Results 11 to 20 of about 2,244,366 (395)

Response Surface Methodology for Optimizing Hyper Parameters [PDF]

open access: yesResearch Papers in Economics, 2006
The performance of an algorithm often largely depends on some hyper parameter which should be optimized before its usage. Since most conventional optimization methods suffer from some drawbacks, we developed an alternative way to find the best hyper parameter values.
Weihs, Claus   +2 more
core   +11 more sources

Maximization of fructose esters synthesis by response surface methodology [PDF]

open access: yesNew Biotechnology, 2011
Enzymatic synthesis of fructose fatty acid ester was performed in organic solvent media, using a purified lipase from Candida antartica B immobilized in acrylic resin. Response surface methodology with a central composite rotatable design based on five levels was implemented to optimize three experimental operating conditions (temperature, agitation ...
Neta, Nair S.   +3 more
openaire   +8 more sources

Extraction and Analysis of Antimicrobial Compounds from Onosma Bracteatum Using Response Surface Methodology

open access: yesJournal of Pharmacy and Bioallied Sciences
Background: Infectious diseases are the world’s leading cause of premature mortality, almost killing 65,000 people every day. Objective: The main objective of the study is to find the extraction and analysis of antimicrobial compounds from Onosma ...
Syed Y. Kazmi, Habeeb A. Baig
doaj   +2 more sources

Response surface methodology (RSM): An overview to analyze multivariate data

open access: yesIndian Journal of Microbiology Research, 2023
In recent years, the fascinating range of Response surface methodology (RSM) applications has captured the interest of many researchers and engineers worldwide.
Rupak Kumar, Meega Reji
semanticscholar   +1 more source

Response Surface Methodology Using Observational Data: A Systematic Literature Review

open access: yesApplied Sciences, 2022
In the response surface methodology (RSM), the designed experiment helps create interfactor orthogonality and interpretable response models for the purpose of process and design optimization.
M. A. Hadiyat, B. M. Sopha, B. Wibowo
semanticscholar   +1 more source

Improved Multiple Comparisons With The Best In Response Surface Methodology [PDF]

open access: bronze, 2003
A method to construct simultaneous confidence intervals about the difference in mean responses at the stationary point and at x for all x within a sphere with radius I R is proposed.
Laura K. Miller, Ping Sa
openalex   +4 more sources

Optimization for biogenic microbial synthesis of silver nanoparticles through response surface methodology, characterization, their antimicrobial, antioxidant, and catalytic potential

open access: yesScientific Reports, 2021
Silver is a poisonous but precious heavy metal that has widespread application in various biomedical and environmental divisions. Wide-ranging usage of the metal has twisted severe environmental apprehensions.
Saba Ibrahim   +5 more
semanticscholar   +1 more source

OPTIMIZATION OF DIFFERENT CHEMICAL PROCESSES USING RESPONSE SURFACE METHODOLOGY- A REVIEW

open access: yesJournal of Engineering and Sustainable Development, 2022
Several chemical and biological processes have been investigated and predicted using Response Surface Methodology (RSM) models. Response Surfaces Methodology is a useful instrument for designing laboratory-scale experiments that optimize and support the ...
Hiba Zaid   +3 more
semanticscholar   +1 more source

Central Composite Design for Response Surface Methodology and Its Application in Pharmacy

open access: yesResponse Surface Methodology in Engineering Science [Working Title], 2021
The central composite design is the most commonly used fractional factorial design used in the response surface model. In this design, the center points are augmented with a group of axial points called star points.
Sankha Bhattacharya
semanticscholar   +1 more source

Application of response surface methodology to stiffened panel optimization [PDF]

open access: yes, 2006
In a multilevel optimization frame, the use of surrogate models to approximate optimization constraints allows great time saving. Among available metamodelling techniques we chose to use Neural Networks to perform regression of static mechanical criteria,
Grihon, Stéphane   +2 more
core   +1 more source

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