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A semi-supervised GCN-based community detection algorithm 22

A semi-supervised GCN-based community detection algorithm

引用

22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Shi, Shu-Han Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Chen, Fu-Xiang Jiangsu University School of Computer Science and Communication Engineering China University of Exeter Department of Computer Science United Kingdom University of Science and Technology School of Ocean Jiangsu China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798331509712

Community detection reveals the unique characteristics and relationships of in-network members, differentiated from out-of-community members and plays a pivotal role in network analysis. In recent years, deep learning techniques have made great strides in the application of community detection, especially on label sampling models, which train graph convolutional networks by constructing a balanced training set through structural centre localization and neighbourhood node expansion. However, such algorithms are limited on centre selection in community structure. To address this, this paper introduces a novel community detection algorithm based on peak density adaptive iterative segmentation, or LDACN in short. First, labels are assigned by adaptively selecting the centre node of the community structure, which results in a more even distribution of labels in the network. Subsequently, more accurate community segmentation is achieved by taking advantage of GCN's combined ability in capturing the connectivity relationships between the nodes and their intrinsic characteristics. Experimental results on synthetic and real-world network datasets show that our algorithm improves the effectiveness of community segmentation compared to the state-of-the-art algorithms. © 2024 IEEE.

关键词： Deep learning

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Multi-Dimensional Recurrent Neural networks Hyperspectral image classification

Multi-Dimensional Recurrent Neural Networks Hyperspectral im...

引用

2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Sangeetha, V. Padmakala, S. Mahaboob John, Y.M. Elayaraja, P. Parthiban, K.G. Saveetha School of Engineering Dept of Cse Chennai India Department of Computer Science and Engineering Chennai India Mahendra College of Engineering Department of Ece Salem India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350375442

Classifying hyperspectral images (HSIs) is a top priority for the remote sensing field. Discriminative feature extraction of HSI using conventional ML techniques is difficult because of the abundance of strongly correlated bands and the scarcity of training data. The deep learning approach has recently achieved state-of-the-art results in a number of visual identification tasks and is widely regarded as a potent tool for feature extraction. The use of deep learning in HSI categorization was spurred by these fruitful uses, and the results have been impressive. Joint spectral-spatial feature extraction for HSI classification has piqued the attention of researchers in convolutional neural network (RNN) based classification techniques among other deep learning approaches. There have been claims in the literature that convolution operations on non-informative spectral bands can cause RNN-based joint feature extraction to underutilize spectral information. In order to minimize information loss, a model was built that extracts spectral and spatial characteristics independently. Then, to take use of the semantic information, the extracted features are fused. Using the datasets from Kennedy Space Center (KSC), Indian Pines (IP), University of Pavia (UP), and Salinas (SA), our model has attained 99.17%, 98.70%, 99.18%, and 99.24% overall accuracy, respectively. For HSI's limited training samples and high dimensional characteristics, researchers have conducted analyses on band selection and training sample quantity, respectively. In comparison to both older and more modern fusion-based approaches, the suggested model has attained state-of-the-art performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Fine-Grained Image Classification Based on Data Enhancement and Attention Mechanism 5

Research on Fine-Grained Image Classification Based on Data ...

引用

5th international Conference on Information Science, Parallel and distributed systems, ISPDS 2024

作者： Xin, Haitao Jiang, Qilu Harbin University of Commerce School of Computer and Information Engineering Harbin China Harbin Business University School of Computer and Information Engineering Harbin China

ISBN: (纸本)9798350352719

The research content of fine-grained image recognition is the problem of sub-category recognition under a broad category. Since fine-grained image classification has the data characteristics of little difference between classes and big difference within classes, it becomes a very challenging research task. The key to solving this task is to find the key regions in images and extract effective features from them. Based on the analysis and research of existing fine-grained image classification algorithms and models, this paper introduces a weakly supervised fine-grained classification method that utilizes cutmix data augmentation. SE attention module is introduced to enhance the significant regional features of images, and label smoothing mechanism is used to improve the model generalization ability. Compared with traditional methods on CUB_200_2011 dataset, the experimental results demonstrate that our method achieves improved classification performance. © 2024 IEEE.

关键词： Convolutional neural networks

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10th international symposium on Security and Privacy in Social networks and Big Data, SocialSec 2024

10th International Symposium on Security and Privacy in Soci...

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10th international symposium on Security and Privacy in Social networks and Big Data, SocialSec 2024

ISBN: (纸本)9789819637737

The proceedings contain 10 papers. The special focus in this conference is on Security and Privacy in Social networks and Big Data. The topics include: ChatGPT Through the Users’ Eyes: Sentiment Analysis of Privacy and Security Issues;Extended Abstract: Evaluating Cross-Chain Platforms for EV Charging Payment systems;Using M5Stack Core 2 ESP32 to Raise Children’s Awareness About Cyber Security;ELITE: Efficient and Secure Machine Learning for Intelligent Perception in Smart Road Infrastructure;gamma Sampling for Intrusion Detection with Imbalanced Data;smartAudit: Smart Contract Vulnerability Detection Using Transfer Learning;FLAIR: A Federated Learning Approach Against Inference Attacks and Risks;Secure Aggregation of Smartwatch Health Data with LDP.

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Research on Building a Standardized Library for Auditing Heterogeneous Information Models in distributed Data 7

Research on Building a Standardized Library for Auditing Het...

引用

7th IEEE international Conference on Information systems and computer Aided Education, ICISCAE 2024

作者： Heping, Huang Zhonglu, Zou Zhicai, Huang Dongguan Power Supply Bureau Guangdong Power Grid Corporation Guangdong Dongguan523000 China

ISBN: (纸本)9798350350760

With the advancement of smart grid transformation, the configuration audit of distribution network communication networks faces challenges such as diversified network devices, numerous manufacturers, and multi-source data management. Traditional methods exhibit significant deficiencies in processing efficiency and data consistency, failing to meet the demands of large-scale data management. This paper proposes a standard library construction method for configuration audit of distribution network data networks. By integrating knowledge graph technology with convolutional neural networks, this method establishes dynamic and static audit libraries, achieving accurate, rapid, and comprehensive management of audit data. The specific process includes the collection and preprocessing of multi-source data, construction of the knowledge graph, application of the inference mechanism, and intelligent retrieval and updating of the standard library. This method significantly improves the accuracy and completeness of data security configuration and optimizes the configuration audit process of distribution network communication networks. ©2024 IEEE.

关键词： Data consistency

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Shallow Convolution Neural Network for Real-Time Change Detection in Agricultural Lands

Shallow Convolution Neural Network for Real-Time Change Dete...

引用

2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Ramyateja, O. Al-Jawahry, Hassan M. Shreyas, A.V. Nagendar, Y. Preetha, R. Maisammaguda Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India Sr University School of Computer Science and Engineering Warangal India Bannari Amman Institute of Technology Department of Mathematics Sathyamangalam India

ISBN: (纸本)9798350375442

The remote sensing technology is helpful for collecting information regarding usage of multispectral data from backscattered coefficients and optical sensors through sensors. The main challenge for an unsupervised change detection is distinguishing among a potential change to significant changes from noise. The Shallow Convolution Neural Network (SCNN) is proposed for change detection in agriculture lands. The Onera Satellite Change Detection (OSCD) dataset is used and it is preprocessed by Non-Local Means (NLM) filters which is helpful to denoise hyperspectral or multispectral images. Through preserving land structures, it enables accurate detection of changes in agricultural areas. Then, the CNN is used for extracting features in which primary layer captures low-level features such as textures and edges while deep layers recognize much complex structures and patterns. The SCNN mitigates overfitting issues particularly when training which leads in satellite-based monitoring thereby providing effective change detection. The Overall Accuracy (OA), Kappa, IoU and mIoU are taken to estimate SCNN performance. The SCNN obtains 95.19%, 0.896, 0.842 and 0.875 of OA, kappa, IoU and mIoU for OSCD dataset which is better than Deep Neural Network (DNN). © 2024 IEEE.

关键词： Change detection

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Dynamic Radial Movement Optimization for Throughout Maximization in Energy Harvesting Cognitive Radio Network

Dynamic Radial Movement Optimization for Throughout Maximiza...

引用

2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Vasantha, B. Kumar, Komuravelly Sudheer Husain, Saif O. Naveen, I.G. Kalpana, Kasapogu Maisammaguda Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

The Energy Harvesting (EH) and corrperative communication technique and information accessibility of Energy Harvesting Cognitive Radio Network (EH-CRN). However, minimized throughput in EH, leading to longer EH periods where nodes are inactive and multi-use CRN secondary user interfere with each other transmissions overlap in time due to require maximum energy. In this paper, proposed Dynamic Inertia Weight with Radial Movement Optimization (DRMO) approach provides a framework for dynamic management of both energy and spectrum resources, allowing the network to adapt to changing conditions and maintain high throughput. The DRMO improves the utilization of under used spectrum bands by allowing secondary users to opportunistically transmit data while minimizing interference with primary user. The RMO adapts to changing environmental factors such as variations in harvested energy and spectrum access opportunities making it minimized energy for using dynamic wireless network. The DRMO method exhibits superior performance compared to existing method such as Whale Colony Optimization algorithm (WCO) and Particle Swarm Optimization (PSO). The proposed DRMO method achieving maximum throughput of 0.45, energy of 0.25mJ and time of 0.2, respectively. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Sentiment Analysis in Social Media using Bidirectional Long Short Term Memory with Focus Loss function

Sentiment Analysis in Social Media using Bidirectional Long ...

引用

2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Sivakumaran, A.R. Shnain, Ammar H. Lande, Jayapal Sindgi, Anupama Sruthi, Patlolla Department of Information Technology Hyderabad India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq Sr University Department of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Sentiment Analysis (SA), is often known as "Opinion Mining"which seeks to analyze sentiment polarities in opinion-based texts. Several Deep Learning (DL) approaches are developed to effectively solves the complex SA challenges. However, existing methods failed to identify the sentiments correctly due to contextual embedding's and different meaning for same word in a sentence or document. To overcome this, a Bidirectional - Long Short Term Memory with Focal Loss (Bi-LSTM-FL) function for SA in social media. The proposed Bi-LSTM-FL model enhanced the SA process by capturing the context from both past and future words in a sentence which lead to understand complex sentence structures. The twitter dataset is utilized and preprocessed by to enhance the text format for classification. The preprocessed texts are fed to Principal Component Analysis (PCA) for selecting optimal set of features and to reduce dimensionality of features. Finally, with the help of selected features the tweets are categorized as positive, negative or neutral effectively. The experimental results of proposed Bi-LSTM-FL achieved accuracy of 98.2% which is greater when compared to previous method such as Gated Attention Recurrent Network (GARN). © 2024 IEEE.

关键词： Long short-term memory

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A secure architecture for exploring patient-level databases from distributed institutions 35

A secure architecture for exploring patient-level databases ...

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35th IEEE international symposium on computer-Based Medical systems (CBMS)

作者： Almeida, Joao Rafael Barraca, Joao Paulo Oliveira, Jose Luis Univ Aveiro DETI IEETA Aveiro Portugal Univ Aveiro DETI IT Aveiro Portugal Univ A Coruna Dept Informat & Commun Technol La Coruna Spain

ISBN: (数字)9781665467704

ISBN: (纸本)9781665467704

One of the main goals of clinical studies consists of identifying diseases' causes and improving the efficacy of medical treatments. Sometimes, the reduced number of participants is a limiting factor for these studies, leading researchers to organise multi-centre studies. However, sharing health data raises certain concerns regarding patients' privacy, namely related to the robustness of anonymisation procedures. Although these techniques remove personal identifiers from registries, some studies have shown that anonymisation procedures can sometimes be reverted using specific patients' characteristics. In this paper, we propose a secure architecture to explore distributed databases without compromising the patient's privacy. The proposed architecture is based on interoperable repositories supported by a common data model.

关键词： distributed query Patient privacy Data anonymisation OMOP CDM OHDSI

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A Segmentation Residual Network with Adaptive Normalization for Crop Type Classification using Satellite Imagery

A Segmentation Residual Network with Adaptive Normalization ...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Kankala, Kumaraswamy Meghana, A. Adnan, Myasar Mundher Sudheer Kumar, Komuravelly Mouli, Kathi Chandra Hyderabad India Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

Nowadays, crop type classification with satellite images has become an emerging study in remote sensing data. Till now, various calibrated images captured by satellite are included to monitor the food growth across the world. The analysis of remote sensing images has some difficulties in classification such as identifying patterns and crop type covers and also overfitting of crops with noisy data. To overcome these issues, the model of Segmentation Residual Network with Adaptive Normalization (SegResNet-AN) is proposed to resolve the limitations of Batch Normalization BN) and variations in spatial images. The proposed SegResNet-AN gives an accurate rate in reducing overfitting of crop classification and also handles the noisy data for the large data images while capturing. For those normalization processes, different image features are extracted from the encoding and decoding. Segmentation is done through the SegResNet which works by dividing individual images into maps of pixel regions according to each crop cover type, removes all duplicated images by data redundancy. In the results, the proposed SegResNet-AN method gives results better than the other compared existing Fusion Net model in terms of precision (0.986) recall (0.934) and f1-score (0.956) respectively. © 2024 IEEE.

关键词： Satellite imagery

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