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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Wang, Youming Zhou, Shuwang Shi, Xinyang Chen, Tao Zhang, Jiong Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences Jinan250014 China School of Computer Electronics and Information Guangxi University Nanning China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The Circle of Willis, a critical arterial ring structure located at the base of the brain, is essential for maintaining cerebral blood supply and is closely associated with various cerebrovascular diseases. Accurate segmentation of the Circle of Willis is crucial for assessing its structural integrity and functional status in clinical diagnosis and neurosurgical planning. This study developed a novel deep learning model based on Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) technology. The model significantly improves the continuity of vascular segmentation and the recognition of fine vascular structures through a vascular skeleton-assisted segmentation mechanism and a multi-scale integrated fusion module. The innovatively introduced Multi-task Feature Attention Fusion Module integrates the semantic information of the vascular skeleton, effectively optimizing segmentation accuracy and structural integrity. Compared to existing techniques, our method has achieved promising results. © 2025 SPIE.

关键词： Musculoskeletal system

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AI Driven Transformation: Updated Technologies in 6G Communication System 1

AI Driven Transformation: Updated Technologies in 6G Communi...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Mp, Sunil Das, Neeraj Department of Electronics and Communication Engineering Karnataka India School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The rollout of 5G systems has prompted standards bodies to begin the 6G system design process. The increased complexity of 6G networks will increase the time, money, and effort needed for implementation and administration. Conversely, in order to lower operating expenditures (OPEX), mobile network providers want these networks to be smart, self-organizing, and economical. A subfield of AI known as artificial intelligence (AI) offers practical answers to these problems and has the potential to radically alter the trajectory of wireless network technology in the future. Also rapidly reviewing the most important issues, with a focus on cellular networks' physical layer of operation and link layer where ML may provide substantial benefits by using a few case study examples. By analyzing the standardization efforts around ML in wireless networks and what the future holds for the preparedness of standards organizations to deal with these developments. Finally, by outlining some of the most pressing problems with ML in wireless tech and suggesting ways forward that wireless networks using 6G may address these concerns. © 2025 IEEE.

关键词： 5G mobile communication systems

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Dynamic Real-Time Talent Team Discovery Based on Complex network Analysis 24

Dynamic Real-Time Talent Team Discovery Based on Complex Net...

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5th international conference on computer science and Management technology, ICCSMT 2024

作者： Pan, Yu Yao, Feng Li, Wei Wang, Pei School of Systems Engineering National University of Defense Technology Hunan Changsha China Department of Information Communication Army Academy of Armored Forces Beijing China

ISBN: (纸本)9798400709999

The real-time and precise identification of talent teams can offer essential support for talent development and augmentation. It plays a key role in efficiently and precisely assembling professional talent and constitutes a significant research focus within human resources. This paper utilizes complex network concepts and artificial intelligence methods to analyze dynamically and mine talent teams in real-time within evolving environments. It proposes a method of dynamic real-time talent team discovery that considers the academic relationships and research direction information of talent simultaneously. Firstly, a unified graph modeling method is proposed to fuse academic relationships and research direction information. Then, we apply non-negative matrix factorization to obtain talent team partition at each time snapshot. Additionally, we employ the evolutionary clustering framework to smooth talent evolution, leveraging historical evolution information to achieve accurate talent team discovery at a continuous time. Extensive experiments demonstrate that the proposed method achieves excellent results on real datasets. The method has proven effective in practical applications, enabling accurate and timely talent team discovery. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Non-negative matrix factorization

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The Improved Unet Semantic Segmentation network for Remote Sensing Images

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第4期52卷 1187-1195页

作者： Zhu, Hang Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

With the development of artificial intelligence, deep learning has been increasingly used to achieve automatic detection of geographic information, replacing manual interpretation and improving efficiency. However, remote sensing images themselves have the issue of slight inter-class variance and significant intra-class variance, making it challenging to extract valuable information. Additionally, the increasing resolution and size of remote sensing images in recent years have introduced more complexity in the types of information, further increasing the difficulty of extracting valuable data. This paper proposes an improved Unet semantic segmentation network (referred to as RAUnet). First, in the encoder, continuous convolutional blocks are enhanced to extract features. At the same time, the EMAM multi-scale attention module is employed for cross-channel learning, capturing information from different feature channels of the target and using the surrounding feature information to assist in distinguishing target information. To capture multi-directional long-range dependencies, the Lo2 module is used for long-range modeling, which captures not only local contextual information but also long-range dependencies. In the decoder, a Dysample upsampling module is used to restore feature details, and in the skip connection layer, features are added for feature fusion. Experimental results show that compared to mainstream models, the proposed method achieves superior segmentation results on the Potsdam and Vihingen datasets. © (2025), (international Association of Engineers). All rights reserved.

关键词： Semantic Segmentation

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Multi-objective Optimization of Antenna Based on Improved WOA-BP Neural network 5th

Multi-objective Optimization of Antenna Based on Improved WO...

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5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhuang, Huawei Sun, Fangzhen Li, Gaoqi Liu, Jianzhao Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

During the design of multi-objective antennas, optimizing efficiency and computing expenses are key considerations. In this essay, a rapid antenna optimization strategy that combines the BP neural network with the non-dominated sorting genetic algorithm II is proposed. Firstly, to enhance the global optimization capability, we improve the whale optimization algorithm by improving population initialization, incorporating a nonlinear convergence factor, and introducing adaptive inertia weights. Then, using the IWOA, we optimize the BPNN's initial weights and thresholds to improve model accuracy. Finally, we present a Pareto-optimal three-band antenna optimization, demonstrating that the proposed method can effectively optimize antenna performance while significantly reducing computational cost.

关键词： Antenna optimization BP neural network Improved whale optimization algorithm Pareto-optimal

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Fca-ProRes2Net Speaker Recognition Based on Fusion Feature Dimensionality Reduction

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第4期52卷 1038-1050页

作者： Hu, Zhangfang Wang, Yi Yuan, Yuan Key Laboratory of Optical Information Sensing and Technology School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

With the advancement of the era, speaker recognition technology plays a pivotal role in applications such as remote telephone identity verification and anti-telecom fraud. However, traditional identification methods utilizing single feature parameters often result in inadequate representation and information loss. Additionally, with the introduction of convolutional neural networks, many approaches to adopt deeper and wider network structures to enhance recognition effectiveness, leading to a decline in network performance. In response to this issue, this paper proposes a speaker recognition network model, Fca-ProRes2Net, based on the fusion of feature parameter dimension reduction. Firstly, a novel hybrid parameter is formed by integrating traditional Mel Frequency Cepstral Coefficients (MFCC) with more robust Gammatone frequency cepstral coefficients (GFCC), capturing both mid-to-high frequency and dynamic/static features. Furthermore, 2DPCA is employed to reduce and integrate the feature matrix, addressing the issue of information redundancy caused by high-dimensional feature parameters and thereby enhancing the representation capability of the parameters. Secondly, the Res2Net network is incorporated into the recognition model, utilizing its fully connected form, known as ProRes2Net, to effectively expand the receptive field combination without significantly increasing parameters, thus enlarging the model’s acceptance domain. Lastly, Frequency Channel Attention networks (Fca-Net) are integrated into this model to redistribute weights among feature parameter channels, enhancing the model’s recognition ability. Experimental results demonstrate a stable performance improvement of this method on the complex VoxCeleb dataset, particularly evident when data is abundant and tasks are complex, effectively enhancing the accuracy and robustness of the speaker recognition model. © (2025), (international Association of Engineers). All rights reserved.

关键词： Dimensionality reduction

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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-network on 6G-enabled Mobile Edge networks 17

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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17th international conference on COMmunication Systems and networkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

The advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. The proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. The predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. This research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 IEEE.

关键词： Mobile edge computing

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Deformable Attention-Based Edge-Aware network for Single Image Super-Resolution

Deformable Attention-Based Edge-Aware Network for Single Ima...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Ju Zhong, Baojiang Ma, Kai-Kuang School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368741

Accurately reconstructing object edges is a key challenge in single image super-resolution (SISR), as it greatly influences our visual perception of image quality. To address this fundamental issue, we propose a novel SISR approach named the deformable attention-based edge-aware (DAE) network. The DAE network features a deformable attention block that dynamically adjusts attention weights to align with edge structures, thereby improving edge awareness and reconstruction quality. Moreover, our network incorporates a multi-patterns window block that captures fine-grained edge details and enhances information flow across network layers. This combination results in visually superior SISR outputs. Extensive experiments and comparisons with the state-of-the-art methods demonstrate that our DAE network excels in both quantitative metrics and visual quality. © 2025 IEEE.

关键词： deformable attention edge-aware single image super-resolution window attention

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Sentiment Analysis of Weibo Comments Based on Bi_directional Long Short-Term Memory network 24

Sentiment Analysis of Weibo Comments Based on Bi_directional...

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3rd international conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Li, Lianxiong School of Computer Science & Artifical Intelligence Lanzhou Institute of Technology Lanzhou Institute of Technology Gansu Lanzhou China

ISBN: (纸本)9798400718120

In the problem of sentiment analysis in Weibo comments, contextual information has a significant impact on sentiment analysis. If contextual information cannot be considered, the accuracy of sentiment analysis will be low. The paper uses a Bi_directional Long Short-Term Memory Veural network(BiLSTM) model for sentiment analysis, considering the influence of contextual information to improve the accuracy of sentiment analysis in Weibo comments. To fully consider the impact of facial expression information on emotional orientation in Weibo comments, in the data preprocessing process, facial expression information is extracted from Weibo to retain it for later emotional judgment;Secondly, using CBOW model of Word2Vec to convert word vectors, Weibo comments can be represented in vector form;Finally, sentiment analysis was conducted using the BiLSTM network model to determine the emotional tendencies of Weibo comments. Compared with RNN and LSTM network models, this method has improved accuracy, recall, and F1 value. The experimental results indicate that the method used to implement sentiment analysis has significant practical value in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Long short-term memory

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A Hybrid Deep Learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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