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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401-410 订阅

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Experimental Evaluation of Accuracy of Most Common Machine Learning models using Pulsar data Set 1

Experimental Evaluation of Accuracy of Most Common Machine L...

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1st international conference of Digital Transformation and Innovation Technology, INCODTRIN 2020

作者： Florez, Alexander Ylnner Choquenaira Vinces, Braulio Valentin Sanchez Arroyo, Diana Carolina Roca Saire, Josimar Edinson Chire Franco, Patricia Batista SP São Carlos Brazil

ISBN: (纸本)9781665423199

This work brings together some of the most common machine learning (ML) algorithms, and the objective is to make a comparison at the level of obtained results from a set of unbalanced data. This dataset is composed of almost 17 thousand observations made to astronomical objects to identify pulsars (HTRU2). The methodological proposal based on evaluating the accuracy of these different models on the same database treated with two different strategies for unbalanced data. The results show that in spite of the noise and unbalance of classes present in this type of data, it is possible to apply them on standard ML algorithms and obtain promising accuracy ratios. © 2020 IEEE.

关键词： Pulsars

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Detecting Autism Spectrum Disorder from Functional Magnetic Resonance Imaging data with an Ensemble Machine Learning Method 5

Detecting Autism Spectrum Disorder from Functional Magnetic ...

引用

5th international conference on Intelligent Medicine and Image processing, IMIP 2023

作者： Sun, Chenxi Sun, Duanchen Liu, Zhi-Ping Shandong University School of Control Science and Engineering Jinan Shandong250061 China Shandong University School of Mathematics Jinan Shandong250100 China

ISBN: (纸本)9798350337396

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder caused by functional impairment of the brain. The prevalence of this disease in the population has been increasing in recent years. Early identification of ASD can help children improve with early behavioral interventions. However, traditional testing methods are very expensive and time-consuming. Machine learning methods based on functional Magnetic Resonance Imaging(fMRI) have emerged as a research direction for autism-assisted diagnosis. In this paper, we developed an ensemble approach to distinguish ASD from normal controls by a stacking strategy, integrating the results of multiple machine learning classifiers. The dataset included 569 subjects from different centers in the study. We extracted the functional connectivity (FC) as features and removed the redundant ones by SVM-RFE, building an ensemble model for prediction by the stacking strategy. Specifically, KNN, NB, RF, GBDT, and XGBoost were used as base models, and logistic regression was employed as a meta-model. The experimental results illustrated that the ensemble method could improve the evaluation metrics of prediction results by about 2% on average compared with individual classifiers. Thus, the stacking strategy has better classification performance for intelligent diagnosis of ASD via fMRI. © 2023 IEEE.

关键词： Diseases

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Research on Clinical Trial data Analysis Based on Bayesian Network and Deep Learning

Research on Clinical Trial Data Analysis Based on Bayesian N...

引用

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

作者： Tingyi Hu Department of Emergency Medicine Xinqiao Hospital Army Medical University Chongqing China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This paper delves into an innovative approach to analyzing clinical trial data by harnessing the power of Bayesian networks and deep learning methods, particularly in combination with convolutional neural networks (CNN). This fusion of techniques allows the meticulous extraction of valuable features from complex data sets by applying the convolution and pooling operations of CNN. The extracted features are then carefully modeled using Bayesian networks, facilitating the construction of complex relationships between various variables. This methodological framework is particularly good at dissecting and interpreting nuances in clinical trial data. The empirical results shown in the study highlight the model's superior predictive power, particularly in estimating coronary blood flow. This advanced technique offers a significant improvement in prediction accuracy compared to traditional models.

关键词： Deep learning Analytical models data analysis Clinical trials Predictive models Feature extraction data models Bayes methods Convolutional neural networks Biomedical imaging

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AD Classification and Detection Using Neuroimaging data 3

AD Classification and Detection Using Neuroimaging Data

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abed, Salma Mourad, Mohamed Abdelkreem, Mohamed Mohamed Ashraf, Mostafa Talaat, Youssef Elashmawi, Walaa H. Misr International University Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is the most prevalent type of dementia, a progressive brain illness that typically manifests in old age. Alzheimer's disease will affect 1.2 percent of the world's population by 2046, according to the World Health Organisation. If nothing is done to reduce that figure, the number of fatalities from Alzheimer's and dementia in Egypt will reach 6,975 in 2020. Egypt is rated 148th in the world. Early Alzheimer's diagnosis is critical for informing patients about the severity of their condition and empowering them to take medication and other preventative measures. MRI and PET scans, have been used to aid in diagnosing along with machine learning approaches. Early AD diagnosis was required to accurately distinguish AD cases from standard control (NC) subjects. Naive Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) are machine learning algorithms that have been utilized to predict the possibility of Mild Cognitive Impairment (MCI) participants getting Alzheimer's, allowing the disease's progression rate to be determined. These strategies, however, were unreliable due to the difficulties of defining the condition as AD or ND, the likelihood of late or seriously demented patients, and a lack of access to large or diverse data sets. A thorough analysis discovered that the proposed workflows might be substantially enhanced by focusing on the constraints of previous studies. The suggested Alzheimer's disease technique categorizes AD patients into one of four stages, using a data set of 1288 MRI images for training and pre-processing procedures such as skull stripping, noise reduction, and motion correction. This method utilizes a convolutional neural network model (CNN) and deep learning architecture in order to enhance accuracy while minimizing the computational resources required to access ROIs (regions of interest). The technology can effectively diagnose patients with minimal work and time while providing much potential. © 2

关键词： Neuroimaging

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data Collection Website with Brain Tumor and Pneumonia Detection 3rd

Data Collection Website with Brain Tumor and Pneumonia Detec...

引用

3rd international conference on VLSI, Communication and Signal processing, VCAS 2020

作者： Awasthi, Atul Mohit, Kumar Gupta, Rajeev Kumar, Basant MNNIT UP Allahabad Prayagraj211004 India

ISBN: (纸本)9789811627606

Medical image processing techniques are meant to extract relevant information from medical images. Computer-aided diagnosis (CAD) system for processing medical images leads to accurate and early detection of diseases which is based on training and testing of various artificial neural network (ANN) models. The number of data required for training and testing a model is not easily available and accessible online due to its large size. We in this paper provide solution to medical data shortage problem by proposing a website algorithm designed for medical data collection. Besides data collection, we also integrated brain tumor and pneumonia detection models into the website, making our website useful not only for medical data collection but also helping doctors in decision-making process. Accuracy has been used as performance parameter to compare various models. The results obtained from the proposed CAD model are satisfying and motivating toward clinical applications as a help to experts in brain tumor and pneumonia diseases interpretations. We integrated these models into the predicted page of our website where doctors can easily upload image of the patient and get the prediction from these models for early and accurate diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Websites

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Feature Selection Techniques Analysis for Identification of Cognitive and Resting States Based on EEG data 8

Feature Selection Techniques Analysis for Identification of ...

引用

8th international conference on Information Technology and Nanotechnology, ITNT 2022

作者： Murtazina, Marina Avdeenko, Tatiana Novosibirsk State Technical University Automated Control System Department Novosibirsk Russia Novosibirsk State Technical University Theoretical and Applied Computer Science Department Novosibirsk Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper investigates the application of feature selection methods included in the Weka software package to solve the problem of classifying cognitive states and resting states by EEG data. For the experiment, the data set 'EEG During Mental Arithmetic Tasks' was used. From the EEG records for 19 scalp electrodes, Hjort parameters and power in the delta, theta, alpha, and beta frequency bands were extracted. At the first stage of the experiment, classifying models were trained on the obtained feature space. At the second stage of the experiment, a study was made of the change in the accuracy of classifiers trained on the same set of algorithms on a subset of features obtained using feature selection methods. To obtain a subset of features, the attribute evaluators CfsSubsetEval and WrapperSubsetEval were used. We selected 25 machine learning algorithms included in the Weka software package, for which the classification accuracy exceeded 75% when trained on the original feature space. Feature selection using the CfsSubsetEval attribute estimator and the GreedyStepwise search method was successful for 13 algorithms. Feature selection using the WrapperSubsetEval estimator with the Logistic classifier and the GreedyStepwise search method was successful for12 algorithms. © 2022 IEEE.

关键词： Classification (of information)

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An Unsupervised Learning Model Investigation for data Inaccuracies in Comparison with Machine Learning algorithms

An Unsupervised Learning Model Investigation for Data Inaccu...

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

作者： Usha. N P.S. Manoharan D. Suriya Kumari M. Kalimuthu R. Priyadarshini Department of Electrical & Electronics Engineering Thiagarajar College of Engineering Madurai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Solar energy, a crucial renewable resource, demands accurate forecasting for efficient grid integration. This study employs unsupervised machine learning techniques to predict solar power generation using a five-year dataset. data preprocessing, including imputation and feature reduction, was conducted to enhance model performance. Autoencoder, Mean Shift Clustering, and DBSCAN algorithms were applied, with performance evaluated using RMSE, R2, MAE, and MAPE metrics. Comparative analysis revealed DBSCAN as the superior model, achieving an accuracy exceeding 70%. This research contributes to the advancement of solar power forecasting and grid management.

关键词： Measurement Machine learning algorithms Accuracy Time series analysis Clustering algorithms Prediction algorithms data models Solar power generation Forecasting Unsupervised learning

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Logistics Demand Fluctuation Forecasting and Real-Time Decision Support Based on XGBoost

Logistics Demand Fluctuation Forecasting and Real-Time Decis...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Xin Chen Yong Liu South China University of Technology Guangzhou China Infinity Equity Management Ltd Shenzhen China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

In this paper, XGBoost is used to forecast logistics warehousing. We fit the demand of 5 warehouses from 2010 to 2020, and forecast the demand from 2020 to 2025. First, the predictive performance of the model is quantified by a number of evaluation measures, including mean error (ME), root mean square error (RMSE), mean absolute error (MAE), mean percentage error (MPE), mean absolute percentage error (MAPE), and mean absolute normalization error (MASE). These indicators give us a comprehensive perspective on the predictive power of our models. The results show that the model can not only accurately model the historical demand data, but also provide real-time forecast results in the face of future demand fluctuations, and the decision support.

关键词： Fluctuations Accuracy Warehousing Predictive models Prediction algorithms Real-time systems data models Forecasting Long short term memory Logistics

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Machine Learning for Subgraph Extraction: Methods, Applications and Challenges 49th

Machine Learning for Subgraph Extraction: Methods, Applicati...

引用

49th international conference on Very Large data Bases, VLDB 2023

作者： Yow, Kai Siong Liao, Ningyi Luo, Siqiang Cheng, Reynold School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science Guangdong-Hong Kong-Macau Joint Laboratory HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong

Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the associated original graphs, and many graph properties are known to be inherited by subgraphs. Subgraphs can be applied in many areas such as social networks, recommender systems, biochemistry and fraud discovery. Researchers from various communities have paid a great deal of attention to investigate numerous subgraph problems, by proposing algorithms that mainly extract important structures of a given graph. There are however some limitations that should be addressed, with regard to the efficiency, effectiveness and scalability of these traditional algorithms. As a consequence, machine learning techniques—one of the most latest trends—have recently been employed in the database community to address various subgraph problems considering that they have been shown to be beneficial in dealing with graphrelated problems. We discuss learning-based approaches for four well known subgraph problems in this tutorial, namely subgraph isomorphism, maximum common subgraph, community detection and community search problems. We give a general description of each proposed model, and analyse its design and performance. To allow further investigations on relevant subgraph problems, we suggest some potential future directions in this area. We believe that this work can be used as one of the primary resources, for researchers who intend to develop learning models in solving problems that are closely related to subgraphs. © 2023, VLDB Endowment. All rights reserved.

关键词： Machine learning

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Node Classification Beyond Homophily: Towards a General Solution 23

Node Classification Beyond Homophily: Towards a General Solu...

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29th ACM SIGKDD conference on Knowledge Discovery and data Mining (KDD)

作者： Xu, Zhe Chen, Yuzhong Zhou, Qinghai Wu, Yuhang Pan, Menghai Yang, Hao Tong, Hanghang Univ Illinois Champaign IL 61820 USA Visa Res Palo Alto CA 94306 USA

ISBN: (纸本)9798400701030

Graph neural networks (GNNs) have become core building blocks behind a myriad of graph learning tasks. The vast majority of the existing GNNs are built upon, either implicitly or explicitly, the homophily assumption, which is not always true and could heavily degrade the performance of learning tasks. In response, GNNs tailored for heterophilic graphs have been developed. However, most of the existing works are designed for the specific GNN models to address heterophily, which lacks generality. In this paper, we study the problem from the structure learning perspective and propose a family of general solutions named ALT. It can work hand in hand with most of the existing GNNs to handle graphs with either low or high homophily. At the core of our method is learning to (1) decompose a given graph into two components, (2) extract complementary graph signals from these two components, and (3) adaptively integrate the graph signals for node classification. Moreover, analysis based on graph signal processing shows that our framework can empower a broad range of existing GNNs to have adaptive filter characteristics and further modulate the input graph signals, which is critical for handling complex homophilic/heterophilic patterns. The proposed ALT brings significant and consistent performance improvement in node classification for a wide range of GNNs over a variety of real-world datasets.

关键词： graph machine learning graph data augmentation graph neural network node classification

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