[1]
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Ghorban Farahi Faculty of Chemical Engineering, Noushirvani University of Technology, Babol, Iran.
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NULL |
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[2]
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Soft computing-based models and decolorization of Reactive Yellow 81 using Ulva Prolifera biochar
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Chemosphere,
2022 |
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[3]
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Modeling of thermo-chemical pretreatment of yam peel substrate for biogas energy production: RSM, ANN, and ANFIS comparative approach
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Applied Surface Science Advances,
2022 |
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[4]
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Investigation of double-layered wavy microchannel heatsinks utilizing porous ribs with artificial neural networks
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International Communications in Heat and …,
2022 |
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[5]
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Preparation of ε-Caprolactone/Fe3O4 Magnetic Nanocomposite and Its Application to the Remazol Brilliant Violet 5R Dye Adsorption from Wastewaters by Using RSM
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Journal of Polymers and the …,
2022 |
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[6]
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Estimation of biosurfactant production parameters and yields without conducting additional experiments on a larger production scale
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Journal of …,
2022 |
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[7]
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Removal of Reactive Red 120 in a Batch Technique Using Seaweed-Based Biochar: A Response Surface Methodology Approach
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Journal of Nanomaterials,
2022 |
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[8]
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Evaluation of the Estimation Capability of Response Surface Methodology and Artificial Neural Network for the Optimization of Bacteriocin-Like Inhibitory Substances …
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2021 |
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[9]
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Exhaust gas recirculation effect on the performance of a CRDI diesel engine fuelled with linseed biodiesel/diesel blend through response surface methodology
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2021 |
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[10]
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Evaluation of optimization techniques for predicting exergy efficiency of the cement raw meal production process
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Cogent Engineering,
2021 |
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[11]
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Comparative adsorptive removal of Reactive Red 120 using RSM and ANFIS models in batch and packed bed column
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2021 |
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[12]
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Comparison of artificial neural network (ANN) and response surface methodology (RSM) in predicting the compressive and splitting tensile strength of …
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2021 |
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[13]
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Optimization of process conditions using RSM and ANFIS for the removal of Remazol Brilliant Orange 3R in a packed bed column
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2021 |
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[14]
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Engineering culture medium for enhanced carbohydrate accumulation in Anabaena variabilis to stimulate production of bioethanol and other high-value co-products …
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2021 |
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[15]
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Comparative analysis of RSM, ANN and ANFIS and the mechanistic modeling in eriochrome black-T dye adsorption using modified clay
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2021 |
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[16]
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Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: a novel MCDM amalgamated MORSM approach
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2020 |
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[17]
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Application of Neural Networks in Optimizing Different Food Processes
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2020 |
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[18]
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Comparative analyses of response surface methodology and artificial neural networks on incorporating tetracaine into liposomes
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2020 |
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[19]
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Evaluation of optimization techniques in predicting optimum moisture content reduction in drying potato slices
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2020 |
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[20]
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Performance, combustion and emission characteristics of a diesel engine fueled with diesel-kerosene-ethanol: A multi-objective optimization study
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2020 |
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[21]
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Modelling and optimizing performance parameters in the wire‑electro discharge machining of Al5083/B
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2020 |
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[22]
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Optimization and scale-up of ethanol production by a flocculent yeast using cashew apple juice as feedstock
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2020 |
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[23]
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Application of Neural Networks in Optimizing Different Food Processes Case Study
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2020 |
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[24]
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Optimization of bioethanol production from cassava peels
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2020 |
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[25]
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Process Analysis and Optimization of Crude Glycerol Autothermal Reforming Using Response Surface Methodology and Artificial Neural Network
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2020 |
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[26]
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Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface …
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Journal of the Brazilian Society of …,
2020 |
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[27]
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Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach/Seef Saadi Fiyadh
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2019 |
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[28]
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Microalgae Biomass Production: A Case of Municipal Wastewater Remediation and Microalgae Harvesting
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2019 |
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[29]
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Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using …
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2019 |
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[30]
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Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre …
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2019 |
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[31]
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Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete
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2019 |
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[32]
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Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural …
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2019 |
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[33]
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Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents
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2019 |
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[34]
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Accepted Manuscrpt
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2019 |
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[35]
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COMPARATIVE STUDIES OF RESPONSE SURFACE METHODOLOGY (RSM) AND PREDICTIVE CAPACITY OF ARTIFICIAL NEURAL NETWORK (ANN) ON MILD STEEL CORROSION INHIBITION USING WATER HYACINTH AS AN INHIBITOR
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FUW Trends in Science & Technology Journal,
2019 |
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[36]
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Artificial neural network evaluation of cement-bonded particle board produced from red iron wood (Lophira alata) sawdust and palm kernel shell residues
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Case Studies in Construction Materials,
2018 |
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[37]
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Preparation and Optimization of Chitosan/pDNA Nanoparticles Using Electrospray
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Academy of Sciences, India,
2018 |
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[38]
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Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization
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Energy,
2018 |
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[39]
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Comparison of modeling methods for wind power prediction: a critical study
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Frontiers in Energy,
2018 |
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[40]
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Rice bran oil based biodiesel production using calcium oxide catalyst derived from Chicoreus brunneus shell
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Energy,
2018 |
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[41]
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Predicting the optimum compositions of a transdermal nanoemulsion system containing an extract of Clinacanthus nutans leaves (L.) for skin antiaging by artificial …
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Journal of chemometrics,
2017 |
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[42]
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Comparative analyses on medium optimization using one-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine …
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Instrumentation Science & Technology,
2017 |
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[43]
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The modeling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach
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2017 |
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[44]
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ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp. strain B67 and its antifungal activity against Botrytis cinerea
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International Journal of Sustainable Transportation,
2017 |
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[45]
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Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust.
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International Journal of Engineering Research in Africa,
2017 |
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[46]
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Optimization of copper extraction from spent LTS catalyst (CuO–ZnO–Al2O3) using chelating agent: Box-behnken experimental design methodology
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Russian Journal of Non-Ferrous Metals,
2017 |
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[47]
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DESIGN AND OPTIMIZATION OF NANOEMULSION FORMULATION CONTAINING Clinacanthus nutans LINDAU LEAF EXTRACT FOR COSMECEUTICAL …
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2017 |
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[48]
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Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust
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2017 |
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[49]
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Predicting the optimum compositions of a transdermal nanoemulsion system containing an extract of Clinacanthus nutans leaves (L.) for skin antiaging by artificial …
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2017 |
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[50]
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Optimization of copper extraction from spent LTS catalyst (CuO–ZnO–Al 2 O 3) using chelating agent: Box-behnken experimental design methodology
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2017 |
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[51]
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Artificial intelligence in agriculture
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2017 |
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[52]
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PHYTOREMEDIATION OF PALM OIL MILL SECONDARY EFFLUENT USING VETIVER SYSTEM
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2016 |
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[53]
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Modeling of the lycopene extraction from tomato pulps
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Food chemistry,
2016 |
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[54]
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MODELING AND PREDICTION OF FLEXURAL STRENGTH OF HYBRID MESH AND FIBER REINFORCED CEMENT-BASED COMPOSITES USING ARTIFICIAL NEURAL NETWORK (ANN)
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2016 |
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[55]
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Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network
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Algal Research,
2016 |
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[56]
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Optimization of Brilliant Green Dye Removal Efficiency by Electrocoagulation Using Response Surface Methodology
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World Journal of Environmental Engineering,
2016 |
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[57]
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Scheduling the blended solution as industrial CO 2 absorber in separation process by back-propagation artificial neural networks
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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,
2015 |
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[58]
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Modeling and optimization by response surface methodology and neural network–genetic algorithm for decolorization of real textile dye effluent using Pleurotus ostreatus: a comparison study
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Desalination and Water Treatment,
2015 |
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[59]
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Enhancement of electronic protection to reduce e-waste
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Journal of Industrial and Engineering Chemistry,
2015 |
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[60]
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OPTIMIZATION OF TRANSPORTATION SYSTEM BASED ON COMBINED MODEL USING ARTIFICIAL NEURAL NETWORKS AND RESPONSE SURFACE METHODOLOGY
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International Journal of Technical Research and Applications,
2015 |
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[61]
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Bioprocess modelling of biohydrogen production by Rhodopseudomonas palustris: model development and effects of operating conditions on hydrogen yield and …
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Chemical Engineering Science,
2015 |
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[62]
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Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,
2015 |
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[63]
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Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil
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Ultrasonics sonochemistry,
2015 |
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[64]
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Bioprocess modelling of biohydrogen production by Rhodopseudomonas palustris: Model development and effects of operating conditions on hydrogen yield and glycerol conversion efficiency
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Chemical Engineering Science,
2015 |
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[65]
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Enhance protection of electronic appliances through multivariate modelling and optimization of ceramic core materials in varistor devices
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RSC Advances,
2015 |
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[66]
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Modeling the red pigment production by Monascus purpureus MTCC 369 by Artificial Neural Network using rice water based medium
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Food Bioscience,
2015 |
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[67]
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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)
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Ultrasonics Sonochemistry,
2015 |
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[68]
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A Methodology for Capturing the Impacts of Bleed Flow Extraction on Compressor Performance and Operability in Engine Conceptual Design
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2015 |
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[69]
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PRODUCTION AND CHARACTERIZATION OF BIOCHAR DERIVED FROM OIL PALM WASTES, AND OPTIMIZATION FOR ZINC ADSORPTION
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2015 |
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[70]
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Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from …
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2015 |
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[71]
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Establishing a formulation design space for a generic clobetasol 17-propionate cream using the principles of quality by design
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2014 |
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[72]
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A STUDY ON ALKALI PRETREATMENT CONDITIONS OF SORGHUM STEM FOR MAXIMUM SUGAR RECOVERY USING STATISTICAL APPROACH.
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2014 |
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[73]
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Artificial neural network modelling of photodegradation in suspension of manganese doped zinc oxide nanopartic les under visible-light irradiation.
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Thescientificworldjournal,
2014 |
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[74]
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Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation
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The Scientific World Journal,
2014 |
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[75]
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Robust parameter design optimization using Kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques
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Applied Mathematics and Computation, Elsevier,
2014 |
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[76]
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Artificial neural network modelling of photodegradation in manganese doped Zinc oxide nano-partcles suspension under visible-light irradiation
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Y Abdollahi - umexpert.um.edu.my,
2014 |
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[77]
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Artificial neural network modelling of photodegradation in suspension of manganese 1 doped Zinc oxide nano-particles under visible-light irradiation 2
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Y Abdollahi - downloads.hindawi.com,
2014 |
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[78]
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Artificial neural network analysis in preclinical breast cancer
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Cell Journal (Yakhteh),
2014 |
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[79]
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Modeling of Alkali Pretreatment of Rice Husk Using Response Surface Methodology and Artificial Neural Network
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Chemical Engineering Communications?just-accepted? ,
2014 |
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[80]
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A study on alkali pretreatment conditions of sorghum stem for maximum sugar recovery using statistical approach
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Chemical Industry and Chemical Engineering Quarterly,
2014 |
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[81]
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Assessment of water quality index using cluster analysis and artificial neural network modeling: a case study of the Hooghly River basin, West Bengal, India
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Desalination and Water Treatment,
2014 |
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[82]
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Modeling tensile modulus of (polyamide 6)/nanoclay composites: Response surface method vs. taguchi‐optimized artificial neural network
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Journal of Vinyl and Additive Technology, Wiley Online Library,
2014 |
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[83]
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Kinetic fluorescence quenching of CdS quantum dots in the presence of Cu (II): Chemometrics-assisted resolving of the kinetic data and quantitative analysis of Cu (II)
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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,
2014 |
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[84]
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Research on Ability Evaluation Model of Compound Talents of Information Technology Based on Artificial Neural Network
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Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on.? ,
2013 |
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[85]
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Modeling of microwave-assisted extraction of natural dye from seeds of Bixa orellana(Annatto) using response surface methodology (RSM) and artificial neural network (ANN)
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Industrial Crops and Products, Elsevier,
2013 |
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[86]
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Artificial neural network modeling of p-cresol photodegradation
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Chemistry Central Journal,
2013 |
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[87]
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Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin
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Bioresource technology, Elsevier,
2012 |
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[88]
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Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN
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Chemical Engineering Journal, Elsevier,
2012 |
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[89]
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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
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Talanta, Elsevier,
2012 |
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[90]
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人工神经网络在水产领域中的应用
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1970 |
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