizes a Threat Assessment & Remediation Analysis (TARA) model based on threats and security controls that meet CCSDS/NIST standards. To complement the static evaluation structure of the existing TARA, we propose a dynamic risk evaluation method that considers the time-based risk change rate by applying a Stochastic Differential Equation (SDE). The derived quantitative risk is linked to the lifecycle perspective of S-RMF and enables risk evolution analysis reflecting the time lags of attack, response, and control effectiveness. This framework can strengthen security reliability by linking the threat-assessment-control-assurance steps and can serve as a standardization basis for space cybersecurity policies.
{"title":"Dynamic Threat Modeling and Risk Assessment for Space Systems","authors":"Dohoon Kim","doi":"10.1109/ACCESS.2025.3649777","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649777","url":null,"abstract":"With the advent of the NewSpace era, space-based systems are facing complex, multi-vector-based cyber threats. Accordingly, a lifecycle-oriented approach to internalizing security becomes essential., and the concept of a Space Risk Management Framework (S-RMF), similar to the existing Risk Management Framework (RMF) system in the defense field, is required for space cybersecurity. Based on Model-Based Security Engineering (MBSE), this study references MITRE ATT&CK and Security and Privacy Architecture Through Threat Assessment (SPARTA) and formalizes a Threat Assessment & Remediation Analysis (TARA) model based on threats and security controls that meet CCSDS/NIST standards. To complement the static evaluation structure of the existing TARA, we propose a dynamic risk evaluation method that considers the time-based risk change rate by applying a Stochastic Differential Equation (SDE). The derived quantitative risk is linked to the lifecycle perspective of S-RMF and enables risk evolution analysis reflecting the time lags of attack, response, and control effectiveness. This framework can strengthen security reliability by linking the threat-assessment-control-assurance steps and can serve as a standardization basis for space cybersecurity policies.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"4080-4088"},"PeriodicalIF":3.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11320256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929413","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 : 2025-12-30DOI: 10.1109/ACCESS.2025.3649591
Yang Chen;Hanieh Ahmadi;Saba Al-Rubaye
However, traditional model-based phase-shift optimization is highly sensitive to imperfect CSI and becomes computationally prohibitive for large UPA-based RIS, while existing model-free solutions relying on single-agent DRL struggle with the exponentially growing action space. This paper presents a scalable multi-agent deep Q-network (MADQN)–based RIS controller designed for large-scale UAV–RIS systems under realistic channel dynamics. An end-to-end channel inference architecture is first introduced to mitigate CSI imperfection and reconstruct stable channel representations under UAV mobility. A multi-objective formulation is then developed to jointly optimize sum rate, energy consumption, and control latency, which is transformed into a multi-agent Markov decision process (MMDP) compatible with quantized RIS hardware. Building on this formulation, a dual-agent RIS controller is proposed, in which row and column agents cooperatively determine the quantized phase configuration of a large UPA RIS. Extensive simulations demonstrate that the proposed framework significantly outperforms benchmark schemes, showing acceptable robustness against varying Rician factor SNRs, UAV densities, and RIS sizes. These results confirm that the proposed MADQN-based controller is a promising and practical solution for scalable RIS control in large-scale multi-UAV communication systems.
{"title":"Multi-Agent Deep Reinforcement Learning-Based RIS-Aided UAV Communications","authors":"Yang Chen;Hanieh Ahmadi;Saba Al-Rubaye","doi":"10.1109/ACCESS.2025.3649591","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649591","url":null,"abstract":"However, traditional model-based phase-shift optimization is highly sensitive to imperfect CSI and becomes computationally prohibitive for large UPA-based RIS, while existing model-free solutions relying on single-agent DRL struggle with the exponentially growing action space. This paper presents a scalable multi-agent deep Q-network (MADQN)–based RIS controller designed for large-scale UAV–RIS systems under realistic channel dynamics. An end-to-end channel inference architecture is first introduced to mitigate CSI imperfection and reconstruct stable channel representations under UAV mobility. A multi-objective formulation is then developed to jointly optimize sum rate, energy consumption, and control latency, which is transformed into a multi-agent Markov decision process (MMDP) compatible with quantized RIS hardware. Building on this formulation, a dual-agent RIS controller is proposed, in which row and column agents cooperatively determine the quantized phase configuration of a large UPA RIS. Extensive simulations demonstrate that the proposed framework significantly outperforms benchmark schemes, showing acceptable robustness against varying Rician factor SNRs, UAV densities, and RIS sizes. These results confirm that the proposed MADQN-based controller is a promising and practical solution for scalable RIS control in large-scale multi-UAV communication systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"1522-1536"},"PeriodicalIF":3.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898247","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 : 2025-12-30DOI: 10.1109/ACCESS.2025.3649703
Jibaek Oh;Kwangphil Park;Jihoon Yoon;Junghan Kwon
Projector-based Augmented Reality (AR) provides intuitive guidance by projecting virtual content directly onto a physical workspace. However, when projected images overlap real objects, they distort the camera view, and detection accuracy drops for models like MediaPipe and YOLO. Moreover, a phenomenon known as ‘Visual Echo’, situation that system mistakes bright projected shapes as real objects, can occur. In addition, the projector-camera response is highly non-linear, which makes simple real-time correction difficult. To overcome these issues, we present a two-stage image preprocessing algorithm designed to suppress projection interference. Our method combines Color Refinement based on a Color Transformation Table and masked Lightness Compensation to effectively remove projection artifacts and enhance the visibility of physical objects. Experimental results show that the algorithm significantly reduces positional error by 70.47% and instability by 70.17% in MediaPipe hand landmark detection, while achieving 100% correct detection rate and reducing positional error by 86.32% in YOLOv8 object detection by effectively eliminating visual echoes. Furthermore, our algorithm maintains real-time performance at 27.4 FPS, making it suitable for practical applications. We successfully demonstrate the robust performance of our method through three distinct use cases: AR-based virtual ring try-on, dining etiquette education, and assembly training, highlighting its potential to enhance the reliability of projector-based AR systems across various fields.
{"title":"Visual Interference Suppression for Physical Object Detection in Projector-Based AR System","authors":"Jibaek Oh;Kwangphil Park;Jihoon Yoon;Junghan Kwon","doi":"10.1109/ACCESS.2025.3649703","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649703","url":null,"abstract":"Projector-based Augmented Reality (AR) provides intuitive guidance by projecting virtual content directly onto a physical workspace. However, when projected images overlap real objects, they distort the camera view, and detection accuracy drops for models like MediaPipe and YOLO. Moreover, a phenomenon known as ‘Visual Echo’, situation that system mistakes bright projected shapes as real objects, can occur. In addition, the projector-camera response is highly non-linear, which makes simple real-time correction difficult. To overcome these issues, we present a two-stage image preprocessing algorithm designed to suppress projection interference. Our method combines Color Refinement based on a Color Transformation Table and masked Lightness Compensation to effectively remove projection artifacts and enhance the visibility of physical objects. Experimental results show that the algorithm significantly reduces positional error by 70.47% and instability by 70.17% in MediaPipe hand landmark detection, while achieving 100% correct detection rate and reducing positional error by 86.32% in YOLOv8 object detection by effectively eliminating visual echoes. Furthermore, our algorithm maintains real-time performance at 27.4 FPS, making it suitable for practical applications. We successfully demonstrate the robust performance of our method through three distinct use cases: AR-based virtual ring try-on, dining etiquette education, and assembly training, highlighting its potential to enhance the reliability of projector-based AR systems across various fields.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"1537-1551"},"PeriodicalIF":3.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898227","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 : 2025-12-30DOI: 10.1109/ACCESS.2025.3649761
Waseem Akram;Muhayy Ud Din;Tarek Taha;Irfan Hussain
Thruster allocation is critical for the reliable operation of underwater vehicles, particularly under actuator degradation, power limitations, and thermal stress. Existing methods, such as pseudo-inverse or standard quadratic programming (QP)-based approaches, mainly minimize allocation error or energy consumption but often overlook real-time degradation and resource constraints. In this paper, we propose an adaptive fault-tolerant thrust allocation framework integrated with a PID plus Sliding Mode Control (PID+SMC) law for robust trajectory tracking. The approach leverages convex optimization to simultaneously enforce: 1) residual-driven health adaptation that down-weights degraded thrusters online; 2) power-aware allocation ensuring operation within a global energy budget; and 3) thermal-aware constraints that actively prevent overheating. A lightweight residual filter continuously updates thruster health indices, enabling rapid reallocation under faults and efficiency loss. Simulation results across nominal, power-limited, thermal-limited, faulted, and combined scenarios show that the proposed method reduces trajectory tracking error by up to 4.3% and completely eliminates power and thermal violations compared to conventional baselines. This unified framework establishes a foundation for real-time, safety-aware thruster management in marine robotics.
{"title":"Adaptive Fault-Tolerant Thrust Allocation for Underwater Vehicles With Resource Constraints","authors":"Waseem Akram;Muhayy Ud Din;Tarek Taha;Irfan Hussain","doi":"10.1109/ACCESS.2025.3649761","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649761","url":null,"abstract":"Thruster allocation is critical for the reliable operation of underwater vehicles, particularly under actuator degradation, power limitations, and thermal stress. Existing methods, such as pseudo-inverse or standard quadratic programming (QP)-based approaches, mainly minimize allocation error or energy consumption but often overlook real-time degradation and resource constraints. In this paper, we propose an adaptive fault-tolerant thrust allocation framework integrated with a PID plus Sliding Mode Control (PID+SMC) law for robust trajectory tracking. The approach leverages convex optimization to simultaneously enforce: 1) residual-driven health adaptation that down-weights degraded thrusters online; 2) power-aware allocation ensuring operation within a global energy budget; and 3) thermal-aware constraints that actively prevent overheating. A lightweight residual filter continuously updates thruster health indices, enabling rapid reallocation under faults and efficiency loss. Simulation results across nominal, power-limited, thermal-limited, faulted, and combined scenarios show that the proposed method reduces trajectory tracking error by up to 4.3% and completely eliminates power and thermal violations compared to conventional baselines. This unified framework establishes a foundation for real-time, safety-aware thruster management in marine robotics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"1341-1357"},"PeriodicalIF":3.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898248","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}
Parallel back-to-back converters are highly demanded in many high-power applications such as adjustable speed drive (ASD) systems, which reduce harmonics and improve the power factor and reliability compared to single two-level converters. It is evident that common-mode voltage (CMV) is the root cause of many challenges in ASD systems, such as shaft voltage and bearing damage, which may reduce equipment lifespan. On the other hand, Zero Sequence Circulating Current (ZSCC) leads to an additional current of switches which increases power loss and decreases the current capacity of converters. Simultaneous reduction of these two critical issues has to be considered in any switching strategy. In this regard, this paper presents a switching strategy based on a modified three-level space vector modulation scheme, which completely eliminates the common-mode voltage (CMV = 0 V). Moreover, the proposed switching sequence keeps the ZSCC within a low-amplitude and fully symmetric ripple, ensuring controlled circulating-current behavior without requiring any additional hardware. The method also generates a three-level line voltage and achieves an input-current THD of 3.92%. The simulation and experimental results confirm the effectiveness of the proposed approach.
{"title":"Common-Mode Voltage Elimination and Zero-Sequence Circulating Current Reduction of Parallel Back-to-Back Converters","authors":"Reza Farajpour;Mojtaba Sarparast;Jafar Adabi;Mohammad Rezanejad;Edris Pouresmaeil","doi":"10.1109/ACCESS.2025.3649536","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649536","url":null,"abstract":"Parallel back-to-back converters are highly demanded in many high-power applications such as adjustable speed drive (ASD) systems, which reduce harmonics and improve the power factor and reliability compared to single two-level converters. It is evident that common-mode voltage (CMV) is the root cause of many challenges in ASD systems, such as shaft voltage and bearing damage, which may reduce equipment lifespan. On the other hand, Zero Sequence Circulating Current (ZSCC) leads to an additional current of switches which increases power loss and decreases the current capacity of converters. Simultaneous reduction of these two critical issues has to be considered in any switching strategy. In this regard, this paper presents a switching strategy based on a modified three-level space vector modulation scheme, which completely eliminates the common-mode voltage (CMV = 0 V). Moreover, the proposed switching sequence keeps the ZSCC within a low-amplitude and fully symmetric ripple, ensuring controlled circulating-current behavior without requiring any additional hardware. The method also generates a three-level line voltage and achieves an input-current THD of 3.92%. The simulation and experimental results confirm the effectiveness of the proposed approach.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"1256-1268"},"PeriodicalIF":3.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898250","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 : 2025-12-30DOI: 10.1109/ACCESS.2025.3649668
Yousef Nejatbakhsh;Malihe Aliasgari
Accurate stock market prediction remains a critical yet challenging task due to the highly non-linear, volatile, and sentiment-driven nature of financial markets. In this paper, we present a hybrid deep learning framework that integrates long-short-term memory (LSTM) networks with Transformer-based attention mechanisms, sentiment analysis from financial news, and a privacy-preserving Federated Learning (FL) strategy. First, we benchmark traditional forecasting approaches, including ARIMA, SARIMAX, Prophet, Random Forest, and Support Vector Regression, against the baseline LSTM models. Our results show that LSTMs consistently outperform conventional methods in capturing temporal dependencies. To further enhance predictive accuracy, we incorporate Transformer attention to improve long-range dependency modeling and apply sentiment analysis using FinBERT-tone to embed market sentiment signals into the model. Finally, we simulate a Federated Learning environment, enabling decentralized model training without sharing raw financial data, thus addressing privacy concerns in the financial domain. Experimental results in ten major technology companies (Tesla, Apple, Amazon, Microsoft, Google, etc.) demonstrate that our hybrid model achieves superior short-term forecasting performance, with an average $R^{2}$ variance score of 0.91 across ten major technology companies and a trend precision of $65.36~%$ , demonstrating strong prediction performance for short-term stock forecasting. These findings highlight the potential of combining deep sequential models, attention mechanisms, and privacy-sensitive training strategies for robust and secure stock market forecasting.
{"title":"Enhancing Stock Market Prediction With Hybrid Deep Learning: Integrating LSTM, Transformer Attention, Federated Learning, and Sentiment Analysis","authors":"Yousef Nejatbakhsh;Malihe Aliasgari","doi":"10.1109/ACCESS.2025.3649668","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649668","url":null,"abstract":"Accurate stock market prediction remains a critical yet challenging task due to the highly non-linear, volatile, and sentiment-driven nature of financial markets. In this paper, we present a hybrid deep learning framework that integrates long-short-term memory (LSTM) networks with Transformer-based attention mechanisms, sentiment analysis from financial news, and a privacy-preserving Federated Learning (FL) strategy. First, we benchmark traditional forecasting approaches, including ARIMA, SARIMAX, Prophet, Random Forest, and Support Vector Regression, against the baseline LSTM models. Our results show that LSTMs consistently outperform conventional methods in capturing temporal dependencies. To further enhance predictive accuracy, we incorporate Transformer attention to improve long-range dependency modeling and apply sentiment analysis using FinBERT-tone to embed market sentiment signals into the model. Finally, we simulate a Federated Learning environment, enabling decentralized model training without sharing raw financial data, thus addressing privacy concerns in the financial domain. Experimental results in ten major technology companies (Tesla, Apple, Amazon, Microsoft, Google, etc.) demonstrate that our hybrid model achieves superior short-term forecasting performance, with an average <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> variance score of 0.91 across ten major technology companies and a trend precision of <inline-formula> <tex-math>$65.36~%$ </tex-math></inline-formula>, demonstrating strong prediction performance for short-term stock forecasting. These findings highlight the potential of combining deep sequential models, attention mechanisms, and privacy-sensitive training strategies for robust and secure stock market forecasting.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"3926-3942"},"PeriodicalIF":3.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929449","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 : 2025-12-30DOI: 10.1109/ACCESS.2025.3649751
Jeongwoo Son;Chansu Kim;Sang Hoon Kang
This paper proposes an electrical contact-based autonomous charging station for uncrewed aerial vehicles (UAVs) that reliably initiates charging regardless of landing position and orientation inaccuracies. Unlike existing UAV charging methods – which may suffer from efficiency losses due to wireless power transfer or require mechanical actuators, specially shaped structures, or diode bridges – the proposed autonomous charging station uses modular units with Hall-effect sensors to detect a magnet mounted on the UAV’s positive charging electrode. Thus, the proposed charging station was designed to allow direct electrical contact without rectifier diodes or actuators, reducing unnecessary losses. Across all 832 possible landing poses of the UAV, the power transfer efficiency exceeded 98.34% – surpassing the 91.02% reported in prior work; in outdoor repeated-flight tests, charging initiated and succeeded in all trials (30/30) with randomized landing positions and orientations. Preliminary field trials at a 765-kV substation demonstrated feasibility under elevated electromagnetic interference. These results highlight the robustness of the proposed system to substantial landing inaccuracies, providing a strong foundation for prolonged, unattended UAV missions in demanding real-world environments.
{"title":"Robust and Efficient Autonomous Charging Station for Uncrewed Aerial Vehicles Under Large Landing Inaccuracies","authors":"Jeongwoo Son;Chansu Kim;Sang Hoon Kang","doi":"10.1109/ACCESS.2025.3649751","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649751","url":null,"abstract":"This paper proposes an electrical contact-based autonomous charging station for uncrewed aerial vehicles (UAVs) that reliably initiates charging regardless of landing position and orientation inaccuracies. Unlike existing UAV charging methods – which may suffer from efficiency losses due to wireless power transfer or require mechanical actuators, specially shaped structures, or diode bridges – the proposed autonomous charging station uses modular units with Hall-effect sensors to detect a magnet mounted on the UAV’s positive charging electrode. Thus, the proposed charging station was designed to allow direct electrical contact without rectifier diodes or actuators, reducing unnecessary losses. Across all 832 possible landing poses of the UAV, the power transfer efficiency exceeded 98.34% – surpassing the 91.02% reported in prior work; in outdoor repeated-flight tests, charging initiated and succeeded in all trials (30/30) with randomized landing positions and orientations. Preliminary field trials at a 765-kV substation demonstrated feasibility under elevated electromagnetic interference. These results highlight the robustness of the proposed system to substantial landing inaccuracies, providing a strong foundation for prolonged, unattended UAV missions in demanding real-world environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"4027-4037"},"PeriodicalIF":3.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929348","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}
The inherent variability in human performance introduces stochastic perturbations into manufacturing environments, undermining the seamless coordination required for effective human-robot collaboration (HRC) systems. While human cognitive flexibility enhances adaptability, it simultaneously acts as a source of operational uncertainty, complicating the modeling and optimization of integrated robotic systems. Given these challenges, there is an urgent need to substantially expand the adaptability of robotic systems through real-time detection, algorithmic analysis and dynamic behavioral adjustments in response to human performance fluctuations. The systematic development of such systems capable of precisely detecting task-specific variations, analyzing them via advanced AI algorithms and adapting their behavior accordingly remains a critical focus of contemporary research. To evaluate progress in this domain, this study conducts a systematic literature review, synthesizing advancements across 124 publications and identifying underexplored research frontiers. The findings reveal a persistent misalignment between current technical capabilities and the requirements of adaptive collaboration in dynamic industrial environments. Key gaps include the absence of explainable AI frameworks for transparent decision-making, limited generalizability of adaptive control architectures and a lack of proactive strategies that anticipate rather than merely react to performance deviations.
{"title":"Adaptive Robotic Behavior in Industrial Human–Robot Collaboration: A Systematic Review of Taxonomies, Enabling Mechanisms, and Research Frontiers","authors":"Bsher Karbouj;Rajwinder Garha;Konstantin KeßLer;Jörg Krüger","doi":"10.1109/ACCESS.2025.3649702","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3649702","url":null,"abstract":"The inherent variability in human performance introduces stochastic perturbations into manufacturing environments, undermining the seamless coordination required for effective human-robot collaboration (HRC) systems. While human cognitive flexibility enhances adaptability, it simultaneously acts as a source of operational uncertainty, complicating the modeling and optimization of integrated robotic systems. Given these challenges, there is an urgent need to substantially expand the adaptability of robotic systems through real-time detection, algorithmic analysis and dynamic behavioral adjustments in response to human performance fluctuations. The systematic development of such systems capable of precisely detecting task-specific variations, analyzing them via advanced AI algorithms and adapting their behavior accordingly remains a critical focus of contemporary research. To evaluate progress in this domain, this study conducts a systematic literature review, synthesizing advancements across 124 publications and identifying underexplored research frontiers. The findings reveal a persistent misalignment between current technical capabilities and the requirements of adaptive collaboration in dynamic industrial environments. Key gaps include the absence of explainable AI frameworks for transparent decision-making, limited generalizability of adaptive control architectures and a lack of proactive strategies that anticipate rather than merely react to performance deviations.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"1398-1422"},"PeriodicalIF":3.6,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898239","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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