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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921-930 订阅

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Creating a credit card anti-fraud prediction model using TensorFlow and machine learning 2

Creating a credit card anti-fraud prediction model using Ten...

引用

2nd international conference on machine learning and Intelligent Systems engineering, MLISE 2022

作者： Du, Yan Tongji University Tongji University College Of Mechanical Engineering Shanghai China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

People's desire for credit cards is increasing because of the rapid rise and development of e-commerce. There is no doubting that the advancement of e-commerce has made life and work more easier for individuals. But, at the same time, this system has some flaws, which are occasionally used to get fraudulent benefits. In the real world, both fraudulent and legitimate transactions occur, and traditional pattern matching algorithms are typically ineffective in detecting fraud. This article combines tensor flow and machine learning to process historical credit card transaction data, perform machine learning, develop a credit card anti-fraud prediction model, and forecast consumer credit card theft in advance. The recall rate and accuracy rate of the model are obtained, which is conducive to building an efficient and accurate credit card fraud detection mechanism. © 2022 IEEE.

关键词： machine learning

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Estimation of loosening torque in bolted joints from experimental data and regression models 14

Estimation of loosening torque in bolted joints from experim...

引用

14th international conference on Recent Advances in Structural Dynamics, RASD 2024

作者： Coelho, Jefferson Machado, Marcela Dutkiewicz, Maciej Department of Mechanical Engineering University of Brasília Brazil Institute of Exact Sciences and Technology Federal University of Amazonas Brazil Faculty of Civil Environmental Engineering and Architecture University of Science and Technology Bydgoszcz Poland

Structures are often joined using fasteners such as rivets or bolts, which are chosen based on their ability to meet performance requirements. Bolts are popular due to their advantages, such as avoiding movement and ensuring the stability and security of bolted joints. However, one of the main issues with fasteners is loosening, which can be caused by shock and vibration and lead to serious damage and structural failure. The use of machine learning techniques for bolt joint verification is limited. Hence, this study proposes a machine learning workflow centred on estimating torque by analysing raw spectral signals derived from experimental tests. This approach accounts for intrinsic variabilities in torque estimation and enhances our results performance. To enrich the experimental dataset, the study employs a technique involving the generation of synthetic datasets based on statistical moments derived from experimental data. Results show that machine learning can estimate torque in joint structures based on data collected under various conditions, improving performance with the lowest error rate. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Bolts

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Application of Extreme learning machine-Autoencoder to Medium Term Electricity Price Forecasting

Application of Extreme Learning Machine-Autoencoder to Mediu...

引用

IEEE international conference on Environment and Electrical engineering (EEEIC) / IEEE Industrial and Commercial Power Systems Europe (I and CPS Europe) conference

作者： Najafi, Arsalan Homaee, Omid Jasinski, Michal Golshan, Mahdi Leonowicz, Zbigniew Wroclaw Univ Sci & Technol Dept Elect Engn Fundamentals Wroclaw Poland Islamic Azad Univ Sepidan Branch Dept Engn Sepidan Iran

ISBN: (纸本)9781665485371

This paper proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind is to use trained weights for hidden layer instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.

关键词： Forecasting electricity price extreme learning machine-autoencoder.

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Classification of Daily Activities by Different machine learning Models Based on Characteristics in the Time Domain 1

引用

5th international conference on Biomedical and Health Informatics (ICBHI)

作者： Ojeda Prado, Luis Antony Borja Inga, Rolando Samuel Ojeda Quispe, Fiorella Cristina Sifuentes Llatas, Mauricio Daniel Paredes Arellano, Alexander Pontificia Univ Catolica Peru Univ Peruana Cayetano Heredia Lima Peru

ISBN: (数字)9783031592164

ISBN: (纸本)9783031592157;9783031592164

Portable physical activity monitors provide detailed, continuous and objective measurements of individual physical activity in the environment of daily activities. A major problem with wristbands, pedometers, and smartphones that use accelerometer technology is that they measure involuntary jerks as steps. Therefore, they generate inaccurate values resulting in erroneous data. Therefore, the purpose of this study is to determine and contrast the performance obtained for the classification of daily activities of different machine learning models based on characteristics in the time domain of signals obtained from accelerometry collected at various points of the body. The development of the activity identifier is based on models and characteristics of existing developments of calorie counters by accelerometric signals;these features are extracted in the time domain. The following classifiers were applied: Logistic Regression, K-Nearest Neighbors, Support Vector machine, Gaussian Naive Bayes, Decision Tree, Random Forest, Light Gradient Boosting and Extreme Gradient Boosting. The performance of each model was measured by how accurately it emerged to classify 4 daily activities based on the test set. The results show that, to have an accuracy greater than 70% in most models, at least 2 accelerometers are required.

关键词： machine learning physical activity classification performance characteristics in the time domain signals from accelerometry

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Ranking-based Client Imitation Selection for Efficient Federated learning 41

Ranking-based Client Imitation Selection for Efficient Feder...

引用

41st international conference on machine learning, ICML 2024

作者： Tian, Chunlin Shi, Zhan Qin, Xinpeng Li, Li Xu, Chengzhong University of Macau China University of Texas Austin United States University of Electronic Science and Technology of China China

Federated learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency, especially given the vast heterogeneity in training capabilities and data distribution across devices. To deal with these challenges, we introduce a novel device selection solution called FedRank, which is based on an end-to-end, ranking-based model that is pre-trained by imitation learning against state-of-the-art analytical approaches. It not only considers data and system heterogeneity at runtime but also adaptively and efficiently chooses the most suitable clients for model training. Specifically, FedRank views client selection in FL as a ranking problem and employs a pairwise training strategy for the smart selection process. Additionally, an imitation learning-based approach is designed to counteract the cold-start issues often seen in state-of-the-art learning-based approaches. Experimental results reveal that FedRank boosts model accuracy by 5.2% to 56.9%, accelerates the training convergence up to 2.01× and saves the energy consumption up to 40.1%. Copyright 2024 by the author(s)

关键词： Federated learning

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Prediction of Rainfall Using machine learning 4

Prediction of Rainfall Using Machine Learning

引用

4th international conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022

作者： Mahajan, Deepika Sharma, Sandeep Guru Nanak Dev University Department of Computer Engineering and Technology Amritsar India

ISBN: (纸本)9781665456357

Rainfall Prediction is a challenging task due to irregular patterns of rainfall and climate variations all around the world. Rainfall forecasts helps to prevent floods and even helps in agriculture for growing crops. machine learning aids in rainfall prediction by using unknown patterns from historical weather data. In this paper, classification technique of machine learning is used for rainfall prediction. The trained data is 80% and tested data is 20% of the whole data. The count of actual forecast and predicted forecast is analyzed. The classification model used is found to be 85% accurate. Many other machine learning algorithms are also compared on means of accuracy. © 2022 IEEE.

关键词： machine learning

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Station-Wise Boarding Passenger Flow Prediction for Public Transport Using Various machine-learning Methods 7th

Station-Wise Boarding Passenger Flow Prediction for Public T...

引用

7th international conference of Transportation Research Group of India, CTRG 2023

作者： Patel, Madhuri Patel, Samir B. Swain, Debabrata Patel, Shubh Department of Computer Engineering Pandit Deendayal Energy University Gandhingar India Department of Information Technology L D College of Engineering Ahmedabad India Department of Computer Science & Engineering Nirma University Ahmedabad India

ISBN: (纸本)9789819610365

The migration behavior of regular passengers in city transport refers to the movement patterns and preferences of individuals who use public transportation on a regular basis. These passengers may have specific routes, travel times, and preferences for certain modes of transportation. Passenger flow prediction is crucial for understanding and managing this behavior. Accurate predictions enable transportation operators to optimize their services by adjusting vehicle frequency and capacity, deploying additional services during peak periods, providing real-time information, identifying high-demand areas, and expanding the network. This improves the efficiency and reliability of public transportation, catering to the needs of regular passengers. data science and machine-learning methods allow us to extract correlations from historical data, improving the accuracy of passenger flow prediction. The passenger flow on a station is highly affected by various factors such as the day of the week, holiday, rain, available routes from that station, and some uncertain events like COVID-19. In this study, we have successfully reported passenger flow prediction at various stations using ensemble machine-learning algorithms. Comparative analysis of implemented work has been carried out with the help of statistical parameters and visual infographic details. For accurate prediction model, accurate data cleaning, pre-processing and feature selection based on correlation have been performed on real dataset of Thane Municipal Transport (TMT) from April 2021 to May 2022. We have also compared the performance of prediction models based on month, day and individual station for moderately deviated data and highly deviated data resulted due to effect of COVID-19 pandemic and Taukte cyclone. An exhaustive comparative analysis between train set and test set have been reported with necessary parameters. Comparing to benchmark models, the XGB regressor and Random forest model can reach most accur

关键词： Decision trees

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Breast Cancer Prediction Using Advance machine learning Algorithms 8

Breast Cancer Prediction Using Advance Machine Learning Algo...

引用

8th international conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Jain, Vinod Agrawal, Mayank Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665408165

Breast cancer is very common type of cancer now a day. It is observed in many of the women and responsible for many deaths in recent days. In this work the power of machine learning classifiers is applied in prediction of breast cancer. Three machine learning classifiers LR, KNN and Decision Tree are applied on a standard data set and their prediction accuracy is calculated. From the experiment it is concluded that the LR algorithms is best for breast cancer prediction. © 2022 IEEE.

关键词： machine learning

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Garbage Classification System Based on Deep learning

Garbage Classification System Based on Deep Learning

引用

2022 international conference on Optics and machine Vision, ICOMV 2022

作者： Han, Xiao Sun, De-Zhi Beijing Institute of Graphic Communication. Beijing100000 China

ISBN: (纸本)9781510653238

In recent years, sustainable development has attracted more and more attention, and waste classification is a social problem related to people's livelihood and social sustainable development. Therefore, this paper proposes a waste classification system based on deep learning. The system is built with tensorflow framework in deep learning and data training with mobilenet model, The data set is obtained by the method of web crawler, cleaned and classified, and the graphical interface is constructed by pyqt5. Finally, by uploading the pictures, the system can effectively classify the objects in the pictures into the correct garbage name and category. © 2022 SPIE

关键词： Sustainable development

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A Scene-Aware Prompt Template of Chinese Text data Augmentation Method Based on Large Language Model 5

A Scene-Aware Prompt Template of Chinese Text Data Augmentat...

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

作者： Kong, Lingfei Wang, Gang He, Jingheng Wang, Ying Gao, Yunpeng School of Data Science and Applications Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9798350355925

In Natural Language Processing (NLP) tasks, the target domain data is usually limited and the quality is poor. Text data augmentation is one of the effective methods to solve the problem of insufficient sample size in many NLP tasks. At present, on the one hand, the text data augmentation method is difficult to achieve the diversity and authenticity of the generated data text structure, on the other hand, it is also a challenge for the screening and elimination of low-quality samples in the generated data. Therefore, this paper proposes a scene-aware template of chinese text data augmentation method (Scene-Aware Prompt Template, SAPT) based on large language model. The SAPT method first uses prompt learning to construct a Scene-Aware Prompt (SAP)template. Secondly, combined with the text understanding and generation ability of the large language model, the SAP is improved by analyzing the semantic and contextual features of the original data to generate scene prompt words. Then, the original data is re-expressed into multiple data samples with different text structures but similar semantics by using the perfect SAP and large language model. Further, the quality of generated data samples is evaluated by fusing Rouge score and cosine similarity score, and low-quality data samples are filtered. The final generated data will be used for training the target domain task model. The experimental results on the Chinese news dataset THUCNews show that the overall performance of the classification model trained by the data generated by the SAPTDA method proposed in this paper reaches 89.89 %, which is 2.79 % higher than the baseline model, and has superior performance compared with the existing text data augmentation methods. © 2024 IEEE.

关键词： Semantics

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