scholarly journals Use of neural networks to predict vault values after implantable collamer lens surgery

2021 ◽  
Author(s):  
Ke Xu ◽  
Xiaoxiao Liu ◽  
Yiming Lei ◽  
Hong Qi ◽  
Chun Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Regression Analysis ◽  
Implantable Collamer Lens ◽  
Network Analyses ◽  
The Neural Network ◽  
Input Variables

Abstract Background Appropriate sizing of the implantable collamer lens (ICL) and accurate prediction of the vault are crucial prior to surgery. However, sometimes, the vault value is higher or lower than predicted, necessitating reoperation. The present study aimed to develop neural networks for improving predictions of vault values following ICL implantation based on preoperative biometric data. Methods This retrospective study included 137 eyes of 74 patients with ICLs. Linear regression and neural network analyses were used to examine the relationship between vault values at the 6-month follow-up and preoperative parameters (e.g., ICL characteristics and biometrics). Results Linear regression analysis revealed that vault values were correlated with five variables: ICL size, anterior chamber depth (ACD), angle-to-angle (ATA), white-to-white (WTW), and lens thickness (LT) (adjusted R2 = 0.411). Inclusion of more input variables was associated with better performance in the neural network analysis. The degree of fit when all 11 variables were included in the neural network model was close to 1 (R2 = 0.98). R2 values for the quaternary neural network model enrolling four input variables (ICL size, ATA, ACD, and LT) reached 0.90. Conclusions A neural network equation including the ICL size and biometric parameters of the anterior segment (ATA, ACD, and LT) can be used to predict the postoperative vault, aiding in the selection of an appropriate ICL size and reducing the need for reoperation after surgery.

scholarly journals Improving prediction of neural networks: a study of tow financial prediction tasks

2004 ◽  
Vol 8 (4) ◽  
pp. 219-233
Author(s):  
Tarun K. Sen ◽  
Parviz Ghandforoush ◽  
Charles T. Stivason
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Models ◽  
Prediction Performance ◽  
Financial Prediction ◽  
The Neural Network ◽  
Input Variables ◽  
Relationship Of

Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is determinal to generalizabilitiy of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Credit Scoring Using Supervised and Unsupervised Neural Networks

2002 ◽  
pp. 154-166 ◽  
Author(s):  
David West ◽  
Cornelius Muchineuta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Scoring ◽  
Self Organizing Map ◽  
Subprime Lending ◽  
The Neural Network ◽  
Combined Use

Some of the concerns that plague developers of neural network decision support systems include: (a) How do I understand the underlying structure of the problem domain; (b) How can I discover unknown imperfections in the data which might detract from the generalization accuracy of the neural network model; and (c) What variables should I include to obtain the best generalization properties in the neural network model? In this paper we explore the combined use of unsupervised and supervised neural networks to address these concerns. We develop and test a credit-scoring application using a self-organizing map and a multilayered feedforward neural network. The final product is a neural network decision support system that facilitates subprime lending and is flexible and adaptive to the needs of e-commerce applications.

APPLYING NEURAL NETWORKS TO SOFTWARE RELIABILITY ASSESSMENT

2010 ◽  
Vol 17 (04) ◽  
pp. 313-329 ◽  
Author(s):  
NORMAN SCHNEIDEWIND
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Software Reliability ◽  
Likelihood Estimation ◽  
Time To Failure ◽  
The Neural Network ◽  
Method Of Maximum Likelihood ◽  
Network Method

We adapt concepts from the field of neural networks to assess the reliability of software, employing cumulative failures, reliability, remaining failures, and time to failure metrics. In addition, the risk of not achieving reliability, remaining failure, and time to failure goals are assessed. The purpose of the assessment is to compare a criterion, derived from a neural network model, for estimating the parameters of software reliability metrics, with the method of maximum likelihood estimation. To our surprise the neural network method proved superior for all the reliability metrics that were assessed by virtue of yielding lower prediction error and risk. We also found that considerable adaptation of the neural network model was necessary to be meaningful for our application – only inputs, functions, neurons, weights, activation units, and outputs were required to characterize our application.

scholarly journals Neural network method as means of processing experimental data on grain crop yields

2020 ◽  
Vol 161 ◽  
pp. 01031
Author(s):  
Aleksandr Nikiforov ◽  
Aleksei Kuchumov ◽  
Sergei Terentev ◽  
Inessa Karamulina ◽  
Iraida Romanova ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Crop Yields ◽  
Winter Triticale ◽  
The Neural Network ◽  
Grain Crop ◽  
Scientific Results

In the work based on agroecological and technological testing of varieties of grain crops of domestic and foreign breeding, winter triticale in particular, conducted on the experimental field of the Smolensk State Agricultural Academy between 2015 and 2019, we present the methodology and results of processing the experimental data used for constructing the neural network model. Neural networks are applicable for solving tasks that are difficult for computers of traditional design and humans alike. Those are processing large volumes of experimental data, automation of image recognition, approximation of functions and prognosis. Neural networks include analyzing subject areas and weight coefficients of neurons, detecting conflict samples and outliers, normalizing data, determining the number of samples required for teaching a neural network and increasing the learning quality when their number is insufficient, as well as selecting the neural network type and decomposition based on the number of input neurons. We consider the technology of initial data processing and selecting the optimal neural network structure that allows to significantly reduce modeling errors in comparison with neural networks created with unprepared source data. Our accumulated experience of working with neural networks has demonstrated encouraging results, which indicates the prospects of this area, especially when describing processes with large amounts of variables. In order to verify the resulting neural network model, we have carried out a computational experiment, which showed the possibility of applying scientific results in practice.

scholarly journals Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 381 ◽  
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu ◽  
Kayoung Park
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Multivariate Data ◽  
Principal Component ◽  
Binary Response ◽  
Link Functions ◽  
The Neural Network ◽  
Input Variables

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

scholarly journals Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

2021 ◽  
Vol 13 (2) ◽  
pp. 777
Author(s):  
Irena Ištoka Otković ◽  
Aleksandra Deluka-Tibljaš ◽  
Sanja Šurdonja ◽  
Tiziana Campisi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Multiple Linear Regression Model ◽  
Urban Environments ◽  
The Neural Network ◽  
The City ◽  
Traffic Behavior

Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.

scholarly journals Modeling Exchange Rates With Neural Networks

2011 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
A. M. M. Jamal ◽  
Cuddalore Sundar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rates ◽  
Network Model ◽  
Neural Network Model ◽  
Econometric Model ◽  
Short Term ◽  
Bilateral Exchange ◽  
The Neural Network ◽  
The U.S

<span>This paper applies the neural network model to forecast bilateral exchange rates between the U.S. and Germany and U.S. and France. The predictions from the neural network model were compared to those based on a standard econometric model. The results suggest that the neural network model may have some advantages when frequent short term forecasts are needed.</span>

scholarly journals Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks

2019 ◽  
Vol 14 ◽  
pp. 65
Author(s):  
S. Athanassopoulos ◽  
E. Mavrommatis ◽  
K. A. Gernoth ◽  
J. W. Clark
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Predictive Performance ◽  
Finite Range ◽  
Mass Excess ◽  
Nuclear Mass ◽  
Droplet Model ◽  
The Neural Network

A neural-network model is developed to reproduce the differences between experimental nuclear mass-excess values and the theoretical values given by the Finite Range Droplet Model. The results point to the existence of subtle regularities of nuclear structure not yet contained in the best microscopic/phenomenological models of atomic masses. Combining the FRDM and the neural-network model, we create a hybrid model with improved predictive performance on nuclear-mass systematics and related quantities.

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