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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Jiang, Bohou Wang, Jiahui Yue, Kun Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350394375

Node classification on heterogeneous graphs with multiple node types and relationships has been widely used in commodity classification, friend recommendation, and so on. Most of the existing models need to define the meta paths manually with incomplete attribute information, leading to the low accuracy of classification. To improve the accuracy of node classification task, we propose a novel graph neural network with heterogeneous attributes (GNNHA) by introducing the heterogeneous attributes of nodes and edges without defining meta paths. First, we design a spatial mapping for heterogeneous edges by mapping and fusing the source and target nodes to a hidden layer space. Then, we make a unique linear projection for each node to obtain the embeddings containing the attributes of nodes and edges by aggregating the information of neighbor nodes. Finally, we provide an improved loss function by leveraging the information loss of mapping and node class imbalance. Our model can capture more complete heterogeneous graph attributes and obtain more objective and accurate classification results. Experimental results show that our proposed model outperforms state-of-the-art ones. © 2024 IEEE.

关键词： Classification (of information)

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Probabilistic Distributed Intrusion Detection For Zero-Trust Multi-Access Edge computing 36

Probabilistic Distributed Intrusion Detection For Zero-Trust...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Sharma, Rahul Chan, Chien Aun Leckie, Christopher The University of Melbourne School of Computing and Information Systems Australia The University of Melbourne Department of Electrical and Electronic Engineering Australia

ISBN: (纸本)9781665477161

With the advent of Multi-Access Edge computing (MEC) in 5G, there is a shift in the core processing and deployment of applications to the edge of the network. This enables applications requiring ultra low latency response times and high bandwidth but also exposes them to new cyber attacks. One of the proposed ways to strengthen application security in MEC is to use Zero Trust Strategies (ZTS). However, despite its strong authentication and authorisation capabilities, it is still vulnerable to attacks and requires further mechanisms to detect anomalies, especially across multiple clusters. Collaborative Intrusion Detection systems (CIDS) have been proposed for detecting sophisticated coordinated attacks but often fall short in detecting focused stealthy attacks. In this paper, we present a novel tree-based Probabilistic Distributed CIDS to detect stealthy and volumetric anomalies in service-to-service interactions deployed across multiple edge clusters with a Zero Trust Network setup. We perform our evaluation based on detection accuracy and network overhead and outline its performance improvement against a traditional Distributed CIDS and a previously proposed Hybrid CIDS. Based on an evaluation using telemetry data from real-world application clusters, our novel CIDS resulted in improvements in detection accuracy of 99.4% and network overhead of up to 51.81% when detecting anomalies in service path interactions, HTTP methods, service endpoint interactions, and unusual variance in numerical values comprising request size, response size, and traffic processing duration. © 2023 IEEE.

关键词： Intrusion detection

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Skin Cancer Classification using CNN and Transfer Learning (TL) 7

Skin Cancer Classification using CNN and Transfer Learning (...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Patel, Shashank Pandey, Mudita Rajeswari, D. Srm Institute of Science And Technology School of Computing College of Engineering And Technology Department of Data Science And Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350359299

Skin cancer is a predominant and possibly lethal condition that distresses people across the world. Primary detection and precise finding are crucial for leveraging efficacious treatment and enhanced patient consequences. This research study explores the application off Artificial Intelligence (AI) resolutions for the primary finding and analysis of skin cancer. The study leverages a dataset of dermatological images and employs various ML methods, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees, to analyse the existence of skin cancer with a higher degree of accuracy. By utilizing a combination of image processing and feature extraction, the AI model demonstrates higher performance in classifying skin lesions into malignant or benign categories. The proposed model has achieved an accuracy rate of 98.52%, making the AI-based system a promising tool for dermatologists and healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting Air Pollution of Urban Chennai Localities using Artificial Intelligence 3

Forecasting Air Pollution of Urban Chennai Localities using ...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Faiz, Rayaan Agarwal, Tisha Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept. of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350375190

India's air quality deteriorated significantly in 2023, ranking third worst globally, highlighting the urgency for effective monitoring and mitigation measures. To comprehend past trends and predict future patterns, historical air pollution data from seven urban areas in Chennai was analyzed using the XGBoost Algorithm. This study aims to gain insights into Chennai's air quality, identify abnormalities, and issue timely warnings to the public and governing authorities. XGBoost was employed to forecast AQI, NO2, PM2.5, NOx, PM10, CO, SO2, NH3, and Ozone levels, outperforming traditional LSTM, Prophet, and ARIMA models. Evaluation using RMSE and MAE scores revealed XGBoost's efficacy, with scores of 20.65 and 14.35, respectively. The research highlights regions like Alandur, Arumbakkam, and Manali with notable concentrations of contaminants, emphasizing the need for immediate action. These findings highlight the significance of utilizing advanced algorithms for accurate air quality forecasting and proactive pollution management. © 2024 IEEE.

关键词： Air quality

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Complex Industrial Process Monitoring Using Improved Dynamic Latent Variables - Neighborhood Preserving Embedding

Complex Industrial Process Monitoring Using Improved Dynamic...

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2024 China Automation Congress, CAC 2024

作者： Wang, Hancheng Li, Peng Ai, Mingxi Wu, Jiande Yang, Chuangyan Xu, Qingmin School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

In complex industrial process monitoring, prevalent challenges include high dimensionality, extensive redundancies, and dynamic characteristics. In this paper, we propose an Improved Dynamic Latent Variable-Neighborhood Preserving Embedding (DLV-NPE) algorithm specifically designed to tackle these issues. The methodology begins with the implementation of a variance-covariance feature selection method to minimize redundancies among monitoring variables. This is followed using a dynamic latent variable algorithm to extract dynamic regression properties from the selected variables. Subsequently, the NPE algorithm is employed to preserve the residuals of high-dimensional dynamic latent variables and the high-dimensional residuals of selected variables, thereby preserving the integrity of the low-dimensional manifold structure. Finally, Bayesian inference is applied to develop comprehensive monitoring indicators. The efficacy of the DLV-NPE algorithm is validated through its application to the Tennessee Eastman (TE) process, where it demonstrates superior fault detection capabilities compared to other algorithms. © 2024 IEEE.

关键词： Embeddings

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Bicriteria Algorithms for Approximately Submodular Cover Under Streaming Model

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Tsinghua Science and Technology 2023年第6期28卷 1030-1040页

作者： Yijing Wang Xiaoguang Yang Hongyang Zhang Yapu Zhang Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China School of Mathematics and Statistics Ningbo UniversityNingbo 315211China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China

In this paper,we mainly investigate the optimization model that minimizes the cost function such that the cover function exceeds a required threshold in the set cover problem,where the cost function is additive linear,and the cover function is non-monotone approximately *** study the problem under streaming model and propose three bicriteria approximation ***,we provide an intuitive streaming algorithm under the assumption of known optimal objective *** intuitive streaming algorithm returns a solution such that its cover function value is no less thanα(1−ϵ)times threshold,and the cost function is no more than(2+ϵ)^(2)/(ϵ^(2)ω^(2))⋅κ,whereκis a value that we suppose for the optimal solution andαis the approximation ratio of an algorithm for unconstrained maximization problem that we can call *** we present a bicriteria streaming algorithm scanning the ground set multi-pass to weak the assumption that we guess the optimal objective value in advance,and maintain the same bicriteria approximation *** we modify the multi-pass streaming algorithm to a single-pass one without compromising the performance ***,we also propose some numerical experiments to test our algorithm’s performance comparing with some existing methods.

关键词： approximately submodular linear additive streaming model bicriteria algorithm

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Signs to Speech 2

Signs to Speech

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vardhan, Harsh Harsavardhan, S. Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings. © 2024 IEEE.

关键词： Convolution

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The convergence properties of infeasible inexact proximal alternating linearized minimization

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Science China Mathematics 2023年第10期66卷 2385-2410页

作者： Yukuan Hu Xin Liu State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

The proximal alternating linearized minimization(PALM)method suits well for solving blockstructured optimization problems,which are ubiquitous in real *** the cases where subproblems do not have closed-form solutions,e.g.,due to complex constraints,infeasible subsolvers are indispensable,giving rise to an infeasible inexact PALM(PALM-I).Numerous efforts have been devoted to analyzing the feasible PALM,while little attention has been paid to the *** usage of the PALM-I thus lacks a theoretical *** essential difficulty of analysis consists in the objective value nonmonotonicity induced by the *** study in the present work the convergence properties of the *** particular,we construct a surrogate sequence to surmount the nonmonotonicity issue and devise an implementable inexact *** upon these,we manage to establish the stationarity of any accumulation point,and moreover,show the iterate convergence and the asymptotic convergence rates under the assumption of the Lojasiewicz *** prominent advantages of the PALM-I on CPU time are illustrated via numerical experiments on problems arising from quantum physics and 3-dimensional anisotropic frictional contact.

关键词： proximal alternating linearized minimization infeasibility nonmonotonicity surrogate sequence inexact criterion iterate convergence asymptotic convergence rate

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Automated Input Data Management for Discrete Event Simulation Models of Emergency Departments 25

Automated Input Data Management for Discrete Event Simulatio...

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25th International Conference on Enterprise Information systems, ICEIS 2023

作者： Mogollon, Juan David Goepp, Virginie Avila, Oscar de Guio, Roland Department of Systems and Computing Engineering School of Engineering Universidad de Los Andes Bogota Colombia I-Cube INSA Strasbourg France

ISBN: (纸本)9789897586484

Emergency Department (ED) overcrowding, that relates to congestion due to the high number of patients, has a negative effect on patient waiting time. The analysis of the flow of patients through Discrete Event Simulation (DES), that models the operation of a system through a sequence of events, is a relevant approach to find a solution to such problem. This technique relies on high-quality input data which needs to be previously managed in a complete process known as Input Data Management (IDM). The objective of this research is to propose a tool to automate the IDM process required for DES models of ED. To do so, we used a case with real data in order to contextualize the problem, evaluate required statistical methods, and gather specific requirements. Based on these results, a prototype was designed and developed by using web and cloud application development tools. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Input output programs

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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