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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是941-950 订阅

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Realization of machine learning algorithms for Diagnosing Winding Faults in Induction Motors

Realization of Machine Learning Algorithms for Diagnosing Wi...

引用

International Conference on Power Electronic Drives

作者： W. Abitha Memala Mercy Paul Selvan S. Mohan Ram T. V. Brindavan R. Raja Singh Dept. of Electrical and Electronics Engg. Sathyabama Institute of Science and Technology Chennai India Dept. of Computer Science and Engg. Sathyabama Institute of Science and Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Vellore India Automotive Research Centre Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

The reliability and efficiency of squirrel cage induction motors (SCIMs) are most important in industrial applications, where failures can lead to significant downtime and economic losses. Among the common faults, stator winding faults are particularly detrimental, leading to degraded motor performance and potentially catastrophic failures if undetected at the incipient stage. Various approaches aiding machine learning algorithms are adapted for diagnosing machine faults. This paper realizes different machine learning algorithms to diagnose the winding faults of SCIM. A comprehensive dataset of motor current signatures is trained and validated with several machine-learning models using Python in the Anaconda platform. A detailed comparative analysis of the model's performance is conducted based on metrics such as accuracy, precision, recall, F1 score, and computational efficiency. This research highlights the potential of integrating machine learning techniques into SCIM monitoring systems, offering a proactive approach to maintenance and reducing unscheduled downtimes. Future work will explore the deployment of these models in real-time monitoring systems and the expansion of the dataset to include various motor types and fault conditions.

关键词： Fault diagnosis Adaptation models machine learning algorithms Induction motors Computational modeling Biological system modeling Windings Stator windings machine learning Real-time systems

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Banking Fraud Detection Using machine learning algorithms

Banking Fraud Detection Using Machine Learning Algorithms

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Upagna Rao Balasankula Bonthu Poojitha Sravanthi Chekurtha Kruthika Buyya Harika Bala Punna Rao V dept. of Computer Science Engineering B V Raju Institute of Technology Narsapur Medak Telangana India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

As technology grows rapidly, new innovations have emerged in our generation. Unfortunately, the rate of fraudulent activities has also increased, and as a result great deal of innocent individuals are in misery. Prior to the internet revolutionizing the world, banks employed traditional methods, including manual reviews, data analytics, transaction monitoring in reporting suspicious activities. However, as technology advances, bank fraud cases continue to rise. Banks must use caution when looking for banking system fraud. This project aims to adapt to these technological changes, detect abnormal behaviors in the dataset, and improve the overall performance of the trained model to protect against evolving malpractices. Combining machine learning algorithms such as logistic regression and random forest results in improve the performance of these systems which gives significant result in detecting the fraudulent activities. Implementing these technologies will improve overall security, speed up response time, and improve accuracy by recognizing suspicious activity. In conclusion, using AI technologies, specifically machine learning helps banks stay one step ahead of fraudsters, by recognizing the need of early detection of malpractice, the study can prevent such tragedies from happening in the future.

关键词： Logistic regression Technological innovation machine learning algorithms Reviews Stacking Banking Fraud Time factors Security Random forests

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Deep Neural Network Representation for Explainable machine learning algorithms: A Method for Hardware Acceleration

Deep Neural Network Representation for Explainable Machine L...

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Julian Schauer Payman Goodarzi Andreas Schütze Tizian Schneider Lab for Measurement Technology Saarland University Saarbrücken Germany

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Deep learning and machine learning algorithms achieved good results in various tasks, from computer vision and speech recognition to condition monitoring and predictive maintenance. Besides the good results obtained, most Deep learning algorithms do not provide a precise interpretation of their decisions. For the purpose of interpretability, explainable ML has gained attention. Because hardware accelerators do not support most explainable machine learning algorithms, they frequently experience runtime deficiencies. The resulting need for comparably inefficient software implementations limits their use on edge-computing hardware. This paper presents a method to represent trained explainable ML algorithms as Deep Neural Networks to utilize hardware acceleration to meet the growing demand for accelerated inference of machine learning algorithms on edge hardware. The primary approach of the paper is to disassemble the trained explainable ML algorithms' inference into their basic mathematical operations to represent them as Deep Neural Network layers. The techniques to convert the trained model functionalities to Deep Neural Network layers are described in detail, including the layer functionalities and their usage in the Deep Neural Networks. Due to the wide use of Deep Neural Networks on hardware accelerators, this method allows the usage of affordable and efficient edge hardware instead of high-price, customized hardware and replaces the programming of high-effort compiler. Finally, the method is successfully applied to a part of an open-source ML toolbox, and the resulting Deep Neural network inferences are successfully run on a Neural Processing Unit.

关键词： Deep learning machine learning algorithms Runtime Software algorithms Artificial neural networks Speech recognition Inference algorithms

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An Efficient Model based on machine learning algorithms for Virtual machines Classification in Cloud Computing Environment

An Efficient Model based on Machine Learning Algorithms for ...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Abdelhadi Amahrouch Mehdi Bouhamidi Youssef Saadi Said El Kafhali Faculty of Sciences and Techniques Data Science for Sustainable Earth Laboratory Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Industrial and Human Automation Mechanics and Computer Science Polytechnic University Hauts-de-France France Faculty of Sciences and Techniques Computer Networks Modeling and Mobility Laboratory Hassan First University of Settat Settat Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

In cloud computing, virtual machines consolidation (VMC) techniques are commonly used to improve resource utilization and reduce energy consumption. Task scheduling in cloud systems is a crucial aspect of VMC as it involves mapping clients' requirements to the appropriate computing resources such as Virtual machines (VMs) or Physical machines (PMs). The cloud provider must ensure that tasks are executed efficiently using the available shared computing resources while maintaining the quality of service (QoS) and minimizing the carbon footprint. Therefore, a good VM migration based on the customer's needs and IT resource capacity is required to maintain the best performance of the cloud system. In this work, we introduce an approach that leverage machine learning-based algorithms for VMs classification based on their latency sensitivity to facilitate subsequent migration into a suitable PM for better VMC. These algorithms categorize VMs into two groups: potentially inter-active machines (exhibiting periodic behavior on a daily scale) and latency-insensitive machines (for example, batch workloads, development, and test workloads). Our model demonstrated robust performance, achieving an accuracy of around 83%, thus establishing itself as the most proficient classifier in this study.

关键词： Cloud computing machine learning algorithms Sensitivity Processor scheduling Computational modeling Quality of service Virtual machining

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Enhancing Advertising Initiatives: Using machine learning algorithms to Engage Targeted Customer

Enhancing Advertising Initiatives: Using Machine Learning Al...

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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： Pankaj Kunekar Mohammed Usman CH Veena Atul Singla Nilesh Anute Nikhil Polke Information Technology Vishwakarma Institute of Technology Pune Maharashtra Department of Management College of Economics Management and Information Systems University of Nizwa Sultanate of Oman Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara India ASM'S Institute of Business Management and Research Pune Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Artificial intelligence (AI) and machine learning (ML) systems are developing at a quick pace, which has brought about a major change for many organisations, including digital marketing. machine learning is a tool used by marketers to identify trends in user behaviour on websites and mobile applications. This aids in their ability to promptly optimise advertising offerings and forecast users' future behaviour. In this paper, we present machine learning-based algorithms for information systems that can be used to optimise advertising services, draw in customers, increase sales, customise promotional offers based on user interests, and set up a spam filter for Facebook or email. Our theory shows that managing advertising campaigns to get better results is a feasible technique. Out of the four digital marketing strategies, advertising marketing offers the highest percentage of revenue to the company (75%) and the best amount of brand publicity (48%), according to the trial's final results shown in figure 2. Under the network marketing strategy, clients' brand sensitivity reaches 53% at the same time.

关键词： Technological innovation Sensitivity machine learning algorithms Social networking (online) Unsolicited e-mail machine learning Predictive models

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Improvements in Diagnosing Kawasaki Disease using machine learning algorithms

Improvements in Diagnosing Kawasaki Disease using Machine Le...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Sheshang Degadwala Vinay Nagarad dasavandi Krishnamurthy Dhairya Vyas Department of Computer Engineering Sigma University Gujarat India Bosch USA Michigan-United state of America Karnataka India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This research explores the changing landscape of Kawasaki Disease (KD) diagnosis, specifically examining the machine learning (ML) algorithms to enhance diagnostic capabilities. KD, a complex pediatric vasculitis, presents diagnostic challenges due to its diverse clinical manifestations, often resulting in delayed treatment. The study investigates the incorporation of machine learning techniques to improve the accuracy and efficiency of KD diagnosis. Utilizing a comprehensive dataset encompassing various clinical parameters related to KD, this research aims to create a robust predictive model. The analysis involves exploring patterns and correlations within the dataset to identify key diagnostic markers for KD. The findings demonstrate the potential of machine learning algorithms to effectively distinguish KD cases from other febrile illnesses, assisting clinicians in making more informed and prompt decisions. The integration of these algorithms in the diagnostic process not only enhances accuracy but also shows promise in reducing diagnostic delays, ultimately contributing to improved patient outcomes.

关键词： Support vector machines Measurement Accuracy machine learning algorithms Social networking (online) Computational modeling Nearest neighbor methods

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A Multi-Criteria Evaluation Approach for Ensemble machine learning algorithms in Precision Brain Cancer Detection

A Multi-Criteria Evaluation Approach for Ensemble Machine Le...

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Artificial Intelligence and Signal Processing (AISP), INDIA, International Conference on

作者： Gurugubelli Visalakshi Laavanya Mohan Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

A brain tumour is a lump or collection of abnormal brain cells that has the potential to spread to nearby tissues and a precise diagnosis is necessary for effective treatment planning. The development of an algorithm for multi-criteria ensemble learning to identify brain cancer with decrease the time needed to process the classifier, which will improve accuracy. After using SMOTE and data augmentation, the model pre-processes the images. After that, a convolution neural network is used to analyze the data to extract features with Squared Hinge Loss function. The images that have been optimized are used as input for ensemble learning algorithms that are based on the gradient-boost and Ada-boost decision tree methods. The algorithm was selected with the intention of reducing processing complexity and temporal complexity without sacrificing CNN's accuracy. The system's accuracy for cancer diagnosis is discovered to be 98.45%, which is a superior value than the current approach.

关键词： Accuracy machine learning algorithms Signal processing algorithms Brain cancer Feature extraction Classification algorithms Complexity theory Planning Ensemble learning Tumors

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Forecasting Volatility and Analyzing Importance Using Dual machine learning algorithms and Elastic Net Regression

Forecasting Volatility and Analyzing Importance Using Dual M...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Xi Kuang Jiajun Qin Zhongshan University Guangzhou China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This study delves into the influence of exchange rate volatility, employing an innovative dual machine learning (DML) algorithm for causal effect estimation combined with elastic net regression. This methodology adeptly navigates the complexities of high-dimensional data and variable covariance, selecting optimal model parameters through cross-validation to mitigate overfitting risks. Furthermore, heterogeneity analysis within this study sheds light on the varied impacts of exchange adjustments across different export product types and target, offering nuanced insights into the dynamics. By blending theoretical exploration with empirical analysis, this research contributes significantly to the advancement of theory and its application in decision, providing a robust framework for addressing volatility challenges.

关键词： Exchange rates machine learning algorithms Data analysis Navigation High dimensional data Estimation Complexity theory Forecasting Overfitting

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Enhancing Power Grid Reliability: Evaluating machine learning algorithms for Fault Classification in Inverter-Based Generators Interconnection Lines

Enhancing Power Grid Reliability: Evaluating Machine Learnin...

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International Conference on Harmonics and Quality of Power

作者： Talita M. O. A. Cunha Moisés J. B. B. Davi Mário Oleskovicz School of Engineering of São Carlos University of São Paulo (EESC/USP) São Carlos Brazil

ISBN: (数字)9798350382563

ISBN: (纸本)9798350382570

Through the transition of the energy matrix, which has resulted in a more significant share of renewable energy sources, there is a massive integration of Inverter-Based Resources (IBRs) in Electrical Power Systems (EPS). This integration increases system complexity and affects power quality due to the unconventional fault contributions of IBRs. Therefore, in recent years, several researchers have studied the challenges of IBRs for conventional protection systems associated with EPS. This work addresses the complexity attributed to IBRs for the fault (short circuits) classification task in transmission lines connected to a wind farm. The study explores the potential of intelligent methods using machine learning algorithms based on decision trees and association rules through the WEKA (Waikato Environment for Knowledge Analysis) software. The results demonstrated the effectiveness of intelligent methods for classifying faults, with success rates exceeding 99%. However, it was found that the signal noise level significantly impacts the accuracy of the evaluated methods. Furthermore, some intelligent techniques have demonstrated the advantage of visibility into their decision-making, returning trees or rules with low complexity after training, which would facilitate the interpretation of this process by end users and its practical implementation.

关键词： Training machine learning algorithms Decision making Power quality Software Complexity theory Association rule learning Circuit faults Decision trees Testing

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Application of machine learning algorithms in Earthquake Multi-Attribute Analysis

Application of Machine Learning Algorithms in Earthquake Mul...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Lunbin Li Shiying Wang Heihe University Heihe City Heilongjiang Province China

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The correspondence between seismic attributes and reservoir characteristics is the correspondence, and if a single seismic attribute prediction reservoir is used for multi-attribute analysis, multi-solution will occur, and the classification and quantity of seismic attributes are also more detailed, and the multi-attribute analysis of earthquake attributes through combination optimization and comprehensive analysis has become the future development trend. In recent years, artificial intelligence, big waves technology has developed rapidly, shock waves and acoustic waves based on their own advantages have been applied in various fields, this paper uses shock waves and acoustic waves for earthquake multi-attribute analysis, can accurately obtain seismic attribute information, attribute analysis can maximize the effective information on the profile in-depth mining, based on the multi-attribute analysis results of the detection area structural distribution state comprehensive interpretation, reduce the error caused by human measurement, but different seismic attributes on various structural sensitivity is different, to combine geological conditions, Stratigraphic conditions use shock waves and acoustic waves to select multiple seismic attributes from the best, which can effectively improve the recognition of seismic attributes and reduce the interpretation error.

关键词： Shock waves Technological innovation Seismic measurements machine learning algorithms Sensitivity Earthquakes Reservoirs Resonance Stability analysis Acoustic waves

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