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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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Hybrid CNN and Vision Transformer-Based Multi-Factor Authentication for Enhanced Security in Online Examination Systems 4

Hybrid CNN and Vision Transformer-Based Multi-Factor Authent...

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4th International conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Reddy, Vallem Ranadheer Lingam, Gourishetty Shankar Ilanna, Peesala Kumar, Amgoth Ashok Chaitanya Deemed to be University Department of Computer Science and Engineering Kishanpur Telangana Hanamkonda506001 India Vaagdevi Engineering College Department of Computer Science and Engineering Bollikunta Telangana Warangal506005 India

ISBN: (纸本)9798331529635

With the rapid growth of online examination platforms, maintaining high levels of security, integrity, and user authentication is paramount. While existing methods utilize traditional security measures, the integration of advanced deep learning techniques can further improve the robustness of these systems. In this paper, we proposed a hybrid model that combines Convolutional Neural networks (CNN) and Vision Transformers (ViT) for facial recognition in a multi-factor authentication system for online examinations. The CNN model captures local feature patterns, while the ViT captures the global relationships within the image, by combining these architectures can provide highly effective method for facial recognition tasks. The proposed model use both CNN and ViT in feature extraction and provides robustness. From the experiment results is observed that the proposed model achieves an accuracy of 98%, outperforming conventional methods in the domain of online examination security. © 2024 ieee.

关键词： Convolutional neural networks

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Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning 100

Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep R...

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100th ieee Vehicular Technology conference, VTC 2024-Fall

作者： Fasihi, Mohammad Reza Mark, Brian L. George Mason University Dept. of Electrical and Computer Engineering Wireless Cyber Center FairfaxVA United States

ISBN: (纸本)9798331517786

Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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MVGCL: Multi-View Graph Contrastive Learning for Service Recommendation

MVGCL: Multi-View Graph Contrastive Learning for Service Rec...

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2023 ieee International conference on Web Services, ICWS 2023

作者： Zou, Guobing Lin, Shiyi Hu, Shengxiang Gan, Yanglan Zhang, Bofeng Chen, Yixin Shanghai University School of Computer Engineering and Science Shanghai China Donghua University School of Computer Science and Technology Shanghai China Shanghai Polytechnic University School of Computer and Information Engineering Shanghai China Washington University in St. Louis Department of Computer Science and Engineering MO United States

ISBN: (纸本)9798350304855

In service recommender system, graph neural networks (GNNs) perform message passing through diffusion mechanism based on user-service relationship graph. However, existing GNN-based service recommendation models suffer from two limitations: 1 message passing is only carried out at firstorder neighbors, as higher-order may cause over-smoothing phenomenon, confining feature propagation in GNNs;and 2 due to sparse and noisy interactions, the distribution of embedding vectors is nonuniform in the latent space, resulting in unsatisfactory performance for downstream applications. To this end, we propose a fixed global graph diffusion view that is independent of the original user-service observed local view to form a multi-view learning by building contrastive learning (CL) relationship, named as Multi-View Graph Contrastive Learning (MVGCL). Specifically, it enhances the capability of message passing through constructed local and global multi-view graphs, and alleviates the sparse and noisy influences by performing intra-CL within local/global view and inter-CL between multi-view to obtain a more uniform distribution of user and service node representations. Extensive experiments are conducted on three benchmark datasets within different scales, and the results demonstrate that our proposed MVGCL can remarkably outperforms state-of-the-art competing baselines on various evaluation metrics. © 2023 ieee.

关键词： Collaborative filtering

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Hate Speech Detection Using Enhanced Recurrent Neural Network Based on Elephant Herding Optimization on Social Media 3

Hate Speech Detection Using Enhanced Recurrent Neural Networ...

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3rd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Senthilkumar, K. Sathiyabama, S. Ayyamuthukumar, D. Thiruvalluvar Government Arts College Department of Computer Science Namakkal Rasipuram India Department of Artificial Intelligence and Data Science Muthayammal College of Engineering Rasipuram India

ISBN: (纸本)9798350369083

In the past ten years, there has been a rise in nasty behaviors on social media due to the increased use of these platforms. One of the most offensive of these behaviors is hate speech, so users must safeguard themselves against it on social media. This research presents an approach to detect hate speech from social media using an optimized Elman recurrent neural network (ERNN) based on the hybrid local search algorithm (LSA) and elephant herding optimization (EHO) algorithm called LSAEHO. The LSA-EHO algorithm is used to LSA to obtain the optimal solution of EHO, enhance the convergence rate, and avoid the local optima problem of EHO. The LSA-EHO is used to find appropriate hyperparameters for ERNN to enhance the convergence rate and accuracy. The experimental results confirmed that the developed hate speech prediction-based optimized ERNN produced higher prediction accuracy and a faster convergence rate. © 2024 ieee.

关键词： Elman neural networks

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Enhanced Gray Wolf Optimizer For Exploring Social Network Communities 3

Enhanced Gray Wolf Optimizer For Exploring Social Network Co...

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3rd International conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Khosravi, Arash Azarnik, Ahmad Kalhor, Mohammad Ghasemzadeh, Maliheh Mahallat Institute of Higher Education Faculty of Engineering Mahallat Iran Shahab Danesh University Department of Computer Engineering Qom Iran Islamic Azad University Department of Engineering Karaj Iran

ISBN: (纸本)9798350381580

The identification of communities within social networks holds paramount importance in scientific investigations and social network analyses. Communities refer to compact clusters of nodes that exhibit stronger interconnections with each other rather than with nodes outside their community. By accurately detecting and understanding these communities, we can achieve profound insights into the structure and functionality of social networks. In this research, we introduce an innovative and improved meta-heuristic algorithm specifically designed for detecting community in social networks. Leveraging the powerful gray wolf algorithm as its foundation, our proposed algorithm employs a combination of mutation, combination, and local search operators to significantly enhance its performance. Moreover, to further refine the outcomes, we have integrated the label propagation algorithm into our approach. To measure the efficacy of our proposed algorithm, extensive evaluations were conducted on various social network datasets. The findings consistently validate our algorithm's ability to converge towards optimal results, thanks to its exceptional accuracy and precision. By introducing this advanced meta-heuristic algorithm, we make contributions to the field of social network analysis by providing a robust and efficient solution for community detection. Our algorithm empowers researchers and analysts to acquire a more profound comprehension of social network structures and functions, thereby facilitating improved decision-making and problem-solving. © 2024 ieee.

关键词： Decision making

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Generative Neural Framework for Micro-Vessels Classification 46

Generative Neural Framework for Micro-Vessels Classification

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46th Annual International conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Khan, Muhammad Zubair Gajendran, Mohan Kumar University of Central Missouri Department of Computer Science & Cybersecurity Warrensburg United States University of Missouri Department of Civil and Mechanical Engineering Kansas City United States

ISBN: (纸本)9798350371499

The morphological abnormalities in the retinal blood vessel have a close association with cerebrovascular, cardio-vascular, and systemic diseases. It makes the retinal artery/vein (A/V) classification salient for clinical decision-making. The existing methods find it challenging to correctly classify A/V with non-uniform brightness and vessel thickness, especially at the bifurcation and endpoints. To avoid these problems and increase precision, AV-Net is proposed. It uses the context information and performs data fusion to improve A/V classification. Specifically, the AV-Net offers a module that fuses local and global vessel information for creating a weight map to constrain the A/V features. It helps suppress the background-prone features and improve region extraction at the bifurcation and endpoints. In addition, to improve model robustness, the AV-Net uses a multiscale-feature module that captures coarse and fine details. © 2024 ieee.

关键词： Generative adversarial networks

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Anomaly Detection in Wireless Body Area networks using Generative Adversarial networks

Anomaly Detection in Wireless Body Area Networks using Gener...

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2024 ieee International conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Rao, Vujjini Ashrith Rao, Ravindra Hota, Chittaranjan Department of EEE BITS Pilani Hyderabad Campus Hyderabad India Department of Mathematics BITS Pilani Hyderabad Campus Hyderabad India Department of Computer Science BITS Pilani Hyderabad Campus Hyderabad India

ISBN: (纸本)9798350353464

The Wireless Body Area Network (WBAN) seamlessly interconnects wearable devices on the human body, facilitating the monitoring of various physiological parameters for better health outcomes. Ensuring the accuracy and security of data transmission is paramount to prevent false alarms and maintain the overall reliability of WBANs. In response to these challenges, we propose a cutting-edge solution, the WBAN-GAN model. This innovative approach integrates a Generative Adversarial Network (GAN) to assist in the detection of anomalies within WBANs. We have employed a convolutional neural network with a lookback in the data to design our generator and discriminator to explore the spatiotemporal correlations in detecting anomalies. In this work, we fully utilize the outputs of both the generator and discriminator in identifying anomalies by calculating the anomaly score using reconstruction error and discriminator output. To the best of our knowledge, very little research has been done using GANs for anomaly detection in WBANs. To validate the efficacy of our WBAN-GAN model, we conducted extensive testing using the MIMIC dataset [11]. Comparative analysis with a CNNAutoencoder-based anomaly detection model demonstrates the superior performance of our proposed approach. Our results show an accuracy of 97% in detecting the anomalies. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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Explainable Fetal Ultrasound Classification with CNN and MLP Models 1

Explainable Fetal Ultrasound Classification with CNN and MLP...

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1st International conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Venkatareddy, Dodda Reddy, K.V. Narasimha Sowmya, Yendluri Madhavi, Yarlagadda Asmi, Shaik Chand Moturi, Sireesha Narasaraopeta Engineering College Dept of CSE Palanadu Narasaraopeta India

ISBN: (纸本)9798350376517

Artificial Intelligence has greatly influenced healthcare, most particularly in medical imaging. This paper represents a review in large form that classifies fetal ultrasound images with the use of convolutional neural networks and multi-Layer Perceptrons. While CNN is very good at spatial feature extraction in image classification, their lack of interpretability presents challenges toward applications in health. In this regard, we include methods of Explainable AI (XAI), more precisely local Interpre table Model-Agnostic Explanations (LIME), for giving more transparency and confidence in the decision-making process of such models. The research here utilizes 12,400 fetal ultrasound images, which were classified under six anatomical structures. The CNN and MLP models showed very promising classification performances of 93.24% and 91.17%, respectively. LIME was implemented to interpret model predictions and to more clearly identify factors contributing to the classification. The results also show that explainability enhances not only trust in AI-based diagnostics but also model reliability in clinical settings. © 2024 ieee.

关键词： Convolutional neural networks

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RTS-DETR: Efficient Real-Time DETR for Small Object Detection

RTS-DETR: Efficient Real-Time DETR for Small Object Detectio...

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2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Wenqiang Li, Aimin Li, Zhiyao Kong, Xiaotong Zhang, Yuechen Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

In recent years, object detection models DEtection TRansformer (DETR) series based on Transformer architecture have played a huge role in various fields. However, the DETR series models are not satisfactory in small object detection. Mainly due to the huge amount of calculation of DETR, a lot of feature information will be lost in the feature fusion stage and the low tolerance of small objects to Intersection over Union (IoU). In order to solve the above problems, we propose a near real-time detection model RTS-DETR. In this paper, we revisit Real-Time DEtection TRansformer (RT-DETR), which effectively handles multi-scale features by decoupling intra-scale interaction and cross-scale fusion, but this will lose a lot of positive local information. To this end, we have improved the efficient hybrid encoder. We propose a new positional encoding method that enables the hybrid encoder to more accurately convert the input feature sequence into a high-dimensional representation, and propose a new feature fusion module to enhance the model's ability to capture local features. Furthermore, in order to improve the tolerance of small objects to IoU, we combine Normalized Wasserstein Distance (NWD) with Shape-IoU for the optimization model. This method more accurately takes into account the shape and size of objects, thereby improving detection accuracy. Our model achieves an accuracy of 38.8% (in terms of mAP@0.5) on the widely used VisDrone dataset, which improves the accuracy by 2.5% compared to RT-DETR with ResNet-18 as the backbone network. © 2024 ieee.

关键词： Signal encoding

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Enhancing Apple Quality Identification: A Multi-Layer Visual Feature Fusion Approach with Convolutional Neural networks 2

Enhancing Apple Quality Identification: A Multi-Layer Visual...

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2nd International conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Trivedi, Naresh Kumar Maheshwari, Himani Tiwari, Raj Gaurang Gautam, Vinay Agarwal, Ambuj Kumar Kaswan, Suresh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350371314

This research uses real-world images with complicated disturbance information (the backdrop was identical to the apples' surfaces) to identify and categorize apple quality. This research introduces a new model for the quick and accurate evaluation of apple quality based on convolutional neural networks (CNNs). The proposed model captures detailed, unique, and valuable image features for detection and classification. In contrast to the deep features, complex interpretation needs and large training sample requirements, the handcrafted features used in this work are mainly produced intuitively. A new feature fusion approach called multi-layer visual feature fusion (MLVSF) is created for better classification of apple images. The MLVSF model, which incorporates deep and handcrafted components obtained from bag-of-visual words, local binary pattern variations, and CNN, can improve the discriminatory power of features used for apple quality identification. With its ability to improve CNN and achieve more accurate classification - resulting in a classification accuracy of 98.93% - MLVSF beats other modern approaches when evaluated on apple image datasets. © 2024 ieee.

关键词： Classification (of information)

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