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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6841 条记录，以下是11-20 订阅

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Research on User Demand Mining and Repurchase Behavior Prediction Algorithm Selection Based on Big data 3

Research on User Demand Mining and Repurchase Behavior Predi...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Zhou, Tingwei Shanghai University Shanghai China

ISBN: (纸本)9798350380989

Based on the text data of authoritative skin care community, this study analyzes the user's needs and summarizes six topics on the premise that it has the option of repurchase intention. We need to calculate the emotional scores of six topics of each text data and contact the repurchase intention, thus constructing a feature data set, and then exploring which machine learning method is used to predict the repurchase behavior of online community review texts, which is relatively more accurate and reliable. © 2024 IEEE.

关键词： big data machine learning sentiment analysis text mining topic clustering

来源：评论

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Credit Card Fraud Detection Using Tree-based Algorithms For Highly Imbalanced data 3

Credit Card Fraud Detection Using Tree-based Algorithms For ...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Rezaei, Abdolazim Yazdinejad, Mohsen Sookhak, Mehdi Texas A&M Univ Dept Comp Sci Corpus Christi TX 78412 USA Univ Isfahan Dept Comp Engn Esfahan Iran

ISBN: (纸本)9798350372977;9798350372984

The escalation of cybercrime in recent years, moves on by the proliferation of electronic devices, poses a significant threat as users engage in diverse online activities, including financial transactions. Cybercriminals exploit phishing information and manipulate cardholders' account balances to illicitly appropriate funds, presenting challenges in traceability. This study addresses detecting fraudulent banking transactions within customers' account data, characterized by highly imbalanced fraud records. Employing diverse machine learning algorithms, particularly focusing on tree-based approaches, this research reveals that LightGBM and XGBoost exhibit superior performance, with LightGBM demonstrating notable supremacy. The resultant evaluation metric, the Area Under the Curve (AUC), attains a commendable value of 0.94649, underscoring the efficacy of the proposed machine learning models in cybersecurity measures against fraudulent financial activities.

关键词： Credit card Fraud detection machine learning Feature engineering LightGBM

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SmartBlend: Smart Video Recommendations on YouTube 3

SmartBlend: Smart Video Recommendations on YouTube

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Singh, Kulvinder Maurya, Aditya Kumar Solanki, Drishti Chandigarh University Dept of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350375190

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. A transformative approach in online education by integrating Multimodal data Mining and data Synthesis to develop a Smart System for learning Video Recommendations. With a focus on Personalized learning and Adaptability, the methodology employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials. SmartBlend introduces a novel approach to personalized learning video recommendations on YouTube, leveraging Multimodal data Mining and data Synthesis. The SmartBlendEdu framework employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials. © 2024 IEEE.

关键词： data mining

来源：评论

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engineering Carbon Emission-aware machine learning Pipelines 24

Engineering Carbon Emission-aware Machine Learning Pipelines

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Husom, Erik Johannes Sen, Sagar Goknil, Arda SINTEF Oslo Norway

ISBN: (纸本)9798400705915

The proliferation of machine learning (ML) has brought unprecedented advancements in technology, but it has also raised concerns about its environmental impact, particularly concerning carbon emissions. To address the imperative of environmentally responsible ML, we present in this paper a novel ML pipeline, named CEMAI, designed to monitor and analyze carbon emissions across the entire lifecycle of ML model development, from data preparation to training and deployment. Our endeavor involves an exhaustive evaluation process underpinned by three industrial case studies. These case studies are structured around the application of ML models to predict tool wear, estimate remaining useful lifetimes, and detect anomalies in the Industrial Internet of Things (IIoT). Leveraging sensor data originating from CNC machining and broaching operations, our research shows empirically the efficacy of carbon emissions as a dependable metric guiding the configuration of an ML development process. The essence of our approach lies in striking a balance between superior performance and minimal carbon emissions. Our findings reveal the potential to optimize pipeline configurations for ML models in a manner that not only enhances performance but also drastically reduces carbon emissions, thereby underlining the significance of adopting ecologically responsible engineering practices.

关键词： machine learning

来源：评论

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Technical Evaluation of the Impact of Industry-Education Integration on College Students’ Employment Rate Based on machine learning Models 24

Technical Evaluation of the Impact of Industry-Education Int...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Zhuang, Yan Su, Ruiqian Xiamen Institute of Technology Fujian Xiamen China

ISBN: (纸本)9798400718120

This study explores the impact of industry-education integration on college students’ employment rate using machine learning models. The original data was preprocessed through feature engineering, and models such as logistic regression, decision tree, and random forest were used, with hyperparameters optimized using techniques like grid search. The experimental results show that, among 58,342 graduate data, the neural network model performs best in evaluation metrics such as accuracy, recall, precision, and F1 score. The model is most accurate in predicting medium employment quality, with errors stemming from feature noise, nonlinear relationships, and data imbalance. Analysis based on major categories and school types indicates that the model performs better on data from science and engineering disciplines and key universities. This study contributes to the formulation of relevant educational policies. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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An Exploratory Study of dataset and Model Management in Open Source machine learning Applications 24

An Exploratory Study of Dataset and Model Management in Open...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Toma, Tajkia Rahman Bezemer, Cor-Paul Univ Alberta Edmonton AB Canada

ISBN: (纸本)9798400705915

datasets and models are two key artifacts in machine learning (ML) applications. Although there exist tools to support dataset and model developers in managing ML artifacts, little is known about how these datasets and models are integrated into ML applications. In this paper, we study how datasets and models in ML applications are managed. In particular, we focus on how these artifacts are stored and versioned alongside the applications. After analyzing 93 repositories, we identified the most common storage location to store datasets and models is the file system, which causes availability issues. Notably, large data and model files, exceeding approximately 60 MB, are stored exclusively in remote storage and downloaded as needed. Most of the datasets and models lack proper integration with the version control system, posing potential traceability and reproducibility issues. Additionally, although datasets and models are likely to evolve during the application development, they are rarely updated in application repositories.

关键词： machine learning (ML) artifact management ML application development SE4AI

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Application Research on Automatic Mapping of Distribution Networks Based on Big data Technology 3

Application Research on Automatic Mapping of Distribution Ne...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Fan, Litao Bi, Yunchuan Jiang, Jigang Yunnan Power Grid Co. Ltd. Electric Power Science Research Institute Kunming Yunnan China Dehong Power Supply Bureau of Yunnan Power Grid Co. Ltd. Kunming Yunnan China

ISBN: (纸本)9798350380989

To solve the problem of automatic mapping of single line diagrams in distribution networks, a large amount of manual adjustment is required to meet practical requirements. This article is based on automatic mapping of a distribution network in Yunnan. The system introduces machine learning technology from the big data Spark framework to the single line graph layout algorithm, and conducts application research. Firstly, this paper introduces the automatic mapping system's Technical architecture, explaining the application environment of machine learning technology, proposing the construction of a decision tree machine learning model based on the iterative binary tree 3rd generation (ID3) algorithm. The distribution network data is used as training samples to describe the construction process of the decision tree model. Furthermore, the graph adjusted sample data is used as the training set to train the layout algorithm library. The algorithm for the layout angle of the child devices is related to the parent device. Finally, it validates the machine learning model and layout algorithm using a single line diagram data instance of the distribution network. As a result, after machine learning, the generated single line diagram runs horizontally and vertically, with balanced layering of the station building and a reasonable and beautiful layout effect, which proves big data and machine learning technology and shows the effectiveness of application in automatic layout of single line diagrams. © 2024 IEEE.

关键词： Big data Decision Tree Layout algorithm machine learning Single line diagram

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Implementation of Predictive machine learning Algorithms on Heated Geothermal Bridge data 3

Implementation of Predictive Machine Learning Algorithms on ...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Soleimani, Faezeh Bidi, Saeed Habibzadeh-Bigdarvish, Omid Yu, Xinbao Ball State Univ Dept Math Sci Muncie IN 47306 USA Queen Mary Univ London Wolfson Inst Populat Hlth London England Univ Texas Arlington Dept Civil Engn Arlington TX USA

ISBN: (纸本)9798350372977;9798350372984

In recent years, there has been a notable surge in exploring statistical methodologies and artificial intelligence (AI) approaches, such as machine learning and deep learning, across various domains, including engineering. These data-driven techniques hold promise for delivering faster and more accurate predictions, representing a significant avenue for progress. Building upon prior research, this study extends the scope by incorporating additional features and utilizing a distinct dataset for forecasting the average heated bridge deck surface temperature and the outlet fluid temperature from the hydronic heating loops. Leveraging machine learning algorithms on data collected from a bridge de-icing project in Texas, we examine the effectiveness of Multiple Linear Regression (MLR) and Support Vector Regression (SVR). Through rigorous comparison with field data, we validate the robustness of these methodologies in temperature forecasting tasks, noting high accuracy across all algorithms. Notably, while both MLR and SVR demonstrate commendable performance, MLR marginally outperforms SVR, achieving an R-2 value of 0.79.

关键词： temperature prediction machine learning multiple linear regression support vector regression geothermally heated bridge

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Research on Sports Competition Result Prediction System Based on Computer Big data machine learning Model 3

Research on Sports Competition Result Prediction System Base...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lu, Xiaolong Li, Ming Ye, Chao Yunnan Vocational College of Sports Yunnan Kunming China Yunnan Youth Sports Training Center Yunnan Kunming China

ISBN: (纸本)9798350380989

Using big data theory to analyze athletes' performance data, this paper finds out the changing characteristics of athletes' performance. Using the principle of big data, we find the performance characteristics of athletes in the training process. The particle swarm optimization (PSO) method is used to predict the network parameters of support vector machine (SVM) online. The validity of this method is verified by testing on college students. This method can track athletes' performance characteristics more accurately, so as to build an excellent sports performance prediction model. The model can detect future trends from athletes' performance data in data mining and machine learning. This kind of system has a wide application prospect, and can greatly improve the efficiency and quality. The method combines machine learning theory with support vector machine network, which can effectively improve the prediction accuracy. This method has a very broad application prospect. © 2024 IEEE.

关键词： Athlete performance changing situation machine learning model prediction model

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Productivity App for ADHD affected people 3

Productivity App for ADHD affected people

引用

3rd IEEE international conference on data, Decision and Systems, ICDDS 2024

作者： Bhandarkar, Sandeep N. Manjunath, Poornima Shanbhag, Ramdas Dinesh Shravanth, G.N. CMR Institute of Technology VTU Information Science and Engineering Bangalore India

ISBN: (纸本)9798350363890

This project introduces the creation of a productivity application specifically designed to cater to the requirements of individuals impacted by Attention Deficit Hyperactivity Disorder (ADHD). Leveraging machine learning techniques, the application aims to augment emotional intelligence and promote self-reflection by analyzing written text. By identifying and acknowledging their emotions, users can cultivate deeper awareness of their mental conditions, fostering personal development and enhancing productivity. © 2024 IEEE.

关键词： Adversarial machine learning

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