计算机科学最新文献_第5页

Flexible and objective diagnosis of type II diabetes by using a fuzzy deep learning ensemble approach

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-25 DOI: 10.1007/s40747-025-01894-w

Min-Chi Chiu, Tin-Chih Toly Chen, Yu-Cheng Wang

{"title":"Flexible and objective diagnosis of type II diabetes by using a fuzzy deep learning ensemble approach","authors":"Min-Chi Chiu, Tin-Chih Toly Chen, Yu-Cheng Wang","doi":"10.1007/s40747-025-01894-w","DOIUrl":"https://doi.org/10.1007/s40747-025-01894-w","url":null,"abstract":"<p>Deep learning (DL) applications have potential for improving the accuracy of type II diabetes diagnoses. However, existing DL applications for the diagnosis of type II diabetes have several drawbacks. For example, they maximize overall diagnostic performance rather than the diagnostic performance for each patient, they do not use objective rules to identify whether a patient has type II diabetes, and they sometimes provide the same diagnostic results for patients with different real diagnoses. To address these drawbacks, the present study developed a fuzzy DL ensemble (FDLE) approach. In this approach, several autoencoder (AE)–fuzzy deep neural networks (FDNNs) with different configurations are constructed and used to predict the probability of a patient having type II diabetes. The probability predictions are fuzzy values based on the patient’s attributes. The fuzzy probabilities predicted by the constructed AE-FDNNs are then aggregated using the fuzzy weighted intersection–radial basis function method. Subsequently, on the basis of the aggregated result, several objective and subjective diagnostic rules are created. The developed FDLE approach was applied to a real case to examine its effectiveness. According to the experimental results, this approach outperformed 10 existing methods by up to 21% in terms of accuracy in diagnosing type II diabetes. The different diagnostic rules created in the FDLE approach complement each other and facilitate an accurate diagnosis.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"8 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe event-triggered control of unmanned surface vehicles with Gaussian processes: Resilience in denial of service attacks and uncertain dynamics

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-25 DOI: 10.1016/j.engappai.2025.110776

Zewen Yang , Xiaobing Dai , Liang Fang , Jiajia Zhou , Zheping Yan

引用次数: 0

Scalable and transferable graph neural networks for predicting temperature evolution in laser powder bed fusion

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-25 DOI: 10.1016/j.engappai.2025.110898

Riddhiman Raut, Amit Kumar Ball, Amrita Basak

{"title":"Scalable and transferable graph neural networks for predicting temperature evolution in laser powder bed fusion","authors":"Riddhiman Raut, Amit Kumar Ball, Amrita Basak","doi":"10.1016/j.engappai.2025.110898","DOIUrl":"10.1016/j.engappai.2025.110898","url":null,"abstract":"<div><div>Predicting temperature distributions in laser powder bed fusion (L-PBF) processes is essential for mitigating thermal distortions and ensuring the structural integrity of manufactured parts. Traditional finite element analysis (FEA) methods, while accurate, are computationally intensive and struggle to scale to larger domains. To address these limitations, this study proposes novel predictive models based on Graph Neural Networks (GNNs) to simulate thermal dynamics in L-PBF processes. The models leverage high-fidelity FEA data from small-scale domains to generalize effectively to larger domains with minimal retraining. For single-laser setups, the GNN achieves a Mean Absolute Percentage Error (MAPE) of 3.77 %, while significantly reducing computational costs. For instance, a thermomechanical simulation for a 2 mm square domain typically takes about 4 h, whereas the single-laser model predicts thermal distributions almost instantly. When calibrated for larger domains, the models significantly enhance predictive performance, showing notable improvements for square domains of 3 mm and 4 mm. Additionally, the models show a decreasing trend in Root Mean Square Error when tuned to larger domains, suggesting the potential for becoming geometry-agnostic. The interaction of multiple lasers complicates heat transfer, necessitating larger model architectures and advanced feature engineering. Using hyperparameters from Gaussian process-based Bayesian optimization, the best multi-laser surrogate model demonstrates a 46.4 % improvement in MAPE over the baseline model. By providing scalable and efficient predictive tools alongside FEA, this work paves the way for thermal modeling in L-PBF.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"153 ","pages":"Article 110898"},"PeriodicalIF":7.5,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2022-2024 Index Proceedings of the IEEE Vol. 110-112

IF 23.2 1区计算机科学

Proceedings of the IEEE Pub Date : 2025-04-25 DOI: 10.1109/JPROC.2025.3564448

引用次数: 0

HLNet: high-level attention mechanism U-Net + + for brain tumor segmentation in MRI

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-25 DOI: 10.1007/s10489-025-06568-1

Wenyang Yang, Zhiming Li, Chao Du, Steven Kwok Keung Chow

{"title":"HLNet: high-level attention mechanism U-Net + + for brain tumor segmentation in MRI","authors":"Wenyang Yang, Zhiming Li, Chao Du, Steven Kwok Keung Chow","doi":"10.1007/s10489-025-06568-1","DOIUrl":"10.1007/s10489-025-06568-1","url":null,"abstract":"<div><p>The high-level attention mechanism enhances object detection by focusing on important features and details, making it a potential tool for tumor segmentation. However, its effectiveness and efficiency in this context remain uncertain. This study aims to investigate the efficiency, feasibility and effectiveness of integrating a high-level attention mechanism into the U-Net and U-Net + + model for improving tumor segmentation. Experiments were conducted using U-Net and U-Net + + models augmented with high-level attention mechanisms to compare their performance. The proposed model incorporated high-level attention mechanisms in the encoder, decoder, and skip connections. Model training and validation were performed using T1, FLAIR, T2, and T1ce MR images from the BraTS2018 and BraTS2019 datasets. To further evaluate the model's effectiveness, testing was conducted on the UPenn-GBM dataset provided by the Center for Biomedical Image Computing and Analysis at the University of Pennsylvania. The segmentation accuracy of the high-level attention U-Net + + was evaluated using the DICE score, achieving values of 88.68 (ET), 89.71 (TC), and 91.50 (WT) on the BraTS2019 dataset and 90.93 (ET), 92.79 (TC), and 93.77 (WT) on the UPEEN-GBM dataset. The results demonstrate that U-Net + + integrated with the high-level attention mechanism achieves higher accuracy in brain tumor segmentation compared to baseline models. Experiments conducted on comparable and challenging datasets highlight the superior performance of the proposed approach. Furthermore, the proposed model exhibits promising potential for generalization to other datasets or use cases, making it a viable tool for broader medical imaging applications.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06568-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Multimodal intent recognition based on text-guided cross-modal attention

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-25 DOI: 10.1007/s10489-025-06583-2

Zhengyi Li, Junjie Peng, Xuanchao Lin, Zesu Cai

{"title":"Multimodal intent recognition based on text-guided cross-modal attention","authors":"Zhengyi Li, Junjie Peng, Xuanchao Lin, Zesu Cai","doi":"10.1007/s10489-025-06583-2","DOIUrl":"10.1007/s10489-025-06583-2","url":null,"abstract":"<div><p>In natural language understanding, intent recognition stands out as a crucial task that has drawn significant attention. While previous research focuses on intent recognition using task-specific unimodal data, real-world scenarios often involve human intents expressed through various ways, including speech, tone of voice, facial expressions, and actions. This prompts research into integrating multimodal information to more accurately identify human intent. However, existing intent recognition studies often fuse textual and non-textual modalities without considering their quality gap. The gap in feature quality across different modalities hinders the improvement of the model’s performance. To address this challenge, we propose a multimodal intent recognition model to enhance non-textual modality features. Specifically, we enrich the semantics of non-textual modalities by replacing redundant information through text-guided cross-modal attention. Additionally, we introduce a text-centric adaptive fusion gating mechanism to capitalize on the primary role of text modality in intent recognition. Extensive experiments on two multimodal task datasets show that our proposed model performs better in all metrics than state-of-the-art multimodal models. The results demonstrate that our model efficiently enhances non-textual modality features and fuses multimodal information, showing promising potential for intent recognition.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-embedded lion pride labor division algorithm for solving dynamic coupling SEAD task allocation problem

IF 4.4 2区计算机科学

IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2025-04-25 DOI: 10.1109/taes.2025.3564582

Bowen Wu, Renbin Xiao

引用次数: 0

IPv6 Routing Protocol for Low-Power and Lossy Networks Security Vulnerabilities and Mitigation Techniques: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-25 DOI: 10.1145/3732776

Aviram Zilberman, Amit Dvir, Ariel Stulman

{"title":"IPv6 Routing Protocol for Low-Power and Lossy Networks Security Vulnerabilities and Mitigation Techniques: A Survey","authors":"Aviram Zilberman, Amit Dvir, Ariel Stulman","doi":"10.1145/3732776","DOIUrl":"https://doi.org/10.1145/3732776","url":null,"abstract":"The proliferation of the Internet of Things (IoT) has reshaped the way we interact with technology, propelling the Routing Protocol for Low-Power and Lossy Networks (RPL) into a critical role as a communication framework. Amid this transformative landscape, security vulnerabilities within RPL-based IoT networks emerge as a substantial concern. This survey delves into these vulnerabilities, offering insights into their intricacies, potential consequences, and robust mitigation strategies. Commencing with a foundational understanding of IoT networks and their real-world applications, the survey sets the stage for comprehending the significance of Routing Protocol for Low-Power and Lossy Networks (RPL). It unravels the unique characteristics of RPL networks, their Destination-Oriented Directed Acyclic Graph (DODAG) topologies, and their pivotal role in enabling seamless device communication. The survey then delves into the heart of RPL security vulnerabilities. It navigates through diverse attack vectors, such as rank attacks and version number attacks. Each vulnerability is scrutinized, unraveling its technical mechanisms and implications for network stability. Transitioning from vulnerabilities to resilience, the survey offers a panoramic view of mitigation strategies. It dissects the nuances of intrusion detection systems (IDS), exploring trust models, location-based approaches, and hybrid systems. Signature-based, anomaly-based, and specification-based detection mechanisms are evaluated for their potential to mitigate threats within RPL networks. As standards shape the IoT landscape, the survey underscores the pivotal role of RPL within this framework. It emphasizes the necessity of secure standards in mitigating vulnerabilities across interconnected IoT devices.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"41 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Provably Secure and Efficient One-to-Many Authentication and Key Agreement Protocol for Resource-Asymmetric Smart Environments

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-04-25 DOI: 10.1109/jiot.2025.3564512

Long Li, Chingfang Hsu, Jianqun Cui, Man Ho Au, Lein Harn, Quanrun Li

引用次数: 0

LLMorpheus: Mutation Testing using Large Language Models

IF 7.4 1区计算机科学

IEEE Transactions on Software Engineering Pub Date : 2025-04-25 DOI: 10.1109/tse.2025.3562025

Frank Tip, Jonathan Bell, Max Schäfer

引用次数: 0