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

共 33943 条记录，以下是821-830 订阅

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Exploring machine learning Applications for Additive Manufacturing 3rd

Exploring Machine Learning Applications for Additive Manufac...

引用

3rd Biennial international conference on Future learning Aspects of Mechanical engineering, FLAME 2022

作者： Chouhan, Kshitij Gupta, Sumit Chaudhary, Vijay Gupta, Pallav Kumar, Sundeep Department of Mechanical Engineering ASET Amity University Uttar Pradesh Noida201313 India Management Studies Engineering College Ajmer Rajasthan305025 India

ISBN: (纸本)9789819918935

The use of data and algorithms to open the ways that human learn, which gradually increase the accuracy, is all considered under a branch of computer science and machine learning known as machine learning (ML). This is a rapidly growing field of data science majorly includes the use of trained algorithms and statistical methods to make classifications, which uncovering basic insights of data mining projects. AM is a relatively new technology that has the potential to drastically alter the future of digital manufacturing. In recent years, machine learning (ML) has attracted a lot of attention in AM because of its amazing performance in data tasks including classification, regression, and clustering. One of the primary justifications is this very fact. The complexity of production systems leads to a variety of problems with regard to design principles, standardization, and quality control. There is a growing need, however, for more sophisticated and superior products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D printing

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Gestational Diabetes and Poor Healthcare Affordability among Pregnant Women: A Double Whammy! 2

Gestational Diabetes and Poor Healthcare Affordability among...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Parikh, Riya Bhargava, Yesoda Shukla, Anupam Indian Institute of Information Technology Pune India

The prevalence of Gestational Diabetes Mellitus (GDM) is increasing at a rapid pace globally. This is concerning because GDM can lead to serious health problems like Type 2 Diabetes, Cardiovascular Diseases, and Depression in both the mother and her baby. GDM also increases healthcare expenditure during and after pregnancy and this is particularly worrisome for women belonging to households with poor healthcare affordability. Given the rising prevalence of GDM and its adverse impact, it is crucial to identify its risk factors and explore the factors associated with healthcare affordability among pregnant women. Our work attempts to identify the association between lifestyle (exercise, fruit, and vegetable consumption), sociodemographic factors, and Gestational Diabetes. Second, attempts to investigate the determinants of Health care affordability among pregnant women. Multivariate stepwise logistic regression is used for investigation purposes. We found Depression, higher BMI, and lower fruit intake to be associated with higher odds of GDM among pregnant women. Whereas the presence of GDM, lower socioeconomic position, absence of health insurance, and single motherhood were found to be associated with poor healthcare affordability. Lifestyle and mental health are associated with the GDM risk in pregnant women. Marital status, health insurance, and socioeconomic position are associated with healthcare affordability among pregnant women. The study can aid in better health outcomes for pregnant women and the creation of inclusive health policies. © 2024 Elsevier B.V.. All rights reserved.

关键词： machine learning

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Human Fall Analysis with FallAllD dataset using machine learning Approach

Human Fall Analysis with FallAllD Dataset using Machine Lear...

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2024 international conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Singh, Sankalp Malarvizhi, S. Ferents Koni Jiavana, K. SRM Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354539

The growing occurrence of falls among elderly individuals has garnered substantial attention from healthcare experts and researchers. Ensuring the prevention of fall-related injuries and fatalities necessitates precise and streamlined fall-detection mechanisms. This study introduces an innovative strategy for multi-class fall detection through the utilization of machine learning techniques in conjunction with the FallAllD dataset. The outcomes of the evaluation underscore the efficacy of the proposed method and its potential to enhance fall detection within real world scenarios. The CNN based 5 class classifier is able to detect fall with 99.24% accuracy. Investigation of data collected from sensor boards connected with waist and hand were analyzed. © 2024 IEEE.

关键词： Fall detection

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Optimizing Semantic Segmentation for Enhanced Football Analytics: A Pixel-level Approach 2

Optimizing Semantic Segmentation for Enhanced Football Analy...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Malakreddy, Bharathi Venkataraman, Sadanand Khan, Mohammed Sinan Nidhi Padmanabhuni, Srinivas Natarajan, Santhi Research & Consultancy Cell BMS Institute of Technology & Management Bengaluru India Department of Computer Science and Engineering Research Shiv Nadar University Chennai India Department of Artificial Intelligence & Machine Learning BMS Institute of Technology & Management Bengaluru India *** Bengaluru India

Semantic Segmentation, a pivotal technique in image analysis, is adeptly leveraged in this research to bolster sports analytics, with a concentrated focus on football. A comprehensive pipeline is unveiled for an in-depth analysis of a select portion of the IAUFD 100k dataset, encompassing 2030 manually annotated football images. The methodology entails a thorough evaluation and comparison of diverse semantic segmentation models, supplemented by the integration of advanced pre-processing strategies and optimal training techniques. Such a holistic approach culminates in a marked enhancement in model performance, as evidenced by a significant uptick in the mean Intersection over Union (mIoU). This research offers granular, object-oriented insights that substantially augment player tracking, action recognition, and event detection in football. The conclusive remarks of the study highlight prospective avenues for further research, emphasizing the potential incorporation of Explainable AI and advanced Metamorphic and Security Testing to fortify sports analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sports

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An ML-Accelerated Framework for Large-Scale Constrained Traffic engineering 44

An ML-Accelerated Framework for Large-Scale Constrained Traf...

引用

44th IEEE international conference on Distributed Computing Systems (ICDCS)

作者： Gu, Cheng Song, Xin Ng, Ben Hok Xiang, Qiao Guo, Zehua Li, Geng Huawei Technol Beijing Peoples R China Univ Toronto Toronto ON Canada Xiamen Univ Xiamen Peoples R China Beijing Inst Technol Zhengzhou Res Inst BIT Zhejiang Lab Beijing Peoples R China

ISBN: (纸本)9798350386066;9798350386059

Traffic engineering (TE) mechanisms are crucial for achieving optimal levels of network performance over wide-area networks across geographically distributed datacenters. Existing work on traffic engineering formulated the challenges at hand as combinatorial optimization problems, which could take hours to compute on modern wide-area network topologies at the scale of thousands of nodes. To improve the performance of TE mechanisms, we introduce DeepTE, a new TE framework based on machine learning (ML) that is designed for the best possible scalability and performance, capable of completing the computation within milliseconds with networks involving thousands of nodes, and of generating near-optimal TE policies while guaranteeing that all constraints are satisfied. DeepTE is also designed with a distributed ML model architecture, which can be horizontally scaled up to multiple GPUs for even better performance. With real-world traffic matrices, our extensive array of performance evaluations of DeepTE on various network topologies and TE problems show that DeepTE is capable of producing policies within 5% of the optimal results while offering up to 100x performance improvements over state-of-the-art traffic engineering mechanisms.

关键词： Traffic engineering machine learning Wide-area Network

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A machine learning framework to predict the thermal fatigue lifetime of SiC module with sintered silver layer 25

A machine learning framework to predict the thermal fatigue ...

引用

25th international conference on Electronic Packaging Technology (ICEPT)

作者： Yu, Pengju Dai, Yanwei Qin, Fei Beijing Univ Technol Inst Elect Packaging Technol & Reliabil Dept Mech Beijing 100124 Peoples R China

ISBN: (纸本)9798350353808

Sintered nano-silver is a widely used interconnect material in the third generation of power semiconductors, and is often used for service in extremely high-temperature environments due to its advantages of high melting temperature, making the reliability of interconnection more and more important for the overall reliability of the third-generation power semiconductor device. Different from traditional fatigue life prediction methods, a thermal fatigue lifetime dataset is collected based on Coffin-Manson thermal fatigue model and finite element simulations in this paper. A data-driven method based on the Support Vector Regression (SVR) model for predicting fatigue lifetime of sintered nano silver layer is developed. The analysis includes identifying the key factors influencing interconnect reliability, and determining the main factors affecting the thermal fatigue lifetime of SiC modules. The results show that increasing the thickness of the sintered nano-silver interconnecting layer can enhance the lifetime of the interconnect structure, while elastic modulus and chip thickness negatively impact the lifetime of the sintered silver layer. The prediction accuracy and stability of the proposed data-driven SVR model are discussed and studied.

关键词： Reliability Power module Sintered silver Fatigue life machine learning

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Deep learning-based Sentiment Analysis of Movie Reviews 3

Deep Learning-based Sentiment Analysis of Movie Reviews

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Lou, Yuqi School of Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781510664814

Sentiment analysis, sometimes regarded as sentiment classification or opinion mining, can be utilized to assess public perceptions of certain products, occasions, people or ideas. There is a wide range of applications for sentiment analysis, including detecting the overall sentiment of text documents, identifying positive and negative opinions expressed in texts, and predicting the future stock market trend based on social media sentiment. In this study, the convolutional neural networks are employed to categorize the sentiment of English movie reviews, i.e., the CNNs are utilized to detect whether a review is positive or negative. Two text representation techniques, the bag-of-words model and TF-IDF, are applied to convert the movie reviews into numerical representation before transmitting the processed data to the CNN model. According to the model's prediction results on the testing set after training, the text processed with the bag-of-word model achieves a higher classification accuracy of 84.84% in the proposed CNN framework, while the TF-IDF for text processing has better precision of 88.96%. © 2023 SPIE.

关键词： Sentiment analysis

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Exploratory data Analysis and Predict Income Udemy Course Instructors Using machine learning Algorithm

Exploratory Data Analysis and Predict Income Udemy Course In...

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2023 international conference on Computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Setiyani, Lila Herina, Nansy Rostiani, Yeny STMIK ROSMA Information System Departement Karawang Indonesia STMIK ROSMA Computerized Accounting Karawang Indonesia

ISBN: (纸本)9798350320954

Udemy Course as an online course platform that has been recognized by both students and teachers as a medium that facilitates the transfer and receiving of knowledge, while also providing income to the Instructors. This study aims to explore datasets in Udemy Course and predict the income of the Instructors in the Udemy course. datasets are taken from Kaggle. The research procedure begins with the collection of datasets from Kaggle, followed by datasets pre-processing, then exploratory data analysis is carried out and predicted, the implementation of machine learning random forest techniques is carried out. The results of this study show a description of the Udemy Course datasets for 2010 - 2022, course distribution prices, customer distribution, course content length distribution, number of courses for each category, number of subscribers for each category, and date of publication. In addition, this study shows that the percentage score n-estimator of machine learning random forest techniques in predicting the income of Udemy course instructors is 94%. © 2023 IEEE.

关键词： Forecasting

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Comprehensive Analysis on the Performance of Antenna Design Using machine learning Techniques 1

引用

2nd international conference on Signal and data Processing, ICSDP 2022

作者： Muthumeenakshi, K. Radha, S. Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098

Optimization of antenna design using machine learning techniques is investigated in this paper. Popular machine learning algorithms such as linear regression, support vector machines, k Nearest Neighbor, Gaussian Process Regression and artificial neural networks are employed for a reference microstrip patch antenna designed initially for 2.45 GHz. Optimal design parameters are obtained using these techniques. A thorough analysis using this machine learning technique shows exceptional performance for the frequency band from 1 to 3 GHz. The accuracy obtained using these techniques is validated by performing CST simulations as well. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Farming Your ML-based Query Optimizer's Food 38

Farming Your ML-based Query Optimizer's Food

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38th IEEE international conference on data engineering (ICDE)

作者： van de Water, Robin Ventura, Francesco Kaoudi, Zoi Quiane-Ruiz, Jorge-Arnulfo Markl, Volker Tech Univ Berlin TU Berlin Berlin Germany DFKI GmbH Kaiserslautern Germany Hasso Plattner Inst HPI Potsdam Germany TU Berlin Berlin Germany

ISBN: (纸本)9781665408837

machine learning (ML) is becoming a core component in query optimizers, e.g., to estimate costs or cardinalities. This means large heterogeneous sets of labeled query plans or jobs (i.e., plans with their runtime or cardinality output) are needed. However, collecting such a training dataset is a very tedious and time-consuming task: It requires both developing numerous jobs and executing them to acquire ground-truth labels. We demonstrate dataFARM, a novel framework for efficiently generating and labeling training data for ML-based query optimizers to overcome these issues. dataFARM enables generating training data tailored to users' needs by learning from their existing workload patterns, input data, and computational resources. It uses an active learning approach to determine a subset of jobs to be executed and encloses the human into the loop, resulting in higher quality data. The graphical user interface of dataFARM allows users to get informative details of the generated jobs and guides them through the generation process step-by-step. We show how users can intervene and provide feedback to the system in an iterative fashion. As an output, users can download both the generated jobs to use as a benchmark and the training data (jobs with their labels).

关键词： Training Maximum likelihood estimation Runtime Training data machine learning Labeling Iterative methods

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