scholarly journals Wastewater treatment by adsorption process on mineral actived carbon: modeling and prediction using an intelligent artificial approach

2021 ◽  
Vol 1204 (1) ◽  
pp. 012006
Author(s):  
Sediri Meriem ◽  
Hanini Salah
Keyword(s):  
Correlation Coefficient ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Fixed Bed ◽  
Back Propagation ◽  
Dynamic Adsorption ◽  
Back Propagation Algorithm ◽  
Output Layer ◽  
Hidden Layer ◽  
Actived Carbon

Abstract Currently there are several wastewater treatments processes, and several adsorbent materials consist of separating and purifying the various industrial effluents. In this work an artificial neural network (ANN) was developed to describe the dynamic adsorption of sodium decanesulfonate using actived carbon obtained by the calcination of mineral biomass under different conditions. Three inputs (time, mass of adsorbent and fixed bed height) were used in the input layer, three neurons in the hidden layer and one in the output layer for the reduced concentration. The Levenberg Marquardt back-propagation algorithm was applied. The tangent sigmoid and linear transfer functions are used for the hidden layer and the output layer respectively. The results showed a correlation coefficient R2 = 0.9965 with root mean squared error RMSE = 0.0276. An interpolation and an extrapolation stage are made to test the accuracy of the network. The results showed a high correlation coefficient R2 = 0.9969 and 0.984 respectively for the interpolation and the extrapolation. These results show the robustness and the high capacity of ANN to describe the dynamic adsorption of sodium decanesulfonate onto actived carbon.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals Development of combined back propagation algorithm and radial basis function for diagnosing depression patients

2015 ◽  
Vol 4 (1) ◽  
pp. 244
Author(s):  
Bhuvana R. ◽  
Purushothaman S. ◽  
Rajeswari R. ◽  
Balaji R.G.
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Optimum Number ◽  
Back Propagation Algorithm ◽  
Efficient System ◽  
Propagation Algorithm ◽  
Radial Basis

Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

2012 ◽  
Vol 433-440 ◽  
pp. 4320-4323 ◽  
Author(s):  
Jing Wang ◽  
Jin Ying Song ◽  
Ai Qing Tang
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Self Adaptation ◽  
Hidden Layer ◽  
Good Agreement

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

scholarly journals Effect on Neural Pattern Classifier for Intelligent Gas Sensor by Increasing Number of Hidden Layer

2021 ◽  
Vol 9 (8) ◽  
pp. 1376-1383
Author(s):  
Shikha Srivastava
Keyword(s):  
Learning Curve ◽  
Gas Sensor ◽  
Pattern Classification ◽  
Back Propagation ◽  
Published Data ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Recognition Pattern ◽  
Hidden Layer ◽  
Classification Efficiency

Abstract: Neural networks are used to solve complex problem viz., speech and image recognition, pattern recognition (Pattern classification), computer vision etc. Pattern classification by using Back Propagation algorithm for an intelligent gas sensor application is presented. The classifier is trained using published data of thick film tin oxide sensor array. Its superior classification and learning performance is demonstrated for discrimination of alcohols and alcoholic beverages by increasing number of hidden layer. The new model proposed in this article give steep and monotone learning curve and better classification efficiency. Keywords: Neural Network classifier, Back Propagation Algorithm, system error, classification efficiency, learning curve

scholarly journals Neural Network Based Pest Identification and Control in Cauliflower Crop using Sounds of Pest

2019 ◽  
Vol 8 (9) ◽  
pp. 3127-3134
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Initial Stage ◽  
Identification Technique ◽  
Pest Detection ◽  
Maximum Protection ◽  
Acoustic Identification ◽  
Hidden Layer ◽  
And Control

The quantity of pesticides usage in the field increases every year, which in turn affects the nutrients present in the crop. These pesticides also cause water contamination, air pollution, serious health problems in humans and finally making the soil infertile. Necessary action has to be taken to protect the environment and crops from chemicals. A well-established automatic acoustic detection of pest for early pest detection is suggested in this paper. Acoustic identification technique of signal analysis is extensively implemented in agriculture to give maximum protection of crops, ultimately resulting in better production. Successful eradication of pest lies in identification of pest without damaging the nutrients and crops. The proposed technique detects the presence of pests in the initial stage. The sounds of different pests which are dreadfully affecting the agricultural crops are collected. The signals of pests are analyzed in time domain and frequency domain specifications. The features of different pests are extracted using Mat lab programming. The various statistical features of pest are trained and given to the hidden layer of neural network where it automatically classifies the pest indicating the presence of pest. The Back-Propagation algorithm is used for training the samples. When the identification of pest has been done, the pest can be killed by the microwave shock rather than applying highly toxic chemicals. This technique also benefits the farmer by avoiding contact with the pesticides. Direct contact with the pesticides sometimes causes skin cancer to farmers. The technique suggested in this paper is harmless to the farmers and crops, hence increases the production.

A Model with Back-Propagation Algorithm for the Estimation of Irreducible Water Saturation

2011 ◽  
Vol 361-363 ◽  
pp. 445-450 ◽  
Author(s):  
Ping Hua Ma ◽  
Hong Fu Fan ◽  
Ke Li
Keyword(s):  
Prediction Models ◽  
Water Saturation ◽  
Back Propagation ◽  
Superior Performance ◽  
Back Propagation Algorithm ◽  
Irreducible Water Saturation ◽  
Long Time ◽  
Multi Phase Flow ◽  
Conventional Models ◽  
Hidden Layer

As one of the most important reservoir parameters, irreducible water saturation, Swir, is a key parameter in evaluating multi-phase flow, as well as its importance in defining oil in-place. Residual oil saturation, the target of tertiary recovery, is also a function of Swir. In traditionally, Swir is determined by conducting capillary pressure experiments, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of Swir with mathematical models is developed in recent years. The study reported in this paper uses artificial neural network to determine Swir. The optimal model is chosen among 25 simulations, subtilizing different combinations of hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with other conventional models, demonstrating the superior performance of the proposed Swir prediction models.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

Development of a Neural Statistical Model for the Relative Humidity Levels Prediction in the Region of Rabat-Kenitra (Morocco)

2021 ◽  
Author(s):  
Kaoutar Elazhari ◽  
Badreddine ABDALLAOUI ◽  
Ali DEHBI ◽  
Abdelaziz ABDALLAOUI ◽  
Hamid ZINEDDINE
Keyword(s):  
Relative Humidity ◽  
Meteorological Parameters ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Neural Model ◽  
Ann Model ◽  
History Of ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Mlp Model

Abstract This work provides the development of a powerful artificial neural network (ANN) model, for the prediction of relative humidity levels, using other meteorological parameters of the Rabat-Kenitra region. The treatment was applied to a database containing a daily history of five meteorological parameters of 9 stations covering this region for a period from 1979 to mid-2014. We have shown that for the prediction of relative humidity in this region, the best performing three-layer ANN (input, hidden and output) mathematical model is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, having an architecture [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer was able to estimate values for relative humidity very close to those observed. Indeed, this was affirmed by a low mean squared error (MSE) and a fairly high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.

Study of Digital Character Recognition Based on BP Neural Networks

2013 ◽  
Vol 333-335 ◽  
pp. 856-859 ◽  
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Solution Space ◽  
Training Sample ◽  
Back Propagation Algorithm ◽  
Sample Object ◽  
Training Error ◽  
Hidden Layer

A digital character recognition method is presented based on BP Neural Network. This paper preprocesses the digital character image and extracts character feature, then uses BP Neural Network to recognize digital character. Back Propagation algorithm seeks network weights to minimize training error in the solution space. A network with hidden layer is created at first, then an input sample vector is sent to network input terminal and the square error E between output values and training sample object output values is calculated. Above process is repeated for input samples of training sets until the error is reduced within the limits of the threshold. The results show that the method presented has good accuracy, quick speed and strong robustness for realtime application.

scholarly journals Prediction of Drape Coefficient by Artificial Neural Network

2015 ◽  
Vol 15 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Adel Ghith ◽  
Thouraya Hamdi ◽  
Faten Fayala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

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