scholarly journals Comparison of Linear Multivariable, Partial Least Square Regression, and Artificial Neural Network Analyses to Study the Effect of Different Parameters on Anode Properties

2015 ◽  
pp. 1129-1134 ◽  
Author(s):  
Dipankar Bhattacharyay ◽  
Duygu Kocaefe ◽  
Yasar Kocaefe ◽  
Brigitte Morais
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Network Analyses ◽  
Least Square Regression ◽  
Artificial Neural

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

scholarly journals Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network

2020 ◽  
Vol 33 (10) ◽  
pp. 1633-1641
Author(s):  
Dae-Hyun Lee ◽  
Seung-Hyun Lee ◽  
Byoung-Kwan Cho ◽  
Collins Wakholi ◽  
Young-Wook Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Carcass Weight ◽  
Body Measurements ◽  
Least Square Regression ◽  
Data Points ◽  
Hanwoo Cattle

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network.Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation.Results: The R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy.Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

scholarly journals Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
E. Chatzimichail ◽  
E. Paraskakis ◽  
A. Rigas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Decision Making ◽  
Persistent Asthma ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Asthma Outcome ◽  
Artificial Neural

The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood.

scholarly journals Comparison of partial least squares and artificial neural network chemometric techniques in determination of sulfamethoxazole and trimethoprim in pharmaceutical suspension by ATR–FTIR spectrometry

2011 ◽  
Vol 26 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Khanmohammadi ◽  
N. Dallali ◽  
A. Bagheri Garmarudi ◽  
M. Zarnegar ◽  
K. Ghasemi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Parameters ◽  
Modified Method ◽  
Ftir Spectrometry ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Partial Least Square (PLS) and Artificial Neural Network (ANN) techniques were compared during development of an analytical method for quantitative determination of sulfamethoxazole (SMX) and trimethoprim (TMP) in Co-Trimoxazole®suspension. The procedure was based on Attenuated Total Reflectance Fourier Transform Infrared (ATR–FTIR) spectrometry. The 800–2500 cm−1spectral region was selected for quantitative analysis.R2and relative error of prediction (REP) in PLS technique were (0.989, 2.128) and (0.986, 1.381) for SMX and TMP, respectively. These statistical parameters were improved using the ANN models considering the complexity of the sample and the speediness and simplicity of the method.R2and RMSEC in modified method were (0.997, 1.064) and (0.997, 0.634) for SMX and TMP, respectively.

scholarly journals Three-Dimensional Dental Analysis for Sex Estimation in the Italian Population: A Pilot Study Based on a Geometric Morphometric and Artificial Neural Network Approach

Healthcare ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Giorgio Oliva ◽  
Vilma Pinchi ◽  
Ilenia Bianchi ◽  
Martina Focardi ◽  
Corrado Paganelli ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pilot Study ◽  
Partial Least Square ◽  
Least Square ◽  
Future Research ◽  
Italian Population ◽  
Neural Network Approach ◽  
Geometric Morphometric ◽  
Artificial Neural

Dental dimorphism can be used for discriminating sex in forensic contexts. Geometric morphometric analysis (GMA) allows the evaluation of the shape and size, separately, of uneven 3D objects. This study presents experiments using a novel combination of GMA and an artificial neural network (ANN) for sex classification, applied to premolars of Caucasian Italian adults (50 females and 50 males). General Procrustes superimposition (GPS) and the partial least square (PLS) method were performed, respectively, to study the shape variance between sexes and to eliminate landmark variations. The “set-aside” approach was used to assess the accuracy of the proposed neural networks. As the main findings of the pilot study, the proposed method applied to the first upper premolar correctly classified 90% of females and 73% of males of the test sample. The accuracy was 0.84 and 0.80 for the training and test samples, respectively. The sexual dimorphism resulting from GMA was low, although statistically significant. GMA combined with the ANN demonstrated better sex classification ability than previous odontometric or dental morphometric methods. Future research could overcome some limitations by considering a larger sample of subjects and other kinds of teeth and experimenting with the use of computer vision for automatic landmark positioning.

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