The development of an artificial neural network – genetic algorithm model (ANN-GA) for the adsorption and photocatalysis of methylene blue on a novel sulfur–nitrogen co-doped Fe2O3 nanostructure surface

In this research an S-N doped Fe2O3 nanostructure is synthesized and its adsorption ability and photocatalytic activity were evaluated. The optimum experimental conditions were obtained and an ANN-GA model was proposed for predicting experimental values.

Download Full-text

Application of Artificial Neural Network-Genetic Algorithm Model in the Prediction of Ibuprofen Release from Microcapsules and Tablets Based on Plant Protein and Its Derivatives

Artificial Intelligence and Renewables Towards an Energy Transition - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-63846-7_59 ◽

2020 ◽

pp. 625-634

Author(s):

Asma Ghennam ◽

Samia Rebouh ◽

Mounir Bouhedda

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Plant Protein ◽

Artificial Neural ◽

Ibuprofen Release ◽

Genetic Algorithm Model

Download Full-text

Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

Journal of the Transportation Research Forum ◽

10.5399/osu/jtrf.46.2.1031 ◽

2010 ◽

Cited By ~ 1

Author(s):

Sergio Davalos ◽

Richard Gritta ◽

Bahram Adrangi

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Feature Selection ◽

Dimensionality Reduction ◽

Local Optima ◽

Air Carrier ◽

Genetic Algorithm Approach ◽

Genetic Algorithm Model

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

Download Full-text

Corrigendum to “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer” [Eng. Appl. Artif. Intell. 26 (2013), 945–950]

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2013.02.005 ◽

2013 ◽

Vol 26 (5-6) ◽

pp. 1641 ◽

Cited By ~ 1

Author(s):

R. Karakış ◽

M. Tez ◽

Y.A. Kılıç ◽

B. Kuru ◽

İ. Güler

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Lymph Node ◽

Axillary Lymph Node ◽

Lymph Node Status ◽

Axillary Lymph ◽

Axillary Lymph Node Status ◽

Genetic Algorithm Model

Download Full-text

Hybrid Artificial Neural Network and Genetic Algorithm Model for Multi-Objective Strength Optimization of Concrete with Surkhi and Buntal Fiber

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering ◽

10.1145/3384613.3384617 ◽

2020 ◽

Author(s):

Dante L. Silva ◽

Kevin Lawrence M. de Jesus ◽

Bernard S. Villaverde ◽

Edgar M. Adina

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Multi Objective ◽

Artificial Neural ◽

Genetic Algorithm Model ◽

Hybrid Artificial Neural Network

Download Full-text

Artificial neural network-genetic algorithm based optimization for the adsorption of methylene blue and brilliant green from aqueous solution by graphite oxide nanoparticle

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2013.12.082 ◽

2014 ◽

Vol 125 ◽

pp. 264-277 ◽

Cited By ~ 68

Author(s):

M. Ghaedi ◽

N. Zeinali ◽

A.M. Ghaedi ◽

M. Teimuori ◽

J. Tashkhourian

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Aqueous Solution ◽

Methylene Blue ◽

Artificial Neural Network ◽

Graphite Oxide ◽

Brilliant Green ◽

Oxide Nanoparticle ◽

Artificial Neural

Download Full-text

Artificial Neural Network for predicting biosorption of methylene blue by Spirulina sp.

Water Science & Technology ◽

10.2166/wst.2011.279 ◽

2011 ◽

Vol 63 (5) ◽

pp. 977-983 ◽

Cited By ~ 5

Author(s):

M. T. Garza-González ◽

M. M. Alcalá-Rodríguez ◽

R. Pérez-Elizondo ◽

F. J. Cerino-Córdova ◽

R. B. Garcia-Reyes ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Methylene Blue ◽

Artificial Neural Network ◽

Removal Efficiency ◽

Sensitivity Analyses ◽

Isotherm Models ◽

Biosorption Capacity ◽

Initial Ph ◽

Artificial Neural

An artificial neural network (ANN) was used to predict the biosorption of methylene blue on Spirulina sp. biomass. Genetic and anneal algorithms were tested with different quantity of neurons at the hidden layers to determine the optimal neurons in the ANN architecture. In addition, sensitivity analyses were conducted with the optimised ANN architecture for establishing which input variables (temperature, pH, and biomass dose) significantly affect the predicted data (removal efficiency or biosorption capacity). A number of isotherm models were also compared with the optimised ANN architecture. The removal efficiency or the biosorption capacity of MB on Spirulina sp. biomass was adequately predicted with the optimised ANN architecture by using the genetic algorithm with three input neurons, and 20 neurons in each one of the two hidden layers. Sensitivity analyses demonstrated that initial pH and biomass dose show a strong influence on the predicted removal efficiency or biosorption capacity, respectively. When supplying two variables to the genetic algorithm, initial pH and biomass dose improved the prediction of the output neuron (biosorption capacity or removal efficiency). The optimised ANN architecture predicted the equilibrium data 5,000 times better than the best isotherm model. These results demonstrate that ANN can be an effective way of predicting the experimental biosorption data of MB on Spirulina sp. biomass.

Download Full-text