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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Klidbari, Mohammadreza Khodaparast Souri, Naser Habibolahi, Zahra Sadat Montazeri Hedeshi, Hamid K. N. Toosi University of Technology Faculty of Electrical Engineering Tehran Iran Virginia Tech The Bradley Department of Electrical and Computer Engineering United States Iran University of Science and Technology Department of Electrical Engineering Tehran Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331516116

Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid multiport isolated DC-DC converter for urban charging stations, incorporating fast and slow charging ports with a fixed switching frequency. It provides isolation and enables soft switching on the primary side of the converter without circulating current on its secondary side. The primary side does not need feedback, which reduces complexity. The second stage generates a wide output voltage range to charge the electric vehicle battery by employing a switch. In addition, the proposed topology offers reduced component count and simple control with fixed-frequency operation. This paper provides the concept and the operation modes. Experimental results are provided to validate its features. The prototype converter achieves 96% peak efficiency. © 2025 IEEE.

关键词： Topology

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Enhanced Body Composition Estimation from 3D Body Scans

Enhanced Body Composition Estimation from 3D Body Scans

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Feng, Boyuan Zheng, Yijiang Cheng, Ruting Feng, Shuya Vaziri, Khashayar Hahn, James Department of Computer Science George Washington University WashingtonDC United States Department of Computer Science and Engineering University of Connecticut StorrsCT United States Department of Surgery The George Washington University Medical Faculty Associates WashingtonDC United States

Accurate body composition assessment is essential for evaluating health and diagnosing conditions like sar copenia and cardiovascular disease. Approaches for accurately measuring body composition, such as Dual Energy X-ray Absorptiometry (DXA) and Magnetic Resonance Imaging (MRI), are precise but costly and limited in accessibility. Some studies have explored predicting body composition by using shapes since 3D scanning techniques allow for precise and efficient digital measurements of body shape. This study introduces an enhanced method using 3D body scanning integrated with a part-to-global Multilayer Perceptron (MLP) network that incorporates predefined high-level features for body composition prediction. For lean mass es timation, our method achieved a root mean square error (RMSE) of 2.85 kg. For fat mass estimation, the RMSE was 2.50 kg, and for bone mineral content (BMC), the RMSE was 193.50 g. These results represent substantial improvements over existing methods, highlighting the effectiveness and reliability of our approach in accurately predicting body composition metrics. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Diagnosis

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Local High-order Structure-aware Graph Neural Network for motif prediction

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Knowledge-Based Systems 2025年 320卷

作者： Yang, Wen Li, Xiang Wang, Bin Qi, Jianpeng Zhao, Zhongying He, Peilan Yu, Yanwei Faculty of Information Science and Engineering Ocean University of China Shandong Qingdao266100 China School of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266100 China

Motifs, serving as fundamental building blocks in complex networks, refer to small, frequently occurring connected subgraphs. Unlike link prediction, motif prediction focuses on whether a given set of nodes will form a particular type of motif and has achieved much more attention. Motif prediction holds significant research value and demonstrates broad application potential across various fields, such as financial default prediction and social network recommendation. However, existing research methods are relatively limited and have largely overlooked the crucial role of local higher-order correlations among nodes and the enclosing subgraph-level structural information in motifs. To overcome these challenges, we propose a novel Local High-order Structure-aware Graph Neural Network for motif prediction, named LHSGNN. It comprehensively predicts motifs from the perspectives of both the node level and subgraph level. LHSGNN incorporates local higher-order correlations among nodes to learn node embeddings. Then, it labels nodes from the views of node roles and distance metrics to capture complex structural information in enclosing subgraph-level motifs. Comprehensive experiments conducted on real-world datasets demonstrate the effectiveness of our LHSGNN. © 2025 Elsevier B.V.

关键词： Small-world networks

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning Data science Feature extraction Convolutional neural networks Data mining Remote sensing Information systems

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Hermitian -(anti-)reflexive solutions to a quaternion matrix equation

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Computational and Applied Mathematics 2025年第6期44卷 1-14页

作者： Liu, Xin Li, Wanqi Yuan, Shifang Zhang, Yang Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology TaiPa China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology TaiPa China School of Mathematics and Computational Science Wuyi University Jiangmen China Department of Mathematics University of Manitoba Winnipeg Canada

In this paper, we consider the Hermitian $$\{P, k + 1\}$$ -(anti-)reflexive solutions to the quaternion matrix equation $$AXB+CXD=E$$ and $$AX=E,$$ respectively. We use the complex representation method to obtain the necessary and sufficient conditions for the existence of the Hermitian $$\{P, k +1\}$$ -reflexive solution (resp. Hermitian $$\{P, k +1\}$$ -anti-reflexive solution), respectively, and derive their solutions when the matrix equations have the Hermitian $$\{P, k +1\}$$ -reflexive solution (resp. Hermitian $$\{P, k +1\}$$ -anti-reflexive solution). Finally, two examples are provided to verify the effectiveness of our method.

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Comparative study of IoT- and AI-based computing disease detection approaches

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Data science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

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Encoding laparoscopic image to words using vision transformer for distortion classification and ranking in laparoscopic videos

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Multimedia Tools and Applications 2025年第10期84卷 7159-7181页

作者： AlDahoul, Nouar Karim, Hezerul Abdul Momo, Mhd Adel Tan, Myles Joshua Toledo Fermin, Jamie Ledesma Faculty of Engineering Multimedia University Cyberjaya63100 Malaysia Computer Science New York University Abu Dhabi United Arab Emirates College of Engineering and Technology University of St. La Salle Bacolod6100 Philippines Department of Natural Sciences University of St. La Salle Bacolod6100 Philippines

Laparoscopic videos are tools used by surgeons to insert narrow tubes into the abdomen and keep the skin without large incisions. The videos captured by a camera are prone to numerous distortions such as uneven illumination, motion blur, defocus blur, smoke, and noise which have impact on visual quality. Automatic detection and identification of distortions are significant to enhance the quality of laparoscopic videos to avoid errors during surgery. The video quality assessment includes two stages: classification of distortions affecting the video frames to identify their types and ranking of distortions to estimate the intensity levels. The dataset generated in ICIP2020 challenge including laparoscopic videos was utilized for training, validation, and testing the proposed solution. The difficulty of this dataset is caused by having five categories of distortions and four levels of severity. Additionally, the availability of multiple distortion categories in one video is considered the most challenging part of this dataset. The work presented in this paper contributes to solve the multi-label distortion classification and ranking problem. This paper aims to enhance the performance of distortion classification solutions. Vision transformer which is a deep learning model was used to extract informative features by transferring learning and representation from the general domain to the medical domain (laparoscopic videos). Additionally, six parallel multilayer perceptron (MLP) classifiers were added and attached to vision transformer for distortion classification and ranking. The experiment showed that the proposed solution outperforms existing distortion classification methods in terms of average accuracy (89.7%), average single distortion F1 score (94.18%), and average of both single and multiple distortions F1 score (96.86%). Moreover, it can also rank the distortions with an average accuracy of 79.22% and average F1 score of 78.44%. Hence, the high performance of t

关键词： Classification (of information)

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Selfish-Free Influential Nodes Detection in Mobile Social Networks 2

Selfish-Free Influential Nodes Detection in Mobile Social Ne...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Abdelmohsen, Doaa Abdelkader, Tamer Hamdy, Mohamed Elbahnasy, Khaled Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt Galala Unversity Faculty of Computer Science and Engineering Suez Egypt King Abdul Aziz University Faculty of Computing and Information Technology Saudi Arabia Jeddah International College Saudi Arabia

ISBN: (纸本)9798331506995

Mobile networks where users group in communities based on their interests, and disseminate data mainly to their community using their mobile devices, are called Mobile Social Networks (MSN). However, the efficiency of data dissemination becomes a challenging problem in these networks. Using influential users can improve the efficiency of data dissemination. An influential user should have an effective activity on the network and influence other users. The participation behavior of nodes in the network dramatically affects the detection process of influential nodes. Self-sufficient nodes who selectively participate in the dissemination of data in the network may reduce the influence of the other nodes. This paper proposes detecting influential nodes that consider the negative effects of selfish behavior. The performance analysis of the proposed selfish-free influential node detection protocol is presented by comparing it to another one that does not consider the selfishness problem. This comparison shows that the proposed protocol achieves efficiency up to 30% in a environment of selfish behavior. © 2025 IEEE.

关键词： Publishing

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FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring

arXiv

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arXiv 2025年

作者： Abbasi, Mostafa Khadivi, Maziyar Ahang, Maryam Lasserre, Patricia Lucet, Yves Najjaran, Homayoun Faculty of Engineering and Computer Science University of Victoria VictoriaBC Canada Computer Science Irving K. Barber Faculty of Science University of British Columbia KelownaBC Canada

The application of AI and machine learning has garnered considerable attention and is evolving rapidly in academia and practice. However, considering the effort required for implementing AI and ML in business process management—particularly regarding data quality and the skills of process analysts—the potential of these technologies has not yet been fully explored. Moreover, beyond data quality issues, continuous changes in business processes introduce significant uncertainty, leading to inefficiencies and the infeasibility of using existing optimization methods. We present a novel 5-step framework called Fine-Tuned Offline Reinforcement Learning Augmented Process Sequence Optimization (FORLAPS), which aims to identify optimal execution paths in business processes using reinforcement learning. We implemented this approach on real-life event logs from our case study an energy regulator in Canada and other real-life event logs, demonstrating the feasibility of the proposed method. Additionally, to compare FORLAPS with the existing models (Permutation Feature Importance and multi-task LSTM-Based model), we experimented to evaluate its effectiveness in terms of resource savings and process time span reduction. The experimental results on real-life event log validate that FORLAPS achieves 31% savings in resource time spent and a 23% reduction in process time span. Using this innovative data augmentation technique, we propose a fine-tuned reinforcement learning approach that aims to automatically fine-tune the model by selectively increasing the average estimated Q-value in the sampled batches. The results show that we obtained a 44% performance improvement compared to the pre-trained model. This study introduces an innovative evaluation model, benchmarking its performance against earlier works using nine publicly available datasets. Robustness is ensured through experiments utilizing the Damerau-Levenshtein distance as the primary metric. In addition, we discussed the su

关键词： Data quality

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The Role of AI and Machine Learning in Cancer Diagnosis and Treatment 4

The Role of AI and Machine Learning in Cancer Diagnosis and ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gawande, Tejas Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artifical Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify cancer indicators which human experts commonly miss leading to better diagnostic accuracy. AI-powered Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan analysis allows doctors to perform quicker and more efficient medical diagnosis. The usage of ML models enables medical professionals to detect prognostic trace elements thus they can enhance treatment choices by analyzing tumor cell biological classification groups. AI actively participates in protein and gene targeting together with clinical trial planning to move personal cancer treatment forward. AI applications face multiple obstacles that stop their effective adoption in the field of oncology. The majority of present-day barriers to AI implementation in oncology stem from problems with data privacy together with algorithmic biases and model validation methods while the high complexity of systems and decreasing model interpretability specifically hinder usage. The success of AI models depends on extensive and diverse datasets although the actual availability of relevant data sets combined with standardization procedures continues to be difficult. Medical diagnostic applications where AI performs its decision-making work present important transparency issues that create doubts about its effectiveness. The analysis of data privacy through federated learning and XAI for interpretability and transfer learning for efficiency and deep learning for accuracy improvement are recent solutions being researched to resolve these problems. The primary aim of this research explores how AI and ML influence cancer diagnosis together with treatment methods while studying present challenges alongside suggested methods to boost their operational performance. The combination of better data governance toget

关键词： Federated learning

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