scholarly journals In silico methods in stability testing of hydrocortisone, powder for injections: Multiple regression analysis versus dynamic neural network

2012 ◽  
Vol 66 (5) ◽  
pp. 647-657 ◽  
Author(s):  
Ljiljana Solomun ◽  
Svetlana Ibric ◽  
Vjera Pejanovic ◽  
Jelena Djuris ◽  
Jelena Jockovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
In Silico ◽  
Sodium Succinate ◽  
Coefficient Of Determination ◽  
Stability Studies ◽  
Dynamic Neural Network ◽  
Freeze Dried

This article presents the possibility of using of multiple regression analysis (MRA) and dynamic neural network (DNN) for prediction of stability of Hydrocortisone 100 mg (in a form of hydrocortisone sodium succinate) freeze-dried powder for injection packed into a dual chamber container. Degradation products of hydrocortisone sodium succinate: free hydrocortisone and related substances (impurities A, B, C, D and E; unspecified impurities and total impurities) were followed during stress and formal stability studies. All data obtained during stability studies were used for in silico modeling; multiple regression models and dynamic neural networks as well, in order to compare predicted and observed results. High values of coefficient of determination (0.950.99) were gained using MRA and DNN, so both methods are powerful tools for in silico stability studies, but superiority of DNN over mathematical modeling of degradation was also confirmed.

scholarly journals Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
S. Karthiyaini ◽  
K. Senthamaraikannan ◽  
J. Priyadarshini ◽  
Kamal Gupta ◽  
M. Shanmugasundaram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Mechanical Strength ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Mean Squared Error ◽  
Fiber Reinforced Concrete ◽  
Coefficient Of Determination ◽  
Artificial Neural

The present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. The model uses the data from early literatures; the data consist of tensile strength of fiber, percentage of fiber, water/cement ratio, cross-sectional area of test specimen, Young’s modulus of fiber, and mechanical strength of control specimen, and these were used as the input parameters; the respective strength attained was used as the target parameter. The models are created and are used to predict compressive, split tensile, and flexural strength of fiber admixed concrete. These models are evaluated through the statistical test such as coefficient of determination (R2) and root mean squared error (RMSE). The results show that these parameters produce a valid model through both MRA and ANN, and this model gives more precise prediction for the fiber admixed concrete.

Consumers' perception-oriented product form design using multiple regression analysis and backpropagation neural network

2015 ◽  
Vol 30 (1) ◽  
pp. 64-77 ◽  
Author(s):  
Hung-Yuan Chen ◽  
Hua-Cheng Chang
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Neural Network Model ◽  
Integrated Design ◽  
Consumer Response ◽  
Product Form ◽  
Backpropagation Neural Network ◽  
Integrated Design Approach

AbstractConsumers' psychological perceptions of a product are significantly influenced by its appearance aesthetics, and thus product form plays an essential role in determining the commercial success of a product. The evolution of a product's form during the design process is typically governed by the designer's individual preferences and creative instincts. As a consequence, there is a risk that the product form may fail to satisfy the consumers' expectations or may induce an unanticipated consumer response. This study commences developing an integrated design approach based on the numerical definition of product form. A series of evaluation trials are then performed to establish the correlation between the product form features and the consumers' perceptions of the product image. The results of the evaluation trials are used to construct three different types of mathematical model (a multiple regression analysis model, a backpropagation neural network model, and a multiple regression analysis with a backpropagation neural network model) to predict the likely consumer response to any arbitrary product form. The feasibility of an integrated design approach is demonstrated using a three-dimensional knife form. Although this study takes an example for illustration and verification purposes, the methodology proposed in the present study is equally applicable to any form of consumer product.

scholarly journals DETERMINANT OF BUDGETARY SLACK ON OPD OF KEBUMEN DISTRICT GOVERNMENT

2019 ◽  
Vol 4 (2) ◽  
pp. 151
Author(s):  
Eka Fitria Ambarini ◽  
Mispiyanti Mispiyanti
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Causal Relationship ◽  
Multiple Regression ◽  
Sampling Technique ◽  
Self Esteem ◽  
Primary Data ◽  
Coefficient Of Determination ◽  
Budgetary Slack ◽  
District Government

This study aimed to examine the causal relationship between budget emphasis, self-esteem and budget participation by using primary data collected by distributing questionnaires to the respondent. The population was the OPD Regency of Kebumen with a purposive sampling as a sampling technique. From 75 questionnaires that have been distributed, 70 questionnaires have been received and only 43 questionnaires that could be processed. This studied used multiple regression analysis methods. The study showed that based on the t-test:  budget emphasis and self-esteem have a positive and significant effect on budgetary slack, while budget participation does not affect budgetary slack. The coefficient of determination (R²) showed 29,5%, that was mean 29,5% variable of budgetary slack influenced by variables budget emphasis, self-esteem, and budget participation but 70,5% explained by other variables which not exist in this research

Faktor-Faktor yang Mempengaruhi Profitabilitas Bank Muamalat di Indonesia

2020 ◽  
Vol 6 (3) ◽  
pp. 716
Author(s):  
Yuwita Ariessa Pravasanti ◽  
Wikan Budi Utami
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Secondary Data ◽  
Sampling Technique ◽  
Analytical Tool ◽  
Financial Data ◽  
Coefficient Of Determination ◽  
Bank Profitability ◽  
Demand Deposits

This study aims to obtain empirical evidence related to the influence of Indonesian Muamalat bank profitability. Characteristics of the influence of wadi'ah demand deposits, wadi’ah savings and mudharabah savings. This study uses secondary data with quarterly financial data population of PT. Bank Muamalat Indonesia from 2011 to 2018. The sampling technique uses quarterly financial data of PT. Bank Muamalat Indonesia has 32 data. The analytical tool used is multiple regression analysis. The results showed that the wadi'ah giro variable partially had a significant effect on profit / ROA. Show that t count is 2,356 ≥ t table is 1,699 while the value of Sig. t 0.026 ≤ Sig.α 0.05. Wadi'ah savings variable partially has a significant effect on profit / ROA. Shows that t count is - 3,135 ≤ t table is 1,699 while Sig. 0.004 ≤ α 0.05. Mudharabah savings variable partially has a significant effect on profit / ROA. Shows that t arithmetic of - 0.334 ≤ t table of 1.699 while the value of Sig. α 0.741 ≥ 0.05, then Ho is rejected and H1 is accepted. The results showed that multiple regression analysis obtained the coefficient of determination (R2) was 0.412 or equal to 41.2%. This means that Wadi'ah Giro, Wadi'ah Savings and Mudharabah Savings jointly affect bank profitability.

Determination of Meat Quality by Image Processing and Neural Network Techniques

2000 ◽  
Vol 12 (4) ◽  
pp. 474-479
Author(s):  
Kazuhiko Shiranita ◽  
◽  
Kenichiro Hayashi ◽  
Akifumi Otsubo
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Meat Quality ◽  
Distribution Density ◽  
Marbling Score ◽  
Quality Grading

We study the implementation of a meat-quality grading system, using the concept of the marbling score, and image processing, neural network techniques and multiple regression analysis. The marbling score is a measure of the distribution density of fat in the rib-eye region. We identify five features used for grading meat images. For the evaluation of the five features, we propose a method of image binarization using a three-layer neural network developed based on inputs given by a professional grader and a system of meat-quality grading based on the evaluation of three of five features with multiple regression analysis. Experimental results show that the system is effective.

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