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Dynamic Layered Clustering Routing Protocol Based on Hybrid-Optimized Neural Networks for UWSNs

IEEE Internet of Things Journal 2025年第11期12卷 16155-16170页

作者： Lu, Xinmiao Yang, Longyue Wu, Qiong Zhu, Yuna Zou, Yixin Shi, Yunbo Harbin University of Science and Technology School of Measurement-Control Technology and Communications Engineering Harbin150000 China Heilongjiang Network Space Research Center School of Computer Science and Technology Harbin150000 China

To address the limitations of traditional flat routing in large-scale Underwater Wireless Sensor Networks (UWSNs), and to tackle challenges such as long delays, low bandwidth, and high error rates encountered by sensor nodes in underwater environments, this paper proposes a dynamic layered clustering routing protocol based on a hybrid optimized backpropagation neural network (PSB-NN), referred to as PSBDR. The protocol introduces non-uniform vertical layering of the network based on water depth and assigns different communication and sensing radii to nodes according to their initial layer. A trained hierarchical prediction model dynamically adjusts the virtual layer assignments of nodes throughout the network's operation. Additionally, a method is implemented for isolated nodes to join clusters via relay nodes, and a priority mechanism, along with a dynamic weight update strategy, is employed to select the optimal next hop during routing. This protocol effectively balances the workload across nodes at varying depths and maximizes the utilization of residual energy in the nodes, contributing to an extending network lifetime. Simulation results demonstrate that PSBDR enhances network performance by improving load balancing, prolonging network longevity, reducing end-to-end delay, and increasing data collection reliability. © 2014 IEEE.

关键词： Network layers

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Multisource Data Fusion Using MLP for Human Activity Recognition

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computers, Materials & Continua 2025年第2期82卷 2109-2136页

作者： Sujittra Sarakon Wansuree Massagram Kreangsak Tamee Department of Computer Science and Information Technology Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence for Innovation and Technology for Detection and Advanced Materials(ITDAM) Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence in Nonlinear Analysis and Optimizations Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand

This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.

关键词： Multisource data fusion human activity recognition multi-layer perceptron(MLP) artificial intelligent

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning on Language-Video Foundation Models

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IEEE Transactions on Multimedia 2025年 27卷 2875-2885页

作者： Chen, Hong Wang, Xin Zeng, Guanning Zhang, Yipeng Zhou, Yuwei Han, Feilin Wu, Yaofei Zhu, Wenwu Tsinghua University Department of Computer Science and Technology Beijing100084 China Beijing National Research Center for Information Science and Technology China Beijing Film Academy Department of Film and TV Technology China Beijing University of Technology China

Customized text-to-video generation aims to generate text-guided videos with user-given subjects, which has gained increasing attention. However, existing works are primarily limited to single-subject oriented text-to-video generation, leaving the more challenging problem of customized multi-subject generation unexplored. In this paper, we fill this gap and propose a novel VideoDreamer framework, which can generate temporally consistent text-guided videos that faithfully preserve the visual features of the given multiple subjects. Specifically, VideoDreamer adopts the pretrained Stable Diffusion with temporal modules as its base video generator, taking the power of the text-to-image model to generate diversified content. The video generator is further customized for multi-subjects, which leverages the proposed Disen-Mix Finetuning and Human-in-the-Loop Re-finetuning strategy, to tackle the attribute binding problem of multi-subject generation. Additionally, we present a disentangled motion customization strategy to finetune the temporal modules so that we can generate videos with both customized subjects and motions. To evaluate the performance of customized multi-subject text-to-video generation, we introduce the MultiStudioBench benchmark. Extensive experiments demonstrate the remarkable ability of VideoDreamer to generate videos with new content such as new events and backgrounds, tailored to the customized multiple subjects. © 1999-2012 IEEE.

关键词： Text to video Dogs Codes Visualization Generators Text to image Diffusion models Hands Training Noise measurement

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Different-Mode Power Splitters for Optical Testing of Three-Channel and Dual-Mode Waveguide Crossing

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Photonic Sensors 2025年第2期15卷 110-118页

作者： Hansi MA Te DU Xinpeng JIANG Zhaojian ZHANG Xin HE Huan CHEN Yang YU Zhenfu ZHANG Yunxin HAN Junbo YANG Yuanxi PENG Center of Material Science National University of Defense TechnologyChangsha 410073China College of Computer National University of Defense TechnologyChangsha 410073China School of Microelectronics and Physics Hunan University of Technology and BusinessChangsha 410205China

We study that the different-mode(waveguide-connected)power splitter[(W)PS]can provide different-mode testing points for the optical *** the PS or WPS providing two different-mode testing points,the measured insertion losses(ILs)of the three-channel and dual-mode waveguide crossing(WC)for both the fundamental transverse electric(TE0)and TE1 modes are less than 1.8 dB or 1.9 dB from 1540 nm to 1560 *** the same time,the crosstalks(CTs)are lower than-17.4 dB or-18.2 *** consistent test results indicate the accuracy of the(W)PS-based testing ***,combining the tunable tap couplers,the(W)PS can provide multiple testing points with different modes and different transmittances.

关键词： Different-mode(waveguide-connected)power splitter testing points three-channel and dual-mode waveguide crossing

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Enhanced APT detection with the improved KAN algorithm: capturing interdependencies for better accuracy

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Complex and Intelligent Systems 2025年第7期11卷 1-20页

作者： Ren, Weiwu Zhang, Hewen Hong, Yu Wang, Zhiwei School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun 130012 China National Computer Network Emergency Response Center Jilin Changchun 130012 China

In real-world network environments, advanced persistent threats (APTs) are characterized by their complexity and persistence. Existing APT detection methods often struggle to comprehensively capture the complex and dynamic network relationships and covert attack patterns involved in the attack process, and they also suffer from insufficient detection effectiveness. To address this, we propose a model that combines bidirectional dynamic graph attention with the improved KAN network. The improved KAN model smoothly connects control points by using the interpolation properties of the Catmull–Rom spline function. This model also combines the feature extraction capabilities of graph neural networks with a bidirectional dynamic graph attention mechanism. By dynamically updating the states of network nodes, it captures multi-step, cross-node, and highly covert attack features in APT attacks. Experimental results show that this method achieves an accuracy of 97.10% in APT attack detection, with false positive and false negative rates of 0.2% and 9.02%, respectively. The effectiveness of the model in extracting complex behavioral features of APT attacks has been validated, providing a reliable solution for APT detection in complex network environments. © The Author(s) 2025.

关键词： Advanced persistent threats Attack detection Bidirectional dependency Dynamic graph attention Kolmogorov–Arnold network

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies School of English Education Guangdong University of Foreign Studies School of Computer Science and Cyber Engineering Guangzhou University South China University of Technology School of Information Science and Technology Guangdong University of Foreign Studies School of Computer Science University of Technology Sydney

With the rapid development of web technology, Social Networks(SNs) have become one of the most popular platforms for users to exchange views and to express their emotions. More and more people are used to commenting on a certain hot spot in SNs, resulting in a large amount of texts containing emotions. Textual Emotion Cause Extraction(TECE) aims to automatically extract causes for a certain emotion in texts, which is an important research issue in natural language processing. It is different from the previous tasks of emotion recognition and emotion classification. In addition, it is not limited to the shallow-level emotion classification of text, but to trace the emotion source. In this paper, we provide a survey for TECE. First, we introduce the development process and classification of TECE. Then, we discuss the existing methods and key factors for TECE. Finally, we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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A Flexible Garbage Collection Scheme with Low Overhead for ZNS SSDs 24

A Flexible Garbage Collection Scheme with Low Overhead for Z...

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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zou, Weifeng Jiu, Kang Chen, Yaojie Network Information Center Wuhan University of Science and Technology Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400712883

Garbage collection (GC) has long impacted the ability of solid state disks (SSD) to function properly and has become an important issue that many memory manufacturers are eager to address. In recent years, with the advent of zoned namespace SSDs (ZNS SSD), the garbage collection process has become more controllable. The host side is able to selectively trigger the process of garbage collection and the area to be covered, so as to meet the real-time demand of applications. In this paper, we propose a flexible garbage collection scheme that aims to avoid affecting the normal read and write operations of applications as much as possible and further reduce the overhead of garbage collection. The experiments are conducted in a simulator capable of emulating zoned storage. Extensive experiments show that our approach is able to reduce read and write latency while reducing write amplification. © 2024 Copyright held by the owner/author(s).

关键词： Refuse collection

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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