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Deep Learning Models for Performance Improved 6G network 1

Deep Learning Models for Performance Improved 6G Network

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

作者： Rajendran, Naveen Kumar Singh, Daljeet Pal Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka Ramnagar District India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The emergence of sixth generation (6G) mobile networks is poised to transform global communication by providing unmatched speed, dependability, and sophisticated network management. This study examines the essential function of AI and machine learning (ML) is in unlocking the whole potential within 6G networks, emphasizing current developments, primary difficulties, and future directions. Machine learning methods are essential for tackling intricate challenges like as distributing resources, energy efficiency, & latency reduction in 6G networks, enabling novel applications such autonomous systems including holographic communication. Notwithstanding the encouraging advancements, obstacles such as the development of scalable machine learning models, the assurance of network security, and the integration of AI-driven solutions into current infrastructures remain. The paper classifies machine learning applications in 6G according to various network topologies and emphasizes unresolved research challenges, especially in improving the trade-off between computational complexity and performance. This study establishes a foundation for future research, highlighting the significance of machine learning in influencing the future development of wireless networks. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Enhancing Peer-to-Peer network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st international conference on Intelligent Systems and Computational networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 IEEE.

关键词： Peer to peer networks

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A novel large kernel two-branch semantic segmentation network 24

A novel large kernel two-branch semantic segmentation networ...

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4th international conference on Signal Processing and Communication technology, SPCT 2024

作者： Zhang, Xinrui Fu, Peng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710636

For semantic branching in a two-branch network structure, it is crucial to quickly improve the feeling field, in addition, the feature fusion interaction of two-branching needs to take into account the structural and multi-scale information fusion of features under different feeling fields. In this paper, in order to cope with this challenge, we propose to accumulate the receptive field in dense convolution using separable large kernel convolution through an adaptive semantic extraction module, and design a selectable large kernel attention module for the problem of mismatch of receptive field of feature information. Finally, this paper designs a multiscale feature extraction module that fuses local and global information, and uses large kernel dense separable convolution in the module with the ability to extract high-dimensional semantics serially and capture multiscale in parallel. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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An Analysis of Worm Dynamics with Honeypot Feedback in Scale-Free networks

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第1期52卷 90-102页

作者： Xue, Jiali Ren, Jianguo Feng, Liping Wang, Lei College of Computer Science and Technology at Jiangsu Normal University Xuzhou221116 China College of Computer Science and Technology at Jiangsu Normal University Xuzhou221116 China Department of Computer Science at Xinzhou Teacher University Xinzhou034000 China Hydrology Bureau of Yishu-Si Water Resources Management Bureau Xuzhou221116 China

Scale-free networks’ complex topology makes them an ideal target for worm attacks. Research has demonstrated that honeypot technology holds enormous promise for resolving worm-related problems. In this paper, we propose a novel propagation model based on honeypot feedback, namely H-SAIR. After capturing the virus successfully, the honeypot collects assault behaviors, analyzes attack paths, generates immunity information, and provides immune feedback to adjacent nodes to promote active protection. First, we introduce honeypot node H and affected node A based on the SIR model. Then, we use dynamics analysis to compute the model's propagation threshold R0, proving the global stability of the disease-free equilibrium P0 and the existence of the endemic equilibrium P*. Finally, the experimental results indicate that when R0 is less than 1, the H-SAIR model effectively constrains the spread of worms. With the increase in feedback rate, the containment effect will become stronger. Meanwhile, we can effectively control the spread of worms by strategically placing enough honeypots at hub sites, thereby maintaining network security. © (2025), (international Association of Engineers). All rights reserved.

关键词： feedback mechanisms honeypots scale-free networks stability analysis worms

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The Impact of TV White Space (TVWS) and 5G networks on Bimodal Learning for Master's Programs in Engineering science at the Higher Institute of technology, University of Bangui

The Impact of TV White Space (TVWS) and 5G Networks on Bimod...

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2025 IEEE international conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Ndassimba, Edgard Zoh-Ponguele, Nathan Kossingou, Ghislain Mervyl Saint-Juste Faculty of Science University of Bangui Department of Computer Engineering Bangui Central African Republic

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

In this study, we presente digital solution for training master's students in engineering sciences, using a bimodal learning approach at the Higher Institute of technology of Bangui's University. This proposal addresses the lack of master's-level programs in DevOps and network Telecommunications, as well as the insufficient expertise of local educators in Information and Communication technology (ICT). It also aims to strengthen the instruction of science, technology, engineering, and mathematics (STEM). The solution is based on remote learning platforms hosted in the public cloud, engaging students and teachers in a bimodal teaching model that delivers simultaneous instruction to both inperson and remote students via a high-speed TV White Space (TVWS) and 5G network. The expected impact is the training of competent engineers capable of successfully carrying out projects in the domain of information and communication technology, thereby contributing to the development of the Central African Republic. © 2025 IEEE.

关键词： Students

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Research on Target Detection Optimization in Intelligent Solid Waste Classification Based on YOLOv8 Algorithm 24

Research on Target Detection Optimization in Intelligent Sol...

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

作者： Wu, Zixuan Zhan, Xin Shen, Junwei Fu, Zuming Department of Mechanical and Electronic Engineering Zhuhai University of Science and Technology Guangdong Zhuhai China Department of Mechanical and Electronic Engineering Guangdong University of Technology Guangdong Zhuhai China

ISBN: (纸本)9798400709999

Driven by globalization and urbanization, municipal solid waste has *** the front-end link of resource recovery and environmental protection, garbage classification is particularly urgent in order to improve its automation and intelligence *** on the YOLOv8 algorithm, this study proposes a series of optimization strategies such as using GhostBottleneck lightweight module, fusion improvement of CBAM attention mechanism, and using WIoU loss function for target detection tasks in intelligent garbage classification systems, aiming at improving detection accuracy and system *** study considered complex issues such as lighting conditions during garbage classification, the integrity of the garbage itself, the severity of the environment, and the difficulty of *** used multiple shared garbage image data sets on the network to test the garbage classification optimization *** average accuracy rate was significantly improved, reaching 96.12%. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Algorithm optimization

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MPGA-YOLOv8: A Leaf Disease Detection network Combining Multi-Scale Progressive Feature network and Global Attention

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第2期52卷 436-446页

作者： Xu, Jiacheng Yang, Yonghui Chu, Maoxiang Student of School of Electronic Information University of Science and Technology Liaoning Anshan114051 China School of Control Science and Engineering University of Science and Technology Liaoning Anshan114051 China

Considering the diversity of natural conditions, including fog, low light, and strong light, as well as the impact of various diseases on leaves, we propose an improved apple leaf disease detection method based on the YOLOv8n model. This method first uses a Multi-Scale Progressive Feature network(MSPN) as the neck network to integrate multiple morphological lesions and enhance the model’s information integration capability. Then, a Global Self Attention Module (GSAM) is applied at the end of the backbone to help the model capture spatial relationships in the image and reduce interference from extremely complex background conditions. Next, we replace the convolution blocks in the backbone with Receptive Field-Focused Convolution Block (RFFconv) to effectively improve the model’s recognition accuracy through shared receptive field weights. Finally, we add a small object detection layer for small lesions to enhance the model’s generalization performance. Experimental results show that the proposed MPGA-YOLOv8 model effectively detects six types of apple leaf diseases in complex backgrounds, including healthy samples, with an average mAP accuracy of 74.3%. Compared to classic models like SSD, Faster RCNN, YOLOv3 tiny, YOLOv5, and YOLOv6, the mAP performance improves by 20.9%, 38.4%, 9.8%, 10.0%, and 14.3%, respectively. This model enables rapid and accurate detection and recognition of apple leaf diseases, providing viable technical support and solutions for disease prevention and control. © (2025), (international Association of Engineers). All rights reserved.

关键词： Disease control

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A Deep Multi-Task Learning network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-Than-ideal performance and restricted applicability. This research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-The-Art methods. This demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 The Author(s).

关键词： Convolutional neural networks

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Relational Representation Augmented Graph Attention network for Knowledge Graph Completion 24

Relational Representation Augmented Graph Attention Network ...

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8th international conference on computer science and Artificial Intelligence, CSAI 2024

作者： Aili, Elyar Yilahun, Hankiz Imam, Seyyare Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China School of National Security Studies Xinjiang University Urumqi China

ISBN: (纸本)9798400718182

Knowledge Graph Completion (KGC) is a popular topic in knowledge graph construction and related applications, aiming to complete the structure of knowledge graphs by predicting missing entities or relations and mining unknown facts in the knowledge graph. In the KGC task, Graph Neural network (GNN)-based methods have achieved remarkable results due to their advantage of effectively capturing complex relations among entities and generating more accurate and rich entity representations by aggregating information from neighbouring nodes. These methods mainly focus on the representation of entities, and the representation of relations is obtained using simple dimensional transformations or initial embeddings. This treatment ignores the diversity and complex semantics of relations and restricts the efficiency of the model in utilizing relational information in the reasoning process. In this work, we propose the Relational Representation Augmented Graph Attention network (RRA-GAT), which effectively identifies and weights neighbouring relations that actually contribute to the target relation by filtering out irrelevant information through an attention function based on the information and spatial domain. Furthermore, we capture complex patterns and features in the relational embedding by means of a feed-forward network consisting of a series of linear transformations and nonlinear activation functions. Experiments demonstrate the very advanced performance of RRA-GAT on the link prediction task on standard datasets FB15k-237 and WN18RR (e.g., improved the MRR metric on the WN18RR dataset by 7.8% relative improvement). © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Neural network-Driven Base Station Selection in Next-Generation Cellular networks 17

Neural Network-Driven Base Station Selection in Next-Generat...

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

作者： Harrithha, B. Kotagi, Vijeth J. Thakur, Rahul Indian Institute of Technology Dharwad Department of Computer Science and Engineering Karnataka Dharwad India Indian Institute of Technology Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798331531195

With the exponential increase in data consumption from activities like streaming, gaming, and IoT applications, existing network infrastructure is under significant strain. To address the growing demand for higher data rates and better coverage, increasing base station density and exploring data offloading in unlicensed spectrum have become essential. While higher base station density reduces the distance between devices and network nodes, enhancing signal strength and data throughput, it also complicates the process of base station selection for users, particularly for those at the cell edge. Thus, ever-increasing demand for high-speed, low-latency communication services in 5G and 5G advanced New Radio (NR) unlicensed (NR-U) networks necessitates intelligent and dynamic approaches for user admission control and resource *** paper presents a novel solution employing neural network technology to address these critical challenges. We first propose an efficient algorithm for admission control and resource allocation. Upon generation of training data using the above algorithm, we propose a neural network-based framework for cellular networks in unlicensed spectrum such as LTE/LTE-U and 5G/NR-U networks. © 2025 IEEE.

关键词： Base stations

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