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Attention Based Generative Adversarial networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International conference on Parallel distributed and Grid Computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production Computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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Research on Ubiquitous Power Internet of Things in Distribution networks Using Adaptive Gradient Algorithm with Convolution neural network

Research on Ubiquitous Power Internet of Things in Distribut...

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Data Science and network Security (ICDSNS), International conference on

作者： Songmei Liu Wanjiang University of Technology Ma'anshan China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In China, several distribution automation devices are established in distribution network to increase reliability of power supply. The major challenge of ubiquitous electric Internet of Things (IoT) is a resource aspect which enables distributed computational sources. Therefore, the Adaptive Gradient Algorithm with Convolution neural network (Adagrad-CNN) is proposed for ubiquitous electric IoT in distribution networks. The Adagrad is helpful when dealing with sparse features or data that has extensive-range of values. The CNN has multiple-layer architecture for classifying image pixels patterns through processing and it filtered noise captures significant predictive properties. The Adagrad-CNN leads to better generalization and enhance the CNN performance through mitigating issues based on exploding gradients that is essential when dealing with difficult and large datasets in ubiquitous electric IoT. The dataset is collected from various devices and sensors in the power grid and it is preprocessed by Z-score normalization. This preprocessing enhances convergence rates and reduces the large scales. It is essential when features are in different ranges and it prevents particular feature dominance. The Adagrad-CNN attains 96.54%, 94.21 %, 95.73% and 94.59% of accuracy, recall, precision and f1-score.

关键词： Performance evaluation Adaptive systems neural networks Noise Distribution networks Filtering algorithms Prediction algorithms Power grids Sensors Internet of Things

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Adaptable Machine Learning Methods for a Twitter Followee Recommendation System

Adaptable Machine Learning Methods for a Twitter Followee Re...

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IoT, Communication and Automation Technology (ICICAT), International conference on

作者： Sandeep Yadav Shashank Srivastav Computer Science and Engineering Department Buddha Institute of Technology Gorakhpur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Recommender systems for social networking have recently used machine learning (ML) methods. This paper describes a multi-agent system that simulates a Twitter recommender system to give users a list of helpful suggestions, i.e., a list of users who are followed, or they want to follow. When processing data in parallel on multi-node distributed systems, a machine learning technique (such as a neural network or multilayer perceptron) is evaluated for scalability using a simulator. A multi-agent modelling simulates the dispersed environment. The quantity of nodes, the method used within the model recommender framework, and the real followers and followees data are the first parameters that need to be configured on the simulator. The experimental findings were acquired on three different datasets to evaluate the speed and precision of a simulated recommender system when evaluating the machine learning method under various conditions.

关键词： Social networking (online) Scalability Blogs neural networks distributed databases Machine learning Multilayer perceptrons Data models Recommender systems Multi-agent systems

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Deep Convolutional neural network-Based High-Precision and Speed DDOS Detection in SDN Environments

Deep Convolutional Neural Network-Based High-Precision and S...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Sid Ali Madoune Sarra Senouci Jiang Dingde Abdelkader Senouci School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Mechanical and Electronic Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Engineering and Environment Northumbria University Newcastle United Kingdom

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

The rapid expansion of interconnected infrastructures has dramatically increased the volume of network devices and traffic, introducing significant security vulnerabilities in Software-Defined networking (SDN) environments. distributed Denial-of-Service (DDoS) attacks exploit the centralized control plane characteristic of SDN, underscoring the need for adaptive, efficient detection mechanisms. In response, we present a novel hybrid framework that leverages Principal Component Analysis (PCA) for dimensionality reduction and a supervised Deep Convolutional neural network (DCNN) for high-precision classification of normal and malicious events. Rigorous evaluation on a real-world SDN dataset, comprising both categorical and numerical features, demonstrates that the proposed model achieves an exceptional detection accuracy of 99.97% and a swift detection time of only 3 seconds-making it well-suited for real-time applications in SDN. These results affirm the efficacy of our model in enhancing both security and scalability, contributing a robust solution for mitigating DDoS threats in dynamic SDN environments.

关键词： Dimensionality reduction Scalability Computational modeling Real-time systems Numerical models Convolutional neural networks Security Computer crime Software defined networking Principal component analysis

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Brand Logo Image Classification Based on Deep Convolutional Generative Adversarial network

Brand Logo Image Classification Based on Deep Convolutional ...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Yang Wang Guangzhou Institute of Science Technology Guangzhou China

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Brand logo image examines how a critical dimension of logo design, namely the naturalness of the logo color, influences brand design induced by the logo. However, high complexity is observed, requiring higher quality data and susceptibility to overfitting when there is insufficient training data. The implementation of the proposed system involved the utilization of a Deep Convolutional Generative Adversarial network (DCGAN) reduce overfitting and efficiently handle noise and distortion in the logo image, which is essential in brand logo image processing. The brand logo image dataset forwarded to the preprocessing stage included pixel values, which may lead to the loss of contrast, shadow, sharpness, noise removal, and structure in some relevant images. The classification involved DCGAN, aiming to achieve high-quality logo image prediction, noise reduction, and handling of overfitting by incorporating synthetic logo images. The proposed DCGAN method achieves promising performance, including a mean Average Precision (mAP) of 52.25, Accuracy of 97.09%, Precision of 97.01 %, Recall of 98.10%, and F1-score of 97.11 %. This performance is compared to existing methods such as YOLOv3 and Convolutional neural network (CNN) - Long Short-Term Memory (LSTM).

关键词： YOLO Image color analysis Noise reduction Training data Generative adversarial networks Data models Convolutional neural networks

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Implicit neural Alignment network for Arbitrary-scale Space-Time Video Super-Resolution

Implicit Neural Alignment Network for Arbitrary-scale Space-...

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International Joint conference on neural networks (IJCNN)

作者： Qin Jiang Qinglin Wang Lihua Chi Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment Hunan GuoKe Computility Technology Co. Ltd

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time super-resolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit neural Alignment network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.

关键词： Training Interpolation Correlation Superresolution neural networks Estimation Benchmark testing

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A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning 30

A Framework for Edge Intelligent Smart Distribution Grids vi...

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30th International conference on Computer Communications and networks (ICCCN)

作者： Hudson, Nathaniel Hossain, Md Jakir Hosseinzadeh, Minoo Khamfroush, Hana Rahnamay-Naeini, Mahshid Ghani, Nasir Univ Kentucky Dept Comp Sci Lexington KY 40506 USA Univ S Florida Dept Elect Engn Tampa FL 33620 USA

ISBN: (纸本)9780738113302

Recent advances in distributed data processing and machine learning provide new opportunities to enable critical, time-sensitive functionalities of smart distribution grids in a secure and reliable fashion. Combining the recent advents of edge computing (EC) and edge intelligence (EI) with existing advanced metering infrastructure (AMI) has the potential to reduce overall communication cost, preserve user privacy, and provide improved situational awareness. In this paper, we provide an overview for how EC and EI can supplement applications relevant to AMI systems. Additionally, using such systems in tandem can enable distributed deep learning frameworks (e.g., federated learning) to empower distributed data processing and intelligent decision making for AMI. Finally, to demonstrate the efficacy of this considered architecture, we approach the non-intrusive load monitoring (NILM) problem using federated learning to train a deep recurrent neural network architecture in a 2-tier and 3-tier manner. In this approach, smart homes locally train a neural network using their metering data and only share the learned model parameters with AMI components for aggregation. Our results show this can reduce communication cost associated with distributed learning, as well as provide an immediate layer of privacy, due to no raw data being communicated to AMI components. Further, we show that FL is able to closely match the model loss provided by standard centralized deep learning where raw data is communicated for centralized training.

关键词： Smart grid Federated learning Edge Computing Advanced metering infrastructure Distribution network Cyber-physical systems Non-intrusive Load Monitoring (NILM)

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Generalizing Functional Error Correction for Language and Vision-Language Models

Generalizing Functional Error Correction for Language and Vi...

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International conference on Machine Learning and Applications (ICMLA)

作者： Wenyu Peng Simeng Zheng Michael Baluja Tao Xie Anxiao Jiang Paul H. Siegel Electrical and Computer Engineering Department University of California San Diego La Jolla CA USA Computer Science San Diego State University San Diego CA USA Department of Computer Science and Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

The goal of functional error correction is to preserve neural network performance when stored network weights are corrupted by noise. To achieve this goal, a selective protection (SP) scheme was proposed to optimally protect the functionally important bits in binary weight representations in a layer-dependent manner. Although it showed its effectiveness in image classification tasks on some relatively simple networks such as ResNet-18 and VGG-16, it becomes inadequate for emerging complex machine learning tasks generated from natural language processing and vision-language association domains. To solve this problem, we extend the SP scheme in three directions: task complexity, model complexity, and storage complexity. Extensions to complex natural language and vision-language tasks include text categorization and “zero-shot” textual classification of images. Extensions to more complex models with deeper block structures and attention mechanisms consist of Very Deep Convolutional neural network (VDCNN) and Contrastive Language-Image Pre-Training (CLIP) networks. Extensions to more complex storage configurations focus on distributed storage architectures to support model parallelism. Experimental results show that the optimized SP scheme preserves network performance in all of these settings. The results also provide insights into redundancy-performance tradeoffs, generalizability of SP across datasets and tasks, and robustness of partitioned network architectures.

关键词： Adaptation models Text categorization neural networks Noise Parallel processing network architecture Robustness Error correction Complexity theory Protection

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Combination of Hybrid Feature Selection and LSTM-AE neural network for Enhancing DDOS Detection in SDN

Combination of Hybrid Feature Selection and LSTM-AE Neural N...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Mohamed Ali Setitra Bless Lord Y. Agbley Zine El Abidine Bensalem Mingyu Fan School of Computer Science and Engineering (Cyberspace Security) University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

distributed Denial of Service (DDoS) attacks pose significant threats in Software-Defined network (SDN) environments. To enhance DDoS detection in SDN, this study presents a novel approach that combines Hybrid Feature Selection and Long Short-Term Memory (LSTM)-Autoencoder (AE) neural network. The feature selection process initially utilizes Information Gain (IG) to select 50% of the most important ones. Subsequently, the SHapley Additive exPlanations (SHAP) method is employed to identify relevant and interdependent features. The LSTM-AE neural network then captures the temporal characteristics and nonlinear patterns in the system's response, creating a low-dimensional data representation. Experimental evaluation using an SDN dataset demonstrates the effectiveness of the proposed approach, achieving an overall accuracy of 99.23%. The hybrid feature selection and LSTM-AE model offer improved DDoS detection capabilities in SDN environments.

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An empirical study on the structure evolution of deep learning models: taking SAR image processing a case study

An empirical study on the structure evolution of deep learni...

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IEEE International conference on Joint Cloud Computing (JCC)

作者： Huanxi Liu Xiang He Dawei Feng Han Bao National Laboratory for Parallel and Distributed Processing (PDL) National University of Defense Technology China School of Information and Communication National University of Defense Technology China

With the continuous improvement on model performance, deep learning models have been widely deployed and achieved promising outcomes in various fields in recent years. However, due to the escalating volumes of training data and the complexity of application problems, it becomes more and more challenging to design a neural network with better performance by hand. Analysing the evolution of typical neural network structures has important reference significance for designing a network structure. In this paper, we select the open source models in SAR image processing for an empirical analysis on the evolution of neural network structures. We analyse the evolution of 239 open source deep learning models from the aspects of framework, computing unit, model computation amount and the combined use of various computing units. Results reveal that preference and co-occurrence exist in computing units, while the average number of convolution, activation and normalization layer increases significantly over time. Model complexity shows an overall upward trend, and the characteristics of SAR image are more and more taken into consideration during the model structure design.

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