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检索条件"主题词=Classification Algorithms"

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A Hierarchical Decision Framework for Transformer Fault Diagnosis Based on Support Vector Machine

A Hierarchical Decision Framework for Transformer Fault Diag...

引用

Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Jiahui Gu School of Software Southeast University Suzhou China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

A transformer fault diagnosis decision framework based on Support Vector Machine (SVM) is established to improve the accuracy of transformer fault diagnosis based on machine learning. Firstly, a clustering splitting method combining actual samples is proposed to simplify the multi classification problem of transformer fault diagnosis as multiple binary classification problems. Secondly, a feature selection method is proposed, which can select appropriate features based on the fault type diagnosed by SVM. In addition, an improved snake optimization algorithm is proposed to achieve global optimization of SVM parameters. Finally, the feasibility and applicability of the proposed methods are verified through example simulation. The results show that the accuracy of the established decision framework has increased by 4% compared with traditional diagnostic models, which has certain engineering practical value.

关键词： Support vector machines Fault diagnosis Accuracy Clustering algorithms Feature extraction Transformers Transmission line measurements Vectors classification algorithms Optimization

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Per-CCA Queueing

Per-CCA Queueing

引用

International Conference on Network and Service Management

作者： Yara Mulla Isaac Keslassy Technion Nvidia

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

Due to their increasing aggressiveness, recent congestion control algorithms (CCAs) can starve vanilla TCP flows in their shared router queues. Unfortunately, existing router-based solutions cannot prevent this *** this paper, we introduce a per-CCA queue isolation where incoming flows first undergo CCA classification, and then are mapped to isolated queues based on their classified CCA. We provide a fundamental analysis for this per-CCA isolation, and present two models for its performance. Then, in evaluations, we show how this per-CCA isolation clearly outperforms buffer sharing, and how our advanced model can accurately represent its performance. We further show how we can increase fairness by optimizing the queue service rates.

关键词： Analytical models classification algorithms

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Usage of Basic Geometrical Ideas and Principles of Symmetry for Machine Learning

Usage of Basic Geometrical Ideas and Principles of Symmetry ...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Vatsal Srivastava Sakthitharan Subramanian Department of Computational Intelligence School of Computing SRM Institute of Technology Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The paper discusses the usage of geometric principles and theories in order to manipulate the position of decision boundary itself to classify data points belonging to two different classes. Moving away from the close relation between statistical approaches at classification, we will attempt to create a classifier that establishes a more direct connection, and works with the decision boundary. We explore binary classification problems using the dataset, compare convergence conditions and evaluate our results against some popular classifiers.

关键词： Machine learning algorithms Automation Computational modeling Machine learning Mathematical models classification algorithms Reliability Convergence

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An Approach for Tamil Handwritten Recognition using ABC enabled GAN

An Approach for Tamil Handwritten Recognition using ABC enab...

引用

International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Sasipriyaa N Kavitha S Anand R N. Gunaseelan K. Dharshini M. Devayani Computer Science and Engineering Kongu Engineering College Erode India Information Technology Nandha Engineering College Erode India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Tamil is one of the world's oldest living languages. Recognizing Tamil handwritten character is complex because of insufficient images in datasets, different handwriting styles and sizes. Although many explorations have been done in different languages on character recognition, minimal work has been done so far for Tamil handwritten character recognition. Deep learning has recently been of great interest in computer science as well as in recognizing handwritten characters. DNN-based model has been made use of to recognize the handwritten Tamil characters, where CNNs have played a key role in the area of handwritten character recognition. The proposed approach consists of three stages: pre-processing to remove noise from the images; character augmentation for generating images; and classification for recognizing characters. Since there is a scarcity of Tamil handwritten datasets, we have applied GANs for data augmentation. In order to improve the performance of the GAN, Artificial bee colony Optimization algorithm is incorporated with GAN. This algorithm begins with generating a population of solutions that are then iteratively improved to enhance the efficiency of the GAN. Then, optimized weights are applied to the GAN, and then the model's performance is tested using a specific test dataset to check the efficiency.

关键词： Deep learning Handwriting recognition Image recognition Training data Generative adversarial networks Data augmentation classification algorithms Character recognition Convolutional neural networks Optimization

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Research on Lightweight Intrusion Detection algorithms in Federated Learning

Research on Lightweight Intrusion Detection Algorithms in Fe...

引用

Information Systems and Computing Technology (ISCTech), International Conference on

作者： Zhi Liu Fuyan Wang Mo Wang Dongao Wang Jianlei Gao The School of Automation Beijing Institute of Technology Beijing China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379877

For the problem of high computational overhead and high communication overhead faced by resource-constrained IoT devices during federated learning, this thesis proposes a lightweight intrusion detection model. By integrating hybrid feature enhancement units and multi-scale convolutional units, the model significantly reduces both the number of parameters and the computational demands. Experimental results show that the lightweight model exhibits good classification performance in a variety of federated learning scenarios.

关键词： Performance evaluation Computers Federated learning Computational modeling Intrusion detection classification algorithms Internet of Things Convolutional neural networks Information systems

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Enhancing Heavy Vehicle Safety with Intelligent Driver Behaviour Monitoring via Multi-Sensor Fusion and Embedded Systems

Enhancing Heavy Vehicle Safety with Intelligent Driver Behav...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Kanagamalliga S Srigitha S Nath Nivetha T Baby Shalini M Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This research presents the development of a sensor fusion-based driver assistance system (DAS) aimed at enhancing the safety of heavy vehicle operators. The system integrates an alcohol sensor, an ADXL 335 accelerometer sensor, and an eye blink detection sensor with an Arduino platform for real-time monitoring of driver behaviour. LabVIEW software was employed for data processing and visualization, providing a user-friendly interface to track driver alertness, speed, and alcohol consumption. A classification algorithm was implemented to categorize the driver's condition into various risk levels based on sensor inputs. Alerts were generated in real-time to notify the driver of potential hazards, such as drowsiness, intoxication, or speeding. Experimental trials demonstrated the system's effectiveness in accurately detecting dangerous driving behaviours and issuing timely warnings. The use of sensor fusion enhanced data reliability by cross-verifying multiple inputs, reducing false positives. The system proved to be robust and practical for implementation in heavy vehicle operations, contributing to road safety. Future enhancements may include additional physiological sensors, integration with vehicle control systems, and the practise of machine learning (ML) algorithms to improve classification accuracy. This research highlights the potential of sensor fusion technology in the development of advanced DAS, particularly in the transportation sector.

关键词： Vehicle safety Data visualization Sensor fusion Control systems Real-time systems classification algorithms Driver behavior Biomedical monitoring Monitoring Vehicles

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Analysis and prediction of factors affecting student grades based on machine learning algorithms

Analysis and prediction of factors affecting student grades ...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jing-Meng Zihan Ban Yawen Gong Hanxiao Shen School of Mathematics and Statistics Taishan University Taian P.R. China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

The importance of analyzing and predicting the impact of student achievement is a key topic in education. Based on the open data set provided by the University of California, Irvine as a data sample, we propose a machine learning algorithm to cluster and predict the performance of students and students’ grades, so as to obtain the correlation between relevant factors and students’ grades. The experimental results indicate that this machine learning algorithm can better analyze the relationship between relevant factors and academic performance and predict students’ daily performance. Thus educators can teach students in accordance with their aptitude, thereby improving their grades.

关键词： Learning systems Machine learning algorithms Education Clustering algorithms Prediction algorithms classification algorithms Decision trees

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Study on Automatic Recording and Health Promotion System of University Sports Results

Study on Automatic Recording and Health Promotion System of ...

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Computers, Information Processing and Advanced Education (CIPAE), 2020 International Conference on

作者： Ting Cao Zhen Liu Huanggang Normal University School of Physical Education Huanggang Hubei China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The purpose of this study is to develop a set of automatic recording and health promotion system for college sports. The system uses the classification model based on support vector machine (SVM) to evaluate the health status of college students, and combines the evaluation results with the physical performance records to realize the dynamic monitoring and management of students' physical activity and health status. By integrating smart wearable devices, the system collects physiological data of students in real time, and uses SVM algorithm for data analysis to predict and classify the health risk level of students. At the same time, the system has designed a user-friendly interface for teachers to record and manage students' physical education results, ensuring the accuracy and timeliness of data. In order to verify the effectiveness of the system, detailed algorithm design and simulation experiments are carried out in this study. The experimental results show that the proposed SVM-based health assessment model has high accuracy and reliability in identifying potential health problems. In addition, the automated function of the sports performance recording system significantly improves the efficiency of data management and reduces the occurrence of human error.

关键词： Support vector machines Performance evaluation Accuracy Prediction algorithms Real-time systems Recording classification algorithms Wearable devices Monitoring Sports

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Compositional Study of Ancient Glassware Based on K-Mean Cluster Analysis and Logistic Regression Algorithm

Compositional Study of Ancient Glassware Based on K-Mean Clu...

引用

Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Zhiting Zhao Minghao Li Junhao Deng Jinan University Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper focuses on the in-depth research on the chemical composition analysis and identification of ancient Chinese glass artifacts, and statistically analyzes and classifies and predicts the compositions of ancient glass artifacts using K-means cluster analysis and logistic regression algorithm. Firstly, some invalid data were excluded through data preprocessing, and the missing values were appropriately processed. In the analysis of the relationship between chemical composition and glass properties, the chi-square test was applied to find that there were significant differences between glass type and color and surface weathering. Secondly, by eliminating the SiO2 composition, which had a considerable influence on the clustering results, K-means cluster analysis was used to classify the subclasses of high-potassium and lead-barium glasses, and the reasonableness and sensitivity of the classification results were verified. Finally, the logistic regression algorithm was used to predict the type of unclassified glass artifacts, while the correlation between important chemical components and other components was explored by gray correlation analysis, which provided a new method and perspective for the compositional analysis and identification of ancient glass artifacts.

关键词： Logistic regression Analytical models Correlation Image color analysis Clustering algorithms Glass Prediction algorithms classification algorithms Chemicals Optimization

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Multi-Instance Learning for Parkinson’s Tremor Level Detection with Learnable Discriminative Pool

Multi-Instance Learning for Parkinson’s Tremor Level Detect...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haoyu Wu Yifan Guan Alexei Lisitsa Xiaohui Zhu Po Yang Jun Qi Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China Department of Computer Science The University of Liverpool Liverpool UK School of Computer Science The University of Sheffield Sheffield UK

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremors as its most typical symptom. Wearable accelerometer sensors, along with corresponding machine learning algorithms, can effectively assist in the diagnosis of PD tremors. However, due to the variations in disease progression and symptoms caused by individual differences among PD patients, it is challenging for existing algorithms to eliminate label noise and accurately identify and extract disease-related features across diverse patient data. In this study, we propose a Learnable Discriminative Instance Pool (LDIP) algorithm based on multi-instance learning, which integrates the concept of learnable shapelets. This method transforms the traditional DIP algorithm into a learnable instance pool that can be adaptively adjusted according to discriminative criteria, thereby enhancing the separability between different classes after bag mapping. We evaluated the proposed method on two clinical datasets using three different machine learning classifiers, achieving a maximum 73% accuracy for 5-class classification. The experimental results demonstrate that our proposed method consistently outperforms current baselines across various settings.

关键词： Machine learning algorithms Accuracy Machine learning Transforms Feature extraction classification algorithms Data mining Wearable sensors Diseases Electronics packaging

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