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

共 7177 条记录，以下是861-870 订阅

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Deep learning quantile regression multi-step electricity price prediction based on MIC-CEEMDAN decomposition 3

Deep learning quantile regression multi-step electricity pri...

引用

3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Wang, Xiuna Han, Chao Li, Dongwei Han, Xiaoyu Electric Power Devel-Opment Research Institute Co. Ltd CEC Technical and Economic Consulting Center of Power Construction No. 13 Baiguang Road Xicheng District Beijing China

ISBN: (纸本)9798350352658

As the power market reform deepens, market player competition intensifies, and the challenge of formulating more rational trading strategies becomes significant. Electricity price forecasting, as the core basis for the formulation of trading strategies, has gradually become a focus of research. In this context, this paper proposes an electricity price forecasting model based on MIC-CEEMDAN-BiLSTM-Attention-QRNN. Firstly, the Maximal Information Coefficient (MIC) model is utilized to screen electricity price-related factors, proposing a reasonable factor matrix as input for the forecasting model. Secondly, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose the original data matrix, generating different decomposition sequence curves to reduce the impact of the volatility of the original data on the forecasting model and enhance its stability. Thirdly, Bidirectional Long Short-Term Memorymodel(BiLSTM)Attention-Quasi-Recurrent Neural Networks (QRNN) is used to predict the spot market electricity price based on the different decomposition curves, improving the feature learning and prediction capabilities of machine learning. Finally, a case analysis using PJM electricity market data demonstrates that the electricity price forecasting model proposed in this paper is highly practical and can meet the needs of spot market electricity price forecasting. © 2024 IEEE.

关键词： Power markets

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Content Recommendations Using machine learning-Based Approaches 3rd

Content Recommendations Using Machine Learning-Based Approac...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Indian, Ajay Meena, Gaurav Mohbey, Krishna Kumar Yadav, Bharati Central University of Rajasthan Ajmer India

ISBN: (纸本)9789819624676

In today’s society, recommendation systems are critical. Because there is a huge amount of data around us, whether music, book, or movie data, numerous programs or businesses, including Netflix, Amazon, and Spotify, focus on recommendation systems such as music apps. Systems for recommending content are intended to assist viewers by reducing the need for them to choose from a large variety that may number in the thousands or even millions. With the help of the movie lens dataset, this work aims to conduct a study to analyze the effectiveness of machine learning techniques, like K-NN, XG-Boost algorithms with Pearson Correlation Coefficient, and Count Vectorizer model to develop the Con-tent Recommender System for suggesting the movie. The performance of the proposed systems is evaluated in this study using the RMSE of 1.1062 and 1.1089 for KNN and XG-Boost algorithms, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Research on Intelligent Information System of Automotive Investment and Financing Platform Industry Chain Based on Computer machine learning 3

Research on Intelligent Information System of Automotive Inv...

引用

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

作者： Zhang, Ning Zuo, Peiwen Liu, Qian Li, Xudong China Automotive Technology and Research Center Co. Ltd. Tianjin China Co. Ltd. Tianjin China

ISBN: (纸本)9798350380989

This paper builds a large-scale automobile industry chain financing platform with independent intellectual property rights. The paper is to construct a collaborative investment and financing model of automobile industry chain based on dynamic alliance. In order to enhance the portability and maintainability of software, a five-level architecture is proposed. This platform can complete and support the business collaboration of the whole industry and provide a good model for its management and construction. A mathematical model of optimal matching between investment and financing platform is constructed by using linear programming and weight scoring method based on greedy algorithm. This model can not only ensure that both investors meet the matching needs, but also improve the operability and effectiveness of investment decisions. According to the enterprise dynamic alliance architecture, this paper studies and develops it. This paper designs and implements a platform management system based on dynamic alliance organization structure. The system is developed and rented by enterprise alliance structure. It can set up a good reference model for the construction and operation of financing platform in the automobile industry. © 2024 IEEE.

关键词： automobile industry chain Industrial chain investment and financing system implementation

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Enhancing Electrical Fault Detection in Transmission Lines Through machine learning 3

Enhancing Electrical Fault Detection in Transmission Lines T...

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3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Kulshrestha, Deepansh Chaudhary, Vishal Madhav Institute of Technology & Science Department of Electrcial Engineering Gwalior India

ISBN: (纸本)9798350390728

The purpose of this study is to apply machine learning techniques to improve the detection of electrical irregularities in transmission lines. The major objective is to provide a reliable framework that reliably identifies and categorises different electrical anomalies, improving power distribution networks' reliability and efficiency. The models were trained and assessed using a range of machine learning methods, such as Support Vector machines (SVM), Random Forest Classifiers, and Decision Tree Classifiers, XGB Classifier, and Logistic Regression. The dataset replicated real-world electrical transmission line issues. The major conclusions demonstrate that every model-aside from SVM maintains a high level of accuracy when evaluated on fresh data and that every model has a training accuracy that is always high, frequently surpassing 100%. Evaluation criteria like recall, F1-score, and accuracy show how well the ensemble techniques Decision Tree, Random Forest, and XGB Classifier identify electrical defects. According to the study, ensemble techniques can enhance fault identification in power distribution networks when used with logistic regression. The efficacy and interpretability of logistic regression are noteworthy. This research provides practical solutions to enhance the resilience and dependability of electrical transmission networks while also advancing our understanding of machine learning applications for problem identification. © 2024 IEEE.

关键词： Power distribution faults

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GTCODP: Code Graph-Based Transport learning with Optimal Plan Alignment for Code Defects Prediction 3

GTCODP: Code Graph-Based Transport Learning with Optimal Pla...

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3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Yu, Mingqian Zhao, Yuhai School of Computer Science and Engineering Northeastern University Shenyang110169 China

ISBN: (纸本)9798331506582

Some researchers use GCL on code graphs to realize code defects prediction in an self-supervised manner. While graph contrastive learning is a popular self-supervised method for function level code defects prediction, it faces challenges in finding label-invariant augmented graphs and determining the exact extent of similarity between sample pairs. It means that even small disturbance applied to the code graph can change the label of the code graph, which will significantly reducing the prediction performance. To deal with this problem, we propose GTCODP. Motivated by Optimal Transport (OT) Theory, GTCODP utilizes Optimal Transport Plan Alignment, which eliminates the need of distinguishing between positive/negative pairs thus improving the function level code defects prediction performance. Besides, experiments show that in activemq, lucene and derby datasets, GTCODP average improves F1-Score for 2.6%, Precision for 1.8%, AUC for 0.87% and Recall for 2.9% compared with GCL. © 2024 IEEE.

关键词： Contrastive learning

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Cardio Care: A Predictive Model for Heart Disease Detection 3

Cardio Care: A Predictive Model for Heart Disease Detection

引用

3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Rohith, R. Senthilnayaki, B. Joshua Dayalan, M. Anna University College Of Engineering Department of Information Science and Technology India Anna University College Of Engineering Department of Computer Science and Engineering India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

Heart Disease is still an epidemic global health concern, so trustworthy prediction models are vital for risk assessment and early detection. In this research, we propose a comprehensive predictive framework leveraging ensemble learning techniques alongside traditional machine learning (ML) models to increase forecasting of cardio disease accuracy. Utilizing a dataset sourced from Cardiovascular-Disease-dataset, demographic, clinical, and lifestyle factors were incorporated as features. The performance of Naive Bayes (NB), AdaBoost, Support Vector Classifier (SVC) and Logistic Regression (LR) both individually and within ensemble frameworks, was evaluated using accuracy of 98% and AUROC (Area-Under the Receiver Operating Characteristic Curve) of 99.845% as a primary metric. Our results demonstrate how effective ensemble learning strategies are in obtaining higher accuracy, precision-recall and along with AUROC curve for heart disease prediction compared to individual classifiers. This study underscores the importance of ensemble methods in improving predictive performance and contributing to enhanced clinical decision-making in cardiovascular health management. © 2024 IEEE.

关键词： Contrastive learning

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Musical Instrument Recognition Using machine learning Approaches 3

Musical Instrument Recognition Using Machine Learning Approa...

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

作者： Chethan, B.K. Bharath, K.N. Puttegowda, Kiran Manjula, A.V. Sunil Kumar, D.S. Anitha, K. Yathiraj, G.R. Mysuru India Dayananda Sagar Academy of Technology and Management Department of Ece Bengaluru India Vidyavardhaka College of Engineering Department of Ece Mysuru India The National Institute of Engineering Department of Ece Mysuru India East Point College of Higher Education Department of Computer Applications Bengaluru India Gsss Institute of Engineering Technology for Women Department of Ece Mysuru India Coorg Institute of Technology Department of Cse Ponnampet India

ISBN: (纸本)9798331508456

This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machine learning techniques against deep learning strategies in a clinical dataset solved under a supervised multi-label, multi-output classification paradigm. To enhance the performance of the system, we adjust the analysis parameters for window size, hop size, and label threshold levels. Furthermore, we explore the benefits of auxiliary feature sets as alternative data representations, specifically: Auditory features are based on two categories: (1) four-band spectral decomposition of stereo sound data, left, right, mid, and side channels and (2) the harmonic and residual model of the audio signal. Two methods for fusing the models trained on these representations are suggested and we use the best of these methods to increase recognition accuracy. These enhancements lead to a vast improvement;with a 14.25 % increase M-F score for the macro average and a 24.17 % improvement in the exact match ratio overall compared to the baseline. © 2025 IEEE.

关键词： Deep learning

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A Comparative Analysis of machine learning and Deep learning Algorithms for Skin Cancer Detection and Classification: Exploring Performance Across Multiple Parameters 3

A Comparative Analysis of Machine Learning and Deep Learning...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Goindi, Summi Thakur, Khushal Singh Kapoor, Divneet Chandigarh University Electronics and Communication Engineering Punjab India

ISBN: (纸本)9798350396157

Skin cancer is a substantial health concern, with a yearly incidence of 123,000 new cases and melanoma being the most prevalent variant. Identifying skin diseases is becoming increasingly challenging due to visual similarities. Automated skin cancer screening devices have the potential to enhance accuracy in identifying skin cancers. Advancements in technology and processing capacities have enabled the use of machine and deep learning algorithms for skin lesion examination. However, there is a pressing need for expedited diagnostic systems for life-threatening illnesses. This study provides an overview of various methodologies used in diagnosing skin cancer using image analysis and examines lessons and insights from previous studies. Future directions and scope will help address issues in autonomous detection of skin cancer. In a comparative analysis of various machine learning models, different performance metrics were observed. The Support Vector machines (SVM) yielded an accuracy of 75.23%, with an AUC of 90.22%. While effective in high-dimensional spaces, SVMs may face limitations with non-linear or noisy data. Random Forest, an ensemble method, demonstrated superior performance with an accuracy of 80.12% and an AUC of 93.22%, possibly due to its proficiency in handling non-linear data structures. Neural Networks, with their ability to model intricate relationships, achieved an accuracy of 81.34%. Multi-Layer Perceptron (MLP), a subset of neural networks, slightly varied in performance, indicating potential differences in architecture or training. Convolutional Neural Network (CNN), renowned for image recognition tasks, showcased an impressive accuracy of 93.23%, hinting at the dataset's spatial structures. The Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) emerged as the top performer with an accuracy of 94.344%, suggesting the dataset's potential temporal or sequential patterns. This comparative study underscores the importance of selecting the app

关键词： Multilayer neural networks

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Design of a Free Trade Zone data Management and Analysis Platform Based on Computer machine learning Technology 3

Design of a Free Trade Zone Data Management and Analysis Pla...

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3rd international Signal Processing, Communications and engineering Management conference, ISPCEM 2023

作者： Liu, Chunyang Liu, Yi Wang, Shuli School of Economics Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350307023

This study is dedicated to architecting a data management and analysis platform, integrating advanced computer technologies and machine learning algorithms. Traditional approaches often fall short in handling and analyzing vast data sets. Thus, this paper proposes an innovative solution: enhancing data processing efficiency and precision through machine learning technologies. The research thoroughly examines the application of machine learning in data management, including the selection of appropriate algorithms, effective model training and optimization, and their integration into a user-friendly platform. The platform's design aims to deliver more accurate data analysis, supporting informed decision-making. © 2023 IEEE.

关键词： Information management

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Credit Card Fraud Detection with XAI: Improving Interpretability and Trust 3

Credit Card Fraud Detection with XAI: Improving Interpretabi...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Vihurskyi, Bohdan Business Data Technologies *** Services Llc Sunnyvale United States

ISBN: (纸本)9798350318609

Fraud detection is crucial for financial institutions to protect clients' funds and maintain trust in online payment systems. However, traditional machine learning models often lack interpretability due to advanced fraud methods. This study proposes a framework that integrates advanced machine learning algorithms with XAI to improve the interpretability of fraud detection models. The framework explains model predictions clearly, helping stakeholders understand and verify system decisions. The Kaggle dataset is pre-processed, i.e., classes are balanced before implementing the ML techniques. The pre-processed Kaggle dataset is used to implement various ML algorithms, with the Local Interpretable Model-agnostic Explanations (LIME) method used during the prediction phase. Decision tree and Random Forest outperform other ML techniques, achieving 100% accuracy. This method clarifies the decision- making process, allowing stakeholders like financial analysts, lawmakers, and consumers to trust and confirm system results. © 2024 IEEE.

关键词： Finance

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