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Image Search Results
Journal: Heliyon
Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption
doi: 10.1016/j.heliyon.2024.e25813
Figure Lengend Snippet: RMSE values obtained from ANFIS via BBD approach.
Article Snippet:
Techniques:
Journal: Heliyon
Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption
doi: 10.1016/j.heliyon.2024.e25813
Figure Lengend Snippet: RMSE values obtained via Regression model and ANFIS prediction at optimum conditions.
Article Snippet:
Techniques:
Journal: Heliyon
Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption
doi: 10.1016/j.heliyon.2024.e25813
Figure Lengend Snippet: Train and test data vs FIS output obtained with optimum ANFIS model (trimf 6-6-3).
Article Snippet:
Techniques:
Journal: Heliyon
Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption
doi: 10.1016/j.heliyon.2024.e25813
Figure Lengend Snippet: Predicted data obtained with optimum ANFIS model vs. experimental data.
Article Snippet:
Techniques:
Journal: Heliyon
Article Title: Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption
doi: 10.1016/j.heliyon.2024.e25813
Figure Lengend Snippet: The comparison of other ANFIS modeling studies on Cr(VI) adsorption.
Article Snippet:
Techniques: Comparison, Adsorption, Modification, Concentration Assay
Journal: Scientific Reports
Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
doi: 10.1038/s41598-024-77144-9
Figure Lengend Snippet: The fundamental configuration of ANFIS.
Article Snippet: The
Techniques:
Journal: Scientific Reports
Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
doi: 10.1038/s41598-024-77144-9
Figure Lengend Snippet: ANFIS model variables interactions.
Article Snippet: The
Techniques:
Journal: Scientific Reports
Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
doi: 10.1038/s41598-024-77144-9
Figure Lengend Snippet: ANFIS processing settings.
Article Snippet: The
Techniques: Generated
Journal: Scientific Reports
Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
doi: 10.1038/s41598-024-77144-9
Figure Lengend Snippet: Graphical plot of ANFIS model training sets.
Article Snippet: The
Techniques:
Journal: Scientific Reports
Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
doi: 10.1038/s41598-024-77144-9
Figure Lengend Snippet: Graphical plot of ANFIS model testing sets.
Article Snippet: The
Techniques:
Journal: Scientific Reports
Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
doi: 10.1038/s41598-024-77144-9
Figure Lengend Snippet: ANFIS Membership function plots.
Article Snippet: The
Techniques:
Journal: Scientific Reports
Article Title: Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
doi: 10.1038/s41598-024-77144-9
Figure Lengend Snippet: Statisical computations to evaluate the performance of the developed model.
Article Snippet: The
Techniques: