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

共 33936 条记录，以下是1251-1260 订阅

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Bearing Fault Diagnosis Using machine learning Models 1

引用

7th international conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Chandrvanshi, Shagun Sharma, Shivam Singh, Mohini Preetam Singh, Rahul Department of Electronics and Communication Engineering Meerut Institute of Engineering and Technology Uttar Pradesh Meerut India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Uttar Pradesh Meerut India Product Technology and Development Centre Larsen & Toubro Ltd Mumbai India

ISBN: (数字)9789819995622

ISBN: (纸本)9789819995615

The bearing serves as a crucial element of any machinery with a gearbox. It is essential to diagnose bearing faults effectively to ensure the machinery’s safety and normal operation. Therefore, the identification and assessment of mechanical faults in bearings are extremely significant for ensuring reliable machinery operation. This comparative study shows the performance of fault diagnosis of bearings by utilizing various machine learning methodologies, including SVM, KNN, Linear Regression, Ridge Regression, XGB Regressor, AdaBoost Regressor, and Cat Boosting Regressor. Bearings are like the unsung heroes of the mechanical world, immensely supporting and guiding the smooth motion in everything, from your car’s wheel to the propeller in a ship. However, like other mechanical components, over the course of time, the constant use of bearings can lead to wear and tear, which may ultimately result in a fault. Bearing faults can manifest in several ways, including vibration, noise, heat, and changes in lubrication that reduce the efficiency of a machine. Therefore, it is essential to regularly monitor the bearings and inspect them to detect any issues early on. The aim of this present work is to use the various ML methodology, and their application on the bearing’s data to watch the condition of the machine’s bearing. The present work is carried out in four phases. In the first phase, the data of various loads is collected. In the second phase, the data undergoes an Exploratory data Analysis (EDA). During the third phase, the data undergoes both training and testing processes to evaluate its effectiveness. In the fourth and final phase, the model that gives the highest accuracy among all is chosen. The present approach is based on the various machine learning algorithms and their application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Failure analysis

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On Developing a Social Message Classification Platform for User-Specified Topics 12

On Developing a Social Message Classification Platform for U...

引用

12th international conference on Software Technology and engineering (ICSTE)

作者： Hsueh, Wan-Ting Hong, Wei-Lun Tsai, Hsing-Yun Teng, Wei-Guang Natl Cheng Kung Univ Dept Engn Sci Tainan Taiwan

ISBN: (纸本)9781665463553

The information people receive from social media contains a variety of topics, and these massive amounts of information are often disorganized. It is difficult for people to get valuable information directly from it. The advantage of our approach is two-fold: to accommodate users of various interests, and Human-in-the-Loop can be responsible for guiding AI system learning. And machine learning is one of the standard methods that is widely used for data classification tasks. The development of machine learning processes often requires efforts of data labeling and programming, which is often time-consuming and labor-intensive. To help non-experts better understand the model training process, we design a no-code data classification platform. It uses messages crawled from social media as data sources, filters data, and incorporates active learning methods to reduce the cost of creating datasets. Our platform uses an automated process to help users complete model training through simple web operations without programming. The ultimate goal is that users can intervene in the process of model training, quickly perform classification tasks and obtain information according to their needs.

关键词： human-in-the-loop active learning interactive machine learning no-code development platform social media

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The characteristic of Quantum Kernel in initial learning process 3

The characteristic of Quantum Kernel in initial learning pro...

引用

3rd IEEE international conference on Quantum Computing and engineering (QCE)

作者： Tomono, Takao Natsubori, Satoko Toppan Inc Digital Innovat Div Tokyo Japan

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Outgoing inspection of the production line is very important for many manufacturing companies. In recent years, two class classification of good and defective products must be carried out efficiently as the number of small-quantity, high-mix products have been increasing. The support vector machine with kernel method (kernel learning) is the most popular method in two class classification used in factories. Quantum kernel learning is one of the most promising applications of quantum technology. In this study, we propose an analysis method that allows us to know the difference between classical and quantum machine learning by plotting false positive rate (FPR) and true positive rate (TPR) on Receiver Operating Characteristic (ROC) space. Quantum machine learning uses quantum data with feature maps. We compared quantum and classical data using a small-sized existing datasets. First, it was not possible to confirm that the quantum data incorporating quantum entanglement could achieve the effect in this study. Next, when we examined the learning process of quantum data with Pauli feature map and classical data, we observed differences in the initial learning process. Based on these results, in contrast to the commonly used ROC curve, we plotted the FPR and TPR separately on the ROC space according to the training size. Plotting on ROC space in this experiment shows that quantum kernel learning is a method to reduce only FPR from high FPR and TPR. We found that the kernel learning process using quantum data is different from one using classical data.

关键词： Quantum kernel quantum machine learning Support Vector machine ROC

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A Blueprint for machine learning Accelerators Using Silicon Dangling Bonds 23

A Blueprint for Machine Learning Accelerators Using Silicon ...

引用

IEEE 23rd international conference on Nanotechnology (NANO)

作者： Ng, Samuel S. H. Chiu, Hsi Nien Retallick, Jacob Walus, Konrad Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9798350333466

As we approach the limit of transistor scaling, an appealing alternative in the form of quantum dots made of silicon dangling bonds (SiDBs) has been experimentally demonstrated to be capable of realizing sub-30 nm(2) logic gates. The introduction of SiQAD, a calibrated computer-aided design tool for the design and simulation of SiDBs, has further enabled the rapid exploration of this novel design space outside of experimental laboratories. Motivated by these advances and by identifying recent demands in machine learning acceleration, this paper proposes an architecture for an SiDB inference accelerator. Area and power estimates are made based on existing logic components and power models, the results are compared against Google's TPUv1. At the same clock rate, the proposed SiDB inference accelerator offers up to 10x improvement in area efficiency and orders of magnitude improvement in power efficiency, showing tremendous promise for further research into this novel platform technology.

关键词： machine learning

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Ensemble of Deep learning Models for Brain Tumor Detection

Ensemble of Deep Learning Models for Brain Tumor Detection

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Patil, Suraj Kirange, Dnyaneshwar SSBT's COE KBC North Maharashtra University Bambhori Jalgaon India Department of Computer Engineering SVKMs MPSTME Shirpur India Department of Computer Engineering SSBT's COE KBC North Maharashtra University Bambhori Jalgaon India

In the last two decades, improvement in artificial intelligence and medical imaging technology have made healthcare sector to achieve some remarkable achievements in diseases analysis and prediction. Due to advancement in medical imaging technology the brain images are taken in different modalities, that gives 3D view of different sections of brain for tumor diagnosis. The ability to extract relevant characteristics from magnetic resonance imaging (MRI) scans is a crucial step for brain tumor classifiers. As a result, several studies have proposed various strategies to extract relevant features from different modalities of MRI to predict the growth of abnormal tumor. Most of techniques used conventional techniques of image processing for feature extraction and machine learning for classification. More recently, the use deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumor. Since tumors are located at different regions of brain, the localizing the tumor and classifying it to particular category is challenging task. In this paper, we have solved this problem by designing deep ensemble model. In the proposed approach, first shallow convolutional neural network (SCNN) and VGG16 network were designed with T1C modality MRI image and subsequently loss and accuracy were examined. To improve the performance of model in terms accuracy and loss information, the extracted features from both the deep learning model were fused to improve the classification accuracy of three types of tumors. The obtained results from ensemble deep convolutional neural network model (EDCNN), proved that the fusion of deep learning model improves the accuracy of multiclass classification problem and also tries to address the problem of overfitting of model for imbalance dataset. The proposed model tries to give classification accuracy up to 97.77%. Furthermore, the proposed framework, achieves competitive results when

关键词： Magnetic resonance imaging

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data Augmentation for Code Analysis 3

Data Augmentation for Code Analysis

引用

3rd international conference on Intelligent data Science Technologies and Applications, IDSTA 2022

作者： Shroyer, Alexander Martin Swany, D. Indiana University Intelligent Systems Engineering Bloomington United States

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

A key challenge of applying machine learning techniques to binary data is the lack of a large corpus of labeled training data. One solution to the lack of real-world data is to create synthetic data from real data through augmentation. In this paper, we demonstrate data augmentation techniques suitable for source code and compiled binary data. By augmenting existing data with semantically-similar sources, training set size is increased, and machine learning models better generalize to unseen data. © 2022 IEEE.

关键词： data mining

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How Sustainable is machine learning in Energy Applications? – The Sustainable machine learning Balance Sheet 17

How Sustainable is Machine Learning in Energy Applications? ...

引用

17th international conference on Wirtschaftsinformatik, WI 2022

作者： Wenninger, Simon Kaymakci, Can Wiethe, Christian Römmelt, Jörg Baur, Lukas Häckel, Björn Sauer, Alexander Project Group Business & Information Systems Engineering of the Fraunhofer FIT Augsburg Germany FIM Research Center University of Applied Sciences Augsburg Augsburg Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute for Energy Efficiency in Production EEP University of Stuttgart Stuttgart Germany

Information Systems play a central role in the energy sector for achieving climate targets. With increasing digitization and data availability in the energy sector, data-driven machine learning (ML) approaches emerged, showing high potential. So far, research has focused on optimizing ML approaches’ prediction performance. However, this is a one-sided perspective. ML approaches require large computation times and capacities leading to high energy consumption. With the goal of sustainable energy systems, research on ML approaches should be extended to include the application’s energy consumption. ML solutions must be designed in such a way that the resulting savings in energy (and emissions) are greater than the energy consumption caused using the ML solution. To address this need, we develop the Sustainable machine learning Balance Sheet as a framework allowing to holistically evaluate and develop sustainable ML solutions which we validated in a case study and through expert interviews. © 2022 17th international conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： machine learning

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Diabetes Detection Using machine learning Techniques

Diabetes Detection Using Machine Learning Techniques

引用

2022 international conference on Artificial Intelligence and data engineering, AIDE 2022

作者： Pawar, Sumathi Pandit, Karuna Chiplunkar, Niranjan N Information Science & Engineering NMAMIT Karkala India Computer Science & Engg NMAMIT Karkala India

ISBN: (纸本)9781665493048

Diabetes Mellitus is one of our country's significant public health issues. It is a metabolic disorder that has impacted thousands of people and is caused by excessive amounts of glucose in the human body. Diabetes, if not detected early, can cause a variety of issues in the human body, including heart disease, renal disease, eye damage, nerve damage, and so on. machine learning is one of the most effective and precise methods for predicting diabetes in the human body. On a dataset constructed with PIMA samples, this system used several machine learning approaches. This study implements and compares Naive Bayes and Random Forest methods to detect diabetes, as well as highlight the issues encountered in each of these models. NB and RF methods can work on multiple input variables of PIMA dataset to derive prediction of diabetes. The system performance is measured with accuracy of outcome using different performance metrics. © 2022 IEEE.

关键词： Damage detection

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An operational concept capability requirement analysis method based on deep reinforcement learning

An operational concept capability requirement analysis metho...

引用

2022 international conference on Computer Application and Information Security, ICCAIS 2022

作者： An, Jing Zhang, Xuechao Liu, Wei Rong, Wanting Joint Logistics College NDU Beijing100000 China China Electronic Engineering Design Institute Beijing100000 China

ISBN: (纸本)9781510663459

Based on the perspective of combining qualitative analysis and quantitative calculation, a method of operational concept capability requirement analysis is designed based on deep reinforcement learning. Firstly, it obtains the simulation small sample data set with high reliability based on simulation experiment. Secondly, the operational concept surrogate model is constructed on the empirical data, and the surrogate model is optimized and trained by using the multi-objective optimization algorithm with high credibility simulation data set as input. Finally, the trained surrogate model is interacted with the deep reinforcement learning framework to realize the reverse exploration of operational concept capability requirements. © 2023 SPIE.

关键词： Reinforcement learning

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Sentimental Analysis of Movie Reviews Using machine learning Algorithms

Sentimental Analysis of Movie Reviews Using Machine Learning...

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2022 international conference on machine learning, Big data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Kumar, Suresh Sharma, Kamlesh Veragi, Dhruv Juyal, Ankit Manav Rachna International Institute of Research and Studies Computer Science and Engineering Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Due to the increasing popularity of social media platforms, it has become increasingly common for companies to collect and analyze the opinions and reviews of their customers to understand their sentiments. So, there is a need for a precise and systematic way of speculating and predicting thoughts that have the power to create a favorable or unfavorable influence in the market, making this type of study crucial for both producers and consumers. This paper aims to analyze the reviews of various movies and determine the factors that influenced the ratings of the movies. It also aims to formulate a strategy to improve the customer experience. The reviews play a significant role in the success or failure of a movie;a good sentiment analysis model is needed to classify movie reviews. This paper has analyzed movie reviews using various techniques such as Random Forest, Logistic regression, Linear SVM, Stochastic gradient, and Multinomial NB. © 2022 IEEE.

关键词： machine learning

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