Extending resolution for model construction

1991 ◽  
pp. 153-169 ◽  
Author(s):  
Ricardo Caferra ◽  
Nicolas Zabel
Keyword(s):  
Model Construction

Bankruptcy Model Construction and its Limitation in Input Data Quality

2019 ◽  
Vol 10 (2) ◽  
pp. 117-125
Author(s):  
Dana Kubíčková ◽  
◽  
Vladimír Nulíček ◽  
Keyword(s):  
Data Quality ◽  
Normal Distribution ◽  
Input Data ◽  
Model Construction ◽  
The Third ◽  
Third Stage ◽  
Discriminant Analyses ◽  
One Year ◽  
The University ◽  
Multivariate Discriminant

The aim of the research project solved at the University of Finance and administration is to construct a new bankruptcy model. The intention is to use data of the firms that have to cease their activities due to bankruptcy. The most common method for bankruptcy model construction is multivariate discriminant analyses (MDA). It allows to derive the indicators most sensitive to the future companies’ failure as a parts of the bankruptcy model. One of the assumptions for using the MDA method and reassuring the reliable results is the normal distribution and independence of the input data. The results of verification of this assumption as the third stage of the project are presented in this article. We have revealed that this assumption is met only in a few selected indicators. Better results were achieved in the indicators in the set of prosperous companies and one year prior the failure. The selected indicators intended for the bankruptcy model construction thus cannot be considered as suitable for using the MDA method.

Simulating Self-Regeneration and Self-Replication Processes Using Movable Cellular Automata with a Mutual Equilibrium Neighborhood

2020 ◽  
Vol 29 (4) ◽  
pp. 741-757
Author(s):  
Kateryna Hazdiuk ◽  
◽  
Volodymyr Zhikharevich ◽  
Serhiy Ostapov ◽  
◽  
...  
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Three Dimensional ◽  
Computer Models ◽  
The Self ◽  
Model Construction ◽  
Natural Phenomena ◽  
Different Types ◽  
Movable Cellular Automata ◽  
Self Replication

This paper deals with the issue of model construction of the self-regeneration and self-replication processes using movable cellular automata (MCAs). The rules of cellular automaton (CA) interactions are found according to the concept of equilibrium neighborhood. The method is implemented by establishing these rules between different types of cellular automata (CAs). Several models for two- and three-dimensional cases are described, which depict both stable and unstable structures. As a result, computer models imitating such natural phenomena as self-replication and self-regeneration are obtained and graphically presented.

scholarly journals Contributions and limitations of using machine learning to predict noise-induced hearing loss

2021 ◽  
Author(s):  
Feifan Chen ◽  
Zuwei Cao ◽  
Emad M. Grais ◽  
Fei Zhao
Keyword(s):  
Machine Learning ◽  
Hearing Loss ◽  
Roc Curve ◽  
Prediction Error ◽  
Noise Exposure ◽  
Model Construction ◽  
Support Vector ◽  
Exposure Level ◽  
Noise Induced Hearing Loss ◽  
Current Review

Abstract Purpose Noise-induced hearing loss (NIHL) is a global issue that impacts people’s life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction. Methods The authors searched PubMed, EMBASE and Scopus on November 26, 2020. Results Eight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve. Conclusion In spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk.

Effective diagnostic model construction based on discriminative breast ultrasound image regions using deep feature extraction

2021 ◽  
Author(s):  
Hailong Yu ◽  
Hang Sun ◽  
Jing Li ◽  
Liying Shi ◽  
Nan Bao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Model Construction ◽  
Diagnostic Model ◽  
Deep Feature ◽  
Deep Feature Extraction

Automatic performance model construction for the fast software exploration of new hardware designs

2006 ◽  
Author(s):  
John Cavazos ◽  
Christophe Dubach ◽  
Felix Agakov ◽  
Edwin Bonilla ◽  
Michael F. P. O'Boyle ◽  
...  
Keyword(s):  
Performance Model ◽  
Model Construction ◽  
Hardware Designs

scholarly journals High-precision 3D real scene model construction method based on air-ground data combination

2020 ◽  
Vol 570 ◽  
pp. 042022
Author(s):  
Fei Yang ◽  
Chengrong Ma ◽  
Bowen Zhang ◽  
Xuannan Chen ◽  
Li Cao ◽  
...  
Keyword(s):  
High Precision ◽  
Construction Method ◽  
Model Construction ◽  
Data Combination ◽  
Real Scene
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