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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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Ranking-based Client Imitation Selection for Efficient Federated learning 41

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

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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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data-driven power system small-signal stability assessment and correction control model based on XGBoost

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ENERGY REPORTS 2022年 8卷 710-717页

作者： Yang, Yude Zhang, Xiu Yang, Lizhen Guangxi Univ Key Lab Power Syst Optimizat & Energy Saving Tech Nanning 530004 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Agr Vocat Univ Nanning 530004 Peoples R China

The influence of small-signal stability on the safety and stability of the power system is becoming more prominent. A mapping model based on steady-state operation information is established using the sample learning method, which provides a new technical path for the rapid assessment and correction of significant power grid oscillation characteristics. This paper establishes a small signal stability assessment and correction control model based on the Extreme Gradient Boosting (XGBoost) algorithm. Firstly, the XGBoost model is obtained by analyzing the mapping relationship between generator power, node power, branch power, and minimum damping ratio. Then, the sensitivity of the generator damping ratio is calculated, and the objective is to minimize the active power adjustment amount of the generator. The stability constraint and power balance are the constraint conditions to establish the optimization correction model, obtain the optimal adjustment amount, correct the minimum damping ratio, and improve the system's stability. Finally, the minimum damping ratio after correction is obtained, and the modified damping ratio is estimated by XGBoost algorithm. The performance of the proposed model is verified in IEEE 3-machine 9-node and 10-machine 39-node systems. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 2021 The 2nd international conference on Power engineering, ICPE, 2021.

关键词： Small-signal stability assessment Correction XGBoost machine learning

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Transferable Convolutional Neural Networks for IMU-based Motion Gesture Recognition in Human-machine Interaction 24

Transferable Convolutional Neural Networks for IMU-based Mot...

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24th international conference on Control, Automation and Systems

作者： Kim, Joonhyun Lee, Jungsoo Kim, Wansoo Hanyang Univ Dept Appl Artificial Intelligence Seoul 15588 South Korea Hanyang Univ ERICA Dept Robot Ansan 15588 South Korea

ISBN: (纸本)9798331517939;9788993215380

Hand gestures, a fundamental aspect of human non-verbal communication, are often leveraged in the domain of Human-machine Interaction (HMI) to implement more user-friendly interfaces. In this study, we propose a Convolutional Neural Network (CNN) model designed for efficient motion gesture recognition, designed to be deployed on a smartwatch, using only one Inertial Measurement Unit (IMU) sensor worn on the wrist. By directly processing lowdimensional motion data on linear acceleration and angular velocity, our model demonstrates high performance using a simplified model structure. Furthermore, we explore the potential of applying a transfer learning approach to our CNN model for novel gesture classification problems. This method demonstrates that a well-trained CNN model's backbone network effectively extracts motion features necessary for the recognition of new gestures. Validation processes in scenarios with limited data-employing specific training-to-test ratios of 1:3, 1:7, and 1:19-allowed for a comparison of our model's performance against baseline models trained from scratch. Our approach initially achieves an accuracy rate of 99.48 +/- 0.25% in recognizing ten distinct motion gestures through the directly processing raw data on linear acceleration and angular velocity directly. Moreover, the transfer learning model outperformed the baseline model trained from scratch with 95.62 +/- 0.99%, 93.23 +/- 1.41%, 92.81 +/- 1.62% accuracy in learning four new gestures under data limitations, respectively. This study shows that the proposed model maintains high performance with lightweight structure, while also highlighting how transfer learning approach can address the challenges of data collection and set the stage for creating more intuitive and user-centric interaction systems.

关键词： Hand gesture Recognition (HGR) Human-Robot Interaction (HRI) Convolutional Neural Networks (CNNs) Transfer learning

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Prediction of Rainfall Using machine learning 4

Prediction of Rainfall Using Machine Learning

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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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On-Device Deep learning for Retrieving System and User Timestamps from Noisy Chat Images 3

On-Device Deep Learning for Retrieving System and User Times...

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3rd international conference on machine learning and data engineering, icmlde 2024

作者： Kayarvizhy, N. Choudhury, Antara Roy Rekha, G.S. Bhuvan, G. Sanchi, Chandramouli BMS College of Engineerning Bangalore560019 India

Extracting information from scanned or digital images are helpful in different real time applications. Optical Character Recognition (OCR) is popular in extraction of information from digital images taken in the ideal lighting condition. But OCR method is not efficient when the images are scanned and clicked with the noisy and blurry environment. Deep learning-based OCR method performs good in text detection and localization in the digital images. Existing methods do not focus on the timestamp detection in screenshots. In this paper, YoloV5 (You Only Look Once) model and Faster RCNN (Faster Region-Convolutional Neural Network) are implemented to locate the region of time stamps from the screenshot images from various messaging applications. Our models take mobile screenshot of conversation applications as input and detect the system timestamps and User timestamps. 5k screenshot images have been collected from 16 messaging applications with various backgrounds. The images are annotated with 3 classes. YoloV5 and faster RCNN models are trained with the annotated dataset. Accuracy of YoloV5 model increased to 97.6 with training it with more dataset than RCNN and also the size is limited to 3.85MB. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Convolutional neural networks

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Dynamic modeling for VSG cluster by using data-physical driven method

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ENERGY REPORTS 2022年 8卷 227-234页

作者： Li, Yunlu Ma, Guiqing Yang, Junyou Xu, Yan Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China

In the task of system analysis for VSG cluster, aggregation modeling method is widely used for simplification. However, there are inevitable errors occur from the process of cluster aggregation. To improve the accuracy of VSG cluster modeling, a data-physical driven modeling method is presented. At first, the equivalence between aggregation error and black box modeling issue is analyzed. Secondly, a hybrid model structure is proposed, which consists of single machine aggregation model and deep neural network based aggregated-error model. Then, to illustrate the modeling procedure, test cases are studied under large disturbance and multi-operating points conditions. The simulation results confirm that the proposed method can provide satisfactory modeling accuracy. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the The 2nd international conference on Power engineering, ICPE, 2021.

关键词： Virtual synchronous generator Dynamic modeling Deep learning Neural network

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machine learning algorithm selection for real-time energy management of hybrid energy ship

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ENERGY REPORTS 2022年 8卷 1096-1102页

作者： Gan, Ming Hou, Hui Wu, Xixiu Liu, Bo Yang, Yawei Xie, Changjun Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Wuhan Univ Technol Shenzhen Res Inst Shenzhen 518000 Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Peoples R China

The advantages of hybrid energy ship with photovoltaic in energy conservation and emission reduction are becoming more and more prominent with increasing tension of global fossil energy. However, how to deal with photovoltaic uncertainty in real time and make photovoltaic efficiently connected to ship microgrid has become a key technical problem. Therefore, we propose a real-time energy management strategy for hybrid energy ship based on approximate model predictive control. Firstly, aiming at minimizing the operating cost and deviation from the reference state of charge, an energy management framework based on model predictive control is established. Secondly, the machine learning algorithm is trained to approximate the optimal control action of model predictive control offline, and the performance of different machine learning algorithms is analyzed quantitatively. Finally, taking a ferry equipped with photovoltaic as an example, the appropriate machine learning algorithm and sample number are selected. The results show that the proposed strategy can not only ensure the optimization performance, but also effectively reduce the amount of calculation and realize the real-time operation of energy management in hybrid energy ship. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 2021 The 2nd international conference on Power engineering, ICPE, 2021.

关键词： Hybrid energy ship machine learning Approximate model predictive control Real time energy management

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Garbage Classification System Based on Deep learning

Garbage Classification System Based on Deep Learning

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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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Breast Cancer Prediction Using Advance machine learning Algorithms 8

Breast Cancer Prediction Using Advance Machine Learning Algo...

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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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