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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是1091-1100 订阅

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Reading Technology for Blind People Using OCR and Neura machine learning Method 6

Reading Technology for Blind People Using OCR and Neura Mach...

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Preethi, C. Haripriya, K. Hammed, S. Shahul Pavalarajan, S. Priyadharshini, S. Varshambigai Achithra, B.R. Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350394702

this study focuses on the creation of a Neura Smart Book Reader that can help people who have visual impairments, especially those who are blind or have low vision and do not know Braille. the project uses IoT technology, which includes hardware, infrastructure, and services within the Internet of things framework. the project primarily uses the energy-efficient Raspberry Pi device, which has machine-learning capabilities, making it adaptable. the Raspberry Pi computer is small and portable and operates on minimal power, showcasing advanced computing and machine learning algorithms. the system has a camera for capturing book pages, and the software performs Optical Character Recognition (OCR), combined with machine learning-driven character recognition. this improves the system's proficiency with each use. Once the image is recognized, the Neura Smart Book Reader converts it into audible text, allowing users to listen without tactile interaction. this proves how machine learning can improve accessibility. the device also offers online text-to-voice conversion via WiFi and 4G services and uses machine learning-based algorithms for enhanced linguistic articulation. this ensures a seamless reading experience for users across digital and hardcopy books. this amalgamation of IoT, Raspberry Pi, and machine learning technologies highlights the latest accessible reading solutions for visually impaired individuals,proving how machine learning can improve accessibility technology. © 2023 IEEE.

关键词： machine learning

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A ML-combined closed-loop identification method for thermodynamic process 4

A ML-combined closed-loop identification method for thermody...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Mao, Juecen Yin, Qi Pan, Lei Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and Environment Southeast University Jiangsu Nanjing210096 China Jiaxing Economic and Technological Development Zone Management Committee Zhejiang Jiaxing314000 China

ISBN: (纸本)9798400709449

To deal with the adverse effects of multi-input and multi-disturbance on thermal plant model identification, a thermal closed-loop process identification method integrating machine learning is proposed. Firstly, the optimal method of identification data based on feature classification machine learning is used to select the historical operation data suitable for identification. the method establishes the feature construction rules of historical operation data, reduces the dimension of data and can better represent the dynamic information. the random forest is used to establish the identification data classification rule model, so as to obtain high prediction accuracy of identification model. Secondly, an identification process integrating input variable selection, model order determination and unbiased parameter estimation is proposed. Among them, the variance expansion factor method and the variable projection analysis method of partial least square regression are used to select the input variables of the identification model, and then the model parameters are unbiasedly identified based on the asymptotic identification method. the ML-combined identification method is applied to the deaerator water level system of thermal power unit, and its reliability and accuracy are verified by closed-loop simulation. © 2023 ACM.

关键词： machine learning

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Contrastive learning Enhanced by Transformer Block for Time Series Forecasting 4

Contrastive Learning Enhanced by Transformer Block for Time ...

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Wang, Xinghao Xiao, Huimin Xing, Qiang College of Artificial Intelligence Southwest University Chongqing400715 China

ISBN: (纸本)9781510672505

the analysis of time series data holds significant application value in many real-world scenarios, such as forecasting power generation and traffic flow. Recently, time series forecasting models based on representation learning have garnered widespread attention due to their ability to maximally retain meta-information. However, the current representation models for time series based on learning typically employ stacked convolutional neural networks (CNNs) as their primary encoder, constructing positive and negative samples at the timestamp level for various time series data. CNNs are primarily designed to capture local patterns. Yet, in time series forecasting tasks, long-term dependencies and global patterns are more crucial, and CNNs might not be adept at capturing these patterns. To address this issue, we introduce "Contrastive learning Enhanced by Transformer Block for Time Series Forecasting." this method initially employs the Transformer to extract features characterized by global patterns. It then uses a contrastive loss function to encourage the model to discern similarities among positive samples and differences among negative ones. Ultimately, the features learned by the model are used for forecasting. Experimental results indicate that our approach outperforms traditional methods in time series forecasting tasks. © 2023 SPIE.

关键词： Forecasting

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An Industrial Network Protocol Fuzzing Framework Based on Deep Adversarial Networks 4

An Industrial Network Protocol Fuzzing Framework Based on De...

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4th international conference on computer Engineering and application, ICCEA 2023

作者： Fen, Tao Li, Deming Yuan, Zhanting Unit: School of Computer and Communication Lanzhou University of Technology China. 36 Pengjiaping Road Qilihe District Gansu Lanzhou China Political Consultative Conference Lanzhou China Unit: Gansu Provincial Committee of the Chinese People's Tuanjie Road Chengguan District Gansu Gansu China

ISBN: (纸本)9798350347548

Fuzzing test is one of the major vulnerability methods in recent years and is also widely used for testing protocols. However, most industrial control network protocol norms are unknown and running in a isolateded environment, so it is difficult to directly apply reverse engineering to construct protocol syntax rules. In this paper, we propose an approach to automatically generate test cases by Deep learning to parse the protocol format and learn the grammar, using Sequence Generative Adversarial Networks (SeqGAN) to train a generative model on real protocol messages to learn the protocol grammar, and then generating spurious but plausible messages as fuzzing test cases. Based on this approach, we present a fuzzing framework to automatic test industrial control protocols. Compared to traditional methods, our approach does not rely on protocol specifications and can be applied to most of protocols. the final experimental data also shows that our framework can effectively learn the features of the target protocols and successfully trigger some exceptions. © 2023 IEEE.

关键词： Accident prevention

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Automatic Anomaly Detection Using Unlabelled Log Data 25

Automatic Anomaly Detection Using Unlabelled Log Data

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhu, Yujia Min, Geyong Wu, Yulei Wang, Haozhe University of Exeter Falcuty of Environment Science and Economy Department of Computer Science ExeterEX4 4QF United Kingdom

ISBN: (纸本)9798350330014

In this paper, we embark on a meticulous investigation of anomaly detection within OpenStack system logs, employing a two-pronged approach that ingeniously combines a task splitting method for log grouping with a two-layer stacked Long Short-Term Memory (LSTM) network for the anomaly detection phase. Our exploration commences with the extraction of typical normal operation patterns from system logs, followed by the application of our LSTM-based anomaly detection model trained on these normal patterns. Our study reveals the remarkable potential of this combined methodology in effectively distinguishing between normal and abnormal tasks, thereby offering profound insights into system operation and potential areas of concern. this exploration stands as a proof to the immense potential of modern machine learning techniques in navigating the ever-increasing complexity and volume of log data. © 2023 IEEE.

关键词： Anomaly detection

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Modified Barnacles Mating Optimization with Deep learning based Tomato Disease Detection and Classification 4

Modified Barnacles Mating Optimization with Deep Learning ba...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sharmila, M. Natarajan, M. Annamalai University Department of Computer and Information Science India

ISBN: (纸本)9798350300093

Tomatoes are the second largest crop grown in India. they are more commonly known vegetables and be possible to find in all kitchens. It is the most popularly produced crop after potato and sweet potato. On the other hand, various diseases might damage the quantity and quality of tomato crops. Leaf disease detection and classification employs different DL algorithms which is a cutting-edge image processing technique yet produces comparatively new and proficient outcomes. this study develops a new Modified Barnacles Mating optimization with Deep learning based Tomato Disease Detection and Classification (MBMODL-TDDC) method. the MBMODL-TDDC method accurately determines the effectual identification of different kinds of tomato diseases. To accomplish this, the presented MBMODL-TDDC method uses a Wiener Filtering (WF) based noise removal process at the preprocessing step. In addition, the MBMODL-TDDC technique employs a RetinaNet object detector for tomato disease detection. Moreover, Hybrid Deep learning (HDL) algorithm was applied for classifying tomato diseases into different types. Eventually, the MBMO algorithm is used to adjust the hyperparameter values of the HDL model. the simulation results of the MBMODL-TDDC technique are tested on the tomato disease dataset. the result analysis portrayed the improvement of the MBMODL-TDDC algorithm over other current methodologies. © 2023 IEEE.

关键词： computer vision

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Research on Future Sales Prediction Based on Feature Engineering 4

Research on Future Sales Prediction Based on Feature Enginee...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Li, Junzhe Zhang, Zhaoqin Kang, Songhuang Lu, Yi Gan, Linyun Xu, Yuanchun Jingdezhen Ceramic University Information Engineering College Jingdezhen China

ISBN: (纸本)9798400708831

this paper conducts analysis and exploration using historical sales data combined with algorithms, implementing a machine learning model for future sales prediction based on feature engineering. Relevant data from the Kaggle platform is utilized, and through data cleaning, feature engineering, and other techniques, the model's accuracy is improved. the paper focuses specifically on feature engineering, employing two approaches: one based on time series, and the other introducing an improved feature engineering method based on entity interaction. the latter, which utilizes the dataset derived from this improved feature engineering, results in a superior final model, achieving a top 19% score on the Kaggle platform. © 2023 ACM.

关键词： machine learning

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application of Spectral Element Method and machine learning Technique in the Composite Concrete Beam Damage Prediction 1

引用

17th international conference on Acoustics and Vibration of Mechanical Structures, AVMS 2023

作者： Dutkiewicz, Maciej Machado, Marcela R. da Silva Coelho, Jefferson Faculty of Civil Architecture and Environmental Engineering and Architecture Bydgoszcz University of Science and Technology Al. Prof. S. Kaliskiego 7 Bydgoszcz85-796 Poland Department of Mechanical Engineering University of Brasilia Brasil70910-900 Brazil

ISBN: (数字)9783031480874

ISBN: (纸本)9783031480867

the paper presents the simulation results of the monitoring and prediction of damage of a cement composite beam using the analysis of changes in resonant frequencies with Spectral Element Method and the machine learning technique. For a beam with random generated crack, the effect of cracking on the change of resonance frequencies was shown by analyzing resonance curves comparing to the health beam. For the four basic resonance frequencies, frequencies were randomly generated, constituting the frequency database for the machine learning—Support Vector machine classification. Damage Index was calculated. the presented methodology is a procedure involving the simulation and prediction of beam damage. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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international conference on Electrical, computer, and Energy Technologies, ICECET 2024

International Conference on Electrical, Computer, and Energy...

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4th IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2024

ISBN: (纸本)9798350395914

the proceedings contain 353 papers. the topics discussed include: statistical approach of anaphoric resolution for computational linguistics using rule based system in text summarization;reliability-based design optimization for OPGW cable damper systems;a robust security method for medical image based on 2D Schaffer map;a mathematical model of asymmetric encryption based on linear Diophantine equations;leveraging vehicle predictive analytics through adaptive learning and cloud-aided sensor fusion;the work on the application of electrical machines and the project method in a public university of Peru;on digital art generation using generative adversarial networks;evaluation of blind signal separation objective function constructed using nonlinear functions;CPH white-box cartoon GAN: a patch-based style-swap approach;and unified structure to generate sparsity-inducing regularizers for enhanced low-rank matrix completion.

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application Strategies of computer Technology in the Field of Mathematical Modeling 4

Application Strategies of Computer Technology in the Field o...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Lv, Na School of Architecture and Environmental Engineering Dongying Vocational College Shandong Dongying257091 China

the era of big data has arrived in society, and the technology behind com-puter networks has not only raised the general level of people's living stand-ards but is also making steady headway in the direction of artificial intelli-gence. the most important theoretical basis for it is mathematics. the pro-cess of abstracting and synthesizing the laws of reality through the use of mathematical techniques is known as mathematical modeling. the applica-tion of mathematical modeling in the natural sciences seeks to approach a problem with more precision by employing mathematical rigor and a mathe-matical language that is systematically complete. At this point in time, the progression of mathematical modeling just can't happen without the assis-tance of advances in computer technology. the significance of computer technology in mathematical modeling is demonstrated by a detailed examina-tion of the formula underlying the computer technology model. After con-ducting research on the results of the mathematical modeling competition held over the course of several years, utilizing the artificial neural network (ANN) algorithm to analyze the data, and coming to the conclusion that the implementation of computer technology plays a role in the dissemination of mathematical modeling, we say that computer technology has a part to play in the dissemination of mathematical modeling. © 2024 the Authors.

关键词： artificial intelligence artificial neural network computer technology mathematical modeling

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