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Graph Neural networks for Fraud Detection in E-commerce Transactions 1

Graph Neural Networks for Fraud Detection in E-commerce Tran...

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1st International conference on Computing, Sciences and Communications, ICCSC 2024

作者： Goyal, Gauri Tyagi, Rashi Tyagi, Shiva Ajay Kumar Garg Engineering College Department of Computer Scienceand Engineering Ghaziabad India

ISBN: (纸本)9798350353648

E-commerce platforms face increasing fraud, resulting in significant financial losses, and traditional fraud detection approaches often fail to capture complex patterns. This paper proposes a Graph Neural Network approach to enhance fraud detection by modeling e-commerce data as a graph, where nodes represent consumers, sellers, and transactions, and edges represent interactions. By utilizing a multi-layer Graph Convolutional Network and message-passing algorithms, the GNN captures intricate relationships, uncovering hidden fraud patterns through local and global node interactions. Trained on a large-scale, real-world dataset, this method significantly outperforms traditional approaches like logistic regression and decision trees, demonstrating its superior ability to detect sophisticated fraud in e-commerce environments. © 2024 ieee.

关键词： Graph neural networks

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Accelerating Federated Learning with Adaptive Extra local Updates upon Edge networks 29

Accelerating Federated Learning with Adaptive Extra Local Up...

引用

29th ieee International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Fan, Yitao Ji, Mingtao Qian, Zhuzhong Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350330717

Delayed Gradient Averaging (DGA) has gained massive attention for improving the training efficiency of Federated Learning (FL) at edge networks, by allowing local computation in parallel to communication. However, it faces multiple challenges due to data distribution across heterogeneous edge devices and dynamic network environments. To address these challenges, we present A-DGA, a novel communication learning parallel federated learning algorithm designed for fluctuating, heterogeneous, and high-latency network conditions. Our proposed A-DGA dynamically sets extra local updates based on network status, avoiding inefficient training upon the outdated gradients. Theoretical analysis demonstrates that A-DGA outperforms DGA in terms of convergence rates given the fixed time slot length. We further conduct massive evaluations under various conditions, including different datasets, models, and data distribution. The experimental results show that A-DGA performs the best, compared to the state-of-the-art methods, achieving an acceleration factor of approximately x2 ∼ x4 and x1.2 ∼ x2, compared to FedAvg and DGA, respectively. Besides, our A-DGA also reduces energy consumption by about 40% ∼ 50% compared to DGA. © 2023 ieee.

关键词： Edge networks Federated Learning

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Att-CapsViT: Attention-based CapsNet and Vision Transformer Hybride Network for Handwriting Chinese Characters Recognition 2

Att-CapsViT: Attention-based CapsNet and Vision Transformer ...

引用

2nd International conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Zheng, Xiangling Zhou, Nanxiang Geng, Zibin School of Information Technology & Management University of International Business and Economics Beijing China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350390254

The research on text recognition has long been under the close scrutiny of scholars, especially the recognition of complex Chinese characters, which is highly favored by the academic community. In recent years, handwriting recognition has achieved remarkable results within the traditional framework of deep learning. However, this approach still falls short in capturing the subtle features and spatial hierarchical relationships within Chinese characters. Furthermore, the complexity of Chinese handwritten characters and the diversity of writing styles pose higher challenges to models. To address this, we propose an attention-based CapsN et and vision transformer hybride network for handwriting Chinese characters recognition (Att-CapsViT), which combines the attention mechanism with capsule networks and visual transformer. The Att-CapsViT model integrates the attention mechanism, combining the local detail feature capturing capability of capsule networks (CapsNets) with the global contextual awareness of visual transformer (ViT) to achieve multi-level feature extraction from local to global. This experiment was validated using two open-source datasets, successfully confirming the robust performance of the Att-CapsViT model in the recognition of Chinese handwritten characters, providing a new perspective for the field of complex text recognition. © 2024 ieee.

关键词： Deep learning

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Extremum Seeking and Adaptive Dynamic Programming for Distributed Feedback Optimization 63

Extremum Seeking and Adaptive Dynamic Programming for Distri...

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63rd ieee conference on Decision and Control, CDC 2024

作者： Liu, Tong Krstic, Miroslav Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering BrooklynNY11201 United States University of California Department of Mechanical and Aerospace Engineering San DiegoCA92093-0411 United States

ISBN: (纸本)9798350316339

This paper studies the distributed feedback optimization problem for linear multi-agent systems without precise knowledge of local costs and agent dynamics. The proposed solution is based on a hierarchical approach that uses upper-level coordinators to adjust reference signals toward the global optimum and lower-level controllers to regulate agents' outputs toward the reference signals. In the absence of precise information on local gradients and agent dynamics, an extremum-seeking mechanism is used to enforce a gradient descent optimization strategy, and an adaptive dynamic programming approach is taken to synthesize an internal-model-based optimal tracking controller. The whole procedure relies only on measurements of local costs and input-state data along agents' trajectories. Moreover, under appropriate conditions, the closed-loop signals are bounded and the output of the agents exponentially converges to a small neighborhood of the desired extremum. A numerical example is conducted to validate the efficacy of the proposed method. © 2024 ieee.

关键词： Dynamic programming

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Reptile Search and LLE Based Node localization in WSN-Assisted IoT 4

Reptile Search and LLE Based Node Localization in WSN-Assist...

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4th International conference on Emerging Trends in networks and computer Communications, ETNCC 2024

作者： Singh, Hina Yadav, Preeti Rishiwal, Vinay Jat, Dharm Singh Yadav, Mano Gnagwar, Sushil M.J.P. Rohilkhand University Deptt. of CSIT Bareilly India University of Science and Technology Deptt. of Computer Science Namibia Bareilly College Deptt. of Computer Science Bareilly India

ISBN: (纸本)9798350353266

Wireless Sensor networks (WSNs) are crucial to facilitate the Internet of Things (IoT) ecosystem for continuous data collection and information transmission to ensure the systematic analysis of various applications for decision-making. localizing sensor nodes is essential to determine the physical location of the devices to optimize the network performance. In this article, we proposed an approach for sensor node localization in WSN-assisted IoT networks by commissioning an amalgamation of the Reptile Search Algorithm (RSA) and locally Linear Embedding (LLE) algorithm. RSA adheres to the adaptive behavior of reptiles that efficiently explores the search space to estimate the potential node locations. Later, the LLE algorithm is applied to incorporate high-dimensional sensor node data into a lower-dimensional space. It also facilitates the preservation of local neighborhood relationships by enhancing localization accuracy. Simulation results validate the efficacy of the proposed approach to achieve improved sensor node localization in WSN-assisted IoT environments. Our findings highlight the significant reduction in localization error and computational Complexity for varying sensor nodes and fixed anchor nodes, with improved energy efficiency to underscore the potential of the proposed methodology. The proposed work can be implemented to improve the efficiency in sensor node localization for various real-life applications, viz., smart agriculture, healthcare, and others. © 2024 ieee.

关键词： Sensor nodes

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An Enhanced BERT-CNN-BiLSTM-MH Model for NLP Classification 4

An Enhanced BERT-CNN-BiLSTM-MH Model for NLP Classification

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4th ieee International conference on Data Science and computer Application, ICDSCA 2024

作者： Zhang, Yilin University College London London United Kingdom

ISBN: (纸本)9798350368239

To address the shortcomings of the BERT-BiLSTM model in processing certain features, this study proposed an improved BERT-CNN-BiLSTM-MH model for topic classification tasks in specific scenarios. Specifically, to overcome the limitations in capturing local features, we introduced a CNN module to effectively extract key local features. Additionally, to tackle the problem of insufficient global feature representation in the BERT-CNN-BiLSTM model, we integrated a multi-head (MH) attention mechanism, which improved the model’s capacity to capture long-range dependencies and diverse contextual information. This enhanced architecture aimed to provide better performance in topic classification tasks by addressing these key limitations. The proposed RoBERTa-CNN-BiLSTM-MH model was validated on CLUE TNEWS dataset, demonstrating strong topic classification capabilities. The model achieved a precision of 0.9102, a recall of 0.9207, an accuracy of 0.9092, and an F1 score of 0.9124, confirming its effectiveness. The BERT-CNN-BiLSTM-MH model significantly outperformed others. It showed an accuracy improvement of 9.41% over BERT-GRU-SE, 8.74% over RoBERTa CNN LSTM, 8.75% over BERT-CNN-GRU-SE, 6.66% over BERT LSTM Transformer, and 2.72% over BERT-CNN-BiLSTM-SE. The results validate the superiority of the proposed model over existing approaches. The RoBERTa-CNN-BiLSTM-MH model meets the requirements for accurate and efficient topic classification, demonstrating promising application prospects in real-world scenarios. ©2024 ieee.

关键词： Convolutional neural networks

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LG-GAT: local-Global Graph Attention Network for EEG Emotion Recognition

LG-GAT: Local-Global Graph Attention Network for EEG Emotion...

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2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Yurui Liu, Tianyue Wu, Qingfeng Xiamen University Xiamen China

ISBN: (纸本)9798350386226

Emotion recognition has become a focal research area in brain-computer interfaces, aiming to leverage artificial intelligence for diagnosing and treating clinical disorders. Neuropsychological studies indicate a close relationship between the activities of different brain functional areas and emotions. Consequently, we introduce a local-Global Graph Attention Network (LG-GAT), a model inspired by neurology that learns the intra-regional and inter-regional activities of the brain by studying the local-global graph representations of electroencephalography (EEG). The LG-GAT mainly consists of two modules, namely the temporal convolution layer and kernel-level attention fusion (KAF) module, the feature graph building (LTG) module. The KAF module is to learn specific task-related features from EEG signals and to fuse the features learned from different kernels. The LTG module utilizes graph attention networks combined with local and global graphical representations with neurophysiological significance to model complex relationships within and between functional areas of the brain. To demonstrate LG-GAT's efficacy, it was evaluated on three public datasets: DEAP, DREAMER, and SEED. Furthermore, LG-GAT's performance was compared with state-of-the-art (SOTA) methods. The results indicate that LG-GAT surpasses these methods, validating the enhancement of emotion recognition performance through the integration of neuroscientific prior knowledge into neural network design. © 2024 ieee.

关键词： Electroencephalography

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A Multi-agent Reinforcement Learning based Offloading and Resource Allocation for Vehicle Edge Computing 16

A Multi-agent Reinforcement Learning based Offloading and Re...

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16th International conference on Communication Software and networks, ICCSN 2024

作者： Yang, Jiazhi Yang, Xinjie Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Ningbo Arrowdeep Internet Technology Co. Ltd. Ningbo China

ISBN: (纸本)9798331506322

This paper investigates the the challenges of resource allocation optimization for computation offloading in vehicular edge networks. It is assumed that multiple moving vehicles select Mobile Edge Computing (MEC) servers on Road Side Units (RSUs) to offload their computation tasks, for which, MEC servers allocate appropriate resources. To achieve the best balance between minimizing latency and resource consumption to ensure system performance, this study utilizes a Multi-Agent Reinforcement Learning (MARL) framework, where each vehicle and MEC server acts as an agent and learns optimal strategies through interactions with the environment. To improve performance, a new Global Heterogeneous MARL (GHMARL) framework is purposely designed based on existing MARL approaches. The proposed framework trains two Critic networks, namely, a global Critic network to ensure service quality and a local Critic network to ensure resource utilization efficiency. Extensive simulation results demonstrate the performance superiority of this approach over benchmark algorithms. © 2024 ieee.

关键词： Mobile Edge Computing Multi-Agent Reinforcement Learning Resource allocation Vehicular edge networks

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

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2nd ieee International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 ieee.

关键词： Graph neural networks

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Adaptive Hierarchical GHSOM with Federated Learning for Context-Aware Anomaly Detection in IoT networks

Adaptive Hierarchical GHSOM with Federated Learning for Cont...

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2024 ieee International conference on Big Data, BigData 2024

作者： Alkulaib, Lulwah Kuwait University Department of Computer Science Sabah Al Salem University City Kuwait

ISBN: (纸本)9798350362480

This paper proposes an adaptive hierarchical Growing Hierarchical Self-Organizing Map (GHSOM) integrated with Federated Learning (FL) for scalable and privacy-preserving anomaly detection in distributed Internet of Things (IoT) networks. In IoT environments, vast amounts of data are generated from heterogeneous sensors, requiring robust anomaly detection mechanisms to ensure system reliability. Traditional centralized approaches face significant challenges related to data privacy, communication overhead, and scalability. The proposed model addresses these issues by combining the local processing power of GHSOM with the privacy-preserving capabilities of FL, ensuring that only model updates, rather than raw data, are shared across the network. To further enhance efficiency, we introduce a novel adaptive hierarchy within the GHSOM structure. This hierarchy dynamically adjusts based on the context and characteristics of the incoming sensor data, such as its variance, frequency, and criticality. By embedding this context-awareness, the GHSOM model can tailor its clustering process to the specific requirements of each sensor node, improving anomaly detection accuracy. Additionally, FL is enhanced with a selective update mechanism, where only significant model updates - triggered by detected anomalies - are shared across the network, further reducing bandwidth usage and communication costs. We validate the proposed system on diverse IoT sensor datasets, demonstrating its superior ability to detect anomalies with reduced communication overhead compared to non-adaptive federated GHSOM approaches. This work highlights the potential of combining adaptive GHSOM hierarchies with federated learning to create scalable, privacy-preserving, and efficient anomaly detection systems suitable for real-time IoT applications. © 2024 ieee.

关键词： Federated learning

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