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

共 33684 条记录，以下是81-90 订阅

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data-Driven Automation in Welding: Leveraging Advanced machine learning for Quality and Efficiency 27

Data-Driven Automation in Welding: Leveraging Advanced Machi...

引用

27th international conference on Advanced Communications Technology, ICACT 2025

作者： Khan, Muhammad Omair Mozumder, Md Ariful Islam Azam, Ibad Ullah Akter, Shifat Shima Joo, Mon-Il Kim, Hee Cheol Institute of Digital Anti-Aging Healthcare Inje University Gimhae-si Korea Republic of Department of Industrial & Management Engineering Inje University Gimhae-si Korea Republic of

ISBN: (纸本)9791188428137

Automation in manufacturing has played an important role in optimizing the welding process by attaining the utmost level of quality control with high operational efficiency. This paper analyzes 15,000 welding observations that belonged to the Hanthec organization. Through comparative analysis of SVM, MLP, and Decision Tree algorithms, our research establishes an effective machine learning framework for welding parameter optimization. The Decision Tree model obtained an excellent performance with the best accuracy of 80% and perfect recall. High correlations between power and thermal aspects as high as 0.96 have been obtained. The developed framework enables systematic parameter selection and demonstrates the potential for automated decision-making in industrial welding applications, contributing to improved manufacturing quality and reliability. ©20XX IEEE.

关键词： Smart manufacturing

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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...

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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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Application of machine learning in Enterprise Financial Risk Assessment: A Study About China's A-Share Listed Manufacturing Companies 5th

Application of Machine Learning in Enterprise Financial Risk...

引用

5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Yul, Kexin Yul, Zengyi Ma, Shuomin Xu, Pan Zhejiang Univ Technol Hangzhou 310023 Peoples R China SpringAI LLC Bellevue WA 98007 USA

ISBN: (纸本)9789819770038;9789819770045

Based on the financial data of domestic A-share listed firms from 2000 to 2022, this paper aims to explore the effectiveness of machine learning in identifying the financial risk of Chinese A-share listed manufacturing firms. To this end, a variety of machine learning models, including logistic regression, decision tree, random forest, XGBoost, SVM, and LSTM, are used to assess the financial risk of enterprises, and the key attributes of enterprise financial risk are extracted through the interpretability exploration and importance coefficient measurement. From this paper, the following conclusions are drawn: (i) XGBoost model performs the best on all attributes, showing its strong ability in dealing with complex financial datasets, and LSTM, which adds time-series factors, performs poorly, which is speculated that it may be related to the incompatibility of the characteristics of the financial data, the reliance on the time-series features, and the need for financial fine features. (ii) Audit opinion, Net Profit and ROA are key influencing factors.

关键词： Financial risk machine learning XGBoost LSTM

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Enhancing Academic Feedback Systems with Deep learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive learning

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Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

The integration of the Internet of Things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. This paves the way for a wide variety of intelligent IoT applications to be developed. This article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. These include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015-2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT. © 2025 IEEE.

关键词： Federated learning

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A Spatiotemporal machine learning Framework for the Prediction of Metocean Conditions in the Gulf of Mexico: Application to Loop Current and Loop Current Eddy Forecasting

A Spatiotemporal Machine Learning Framework for the Predicti...

引用

Offshore Technology conference, OTC 2025

作者： Steele, E.C.C. Juniper, M.C.R. Pillai, A.C. Ashton, I.G.C. Chen, J. Jaramillo, S. Zarate, L. Met Office FitzRoy Road Exeter United Kingdom University of Exeter Penryn Campus Penryn United Kingdom University of Plymouth Drake Circus Plymouth United Kingdom Shell Global Solutions US Inc HoustonTX United States

ISBN: (纸本)9781959025610

We are entering an exciting new era of data-driven weather prediction, where forecast models trained on historical data (including observations and reanalyses) offer an alternative to directly solving the governing equations of fluid dynamics. By capitalizing on a vast amount of available information – and capturing their inherent patterns that are not represented explicitly – such machine learning-based techniques have the potential to increase forecast accuracy, augmenting traditional physics-based equivalents. Here, we adapt and apply a promising machine learning framework – originally proposed by the present authors for regional prediction of ocean waves – to the operational forecasting of the Loop Current and Loop Current Eddies (LC/LCEs) in the Gulf of Mexico (GoM). The approach consists of using an attention-based long short-term memory recurrent neural network to learn the temporal patterns from a network of available observations, that is then combined with a random forest based spatial nowcasting model, trained on highresolution regional reanalysis data, to develop a complete spatiotemporal prediction for the basin. Since machine learning approaches are typically physics-agnostic, an identical framework to that developed for the prediction of ocean waves can be used for the prediction of surface currents, with the only difference being the training datasets to which this is exposed. This is illustrated here using a period of three months of training data from October 2022 to December 2022, with the model driven by only three observation sites in the northern GoM. As such, it is unrealistic to expect performance for an unseen week in January 2023 to be equivalent to smaller/simpler domains with a more favorable quantity, quality and coverage/ distribution of input observations but, despite these severe constraints, the ability of the model to forecast a plausible structure of the LC/LCE system is nonetheless impressive. The architecture of the MaLCOM framew

关键词： Long short-term memory

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An Efficient Federated Meta Unlearning Algorithm with Enhanced Privacy Protection 20th

An Efficient Federated Meta Unlearning Algorithm with Enhan...

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20th IFIP WG 10.3 international conference on Network and Parallel Computing, NPC 2024

作者： Wang, Yani Ying, Zuobin Pan, Zijie Zhu, Enmin Zhou, Wanlei City University of Macau Macau S.A.R. Taipa China University of Macau Macau S.A.R. Taipa China

ISBN: (纸本)9789819628636

In machine learning, the ability for models to selectively forget specific data is increasingly crucial. Traditional "machine unlearning" methods often involve extensive retraining, which is computationally expensive and impractical for real-time applications. This paper introduces Federated Meta Unlearning (FMU), a novel approach within the Federated learning (FL) framework that merges the adaptability of meta-learning with the precision of machine unlearning. FMU utilizes a modified Model-Agnostic Meta-learning algorithm, employing model decomposition and weighted aggregation techniques. This approach enables efficient data influence removal while minimizing impact on overall model performance. The key innovation of FMU is the decomposition of the federated model into adjustable components, allowing targeted modifications or removal based on unlearning requests, followed by weighted re-aggregation to maintain model integrity and flexibility. Extensive experiments across diverse datasets show that FMU reduces the computational burden of traditional unlearning while preserving high model performance and adhering to privacy standards. Additionally, FMU enhances adaptability to new data and tasks, making it suitable for dynamic and privacy-sensitive settings. In summary, FMU offers a substantial advancement in addressing the challenge of selective forgetting in machine learning. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning

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data Security Protection System in Human-machine-Object Fusion Intelligent Demand engineering System 3

Data Security Protection System in Human-Machine-Object Fusi...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Qi, Yongjun Li, Chenggao Tang, Hailin Shen, Junxuan Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China School of Information and Communication Technology Mongolian University of Science and Technology Ulaanbaatar Mongolia

ISBN: (纸本)9798331508456

In the engineering system of human-machine physical intelligence requirements, the multi-source and high complexity of data pose significant security risks. Therefore, this article designs a comprehensive data security protection system. Firstly, this article chooses a dynamic encryption strategy based on role-based access control. Then, this article combines differential privacy algorithm to construct a privacy protection model to ensure the privacy of data during sharing and processing. Subsequently, this article proposes a trusted data transmission protocol. Finally, this article introduces federated learning techniques to further improve the security and robustness of data in distributed scenarios. The experimental results show that the system performs well in terms of data security, privacy protection, and processing efficiency. Especially under high-intensity attacks, the system successfully detected and prevented approximately 98% of tampering. In the above data conclusions, the research method has significant advantages in encryption performance, achieving a better balance between privacy protection and data accuracy, demonstrating strong application prospects. © 2025 IEEE.

关键词： Requirements engineering

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A Network Traffic Classification Framework based on Deep learning

A Network Traffic Classification Framework based on Deep Lea...

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2024 international conference on Measurement, Communication, and Virtual Reality, MCVR 2024

作者： Zhang, Long Zhang, Zimu Ding, Yijia Liu, Yifei Sun, Jing Beijing Aerospace Institute for Metrology and Measurement Technology 1 Nanda Hongmen Road Fengtai District Beijing China Key Laboratory of Artificial Intelligence Measurement and Standards for State Market Regulation 1 Nanda Hongmen Road Fengtai District Beijing China

ISBN: (纸本)9781510691773

With the rapid development and widespread popularity of the Internet, the types and quantities of abnormal traffic are also increasing. Abnormal traffic detection, as an important part of the field of computer system and network security, has become a research hotspot in the information age. Traffic classification based on machine learning is one of the most widely studied abnormal traffic detection methods, but how to design and extract the relevant feature set that can achieve accurate description of traffic characteristics is the main problem faced by current research. Compared with traditional machine learning, deep learning does not require manual design and extraction of feature sets, which brings new opportunities for the development of traffic classification and anomaly detection. Therefore, this paper studies traffic classification and anomaly detection based on deep learning. The main research work and innovations are as follows: (1) Optimize and build network traffic data sets. In traffic classification and anomaly detection methods based on machine learning, data sets are the training basis of classification models, and the quality of data sets plays a key guarantee role in verifying the effectiveness of classification methods. (2)Aiming at the complexity of feature design and extraction of traditional machine learning traffic classification methods, a traffic classification method based on Netflow and DNN (Deep Neural Network) is proposed. (3) In order to solve the problem that traffic feature sets are difficult to extract in encrypted traffic classification, a Convolutional Neural Networks (CNN) and SAEN (Stacked Autoencoder Networks, Stack self-coding network), which uses a hybrid feature set construction method combining traffic statistics and spatial features. (4) In order to improve the efficiency of abnormal traffic detection, work is carried out from two aspects: first, combining abnormal traffic detection methods based on statistics;The second i

关键词： Convolutional neural networks

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High Precision Prediction of Electric Emission Carbon Factor by Deep learning Combined Ensemble learning 2nd

High Precision Prediction of Electric Emission Carbon Factor...

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2nd international conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

作者： Zhang, Xiaoshun Xiao, Yu Li, Jincheng College of Information Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan528311 China

ISBN: (纸本)9789819619641

Amid rising global pressure on the energy sector to lower carbon emissions and adopt sustainable practices, accurately predicting electric carbon factors is crucial for carbon reduction in power systems. This study introduces a method for accurately predicting electric carbon factors by utilizing deep learning and ensemble learning techniques. By combining the temporal prediction capabilities of Long Short-Term Memory (LSTM) networks with the high-dimensional data handling capabilities of Random Forest models, the proposed method significantly enhances prediction accuracy and robustness through multi-model integration. Experiments on the IEEE 39-bus and 118-bus power system datasets show that the proposed method surpasses traditional machine learning and single deep learning models in Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The findings of this study provide strong support for carbon emission prediction, low-carbon dispatch, and control in power systems, offering significant practical application value and broad application prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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