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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33935 条记录，以下是661-670 订阅

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Portable data Collection Unit 3

Portable Data Collection Unit

引用

3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Guo, Zhenyan Zhu, Yongcan Bing, Kaiyang He, Jiacheng Xi'an Polytechnic University College of Electronics and Information Xi'an China

ISBN: (纸本)9798350352658

This study aims to address the challenge of inconsistent and difficult-to-unify archiving and analysis of data from live detection and preventive testing of electrical equipment in the intelligent operation and maintenance system of the power grid. To tackle this issue, we propose a portable data aggregation unit. This unit enables seamless integration of live detection and preventive testing instruments, and possesses the capability to extract data from various instruments, regardless of whether they have interfaces or not. Through communication interfaces and protocols of existing instrument devices, the unit collects device data, while for devices without interfaces, deep learning and machine learning algorithms are employed for image differentiation to achieve data recognition and communication. Consequently, collected device data can be transmitted in real-time to a platform server, resolving the problem of devices without interfaces being inaccessible and facilitating online data processing. Additionally, the collected data is utilized for transformer fault diagnosis under multiple feature data conditions. In summary, our research brings substantial improvements to the intelligent operation and maintenance system of the power grid, addressing issues of data recording and analysis inconsistency, and providing crucial support for fault prediction and health management of electrical equipment. © 2024 IEEE.

关键词： Instrument testing

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Research on data Fusion Algorithms for Non-stop Overload Detection on Highways 5

Research on Data Fusion Algorithms for Non-stop Overload Det...

引用

5th IEEE international conference on Pattern Recognition and machine learning, PRML 2024

作者： Tang, Lian Liu, Qing Lin, Hang School of Information Engineering Chongqing Jianzhu College Chongqing China

ISBN: (纸本)9798350355925

With the rapid development of highway network, overloading of freight vehicles has caused serious impact on road safety and infrastructure. In order to improve the data fusion accuracy of the overload control system, combining dynamic weighing, image capture recognition and laser scanning technologies, a complete set of non-stop detection system architecture is researched and designed, a multi-feature volume data fusion model is constructed, and the data matching and fusion process is optimized. Through the application test of comprehensive super-control platform, the results show that the proposed data fusion algorithm of non-stop detection for highway super-control can effectively control the error of total weight within ± 2.5%, and the errors of vehicle length and height within ± 150mm and ± 50mm, respectively, which verifies its application value in highway super-control. © 2024 IEEE.

关键词： data fusion

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A study of machine learning based credit card potential default customer identification 2

A study of machine learning based credit card potential defa...

引用

2nd international conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Xu, Sijie Lin, Peixin Luo, Wanqi Yang, Wenjun Jia, Yuntao Beijing Institute of Technology Zhuhai Campus Zhuhai519088 China

ISBN: (纸本)9781510663244

Fintech is continuously driving the overall upgrade of payment methods. Technologies such as Big data, the Internet of Things, and Artificial Intelligence continue to be applied in the payment field and significantly impact the payment industry. Based on the fusion of multiple machine-learning models, the problem of identifying potential default credit card customers is investigated in this paper. The customers are mined and classified based on their bill amount, education level, marital status and other characteristic information. The various models predict using the AutoML framework, then fused and optimized by bagging and stacking methods, and the models are evaluated using evaluation metrics such as F1 values. The test results show that the F1 value of the integrated model after multiple stacks reaches 54.3%, which is better than that of a single algorithm. © 2023 SPIE.

关键词： machine learning

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Comparison of Affine and DCGAN-based data Augmentation Techniques for Chest X-Ray Classification

Comparison of Affine and DCGAN-based Data Augmentation Techn...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Bali, Mayank Mahara, Tripti SBM Christ University Bangalore560029 India

data augmentation, also called implicit regularization, is one of the popular strategies to improve the generalization capability of deep neural networks. It is crucial in situations where there is a scarcity of high-quality ground-truth data. Also getting new samples is expensive and time consuming. This is a typical issue in the medical domain. Therefore, this study compares the performance of Affine and Generative Adversarial Networks (GAN)- based data augmentation techniques on the chest image X-Ray dataset. The Pneumonia dataset contains 5863 chest X-Ray images. The traditional Affine data augmentation technique is applied as a pre-processing technique to various deep learning-based CNN models like VGG16, Inception V3, InceptionResNetV2, DenseNet-169 and DenseNet-202 to compare their performance. On the other hand, DCGAN architecture is applied to the dataset for augmentation. Evaluation measures like accuracy, recall and AUC depict that DCGAN outperforms other traditional models. The most important advantage of DCGAN is that it is able to identify fake images with 100% accuracy. This is especially relevant for the medical domain as it deals with the life of individuals. Thus, it can be concluded that DCGAN has better performance as compared to affine transformations applied to traditional CNN models. © 2023 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Network Traffic Image dataset Generation from PCAP files for Evaluating Performance of machine learning Models

Network Traffic Image Dataset Generation from PCAP files for...

引用

2022 international conference on engineering and MIS, ICEMIS 2022

作者： Swathi, S. Lakshmeeswari, G. Vnr Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Department of Cse Visakapatnam India

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Detection of network attack traffic in network environments is majorly studied in the literature by applying various data mining and machine learning techniques. The existing studies which applied data and machine learning techniques consider network traffic instances in either pcap or csv representations. In the current contribution, the basic idea is to use network traffic images which are obtained from pcap representations. These generated network traffic images are then used to design and build efficient machine learning models. Another advantage of representing the network traffic in the form of images is that these images can be used to evaluate the computational performance of deep learning models also. Till date, the existing machine learning studies on cloud network traffic attack detection and network environments did not apply network traffic images to build machine learning models but only applied to build deep learning models which form the motivation for the present research. We propose to use the network traffic images for designing new machine learning models. © 2022 IEEE.

关键词： data mining

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Design and Implementation of Pedestrian Detection System Based on Video Image

Design and Implementation of Pedestrian Detection System Bas...

引用

2022 international conference on Computer Vision, Application, and Algorithm, CVAA 2022

作者： Zhang, Jinhui Chongqing Aerospace Polytechnic College Chongqing401147 China

ISBN: (纸本)9781510664036

Facing the problems of low working efficiency, poor accuracy and insufficient stability of traditional motion detection algorithms and machine learning algorithms in pedestrian detection, this paper will take artificial intelligence technology as the core, adopt R-CNN series deep learning models, combine support vector machine (SVM) classifier, and rely on TensorFlow deep learning framework to realize pedestrian detection and recognition in video and picture files with the support of OpengCV Open Source Visual Library. Combined with *** framework, C# language is used to complete the design and development of each functional module, improve the deployment of the corresponding API interface, and build a front-end interactive interface to form a pedestrian detection system based on Web. The overall design of the system chooses B/S architecture, which allows users to access the Web Server through simple request operation to complete the detection of video image data in the database, and return the detection results to the front page for display. The system will greatly improve the precision and accuracy of pedestrian detection, and make a positive attempt to further expand the application scenarios of pedestrian detection in video images. © 2023 SPIE.

关键词： Deep learning

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Short-term mathematical prediction model of air quality based on CEEMD-ELM-PSO

Short-term mathematical prediction model of air quality base...

引用

IEEE international conference on Electrical engineering, Big data and Algorithms (EEBDA)

作者： Qu, Hairong Zhang, Runnan Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Hohai Univ Coll Comp & Informat Nanjing Peoples R China

ISBN: (纸本)9781665416061

After entering the new era, people's living standard has been significantly improved, the concept of environmental protection has been deeply rooted. People pursue a greener and healthier lifestyle, and the concern for air quality has become more and more intense. People need to continuously analyze relevant mechanisms to help people predict air environment quality effectively and prevent relevant hazards in time. It is difficult to obtain a better prediction effect under different AQI fluctuation trends in the traditional single machine learning model for air quality prediction. In order to solve the problem, the prediction method is improved, and the ELM-PSO algorithm is used to predict the future AQI, which helps to analyze the future air change trends from a macro perspective. This bit combines Beijing's short-term air quality data and proposes an air quality short-term prediction model based on complementary ensemble empirical modal decomposition and an optimal limit learning machine. Firstly, the data are decomposed by CEEMD to reduce the non-smoothness of the data, thus reducing the impact of non-smoothness on the prediction accuracy;then ELM model prediction is performed for each decomposed series obtained;the output weights of the limit learning machine are used to PSO optimization search, and then construct the prediction model based on the limit learning machine;finally, all the prediction components are superimposed to obtain the final results. The results surface that the prediction model proposed in this paper has high accuracy in short-term air quality prediction.

关键词： Air quality prediction Extreme learning machine Particle swarm optimization algorithm Complementary ensemble empirical modal decomposition machine learning

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Text Mining Of English Grammar Books In 19th ~ 20th Century

Text Mining Of English Grammar Books In 19th ~ 20th Century

引用

2022 international conference on machine learning and Knowledge engineering, MLKE 2022

作者： Cui, Chao Inner Mongolia Agricultural University College of Foreign Languages Hohhot China

ISBN: (纸本)9781665495677

English grammar books are important materials for English learning, in order to systematically analyze the themes and trends of English grammar books in the 19th and 20th centuries, unstructured data and structured information are correlated, in this paper, text mining, co-word analysis and other research methods were adopted to study 15 books named English Grammar in the Gutenberg Project, which lasted from 1812 to 1918 and had a total of 1863536 English words. Wordstat9.0.4, KH CODER3 and Excel were used to analyze the collected information. This paper presents the results of text mining, such as word frequency analysis, co-occurrence analysis, keyword analysis and part of speech analysis. © 2022 IEEE.

关键词： data mining

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Deletion-Anticipative data Selection with a Limited Budget 41

Deletion-Anticipative Data Selection with a Limited Budget

引用

41st international conference on machine learning, ICML 2024

作者： Sim, Rachael Hwee Ling Fan, Jue Tian, Xiao Jaillet, Patrick Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. However, the resulting high model performance, measured by a data utility function, may not be preserved when some data owners, enabled by the GDPR's right to erasure, request their data to be deleted from the ML model. This raises an important question for learners who are temporarily unable or unwilling to acquire data again: During the initial data acquisition of a training set of size k, can we proactively maximize the data utility after future unknown deletions? We propose that the learner anticipates/estimates the probability that (i) each data owner in the feasible set will independently delete its data or (ii) a number of deletions occur out of k, and justify our proposal with concrete real-world use cases. Then, instead of directly maximizing the data utility function, the learner can maximize the expected or risk-averse post-deletion utility based on the anticipated probabilities. We further propose how to construct these deletion-anticipative data selection (DADS) maximization objectives to preserve monotone submodularity and near-optimality of greedy solutions, how to optimize the objectives and empirically evaluate DADS' performance on real-world datasets. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Research on Collaborative Optimization of Big data Analysis and Artificial Intelligence Based on Deep learning 24

Research on Collaborative Optimization of Big Data Analysis ...

引用

3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Zhang, Xu Li, Suruo Chen, Huafeng Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Big Data Res Ctr Jingmen 448000 Peoples R China

ISBN: (纸本)9798400709777

Drawing upon deep learning techniques, this study investigates big data analysis methods across various scenarios to enhance data value. The proposed intelligent collection device is capable of harvesting both unstructured data, such as videos and images, and structured data, including geographical coordinates, operational metrics, and electronic device identifiers. Building on this, the paper introduces an architecture for a big data analysis system underpinned by deep learning, elaborating on its critical technologies. Furthermore, it underscores how community and end-to-end power transactions bolster the adaptability and maximize the value of power generation entities. Although Multi-Agent Deep Reinforcement learning offers a novel approach to managing energy among multiple prosumers, challenges such as environmental volatility, the safeguarding of prosumer privacy, and computational demands persist. This research aims to explore a multi-agent reinforcement learning algorithm that employs parameter sharing and deep deterministic policy gradients to enhance learning efficiency and mitigate training complexities through shared strategies and experiences among agents. Additionally, by leveraging a reputable third party for disseminating comprehensive community market data to prosumers, this approach not only promises to secure prosumer privacy effectively but also to minimize environmental uncertainty and augment the algorithm's scalability.

关键词： Deep learning Big data analysis Artificial intelligence collaborative optimization

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