Response surface methodology and artificial neural network modeling for optimization of ultrasound-assisted extraction and rapid HPTLC analysis of asiaticoside from Centella asiatica

Industrial Crops and Products ◽

10.1016/j.indcrop.2021.114320 ◽

2022 ◽

Vol 176 ◽

pp. 114320

Author(s):

Poonam Kumari ◽

Prabhjot Kaur ◽

Vijay Kumar ◽

Babita Pandey ◽

Romaan Nazir ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Network Modeling ◽

Centella Asiatica ◽

Neural Network Modeling ◽

Ultrasound Assisted Extraction ◽

Artificial Neural Network Modeling ◽

Assisted Extraction ◽

Ultrasound Assisted

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Comparative study of response surface methodology and artificial neural network in the optimization of the ultrasound-assisted extraction of diarylheptanoid phytoestrogens from Curcuma comosa rhizomes

Chemical Engineering and Processing - Process Intensification ◽

10.1016/j.cep.2021.108461 ◽

2021 ◽

Vol 165 ◽

pp. 108461

Author(s):

Bancha Yingngam ◽

Abhiruj Navabhatra ◽

Wandee Rungseevijitprapa ◽

Chutinun Prasitpuriprecha ◽

Adelheid Brantner

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Comparative Study ◽

Response Surface ◽

Ultrasound Assisted Extraction ◽

Curcuma Comosa ◽

Assisted Extraction ◽

Ultrasound Assisted ◽

Artificial Neural

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Application of response surface methodology and artificial neural network modeling to assess non-thermal plasma efficiency in simultaneous removal of BTEX from waste gases: Effect of operating parameters and prediction performance

Process Safety and Environmental Protection ◽

10.1016/j.psep.2018.08.010 ◽

2018 ◽

Vol 119 ◽

pp. 261-270 ◽

Author(s):

Ahmad Hosseinzadeh ◽

Ali Asghar Najafpoor ◽

Ahmad Jonidi Jafari ◽

Reza Khani Jazani ◽

Mansour Baziar ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Network Modeling ◽

Prediction Performance ◽

Neural Network Modeling ◽

Operating Parameters ◽

Simultaneous Removal ◽

Artificial Neural Network Modeling ◽

Non Thermal Plasma

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Sonocatalytic Tannery Effluent Degradation –Response Surface Methodology and Particle Swarm Optimization Embedded Artificial Neural Network Modeling Approach

CLEAN - Soil Air Water ◽

10.1002/clen.202100095 ◽

2021 ◽

pp. 2100095

Author(s):

Natarajan Arun Prem Anand ◽

Ramasamy Palani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Particle Swarm Optimization ◽

Response Surface ◽

Network Modeling ◽

Neural Network Modeling ◽

Tannery Effluent ◽

Swarm Optimization ◽

Artificial Neural Network Modeling

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Reduction of edge effect using response surface methodology and artificial neural network modeling of a spur gear treated by induction with flux concentrators

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-019-03817-9 ◽

2019 ◽

Vol 104 (1-4) ◽

pp. 103-117

Author(s):

Mohamed Khalifa ◽

Noureddine Barka ◽

Jean Brousseau ◽

Philippe Bocher

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Edge Effect ◽

Network Modeling ◽

Spur Gear ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Artificial Neural

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Application of artificial neural network‐genetic algorithm and response surface methodology for optimization of ultrasound‐assisted extraction of phenolic compounds from cashew apple bagasse

Journal of Food Process Engineering ◽

10.1111/jfpe.13828 ◽

2021 ◽

Author(s):

Abhipriya Patra ◽

S Abdullah ◽

Rama Chandra Pradhan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Phenolic Compounds ◽

Ultrasound Assisted Extraction ◽

Cashew Apple Bagasse ◽

Assisted Extraction ◽

Cashew Apple ◽

Ultrasound Assisted

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Artificial Neural Network Modeling of Cutting Force in Turning of Ti-6Al-4V Alloy and Its Comparison with Response Surface Methodology

Advances in Intelligent and Soft Computing - Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011 ◽

10.1007/978-81-322-0491-6_69 ◽

2012 ◽

pp. 761-768 ◽

Author(s):

Vikas Upadhyay ◽

P. K. Jain ◽

N. K. Mehta

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Cutting Force ◽

Response Surface ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Artificial Neural

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Response surface methodology and artificial neural network approach for the optimization of ultrasound-assisted extraction of polyphenols from garlic

Food and Chemical Toxicology ◽

10.1016/j.fct.2019.110976 ◽

2020 ◽

Vol 135 ◽

pp. 110976 ◽

Author(s):

Andrija Ciric ◽

Bor Krajnc ◽

David Heath ◽

Nives Ogrinc

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Network Approach ◽

Ultrasound Assisted Extraction ◽

Neural Network Approach ◽

Assisted Extraction ◽

Ultrasound Assisted ◽

Artificial Neural Network Approach

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Degradation of ticarcillin by subcritical water oxidation method: Application of response surface methodology and artificial neural network modeling

Journal of Environmental Science and Health Part A ◽

10.1080/10934529.2018.1471023 ◽

2018 ◽

Vol 53 (11) ◽

pp. 975-985 ◽

Author(s):

Erdal Yabalak

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Water Oxidation ◽

Subcritical Water ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Oxidation Method

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Statistical and Intelligent Analysis of Viscosity behavior of MgO-MWCNT (25–75%)/10W40 Hybrid Nanolubricant Using Artificial Neural Network Modeling and Response Surface Methodology

Arabian Journal for Science and Engineering ◽

10.1007/s13369-021-06068-7 ◽

2021 ◽

Author(s):

Mohammad Hemmat Esfe ◽

Milad Goodarzi ◽

Saeed Esfandeh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Viscosity Behavior ◽

Intelligent Analysis ◽

Artificial Neural

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Artificial Neural Network Modeling of Ultrasound-Assisted Polysaccharides Extraction from Potentilla anserina and Anti-Platelet Aggregation Activity

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.618.367 ◽

2014 ◽

Vol 618 ◽

pp. 367-375 ◽

Author(s):

Tao Guo ◽

Jun Qing Wei ◽

Ya Wang ◽

Dan Su ◽

Zhen Zhang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Platelet Aggregation ◽

Neural Network Modeling ◽

Process Conditions ◽

Ultrasound Assisted Extraction ◽

Assisted Extraction ◽

Potentilla Anserina ◽

Ultrasound Assisted ◽

Artificial Neural

In this study, Ultrasound-assisted extraction (UAE) was used for polysaccharides extraction from Potentilla anserina. A computer-stimulated artificial neural network (ANN) was developed to get a correlation between the input variables and the output parameter. Finally, the optimal process conditions were obtained as follows: extraction temperature 55 °C, extraction time 55 min, ratio of liquid to solid 20:1, power 175 W. Under optimized conditions, ultrasound-assisted extraction had obviously higher yield of polysaccharides than the traditional heat reflux method. The optimization procedure showed a close interaction between the experimental and simulated values for polysaccharides extraction. The R2 (0.99271) and MSE (0.0425) values of model suggested good fitness and generalization of the ANN. Moreover, the results also indicated that polysaccharides have inhibitory effect on ADP-induced platelet aggregation.

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