Efficient removal of methylene blue dye using mangosteen peel waste: kinetics, isotherms and artificial neural network (ANN) modelling

2017 ◽  
Vol 86 ◽  
pp. 191-202 ◽  
Author(s):  
Asma Nasrullah ◽  
A.H. Bhat ◽  
Mohamed Hasnain Isa ◽  
Mohammed Danish ◽  
Abdul Naeem ◽  
...  
Keyword(s):  
Neural Network ◽  
Methylene Blue ◽  
Artificial Neural Network ◽  
Blue Dye ◽  
Methylene Blue Dye ◽  
Efficient Removal ◽  
Mangosteen Peel ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye

RSC Advances ◽  
2016 ◽  
Vol 6 (46) ◽  
pp. 40502-40516 ◽  
Author(s):  
A. Asfaram ◽  
M. Ghaedi ◽  
M. H. Ahmadi Azqhandi ◽  
A. Goudarzi ◽  
M. Dastkhoon
Keyword(s):  
Neural Network ◽  
Methylene Blue ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Support Vector ◽  
Blue Dye ◽  
Statistical Experimental Design ◽  
Methylene Blue Dye ◽  
Zinc Sulfide Nanoparticles ◽  
Artificial Neural Network Ann

This study is based on the usage of a composite of zinc sulfide nanoparticles with activated carbon (ZnS-NPs-AC) for the adsorption of methylene blue (MB) from aqueous solutions.

The prediction of optimal conditions for the surface grafting of β-cyclodextrin onto silk fabrics by an artificial neural network (ANN)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abolfazl Zare ◽  
Pedram Payvandy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Crosslinking Agent ◽  
Blue Dye ◽  
Content Type ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Silk Fabrics ◽  
Recovery Angle

Purpose The purpose of this study is the chemical grafting of β-Cyclodextrin (β-CD) onto silk fabrics by the use of butane tetracarboxylic acid (BTCA) as a crosslinking agent and nano-TiO2 (NTO) as a catalyst. The effects of different parameters involved in this particular process, e.g. β-CD, BTCA and NTO concentrations, are examined using the artificial neural network (ANN). The method is evaluated for its ability to predict certain properties of treated fabrics, including grafting yield, dry crease recovery angle (DCRA) and wet crease recovery angle (WCRA), tensile strength, elongation at break and methylene blue dye absorption. Design/methodology/approach This study was conducted to describe the cross-linking of silk with 1,2,3,4-BTCA as a crosslinking agent in a wet state at low temperatures using NTO catalyst to improve the dry and wet wrinkle recovery (DCRA and WCRA) of silk fabrics. An ANN was also used to model and analyze the effects of BTCA, β-CD and NTO concentrations on the grafting percentage and some properties of the treated samples. Findings According to the results, the wet and dry wrinkle recovery of the silk fabrics was improved for about 38% and 11%, respectively, as compared to the non-cross-linked fabrics, without significantly affecting the tensile strength retention of the fabrics. Originality/value This research model and analyze the effects of BTCA, β-CD and NTO concentrations on the grafting percentage and some properties of the treated samples for the first time.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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