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International Conference on computer Supported Cooperative Work in Design

作者： Shumei Yang Kun Wang Kun Ma Fuqiang Wang Peng Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Qingdao Branch of China E-Port Data Center Qingdao China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Monitoring mechanical equipment is crucial for industrial efficiency and safety. Anomalous sound detection faces challenges due to limited labeled data and varying acoustic conditions. Supervised methods require extensive labeled data, which is often scarce. Unsupervised methods, which model the distributions of normal data, are more suitable when normal data is abundant but anomalies are rare. In this paper, we present SAM-AE-GAN, an unsupervised anomaly detection model that combines self-attention, autoencoders, GANs, and spectral perturbation for data augmentation. The self-attention mechanism captures long-range dependencies, autoencoders enable efficient data representation, and GANs enhance generative performance. Spectral perturbation enhances adaptability, improving robustness and accuracy. Experimental results demonstrate the effectiveness of SAM-AE-GAN across five machine types in the 2022 Challenge TASK 2 dataset, validating its capability in complex industrial environments. This model provides an effective solution for anomaly detection and predictive maintenance, even in scenarios with limited labeled data.

关键词： Adaptation models Accuracy Perturbation methods Autoencoders data augmentation Generative adversarial networks data models Robustness Anomaly detection Unsupervised learning

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Examining the Impact of data Mining on Decision-Making Processes 3

Examining the Impact of Data Mining on Decision-Making Proce...

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3rd International Conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Singh, Vaishali Pandey, Pratik Aggarwal, Prateek Reena, R. Bhagwat, Suvarna R. Sunil, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device. © 2023 IEEE.

关键词： discuss extract information overcome relationships

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Enhanced Anomaly Detection in Embedded Payment Systems using Depthwise Separable CNN with Dandelion Optimizer

Enhanced Anomaly Detection in Embedded Payment Systems using...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sevinthi Kali Sankar Nagarajan Rabail Adwani Kardeepa Ponnuchamy Harshal Shah Shashikala Gurpur Ramya Maranan Independent Researcher 12460 Lanthimos San Antonio TX USA MS in Data Science Cranston RI USA Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Gujarat Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Vimannagar Pune Maharashtra Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Due to the prevalence of online payments, it is essential to identify instances of payment fraud. Plans that break rules and regulations within the payment systems not only cost money but also erode customer confidence. Finding these fraudulent transactions can be challenging because of the complexity of fraud and the sheer number of transactions. In this study, a new deep learning technique for identifying online payment fraud is proposed. The classification phase, hybrid feature selection, and sophisticated preprocessing form its foundation. Starting with the IEEE CIS Fraud dataset and the PaySim Financial Credit dataset, the suggested approach is implemented. To ensure that all of the data sets have identical scales, a preprocessing step that uses Min-Max scaler normalization is utilized. The optimal qualities are picked using a hybrid approach that combines Gooseneck Barnacle Optimization (GBO) and the Artificial Hummingbird Algorithm (AHA). A depthwise separable convolutional neural network (DS-CNN) is then employed for classification, which facilitates the differentiation between fraudulent and non-fraudulent transactions. Additionally, to increase the detection rate, the Dandelion Optimizer (DO) optimizes the entire system. The aforementioned study indicates that the suggested approach has a high accuracy rate in detecting fraudulent transactions, which means it has almost infinite potential to speed up the entire process. In both datasets, the proposed method achieves an impressive 99% accuracy rate, proving its dependability and efficiency. This exceptional outcome shows how flexible the approach is to complex patterns and data variations, indicating its high level of reliability.

关键词： Accuracy Prevention and mitigation Feature extraction Regulation Fraud Complexity theory Classification algorithms Timing Convolutional neural networks Reliability

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Investigating the Benefits of Machine Learning Algorithms for Text Classification 3

Investigating the Benefits of Machine Learning Algorithms fo...

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3rd International Conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Gowrishankar, J. Vinithra Banu, T. Parikshit, N. Singh, Vandana Kalra, Hitesh Sahoo, Girija Shankar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper affords a research into using gadget studying algorithms for textual content category duties, with a focal point on understanding the importance of these algorithms and their ability to enhance the accuracy of textual content classification. it's miles located that, while conventional techniques, which include rule based models and hand-engineered features, can attain first-class outcomes, there are compelling motives to explore the capability of device getting to know algorithms to further improve the accuracy and performance of textual content class. The paper discusses the 3 maximum famous gadget gaining knowledge of algorithms for this project: the NaÏve Bayes classifier, help Vector Machines, and synthetic Neural networks, and presents a top level view of the latest research findings that demonstrate the prevalence of these algorithms over traditional methods. The paper additionally presents an outline of the distinctive approaches for introducing era into text type, and discusses modern-day strategies for implementing and deploying those algorithms. Finally, the paper outlines ability areas for destiny research associated with information the results of system studying algorithms for this mission, and suggests viable instructions for exploration. © 2023 IEEE.

关键词： Classification (of information)

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Role of Machine Learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

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Decision-Making lifecycle of the Bid Process Information System

Decision-Making lifecycle of the Bid Process Information Sys...

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2022 International Conference on Industry sciences and computer science Innovation, iSCSi 2022

作者： Trabelsi, Salma Zahaf, Sahbi Zekri, Manel Aouicha, Mohamed Ben Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Tunisia University of Tunis El-Manar Institut Superieur d'Informatique Tunis Tunisia MIRACL Laboratory Higher Institute of Computer and Multimedia Sakiet Ezzit Technopole Sfax242-3021 Tunisia Department of Computer Science Faculty of Sciences Tunis Tunisia

"Three fit" problems in the Bid Process Information System (BPIS) are still an open issue. The BPIS must be: integrated, flexible and interoperable in order to deal with the urbanization approach. Hence, operational, organizational, decision-making, and cooperative dimensions are four dimensions that have been identified to deal with such failures. Therefore, in this work, we are particularly interested in handling this failure by proposing an approach that focuses on the decision dimension of BPIS. This improves at the end of the various stages of the data warehouse lifecycle and takes into consideration the user's knowledge by focusing on their individual needs too. © 2022 Elsevier B.V.. All rights reserved.

关键词： Decision making

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Federated Learning for 6G:Applications,Challenges,and Opportunities

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Engineering 2022年第1期8卷 33-41页

作者： Zhaohui Yang Mingzhe Chen Kai-Kit Wong H.Vincent Poor Shuguang Cui Department of Electronic and Electrical Engineering University College LondonLondon WC1E 6BTUK Department of Electrical and Computer Engineering Princeton UniversityPrincetonNJ 08544USA Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and Future Network of Intelligence Institute The Chinese University of Hong KongShenzhen 518172China

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and ***,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter *** approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and *** paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless *** particular,the essential requirements for applying FL to wireless communications are first *** potential FL applications in wireless communications are *** main problems and challenges associated with such applications are ***,a comprehensive FL implementation for wireless communications is described.

关键词： Federated learning 6G Reconfigurable intelligent surface Semantic communication Sensing Communication and computing

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NextSeg: Automatic COVID-19 Lung Infection Segmentation from CT Images Based on Next-ViT

NextSeg: Automatic COVID-19 Lung Infection Segmentation from...

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International Joint Conference on Neural networks (IJCNN)

作者： Aimei Dong Ruixin Wang Xuening Zhang Jian Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.

关键词： COVID-19 Image segmentation Convolution Computed tomography Neural networks Lung Medical services

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Advanced Bandwidth Management Techniques Using Wireless Mesh networks for High-Speed Optical Fiber Communications 3

Advanced Bandwidth Management Techniques Using Wireless Mesh...

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3rd International Conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Yadav, Rakesh Kumar Sharma, Sunil Seth, Kanika Jeyalaxmi, M. Ingle, Yashwant S. Vivek, V. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This abstract encompasses superior bandwidth management techniques the usage of Wi-Fi mesh networks to improve the performance of high-velocity optical fiber communications. the point of interest lies at the access-, relay-and center-mesh architectures with their diverse, discrete techniques consisting of dynamic frequency allocation, load balancing and node optimization, as well as on highlights from latest research. Within the experiments, the effects exhibit giant enhancement in signal first-rate and effectiveness of bandwidth resources when the use of the proposed version, based totally at the convergence speed of the network fee. Furthermore, it's also evident that the optimized wireless mesh configuration brings about enormous fee financial savings. All the findings obtained at some stage in this research have eminently greater the talents of the proposed structure for growing green communications networks. © 2023 IEEE.

关键词： Communications Convergence Experiments networks Superior

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Volume Hiding Conjunctive Searchable Symmetric Encryption over Cloud

Volume Hiding Conjunctive Searchable Symmetric Encryption ov...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Qinghua Deng Lanxiang Chen Yizhao Zhu Yi Mu Faculty of Data Science City University of Macau Macau Macau China School of Computer and Information Engineering Hanshan Normal University Chaozhou China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (数字)9798331506896

ISBN: (纸本)9798331506902

Symmetric searchable encryption (SSE) is crucial for promoting cloud storage. It enables users to encrypt private data and outsource it to untrusted servers while retaining query capabilities. To prevent the cloud from obtaining users’ private data, researchers have proposed numerous schemes to protect users' privacy. However, the issue of volume hiding has received less attention. Recent studies have demonstrated that volume leakage can be exploited to infer query *** mitigate the aforementioned leakage problem, we introduce an efficient volume-hiding SSE scheme supporting conjunctive queries, denoted as VHXT. The primary strategy for implementing volume hiding comprises two aspects: first, employing a bitmap-based inverted index to obscure the query response length of the least frequent keyword, and second, introducing laconic PSI for efficient membership testing. The laconic PSI enables our scheme to achieve approximately optimal overhead. Moreover, security analysis and performance evaluation demonstrate that VHXT effectively prevents volume leakage with minimal overhead.

关键词： Performance evaluation Cloud computing data privacy Protocols Knowledge discovery Encryption Servers Indexes Distributed computing Testing

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