检索结果-内蒙古大学图书馆

model visualization: Graphical support for DSS problem structuring and knowledge organization

Decision Support Systems 1990年第1期6卷 13-27页

作者： Pracht, William E. The Fogelman College of Business and Economics Memphis State University Memphis TN 38152 United States

model visualization is proposed as an approach for business problem model development and use within a Decision Support System environment. The goal of model visualization is to allow the model builder/user to form clear mental images of a model's structure and function. A model visualization system is a display-based system that: (1) is designed for implementation in a managerially-friendly, interactive problem solving environment;(2) supports the creative task of discovering and modeling the structure of a complex problem;(3) provides the capability for modeling the behavior of a system through time and (4) may serve as the basis for the incorporation of heuristic rules appropriate for model-based reasoning procedures. Efforts are currently underway to design, develop, and implement a prototype model visualization system in Smalltalk/V. © 1990.

关键词： Business Problem modeling Frame-based Knowledge Representation Frames Interactive Graphics Interactive Problem Solving Knowledge Organization model visualization Problem Structuring

来源：评论

学校读者我要写书评

暂无评论

atomes: Analysis, visualization, edition and post-processing of 3D atomic scale models

引用

COMPUTATIONAL MATERIALS SCIENCE 2025年 253卷

作者： Le Roux, Sebastien Univ Strasbourg CNRS Inst Phys & Chim Mat Strasbourg UMR 7504 23 Rue LoessBP 43 F-67034 Strasbourg 2 France

atomes is a cross-platform open source program developed to analyze, to visualize and to edit/create large three-dimensional atomic scale models. By regrouping advanced analysis techniques, 3D visualization and 3D edition, atomes introduces innovative 3D rendering possibilities and intuitive applications of the calculation results. atomes also provides an advanced input preparation system for further calculations using well known classical, ab-initio and QM-MM molecular dynamics codes. To prepare the input files for these calculations is likely to be the key, and most complicated step towards MD simulations. atomes offers a user-friendly assistant to help and guide the user step by step to achieve this crucial step.

关键词： Software Open source Atomic scale modeling 3D models Crystal builder Surface passivation model analysis model visualization model creation model edition

来源：评论

学校读者我要写书评

暂无评论

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning

引用

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2024年 184卷 105341-105341页

作者： Yin, Minyue Lin, Jiaxi Wang, Yu Liu, Yuanjun Zhang, Rufa Duan, Wenbin Zhou, Zhirun Zhu, Shiqi Gao, Jingwen Liu, Lu Liu, Xiaolin Gu, Chenqi Huang, Zhou Xu, Xiaodan Xu, Chunfang Zhu, Jinzhou Soochow Univ Affiliated Hosp 1 Dept Gastroenterol Suzhou 215006 Jiangsu Peoples R China Suzhou Clin Ctr Digest Dis Suzhou 215006 Jiangsu Peoples R China Jiangsu Univ Jintan Hosp Dept Gen Surg Changzhou 213299 Jiangsu Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Soochow Univ Changshu Hosp Changshu Peoples Hosp 1 Dept Gastroenterol Suzhou 215500 Jiangsu Peoples R China Peoples Hosp Hunan Prov Dept Hepatobiliary Surg Changsha 410002 Hunan Peoples R China Soochow Univ Affiliated Hosp 2 Dept Obstet & Gynaecol Suzhou 215004 Jiangsu Peoples R China Soochow Univ Affiliated Hosp 1 Dept Radiol Suzhou 215006 Jiangsu Peoples R China Harbin Med Univ Affiliated Hosp 1 Minist Educ Key Lab Hepatosplen Surg Harbin 150000 Peoples R China Soochow Univ Changshu Affiliated Hosp Dept Gastroenterol 1 Shuyuan St Suzhou 215500 Jiangsu Peoples R China Soochow Univ Affiliated Hosp 1 Dept Gastroenterol 188 Shizi St Suzhou 215006 Jiangsu Peoples R China

Objective: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). Methods: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained model alpha based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and model beta was developed on the features after dimensionality reduction using principal component analysis. Third, model gamma was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. Results: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [model alpha: 0.709 (0.618-0.800);model beta: 0.749 (0.675-0.824);model gamma: 0.687 (0.592-0.782);MCTSI: 0.778 (0.698-0.857);RANSON: 0.642 (0.559-0.725);BISAP: 0.751 (0.668-0.833);SABP: 0.710 (0.621-0.798)]. Conclusion: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

关键词： Multimodal fusion Machine learning Deep learning Severe acute pancreatitis model visualization

来源：评论

学校读者我要写书评

暂无评论

Design and evaluation of visualization support to facilitate decision trees classification

引用

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 2007年第2期65卷 95-110页

作者： Liu, Yan Salvendy, Gavriel Wright State Univ Dept Biomed Ind & Human Factors Engn Dayton OH 45435 USA Purdue Univ Sch Ind Engn W Lafayette IN 47906 USA Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

The loosely coupled relationships between visualization and analytical data mining (DM) techniques represent the majority of the current state of art in visual data mining;DM modeling is typically an automatic process with very limited forms of guidance from users. A conceptual model of the visualization support to DM[ modeling process and a novel interactive visual decision tree (IVDT) classification process have been proposed in this paper, with the aim of exploring humans' pattern recognition ability and domain knowledge to facilitate the knowledge discovery process. An IVDT for categorical input attributes has been developed and experimented on 20 subjects to test three hypotheses regarding its potential advantages. The experimental results suggested that, compared to the automatic modeling process as typically applied in current decision tree modeling tools, lVDT process can improve the effectiveness of modeling in terms of producing trees with relatively high classification accuracies and small sizes, enhance users' understanding of the algorithm, and give them greater satisfaction with the task. (c) 2006 Published by Elsevier Ltd.

关键词： visual data mining interactive modeling model visualization data visualization

来源：评论

学校读者我要写书评

暂无评论

Quality of classification explanations with PRBF

引用

NEUROCOMPUTING 2012年 96卷 37-46页

作者： Robnik-Sikonja, Marko Kononenko, Igor Strumbelj, Erik Univ Ljubljana Fac Comp & Informat Sci Lab Cognit Modeling Ljubljana 1001 Slovenia Univ Ljubljana Fac Comp & Informat Sci Artificial Intelligence Dept Ljubljana 1001 Slovenia

Recently two general methods for explaining classification models and their predictions have been introduced. Both methods are based on an idea that importance of a feature or a group of features in a specific model can be estimated by simulating lack of knowledge about the values of the feature(s). For the majority of models this requires an approximation by averaging over all possible feature values. A probabilistic radial basis function network (PRBF) is one of the models where such approximation is not necessary and therefore offers a chance to evaluate the quality of approximation by comparing it to the exact solution. We present both explanation methods and demonstrate their behavior with PRBF. The explanations make individual decisions of classifiers transparent and allow inspection and visualization of otherwise opaque models. We empirically compare the quality of explanations based on marginalization of the Gaussian distribution (the exact method) and explanation with averaging over all feature values (the approximation). The results show that the approximation method and the exact solution give very similar results, which increases the confidence in the explanation methodology also for other classification models. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Classification explanation model explanation Quality of explanation model comprehensibility Probabilistic RBF networks model visualization

来源：评论

学校读者我要写书评

暂无评论

Estimation of bond strength between UHPC and reinforcing bars using machine

引用

ENGINEERING STRUCTURES 2022年 262卷 1页

作者： Li, Zhijie Qi, Jianan Hu, Yuqing Wang, Jingquan Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 211189 Peoples R China Southeast Univ Bridge Engn Res Ctr Southeast Univ Nanjing 211189 Peoples R China Southeast Univ Natl Prestress Engn Res Ctr Nanjing 211189 Peoples R China

Bond strength estimation plays an important role in structure engineering. This paper proposes to adopt machine learning approaches to conduct a data-driven analysis of bond strength between ultra-high perfor-mance concrete (UHPC) and reinforcing bars. To make up for the lack of experimental data, a new database is established by integrating 557 instances from several published works. A total of nine machine learning models which can be divided into three types are implemented to train the bond strength estimators based on the database, including linear models, tree models, and artificial neural networks. Four strong metrics, i.e. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Ratio of Accurate Estimation (RACC), are used to evaluate the performance of models. Among them, Artificial Neural Network and Random Forest achieve great estimation performances in the top two, which far exceed the empirical formulas. They have 74% and 73% of estimated data to keep the relative error within 10%, respectively. The statistical relative importance of different factors from tree models consistently shows that the ratio of embedded depth to the diameter of reinforcing bars has a significant impact on the bond strength of UHPC, which is conformable with the observations in experiments.

关键词： Bond strength Machine learning UHPC Reinforcing bar model visualization

来源：评论

学校读者我要写书评

暂无评论

Visualizing Statistical models: Removing the Blindfold

引用

STATISTICAL ANALYSIS AND DATA MINING 2015年第4期8卷 203-225页

作者： Wickham, Hadley Cook, Dianne Hofmann, Heike Rice Univ Dept Stat RStudio Houston TX 77004 USA Iowa State Univ Dept Stat Ames IA 50011 USA

visualization can help in model building, diagnosis, and in developing an understanding about how a model summarizes data. This paper proposes three strategies for visualizing statistical models: (i) display the model in the data space, (ii) look at all members of a collection, and (iii) explore the process of model fitting, not just the end result. Each strategy is accompanied by examples, including manova, classification algorithms, hierarchical clustering, ensembles of linear models, projection pursuit, self-organizing maps, and neural networks. (C) 2015 Wiley Periodicals, Inc.

关键词： model visualization exploratory data analysis data mining classification high-dimensional data

来源：评论

学校读者我要写书评

暂无评论

SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens

引用

ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021年第14期55卷 9958-9967页

作者： Wang, Liguo Zhao, Lu Liu, Xian Fu, Jianjie Zhang, Aiqian Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Environm Chem & Ecotoxicol Beijing 100085 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Sch Environm Hangzhou 310012 Peoples R China

Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals.

关键词： deep learning 3D molecular surface electrostatic potential point cloud chemical toxicity classification model visualization data imbalance estrogen receptor agonist activity

来源：评论

学校读者我要写书评

暂无评论

A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第5期25卷 1483-1494页

作者： Jiang, Shancheng Li, Huichuan Jin, Zhi Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China Guangdong Prov Key Lab Fire Sci & Technol Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Biomed Engn Guangzhou 510275 Peoples R China

Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of malignant skin tumors is a significant goal, especially considering treatment is normally effective if the tumor is detected early. Limited published histopathological image sets and the lack of an intuitive correspondence between the features of lesion areas and a certain type of skin cancer pose a challenge to the establishment of high-quality and interpretable computer-aided diagnostic (CAD) systems. To solve this problem, a light-weight attention mechanism-based deep learning framework, namely, DRANet, is proposed to differentiate 11 types of skin diseases based on a real histopathological image set collected by us during the last 10 years. The CAD system can output not only the name of a certain disease but also a visualized diagnostic report showing possible areas related to the disease. The experimental results demonstrate that the DRANet obtains significantly better performance than baseline models (i.e., InceptionV3, ResNet50, VGG16, and VGG19) with comparable parameter size and competitive accuracy with fewer model parameters. Visualized results produced by the hidden layers of the DRANet actually highlight part of the class-specific regions of diagnostic points and are valuable for decision making in the diagnosis of skin diseases.

关键词： Skin Solid modeling Diseases Skin cancer Deep learning visualization Lesions Attention mechanism computer-aided diagnostic system model visualization skin histopathological image

来源：评论

学校读者我要写书评

暂无评论

Keyhole status prediction based on voting ensemble convolutional neural networks and visualization by Grad-CAM in PAW

引用

JOURNAL OF MANUFACTURING PROCESSES 2022年 80卷 805-815页

作者： Zhou, Fangzheng Liu, Xinfeng Zhang, Xuehua Liu, Yang Jia, Chuanbao Wu, Chuansong Shandong Univ Inst Mat Joining MOE Key Lab Liquid Solid Struct Evolut & Mat Proc Jinan 250061 Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China Shandong Coll Elect Technol Jinan 250200 Peoples R China

K-PAW (Keyhole Plasma Arc Welding) has a wide range of applications in welding medium-thick metal workpieces. However, the welding processes are vulnerable due to the fragile force balance on the liquid metal around the keyhole, and burn-through or lack of fusion might be caused. In order to optimize the welding quality, considerable work has been devoted to penetration/keyhole status prediction by visual sensing and deep learning algorithms. However, a single network model is challenging to extract comprehensive features of weakdiscrimination weld pool images. This paper focused on the correspondence between the topside weld pool images and the prediction/keyhole status. A novel prediction method has been proposed based on eight classical CNNs trained, compared, and fused. For these single models, the prediction accuracy is higher than 95 %, with the speed of faster than 10 frames per second (FPS). The model visualization by the Grad-CAM method was performed to show the focused feature regions clearly. Although regarded as black boxes, these prediction models are considered robust when the extracted features are consistent with the prior knowledge. KeyholeVot, a voting ensemble decision model, was established based on the selected three robust models (i.e., InceptionNetV3, InceptionResNetV2, and XceptionNet) and achieved 96.62 % evaluation accuracy.

关键词： Keyhole plasma arc welding Topside weld pool image Convolution neural network Majority voting model visualization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：