|
[1]
|
Bidirectional fermentation of Monascus and Mulberry leaves enhances GABA and pigment contents: establishment of strategy, studies of bioactivity and mechanistic
Preparative Biochemistry & Biotechnology,
2023
DOI:10.1080/10826068.2023.2207111
|
|
|
|
|
[2]
|
Comparative adsorptive removal of Reactive Red 120 using RSM and ANFIS models in batch and packed bed column
Biomass Conversion and Biorefinery,
2023
DOI:10.1007/s13399-021-01444-7
|
|
|
|
|
[3]
|
Artificial Intelligence Techniques and Response Surface Methodology for the Optimization of Methyl Ester Sulfonate Synthesis from Used Cooking Oil by Sulfonation
ACS Omega,
2023
DOI:10.1021/acsomega.2c08117
|
|
|
|
|
[4]
|
RSM- and ANN-based modeling for a novel hydrolysis process of lignocellulose residues to produce cost-effective fermentable sugars
Biomass Conversion and Biorefinery,
2023
DOI:10.1007/s13399-023-04484-3
|
|
|
|
|
[5]
|
Removal of Reactive Red 120 in a Batch Technique Using Seaweed-Based Biochar: A Response Surface Methodology Approach
Journal of Nanomaterials,
2022
DOI:10.1155/2022/3621807
|
|
|
|
|
[6]
|
Modeling of Water Quality in West Ukrainian Rivers Based on Fluctuating Asymmetry of the Fish Population
Water,
2022
DOI:10.3390/w14213511
|
|
|
|
|
[7]
|
Evaluation of the Estimation Capability of Response Surface Methodology and Artificial Neural Network for the Optimization of Bacteriocin-Like Inhibitory Substances Production by Lactococcus lactis Gh1
Microorganisms,
2021
DOI:10.3390/microorganisms9030579
|
|
|
|
|
[8]
|
Comparative adsorptive removal of Reactive Red 120 using RSM and ANFIS models in batch and packed bed column
Biomass Conversion and Biorefinery,
2021
DOI:10.1007/s13399-021-01444-7
|
|
|
|
|
[9]
|
Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology
Journal of the Brazilian Society of Mechanical Sciences and Engineering,
2020
DOI:10.1007/s40430-020-02418-y
|
|
|
|
|
[10]
|
Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres
Cogent Engineering,
2019
DOI:10.1080/23311916.2019.1649852
|
|
|
|
|
[11]
|
Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents
Foods,
2019
DOI:10.3390/foods8040142
|
|
|
|
|
[12]
|
Rice bran oil based biodiesel production using calcium oxide catalyst derived from Chicoreus brunneus shell
Energy,
2018
DOI:10.1016/j.energy.2017.11.073
|
|
|
|
|
[13]
|
Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization
Energy,
2018
DOI:10.1016/j.energy.2018.04.053
|
|
|
|
|
[14]
|
Comparison of modeling methods for wind power prediction: a critical study
Frontiers in Energy,
2018
DOI:10.1007/s11708-018-0553-3
|
|
|
|
|
[15]
|
Optimization of copper extraction from spent LTS catalyst (CuO–ZnO–Al2O3) using chelating agent: Box-behnken experimental design methodology
Russian Journal of Non-Ferrous Metals,
2017
DOI:10.3103/S1067821217010102
|
|
|
|
|
[16]
|
Predicting the optimum compositions of a transdermal nanoemulsion system containing an extract of Clinacanthus nutans
leaves (L.) for skin antiaging by artificial neural network model
Journal of Chemometrics,
2017
DOI:10.1002/cem.2894
|
|
|
|
|
[17]
|
Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust
International Journal of Engineering Research in Africa,
2017
DOI:10.4028/www.scientific.net/JERA.30.125
|
|
|
|
|
[18]
|
Comparative analyses on medium optimization using one-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis by Pediococcus pentosaceus RF-1
Biotechnology & Biotechnological Equipment,
2017
DOI:10.1080/13102818.2017.1335177
|
|
|
|
|
[19]
|
ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp. strain B67 and its antifungal activity against Botrytis cinerea
Biotechnology & Biotechnological Equipment,
2017
DOI:10.1080/13102818.2017.1379359
|
|
|
|
|
[20]
|
The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach
Water Science and Technology,
2017
DOI:10.2166/wst.2017.393
|
|
|
|
|
[21]
|
Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network
Algal Research,
2016
DOI:10.1016/j.algal.2015.11.004
|
|
|
|
|
[22]
|
Modeling and optimization by response surface methodology and neural network–genetic algorithm for decolorization of real textile dye effluent usingPleurotus ostreatus: a comparison study
Desalination and Water Treatment,
2016
DOI:10.1080/19443994.2015.1059372
|
|
|
|
|
[23]
|
Modeling tensile modulus of (polyamide 6)/nanoclay composites: Response surface method vs. taguchi-optimized artificial neural network
Journal of Vinyl and Additive Technology,
2016
DOI:10.1002/vnl.21416
|
|
|
|
|
[24]
|
Modeling of the lycopene extraction from tomato pulps
Food Chemistry,
2016
DOI:10.1016/j.foodchem.2015.06.069
|
|
|
|
|
[25]
|
Enhancement of electronic protection to reduce e-waste
Journal of Industrial and Engineering Chemistry,
2015
DOI:10.1016/j.jiec.2015.04.021
|
|
|
|
|
[26]
|
Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,
2015
DOI:10.1016/j.saa.2015.06.036
|
|
|
|
|
[27]
|
Modeling of Alkali Pretreatment of Rice Husk Using Response Surface Methodology and Artificial Neural Network
Chemical Engineering Communications,
2015
DOI:10.1080/00986445.2013.871707
|
|
|
|
|
[28]
|
Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: Comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN)
Ultrasonics Sonochemistry,
2015
DOI:10.1016/j.ultsonch.2015.01.013
|
|
|
|
|
[29]
|
Dispersive solid phase extraction combined with dispersive liquid-liquid extraction for the determination of BTEX in soil samples: ant colony optimization-artificial neural network
Journal of Chemometrics,
2015
DOI:10.1002/cem.2706
|
|
|
|
|
[30]
|
Bioprocess modelling of biohydrogen production by Rhodopseudomonas palustris: Model development and effects of operating conditions on hydrogen yield and glycerol conversion efficiency
Chemical Engineering Science,
2015
DOI:10.1016/j.ces.2015.02.045
|
|
|
|
|
[31]
|
Enhance protection of electronic appliances through multivariate modelling and optimization of ceramic core materials in varistor devices
RSC Adv.,
2015
DOI:10.1039/C4RA16134C
|
|
|
|
|
[32]
|
Modeling the red pigment production by Monascus purpureus MTCC 369 by Artificial Neural Network using rice water based medium
Food Bioscience,
2015
DOI:10.1016/j.fbio.2015.04.001
|
|
|
|
|
[33]
|
Assessment of water quality index using cluster analysis and artificial neural network modeling: a case study of the Hooghly River basin, West Bengal, India
Desalination and Water Treatment,
2015
DOI:10.1080/19443994.2014.880379
|
|
|
|
|
[34]
|
Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil
Ultrasonics Sonochemistry,
2015
DOI:10.1016/j.ultsonch.2014.10.019
|
|
|
|
|
[35]
|
Modeling of Dispersive Liquid-Liquid Microextraction for Determination of Essential Oil from Borago officinalis L. By Using Combination of Artificial Neural Network and Genetic Algorithm Method
Journal of Chromatographic Science,
2015
DOI:10.1093/chromsci/bmv065
|
|
|
|
|
[36]
|
Robust parameter design optimization using Kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques
Applied Mathematics and Computation,
2014
DOI:10.1016/j.amc.2014.03.082
|
|
|
|
|
[37]
|
Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation
The Scientific World Journal,
2014
DOI:10.1155/2014/726101
|
|
|
|
|
[38]
|
Application of response surface methodology in enzymatic synthesis: A review
Russian Journal of Bioorganic Chemistry,
2014
DOI:10.1134/S1068162014030054
|
|
|
|
|
[39]
|
Modeling of microwave-assisted extraction of natural dye from seeds of Bixa orellana (Annatto) using response surface methodology (RSM) and artificial neural network (ANN)
Industrial Crops and Products,
2013
DOI:10.1016/j.indcrop.2012.04.004
|
|
|
|
|
[40]
|
Research on Ability Evaluation Model of Compound Talents of Information Technology Based on Artificial Neural Network
2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics,
2013
DOI:10.1109/IHMSC.2013.111
|
|
|
|
|
[41]
|
Artificial neural network modeling of p-cresol photodegradation
Chemistry Central Journal,
2013
DOI:10.1186/1752-153X-7-96
|
|
|
|
|
[42]
|
Optimization of solid-phase extraction using artificial neural networks and response surface methodology in combination with experimental design for determination of gold by atomic absorption spectrometry in industrial wastewater samples
Talanta,
2012
DOI:10.1016/j.talanta.2012.04.019
|
|
|
|
|
[43]
|
Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin
Bioresource Technology,
2012
DOI:10.1016/j.biortech.2012.02.084
|
|
|
|
|
[44]
|
Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN
Chemical Engineering Journal,
2012
DOI:10.1016/j.cej.2011.12.019
|
|
|
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