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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10^-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： Anomaly Detection Firmware Attestation Graph Neural Network (GNN) Internet of Things (IoT) Malware Detection Security Swarm Attestation

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InfoGNN: End-to-end deep learning on mesh via graph neural networks

arXiv

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arXiv 2025年

作者： Gao, Ling Shu, Zhenyu Xin, Shiqing School of Information Science and Technology Zhejiang Sci-Tech University Ningbo China School of Computer and Data Engineering NingboTech University Ningbo China School of Computer Science and Technology ShanDong University Qingdao China

3D models are widely used in various industries, and mesh data has become an indispensable part of 3D modeling because of its unique advantages. Mesh data can provide an intuitive and practical expression of rich 3D information. However, its disordered, irregular data structure and complex surface information make it challenging to apply with deep learning models directly. Traditional mesh data processing methods often rely on mesh models with many limitations, such as manifold, which restrict their application scopes in reality and do not fully utilize the advantages of mesh models. This paper proposes a novel end-to-end framework for addressing the challenges associated with deep learning in mesh models centered around graph neural networks (GNN) and is titled InfoGNN. InfoGNN treats the mesh model as a graph, which enables it to handle irregular mesh data efficiently. Moreover, we propose InfoConv and InfoMP modules, which utilize the position information of the points and fully use the static information such as face normals, dihedral angles, and dynamic global feature information to fully use all kinds of data. In addition, InfoGNN is an end-to-end framework, and we simplify the network design to make it more efficient, paving the way for efficient deep learning of complex 3D models. We conducted experiments on several publicly available datasets, and the results show that InfoGNN achieves excellent performance in mesh classification and segmentation tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Wireless Semantic Communication Based on Probability Distribution: An Initial Work

Wireless Semantic Communication Based on Probability Distrib...

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IEEE Conference on Wireless Communications and Networking

作者： Qingxiang Luo Yashuang Guo Aoran Zheng Zhitong Ni F. Richard Yu Victor C.M. Leung School of Computer Science and Technology Beijing Jiaotong University Big Data Technologies Centre University of Technology Sydney Department of Systems and Computer Engineering Carleton University Department of Electrical and Computer Engineering The University of British Columbia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the paper, we consider the general semantic transmission in wireless networks based on probability distribution. Firstly, we extract a multidimensional semantic probability distribution function, independent of any a specific wireless channel model, by using the variational inference technique. Secondly, we propose a new semantic similarity metric for measuring the difference between the received semantics and the expected semantics based on Kullback-Leibler divergence. Then, we formulate the semantic transmission problem as an optimization problem of transmission symbol adjustment with the aim to maximize the semantic similarity. Finally, we develop an optimal semantic transformation and transmission (STT) algorithm to obtain the optimal transmission symbol adjustment decision. This decision makes the closed-form expression of semantic transmission symbol available, which can realize lossless semantic transmission with energy constraint. Simulation results verify the effectiveness and robustness of the proposed STT algorithm.

关键词： Measurement Wireless networks Simulation Semantics Symbols Semantic communication Propagation losses Probability distribution Robustness Optimization

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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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FPGA-Optimized Hardware Accelerator for Fast Fourier Transform and Singular Value Decomposition in AI

arXiv

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arXiv 2025年

作者： Ding, Hong Kang, Chia Chao Xi, SuYang Liu, Zehang Zhang, Xuan Ding, Yi School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Sepang Malaysia School of Computer and Data Science Xiamen University Malaysia Sepang Malaysia

This research introduces an FPGA-based hardware accelerator to optimize the Singular Value Decomposition (SVD) and Fast Fourier transform (FFT) operations in AI models. The proposed design aims to improve processing speed and reduce computational latency. Through experiments, we validate the performance benefits of the hardware accelerator and show how well it handles FFT and SVD operations. With its strong security and durability, the accelerator design achieves significant speedups over software implementations, thanks to its modules for data flow control, watermark embedding, FFT, and SVD. Copyright © 2025, The Authors. All rights reserved.

关键词： Intellectual property core

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Improving Medical Imaging Diagnostics with Deep Convolutional Networks for Early Detection and Treatment

Improving Medical Imaging Diagnostics with Deep Convolutiona...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Krishnakumar, K. Gurrapu, Omprakash Reshma, P. Krishnammal, P. Muthu Khan, Huma Qamar Amane, Ninad Saveetha School of Engineering Chennai India Volvo Trucks North America NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India VIT-AP University Amaravati India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Bms Institute of Technology and Management Department of Ai&ml Bangalore India

ISBN: (纸本)9798331538538

Diagnostic imaging is an important technique which helps in diagnosing several diseases at the initial stage in order to take necessary treatments. Nevertheless, conventional techniques tend to have issues with regard to precision, speed, and quantitatively assessing multidimensional medical information. In this paper, we reveal the potential of deep convolutional networks (DCNs) in the field of medical image diagnostic. It means that, with the help of deep learning algorithms DCNs can learn meaningful features of medical images and improve diagnostic accuracy and speed. The near proposed technique involves the use of deep convolutional network model to analyse medical images for diagnosis of diseases such as cancer, heart diseases and neurological disorders. The obtained experimental results demonstrate that the presented model has the highest accuracy of 96.5% with very high sensitivity and specificity coefficients compared to other methods. Further, the interpretability of the proposed DCN model and different aspects of it are discussed to support clinical validation. Through this research, the effectiveness of deep learning in revolutionizing the diagnosis for medical imaging diagnostics while early disease detection and treatment is established. © 2025 IEEE.

关键词： Neuroimaging

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Real-Time Speech Emotion Recognition Using Hybrid Transformer Networks for Mental Health Monitoring

Real-Time Speech Emotion Recognition Using Hybrid Transforme...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Durga, P. Janani Basha, C. H. Hussaian Christopher, Nelson Kennedy Babu Senthilkumar, S. Kumar, N. Rathina Kavitha, M. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Sr University Department of Electrical and Electronics Engineering Telangana Hanumakonda50637 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore641107 India

ISBN: (纸本)9798331509828

This study proposes a hybrid method to multi dialect speech recognition using the transfer learning with transformer-based architecture. The primary objective of this method is to enhance the automatic speech recognition (ASR) across the different dialectal variations of specific language. The transfer learning methods are utilized to modify the base model that was trained on high resource language to manage the multiple dialects with limited labelled data for each dialect. This is achieved by using pre trained transformer methods such as Wav2Vec2.0 and Whisper. An approach which employs dialect specific embeddings is presented. It enables the method to capture data and differentiate between prosodic elements and phonetic that are distinctive to a variety of dialects. data augmentation technique are utilized in order to overcome the limited availability of labelled data for low resource dialects. These methods are utilized to increase the efficiency of training set and enhance the robustness of the method. Evaluation of the method is carried out using multi dialect speech datasets which include Arabic MGB-3 and common voice. These datasets represent a variety of dialects that are spoken in various linguistic regions. The result of this study indicates that the transformer-based method is effective in managing difficulties of dialectal variances while the reliability is reduced on different datasets. Different real-world applications are provided by the proposed method for improving voice enabled applications. Furthermore, this method leads the way for the development of ASR method that are more accurate and independent in locations that have a significant linguistic variation. © 2025 IEEE.

关键词： Transfer learning

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A Dual Machine Learning Approach for Differentiating Spindle Cell and Giant Cell Carcinomas and Assessing Severity Levels 8

A Dual Machine Learning Approach for Differentiating Spindle...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Rajan, R. Reddy, V.Naresh Kumar Veeresh, U. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chengalpattu India Marri Laxman Institute of Technology and Management Department of Computer Science and Engineering Dundigal Hyderabad India VNR Vignan Jyoti Institute of Engineering & Technology Department of CSEAIML Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

Lung cancer is a major health challenge throughout the world, there is an urgent need for innovative method for detection and diagnosis. This paper introduced a dual machine learning framework to improve the diagnosis and treatment guidelines. Bidirectional Encoder Representations from Transformers is utilized to analysis of patient history, obtained accuracy of 92.5%, showing deep context understanding of Medicals chronicle. And also Convolutional Neural Network (CNN) was utilized to differentiate between Gait cell carcinoma and spindle cell carcinoma and achieved the accuracy of 94.2% and 93.7% respectively. High performance metrics like AUC, ROC ranging from 97% for both carcinoma types. These findings demonstrated a more precise method for lung cancer diagnosis and suitable treatment planning. © 2025 IEEE.

关键词： Diseases

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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