The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
A. Akilli - One of the best experts on this subject based on the ideXlab platform.
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Egg production curve fitting using least square support vector machines and Nonlinear Regression Analysis
2018Co-Authors: O. Görgülü, A. AkilliAbstract:WOS: 000432740200001It was aimed to model egg production curves using Nonlinear Regression Analysis and least squares support vector machines in this study. The accuracy of the models was calculated using the Akaike information criteria, mean square error, mean absolute percentage error, mean absolute deviation, R-2 and AdjR(2). The data set consisted of egg performance values of laying hens recorded from 20 weeks to 70 weeks of age. The longitudinal data had a Nonlinear structure. The results showed that the least squares support vector machines method, which is considered in different parameter combinations, can be used as an alternative to classical methods and predictions have lower errors. The present study shows that least squares support vector machine methods can be used successfully in the modelling of egg production curves in laying hens
Muhammad Farooq Ahmed - One of the best experts on this subject based on the ideXlab platform.
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Prediction Modeling for the Estimation of Dynamic Elastic Young’s Modulus of Thermally Treated Sedimentary Rocks Using Linear–Nonlinear Regression Analysis, Regularization, and ANFIS
Rock Mechanics and Rock Engineering, 2020Co-Authors: Umer Waqas, Muhammad Farooq AhmedAbstract:Thermal cracking significantly affects the dynamic and mechanical stability of rock mass. This study first focuses on the evaluation of dynamic-mechanical behavior of thermally deteriorated rocks in terms of their dynamic elastic Young’s modulus ( E _d), quality factor ( Q -factor), resonance frequency ( F _r), unconfined compressive strength (UCS) and tensile strength (BTS). Secondly, it focuses on the comparison of the performance of different statistical data modeling techniques. The overall reduction in the values of E _d, Q -factor, F _r, UCS, and BTS for all thermally treated rock samples was recorded as 23–49%, 6–28%, 7–21%, 10–38%, and 14–56%, respectively. The previous studies do not show any significant correlation between the strength parameters of thermally deteriorated rocks. In this study, a total of 7 predictive models were developed to estimate E _d for thermally deteriorated rocks using linear-Nonlinear Regression Analysis, regularization, and adaptive-neuro fuzzy inference system (ANFIS). Results of hypothesis testing showed that the linear-Nonlinear Regression equations were statistically significant. Similarly, outcomes of neuro-fuzzy logic Analysis, based on the degree of thermal cracking of rocks satisfied the statistical significance of the ANFIS model. Among all prediction models, the ANFIS model has the lowest value of root mean square error (RMSE) and the highest value of the Nash–Sutcliffe coefficient ( E ). Based on the results of model quality indices, these statistical modeling techniques are arranged in the following order; ANFIS > Nonlinear Regression > Regularization > Linear Regression. The outcomes of this study can provide references to solve complex problems in geostatistics.
Luiz Fernando Capretz - One of the best experts on this subject based on the ideXlab platform.
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CCECE - Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis
2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2020Co-Authors: Ali Bou Nassif, Manar Abutalib, Luiz Fernando CapretzAbstract:Software effort estimation in the early stages of the software life cycle is one of the most essential and daunting tasks for project managers. In this research, a new model based on Nonlinear Regression Analysis is proposed to predict software effort from use case diagrams. It is concluded that, where software size is classified from small to very large, one linear or non-linear equation for effort estimation cannot be applied. Our model with three different non-linear Regression equations can incorporate the different ranges in software size.
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Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis
2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2020Co-Authors: Ali Bou Nassif, Manar Abutalib, Luiz Fernando CapretzAbstract:Software effort estimation in the early stages of the software life cycle is one of the most essential and daunting tasks for project managers. In this research, a new model based on Nonlinear Regression Analysis is proposed to predict software effort from use case diagrams. It is concluded that, where software size is classified from small to very large, one linear or non-linear equation for effort estimation cannot be applied. Our model with three different non-linear Regression equations can incorporate the different ranges in software size.
William C. Ports - One of the best experts on this subject based on the ideXlab platform.
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Predictors of Systemic Exposure to Topical Crisaborole: A Nonlinear Regression Analysis
Journal of clinical pharmacology, 2020Co-Authors: Vivek S. Purohit, Steve Riley, Huaming Tan, William C. PortsAbstract:Crisaborole ointment, 2%, is a nonsteroidal phosphodiesterase 4 inhibitor for the treatment of mild to moderate atopic dermatitis. Results from 2 randomized, double-blind, vehicle-controlled phase 3 studies showed that twice-daily crisaborole in children and adults with mild to moderate atopic dermatitis was efficacious and well tolerated. Initial pharmacokinetics (PK) studies of crisaborole indicated absorption with measurable systemic levels of crisaborole. The current Analysis was conducted to correlate steady-state systemic exposure parameters with ointment dose and identify covariates impacting PK parameters in healthy participants and patients with atopic dermatitis or psoriasis. A Nonlinear Regression Analysis was conducted using ointment dose and noncompartmental PK parameters at steady state (area under the curve [AUCss ] and maximum concentration [Cmax,ss ]). PK data were available from 244 participants across 6 clinical studies (AUCss , N = 239; Cmax,ss , N = 241). Disease condition had the greatest impact on slope in both models, corresponding to 2.5-fold higher AUCss and Cmax,ss values at a given ointment dose in patients with atopic dermatitis or psoriasis relative to healthy participants. Disease severity, race/ethnicity, and sex had marginal effects on AUCss and Cmax,ss . Systemic exposures were similar across age groups ≥2 years of age when the same percentage of body surface area (%BSA) was treated. Predictive performance plots for AUCss and Cmax,ss for different age groups demonstrated that the models adequately describe the observed data. Model predictions indicated that systemic exposure to crisaborole in pediatric patients (2-17 years) is unlikely to exceed systemic exposure in adults (≥18 years), even at the highest possible ointment dose corresponding to a %BSA of 90.
O. Görgülü - One of the best experts on this subject based on the ideXlab platform.
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Egg production curve fitting using least square support vector machines and Nonlinear Regression Analysis
2018Co-Authors: O. Görgülü, A. AkilliAbstract:WOS: 000432740200001It was aimed to model egg production curves using Nonlinear Regression Analysis and least squares support vector machines in this study. The accuracy of the models was calculated using the Akaike information criteria, mean square error, mean absolute percentage error, mean absolute deviation, R-2 and AdjR(2). The data set consisted of egg performance values of laying hens recorded from 20 weeks to 70 weeks of age. The longitudinal data had a Nonlinear structure. The results showed that the least squares support vector machines method, which is considered in different parameter combinations, can be used as an alternative to classical methods and predictions have lower errors. The present study shows that least squares support vector machine methods can be used successfully in the modelling of egg production curves in laying hens
