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检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

共 5726 条记录，以下是501-510 订阅

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Machine Learning-Driven Exploration and Stages Classification of Liver Cirrhosis: A Comprehensive Approach

Machine Learning-Driven Exploration and Stages Classificatio...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Veeraswamy Ammisetty UmaSankar Pechetti Viswanath Veera Krishna Maddinala K John Bunyan Department of Computer Science &amp Engineering Koneru Lakshmaiah Educational Foundation Guntur Andhra Pradesh India Department of CSE Sri Vasavi Engineering College (A) Tadepalligudem A.P Department of CSE Sri Vasavi Engineering College Tadepalligudem Andhra Pradesh India Department CB&DS Bapatla Engineering College Bapatla Andhra Pradesh India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Liver cirrhosis, a progressive liver disease, necessitates accurate and timely staging for optimal patient management. This study proposes a novel approach combining exploratory data analysis (EDA) with machine learning (ML) to enhance liver cirrhosis classification. By leveraging clinical and laboratory data, EDA will identify key predictors of disease progression. Subsequently, KNN, XGBoost, Random Forest, and Gradient Boosting algorithms will be applied to classify disease stages with precision. The integration of EDA and ML aims to improve risk stratification, treatment decisions, and overall patient outcomes in liver cirrhosis management.

关键词： data analysis Machine learning algorithms Liver diseases Predictive models Nearest neighbor methods Boosting Prediction algorithms Feature extraction Random forests Diseases

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An Extensive Analysis of ML Techniques for Predicting and Analysing Medical data

An Extensive Analysis of ML Techniques for Predicting and An...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Akshay Rajan Milan Manoj Gouri Santhosh Sarath S Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The study aims to examine the effectiveness of three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Logistic Regression, navigating through the complex terrain of medical-related datasets. Through an in-depth analysis of three major datasets including demographics, health indicators, and assessments of Parkinson's disease, this study reveals the subtle advantages and disadvantages of each algorithm. Whereas Random Forest has unmatched flexibility in figuring out complex correlations, Logistic Regression stands out as an example of simplicity and openness. SVM struggles with computing loads and parameter complexities at the same time. This thorough investigation avoids claiming a model is supe-rior, allowing practitioners to identify the complex interactions amongst machine learning giants when it comes to healthcare datasets.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Computational modeling Medical services Predictive models data models Real-time systems Random forests

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Attention Synchronous Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Prediction 3

Attention Synchronous Based Spatial-Temporal Graph Convoluti...

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3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Cao, Zhuyou Luo, Tianwang Xiao, Jianhua Xiao, Hongbo Qing, Xinyi School of Computer Science and Engineering Huaihua University Hunan China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huai-hua Hunan418000 China

ISBN: (纸本)9798400716775

This study introduces AS-STGCN, a novel traffic flow prediction model that incorporates an attention mechanism and a synchronized spatio-temporal graph convolutional network. In the field of traffic prediction, deep learning models (e.g., STGCN) have outperformed traditional methods, especially in capturing temporal and spatial correlations. AS-STGCN introduces a spatio-temporal attention mechanism and simultaneous convolution, which improves the prediction accuracy and demonstrates a unique capability in capturing dynamic spatio-temporal patterns in urban traffic networks. This research is of great significance in advancing the field of traffic flow prediction and providing a more comprehensive and accurate method for predicting urban traffic dynamics. © 2023 ACM.

关键词： Convolution

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Prediction of FCEV ownership in China based on the improvement of the GM(1,1) using genetic algorithms

Prediction of FCEV ownership in China based on the improveme...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Jingyi Li Xuefeng Liu Qiuye Yu China Automotive Technology and Research Center Co. Ltd. Tianjin China Automobile Data of China Co. Ltd. Tianjin China

ISBN: (纸本)9781665468138

In order to improve the accuracy of forecasting the Ownership of FCEV in China, this paper optimizes the GM(1,1) by using exponential smoothing and genetic algorithm, and verifies that the improved GM(1,1) is more suitable for forecasting the Ownership of FCEV in China through the data of the Ownership of FCEV in China from 2017 to 2021. Finally, the best model is used to forecast the Ownership of FCEV in China from 2022 to 2026.

关键词： Analytical models Smoothing methods Predictive models Reliability theory Prediction algorithms Market research data models

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data Batch processing: Modelling and Applications

Data Batch Processing: Modelling and Applications

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2020 IEEE international conference on Problems of Infocommunications Science and Technology, PIC S and T 2020

作者： Pichugina, Oksana Muravyova, Nadezhda National Aerospace University 'KHAI' Mathematical Modeling and Artificial Intelligence Department Kharkiv Ukraine South Ural State University Applied Mathematics and Programming Department Chelyabinsk Russia

ISBN: (纸本)9781728191775

Two important problems in infocommunication of scheduling batch processing are investigated having discrete-continuous character. A number of mathematical models of these problems were built including mixed discrete, permutation-based and Boolean ones. It is justified that the models enable applying several optimization techniques taking into account investigated geometric and extreme features of feasible domains. © 2020 IEEE.

关键词： Scheduling

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Ensemble Machine Learning Approaches for Coastal Planetary Boundary Layer Height Estimation Based on Coherent Doppler Wind Lidar

Ensemble Machine Learning Approaches for Coastal Planetary B...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： Yehui Chen Xiaomei Jin Ying Liu Ningquan Weng Wenyue Zhu Qing Liu Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine Mechanics HFIPS Chinese Academy of Sciences Hefei China Science Island Branch of Graduate School University of Science and Technology of China Hefei China Advanced Laser Technology Laboratory of Anhui Province Hefei China School of Environmental Science & Optoelectronic Technology Anhui Xinhua University Hefei China School of Environmental Science and Optoelectronic Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

The planetary boundary layer is instrumental in mediating the exchange of heat and matter between the Earth's surface and the atmosphere. Using continuous high-resolution wind profiling measurements conducted in Qingdao, an eastern coastal city of China, from December 2019 to April 2020, this study constructs and validates an integrated model that integrates the K-means and Random Forest algorithms. The model estimates the planetary boundary layer height across five real-time weather scenarios:clear, rainy, few clouds, moderate clouds, and cloudy, incorporating a suite of meteorological variables sourced from the ERA5 reanalysis dataset. In particular, the training results for $C2$ (a few clouds) demonstrate better performance, with a value of $R^{2}$ of 0.92, indicating a high degree of robustness. This research offers substantial value for meteorological studies and engineering applications in coastal zones.

关键词： Wind Analytical models Laser radar Atmospheric measurements Atmospheric modeling Clouds Sea measurements Estimation data models Random forests

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Research on credit-risk models via machine-learning algorithms and logistic regression for predicting CBA consumer behaviour

Research on credit-risk models via machine-learning algorith...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Ce Fang Te Bu Fang Fang International College Krirk University Bangkok Thailand

Start with the pre-screening of a possible application and continuing through the account's administration during its life and potential write-off, risk assessment techniques are used to handle things a borrower's account. Typically, a logistic regression model is used to estimate how risky it is to lend money to a credit applicant. However, researchers have also looked at a variety of other classifiers, and while early results indicate that support vector machines are the most accurate, practical implementation of these results from the lab may be hampered by issues with data quality. With a regression-based R2 values of forecasted/realized tardiness of 85%, this article generates out-of-sample predictions that greatly increase the categorization rates of credit-card holder defaults and delinquencies. We estimate the cost reductions to range from 6% to 25% of total losses using conservative estimates for the costs and benefits of reducing credit lines based on machine-learning projections.

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Research on Supply Chain Elasticity Modeling and Risk Mitigation Model Based on Big data Analysis

Research on Supply Chain Elasticity Modeling and Risk Mitiga...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Yiping Xu Wuhan Business University Wuhan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

An elastic modeling and risk mitigation method of supply chain based on big data analysis is proposed. The aim is to quantify the deformation characteristics of supply chain system by mathematical model, analyze its response behavior under the action of risk, and identify the critical state of system collapse. This paper constructs an elastic system model that comprehensively considers the interaction of all links in the supply chain, which can describe the dynamic changes of the supply chain in the process of risk loading and unloading, and predict the sensitivity and resilience of the system to external disturbances. By introducing the risk response function, people quantify the absorption and dispersion ability of the supply chain system to different types of risks, and then evaluate the stability of the system under risk shocks. The study reveals the threshold conditions of supply chain system collapse, and provides an early warning mechanism and decision support for risk managers. In order to verify the validity of the theoretical model, people use MATLAB simulation software to carry out a large number of simulation experiments, and the results show that the model can accurately predict the risk response behavior of the supply chain system, and guide the design of risk mitigation strategy in practice.

关键词： Analytical models Supply chains Loading Predictive models Big data Mathematical models data models Load modeling Risk mitigation Resilience

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A Comprehensive Review Analysis on Osteoarthritis Using Deep Learning and Machine Learning Technologies for Arthritis Detection

A Comprehensive Review Analysis on Osteoarthritis Using Deep...

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Tinu Thomas S. Prince Mary Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Department of Electronics and Computer Engineering St. Joseph’s College of Engineering and Technology Palai

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Osteoarthritis is a prevalent degenerative knee joint disease, causing considerable pain and mobility issues, thus impacting the independence and quality of life of millions. Deep learning and Machine Learning techniques have emerged as promising tools for enhancing the diagnosis, prognosis, and treatment of osteoarthritis (OA). These technologies allow for the extraction of valuable insights from medical images, clinical data, and genetic information, thereby improving patient care and management strategies. By employing advanced algorithms, healthcare professionals can improve early detection, and treatment plans, and accurately predict disease progression, ultimately leading to better outcomes for OA patients. This paper proposes a comprehensive review of various DL-based and ML-based methods for OA, spanning the years 2020 to 2024. It aims to provide valuable insights for researchers interested in exploring different models and potentially inspiring the development of integrated approaches for even more effective OA detection.

关键词： Deep learning Reviews Refining Medical services Predictive models Prediction algorithms data models

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A Strategy-based Optimization Algorithm to Design Codes for DNA data Storage System 21st

A Strategy-based Optimization Algorithm to Design Codes for ...

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21st international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Rasool, Abdur Qu, Qiang Jiang, Qingshan Wang, Yang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab High Performance Data Min Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Beijing 100049 Peoples R China

ISBN: (纸本)9783030953881;9783030953874

The exponential increase of big data volumes demands a large capacity and high-density storage. Deoxyribonucleic acid (DNA) has recently emerged as a new research trend for data storage in various studies due to its high capacity and durability, where primers and address sequences played a vital role. However, it is a critical biocomputing task to design DNA strands without errors. In the DNA synthesis and sequencing process, each nucleotide is repeated, which is prone to errors during the hybridization reactions. It decreases the lower bounds of DNA coding sets which causes the data storage stability. This study proposes a metaheuristic algorithm to improve the lower bounds of DNA data storage. The proposed algorithm is inspired by a moth-flame optimizer (MFO), which has exploration and exploitation capability in one dimension, and it is enhanced by opposition-based learning (OBL) strategy with three-dimension search space for the optimal solution;hereafter, it is MFOL algorithm. This algorithm is programmed to construct the DNA storage codes by reducing the error rates of DNA coding sets with GC-content, Hamming distance, and No-runlength constraints. In experiments, 13 benchmark functions and Wilcoxon rank-sum test are implemented, and performances are compared with the original MFO and three other algorithms. The generated DNA codewords by MFOL are compared with a state-of-the-art Altruistic algorithm and KMVO algorithm. The proposed algorithm improved 30% DNA coding rates with shorter sequences, reducing errors during DNA synthesis and sequencing.

关键词： DNA data storage Biocomputing DNA coding sets Opposition-based learning MFO algorithm

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