• Environmental Engineering Research
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• v.25 no.3
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• pp.418-425
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• 2020

In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R² values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.4
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Computers and Concrete
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• v.25 no.2
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• pp.119-132
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• 2020

There is a growing interest in the use of by-product materials such as ceramics as alternative materials in construction. The aim of this study is to investigate the mechanical properties and durability of sustainable concrete containing waste ceramic powder (WCP), and to predict the results using artificial neural network (ANN). In this order, different water to binder (W/B) ratios of 0.3, 0.4, and 0.5 were considered, and in each W/B ratio, a percentage of cement (between 5-50%) was replaced with WCP. Compressive and tensile strengths, water absorption, electrical resistivity and rapid chloride permeability (RCP) of the concrete specimens having WCP were evaluated by related experimental tests. The results showed that by replacing 20% of the cement by WCP, the concrete achieves compressive and tensile strengths, more than 95% of those of the control concrete, in the long term. This percentage increases with decreasing W/B ratio. In general, by increasing the percentage of WCP replacement, all durability parameters are significantly improved. In order to validate and suggest a suitable tool for predicting the characteristics of the concrete, ANN model along with various multivariate regression methods were applied. The comparison of the proposed ANN with the regression methods indicates good accuracy of the developed ANN in predicting the mechanical properties and durability of this type of concrete. According to the results, the accuracy of ANN model for estimating the durability parameters did not significantly follow the number of hidden nodes.

• Computers and Concrete
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• v.23 no.1
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• pp.49-60
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• 2019

In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters.

• Journal of The Korean Astronomical Society
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• v.19 no.2
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• pp.69-89
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• 1986

The global morphology and geometric parameters of 39 barred galaxies are investigated, by using isophote map and isodensity tracings obtained from detailed surface photometry with the Kiso V-band plates. The observed results are as follows: i) There is no strong indication that the ratio of bar length to disk size is correlated with Hubble type, except that the largest bars appear in the SBb galaxies. ii) The mean value of axial ratios of bars is $2.0{\pm}0.4$ and there is a trend that early type galaxies have smaller axial ratios than late type galaxies. iii) About 15% of early type barred galaxies(Sb0-SBb) have triaxial bulges.

• Korean Journal of Food Science and Technology
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• v.48 no.2
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• pp.165-171
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• 2016

Physicochemical properties of waxy maize flours prepared by hydrothermal and enzymatic treatments were evaluated. Waxy maize flours were hydrothermally treated using heat-moisture treatment (HMT) and annealing (ANN) and enzymatically treated using commercial enzymes (cellulase, proteinase, and pectinase). The HMT-modified waxy maize flours had low water absorption index (WAI), melting enthalpy, viscosity, and crystallinity. However, ANN-modified and enzymatically modified waxy maize flours had high WAI, melting enthalpy, and viscosity. X-ray diffraction analysis of ANN-modified and enzymatically modified waxy maize flours revealed a typical A-type pattern and displayed sharper crystalline peaks than those observed for the control groups (native waxy maize flours). In contrast, the crystallinity of HMT-modified waxy maize flours were decreased by hydrothermal treatment.

• Geosystem Engineering
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• v.21 no.5
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• pp.273-281
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• 2018

In this study, the activated red mud was used as a new and appropriate adsorbent for the removal of ferrocyanide and ferricyanide from aqueous solution. Predicting the removal percentage and adsorption capacity of ferro-ferricyanide by activated red mud during the adsorption process is necessary which has been done by modeling and simulation. The artificial neural network (ANN) was used to develop new models for the predictions. A back propagation algorithm model was trained to develop a predictive model. The effective variables including pH, absorbent amount, absorbent type, ionic strength, stirring rate, time, adsorbate type, and adsorbate dosage were considered as inputs of the models. The correlation coefficient value ($R^2$) and root mean square error (RMSE) values of the testing data for the removal percentage and adsorption capacity using ANN models were 0.8560, 12.5667, 0.9329, and 10.8117, respectively. The results showed that the proposed ANN models can be used to predict the removal percentage and adsorption capacity of activated red mud for the removal of ferrocyanide and ferricyanide with reasonable error.

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.59-63
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• 2006

Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.