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

共 34017 条记录，以下是1061-1070 订阅

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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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A Two-Country Study of Default Risk Prediction Using Bayesian machine-learning 1

引用

8th Annual international conference on machine learning, Optimization and data science (LOD)

作者： Incerti, Fabio Bargagli-Stoffi, Falco J. Riccaboni, Massimo IMT Sch Adv Studies Lucca Lucca Italy Harvard Univ Cambridge MA USA

ISBN: (数字)9783031258916

ISBN: (纸本)9783031258909;9783031258916

This paper tackles the prediction problem of firm default based on financial accounts and other firm features. We propose to exploit a novel machine-learning algorithm, the Bayesian Additive Regression Tree with Missingness Incorporated in Attributes (BART-MIA), which has been recently shown to outperform many traditional algorithms in analogous prediction tasks. We address the issue from an international perspective to assess its performance in both Netherlands and Italy over recent years. Despite the structural differences in the financial accounts in the two countries, we find the BART-MIA can take advantage of country-level missingness patterns and outperform state-of-the-art econometric and machine-learning models.

关键词： Supervised machine-learning Bayesian statistical learning Firm default risk Bankruptcy Italy Netherlands

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Rainfall Estimation Using machine learning Approaches with Raingauge, Radar, and Satellite data

Rainfall Estimation Using Machine Learning Approaches with R...

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2022 international conference on Electrical engineering and Informatics, ICELTICs 2022

作者： Putra, Maulana Rosid, Mohammad Syamsu Handoko, Djati Universitas Indonesia Department of Physics Depok Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

Rainfall is an essential variable in studying weather and has a vital role in the hydrological cycle. Therefore, accurate rainfall information becomes an urgent need. machine learning approaches have also shown remarkable developments in various areas of life including rainfall estimation. This research combined rain gauge, radar and satellite data with several algorithms such as Decision Tree, Random Forest, Adaptive Boosting, and Gradient Boosting. Interestingly, it was found that the Gradient Boosting learning ensemble algorithm is best applied in rainfall estimation, with a correlation value (R2) is 0.740 and RMSE value is 1,765. On the other hand, weather radar reflectivity data significantly influences the integration of the data built into the model, suggesting that details of optimizing data are significantly required. © 2022 IEEE.

关键词： Rain

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Multi-Site Mild Traumatic Brain Injury Classification with machine learning and Harmonization 44

Multi-Site Mild Traumatic Brain Injury Classification with M...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Bostami, Biozid Espinoza, Flor A. van der Horn, Harm J. van der Naalt, Joukje Calhoun, Vince D. Vergara, Victor M. Triinst Ctr Translat Res Neuroimaging & Data Sci Atlanta GA 30303 USA Univ Groningen Univ Med Ctr Groningen Dept Neurol Groningen Netherlands

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Traumatic brain injury (TBI) can drastically affect an individual's cognition, physical, emotional wellbeing, and behavior. Even patients with mild TBI (mTBI) may suffer from a variety of long-lasting symptoms, which motivates researchers to find better biomarkers. machine learning algorithms have shown promising results in detecting mTBI from resting-state functional network connectivity (rsFNC) data. However, data collected at multiple sites introduces additional noise called site-effects, resulting in erroneous conclusions. Site errors are controlled through a process called harmonization, but its use in classifying neuroimaging data has been addressed lightly. With the ongoing need to improve mTBI detection, this study shows that harmonization should be integrated into the machine learning process when working with multi-site neuroimaging datasets.

关键词： Neuroimaging machine learning algorithms Process control machine learning Biomarkers Cognition Biology

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Target Recognition Using Pre-Trained Convolutional Neural Networks and Transfer learning 2

Target Recognition Using Pre-Trained Convolutional Neural Ne...

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

作者： Mishra, Gangeshwar Gupta, Prinima Tanwar, Rohit Department of CST MRU Haryana Faridabad India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

In modern surveillance, automatic target recognition (ATR) is a critical challenge, necessitating rapid and precise object identification, especially in military and disaster response scenarios. This research presents a comprehensive framework for ground target classification, focusing on Synthetic Aperture Radar (SAR) imagery. Harnessing the Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Targets dataset, this study integrates Convolutional Neural Networks (CNNs) into SAR image analysis. Transfer learning emerges as a key strategy, adapting insights from pre-trained CNNs to the intricacies of target recognition in SAR imagery. This approach outperforms traditional methods, enhancing efficiency amid dataset annotation challenges. Noteworthy CNN architectures including DarkNet-19, DenseNet-121, InceptionV4, ResNet-152, and VGG19 are explored. The ResNet-152 model demonstrates exceptional performance, emerging as a leading contender with a remarkable testing accuracy of 98.56% when trained from scratch. Furthermore, by employing transfer learning, the model's accuracy may be further enhanced to reach 98.81% on the testing dataset. This research signifies a transformative SAR imagery path guided by pre-trained CNNs and transfer learning. It reshapes ATR's accuracy and efficiency, pointing to a future where innovation surmounts challenges previously deemed insurmountable. © 2024 Elsevier B.V.. All rights reserved.

关键词： Efficiency

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Distilled Neural Networks for Efficient learning to Rank: (Extended Abstract) 40

Distilled Neural Networks for Efficient Learning to Rank: (E...

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40th IEEE international conference on data engineering, ICDE 2024

作者： Nardini, Franco Maria Rulli, Cosimo Trani, Salvatore Venturini, Rossano ISTI-CNR Pisa Italy University of Pisa Italy

ISBN: (纸本)9798350317152

Recent studies in learning to Rank (LtR) have shown the possibility of effectively distilling a neural network from an ensemble of regression trees. This fully enables the use of neural-based ranking models in query processors of modern Web search engines. Nevertheless, ensembles of regression trees outperform neural models both in terms of efficiency and effectiveness on CPU. In this paper, we propose a framework to design and train neural networks outperforming ensembles of regression trees. After distilling the networks from tree-based models, we exploit an efficiency-oriented pruning technique that works by sparsifying the most computationally intensive layers of the model. Moreover, we develop inference time predictors, which help devise neural network architectures that match the desired efficiency requirements. Comprehensive experiments on two public learning-to-rank datasets show that the neural networks produced with our novel approach are competitive in terms of effectiveness-efficiency trade-off when compared with tree-based ensembles by providing up to 4x inference time speed-up without degradation of the ranking quality. © 2024 IEEE.

关键词： Efficiency

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Intelligent Planning of Power Transmission and Transformation Projects Based on AI and Multi-source Heterogeneous data Integration 24

Intelligent Planning of Power Transmission and Transformatio...

引用

1st international conference on Image Processing machine learning and Pattern Recognition

作者： Qu, Zhanfei Dong, Gang Sun, Qigang He, Chunhui Lu, Ling State Grid Shandong Elect Power Co Econ & Technol Res Inst Beijing Peoples R China State Grid Shandong Elect Power Co Beijing Peoples R China

ISBN: (纸本)9798400707032

The acceleration of smart grid construction imposes higher demands on the intelligent planning of power transmission and transformation projects. To address the inefficiencies and high costs in existing planning methods, this paper proposes an intelligent planning approach based on multi-source heterogeneous data integration and artificial intelligence. By integrating multi-source heterogeneous data to analyze on-site information, and utilizing Bayesian networks and Convolutional Neural Networks with Attention Mechanisms (CNNAM), the planning and construction processes of power transmission and transformation projects are optimized. This approach significantly improves the accuracy and reliability of cost estimation, providing strong data support and technical assurance for the intelligent planning of power transmission and transformation projects, thereby making the planning process more effective and practical.

关键词： Smart Grid Intelligent Planning Multi-Source Heterogeneous data Bayesian Networks Convolutional Neural Networks

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Carbon Emission Prediction with Macroeconomic Variables and machine learning 3

Carbon Emission Prediction with Macroeconomic Variables and ...

引用

3rd international conference on Clean and Green Energy engineering, CGEE 2022

作者： Tan, Hongxuan GuangDong-TaiWan College of Industrial & Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9781665452656

ISBN: (纸本)9781665452656

Carbon Emission Prediction is important because China is the world's largest emitter of carbon, and an analysis of the factors affecting China's carbon emissions will help the country to save energy and reduce emissions in the future. This problem has been studied in the literature with 9 methods and 9 kinds of data. Most of the data were collected in the National Bureau of Statistics of China. We predict carbon emission with 44 Macroeconomic Variables and 7 machine learning models, with data from 31 provinces in China from 2011 to 2019. This study uses machine learning algorithms to discuss the impact of macroeconomic indicators in the dataset on carbon emissions in China. We found AdaBoost model is the most precise and Coal consumption variables are more important in the prediction. © 2022 IEEE.

关键词： machine learning

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Research on Fostering Intercultural Communication Competence of Chinese EFL Learners in Online learning Using machine learning Method 24

Research on Fostering Intercultural Communication Competence...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Yu, Juan Huang, Xinwei School of Foreign Languages Wuhan Business University Hubei Wuhan China Center for Chinese and Western Language and Culture Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9798400710094

In the context of Chinese culture going global and the increasing important role of China on the global stage, intercultural communication competence plays an increasingly important role in terms of personal, national and international communication. This paper provides an investigation on ICC, online learning and the integration of ICC development into online learning. This study proposes three ways on the integration of ICC cultivation into online curriculum, including creating online community, constructing real-life situations, and combining technology with online learning. This study also addresses the problem of assessing and improving ICC in online learning contexts by leveraging machine learning (ML)-based sentiment analysis. The key idea of our research study is to integrate ML-based sentiment analysis into the evaluation of students’ intercultural communication skills during online courses. The proposed model’s performance is assessed using various metrics, including accuracy, precision, recall, F1-score, and AUC. The results of the experiments designates that the Random Forest algorithm outperforms other ML models. This research has a capacity enhance the understanding of students’ intercultural communication experiences and provide actionable insights for instructors. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Automatic Assisstance System Based on machine learning for Effective Crowd Management 2

Automatic Assisstance System Based on Machine Learning for E...

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2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Saxena, Divya Kumar, Santosh Tyagi, Pankaj Kumar Singh, Amarendra Pant, Bhasker Reddy Dornadula, Venkata Harshavardhan University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States Lucknow Public College of Professional Studies Computer Science Department India Chandigarh University Mohali India Kamala Nehru Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Sultanpur India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Startups Mentoring Society It &ites Department Andhra Pradesh Tirupati India

ISBN: (纸本)9781665437899

So here we are going to study about the machine learning and automatic assistance system in machine leering which help in the effective crowd management machine learning (ML) is a form of artificial intelligence (AI) that enables software programmers to increase prediction accuracy without being specifically intended to do so. machine learning techniques use previous data as input to anticipate new output values. Because data is so important, developing improved methods for intelligently managing the now-ubiquitous crowd-powered data-gathering systems is a critical next step toward totally autonomous agents. So here we will discuss about the machine learning and its automatic assistance system in crowd management. learning for use in a variety of sectors is becoming more common as the availability of data expands over time. Computer vision and computer vision have improved a wide range of industries, including medical diagnoses, data display and procedures, science and research, and so on. Such approaches have already been used in the fields of Smartphone apps, computer equipment, online websites, and cyber security. So here we have reached a conclusion about the automatic assistance system based on machine learning for effective cored management. © 2022 IEEE.

关键词： Information management

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