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Prediction of storage quality of fresh-cut green peppers using artificial neural network

- Meng, Xiangyong, Zhang, Min, Adhikari, Benu

  • Authors: Meng, Xiangyong , Zhang, Min , Adhikari, Benu
  • Date: 2012
  • Type: Text , Journal article
  • Relation: International Journal of Food Science and Technology Vol. , no. (2012), p.
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  • Description: To extend the shelf-life of fresh-cut fruits and vegetables, it is essential to develop models that can accurately predict their storage quality. In view of this, an artificial neural network (ANN) model based on back propagation (BP) algorithm was developed to predict the storage quality (degree of yellowness, water loss, textural firmness and vitamin C content) of fresh-cut green peppers. The prediction accuracy of ANN was compared with that of multiple linear regression-based models. The root mean square error (RMSE), mean absolute error (MAE), sum of squared residuals (SSR) and standard error of prediction (SEP) were used as comparison parameters. The results showed that the accuracy and goodness of fit of the storage quality parameters predicted by ANN were better than those predicted by multiple linear regression-based models. The RMSE, MAE, SSR and SEP values obtained from the former were much lower than those obtained from the latter. © 2012 The Authors. International Journal of Food Science and Technology © 2012 Institute of Food Science and Technology.

Artificial neural network for prediction of air flow in a single rock joint

- Ranjith, Pathegama, Khandelwal, Manoj

  • Authors: Ranjith, Pathegama , Khandelwal, Manoj
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 21, no. 6 (2012), p. 1413-1422
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  • Description: In this paper, an attempt has been made to evaluate and predict the air flow rate in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure, and air outlet pressure using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network having 3-7-1 architecture network was trained using 37 data sets measured from laboratory investigation. Ten new data sets were used for the, validation and comparison of the air flow rate by ANN and multi-variate regression analysis (MVRA) to develop more confidence on the proposed method. Results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between measured and predicted values of air flow rate. It was found that CoD between measured and predicted air flow rate was 0. 995 and 0. 758 by ANN and MVRA, respectively, whereas MAE was 0. 0413 and 0. 1876. © 2011 Springer-Verlag London Limited.

Application of an expert system for the assessment of blast vibration

- Khandelwal, Manoj

  • Authors: Khandelwal, Manoj
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Geotechnical and Geological Engineering Vol. 30, no. 1 (2012), p. 205-217
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  • Description: The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors. © 2011 Springer Science+Business Media B.V.

Exploring the application of artificial neural network in rural streamflow prediction - A feasibility study

- Choudhury, Tanveer, Wei, Jackie, Barton, Andrew, Kandra, Harpreet, Aziz, Abdul

  • Authors: Choudhury, Tanveer , Wei, Jackie , Barton, Andrew , Kandra, Harpreet , Aziz, Abdul
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 753-758
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  • Description: Streams and rivers play a critical role in the hydrologic cycle with their management being essential to maintaining a balance across social, economic and environmental outcomes. Accurate streamflow predictions can provide benefits in many different ways such as water allocation decision making, flood forecasting and environmental watering regimes. This is particularly important in regional areas of Australia where rivers can play a critical role in irrigated agriculture, recreation and social wellbeing, major floods and sustainable environments. There are several hydrological parameters that effect stream flows in rivers and a major challenge with any prediction methodology, is to understand these parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required for accurate prediction of streamflow under usually unique, waterway-specific conditions using available data. This research employs an approach based on Artificial Neural Network (ANN) to provide this robust methodology. Data from readily available sources has been selected to provide appropriate input and output parameters to train, validate and optimise the neural network. The optimisation steps of the methodology are discussed and the predicted outputs are compared and analysed with respect to the actual collected values. © 2018 IEEE.
  • Description: IEEE International Symposium on Industrial Electronics

A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

- Sayadi, Ahmad, Monjezi, Masoud, Talebi, Nemat, Khandelwal, Manoj

  • Authors: Sayadi, Ahmad , Monjezi, Masoud , Talebi, Nemat , Khandelwal, Manoj
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 5, no. 4 (2013), p. 318-324
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  • Description: In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R2) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.

Prediction of clogging in stormwater filters using artificial neural network

- Lin, Junlin, Kandra, Harpreet, Choudhury, Tanveer, Barton, Andrew

  • Authors: Lin, Junlin , Kandra, Harpreet , Choudhury, Tanveer , Barton, Andrew
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 771-776
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  • Description: Stormwater filtration technologies play a significant role in improving water quality and making treated water available for non-potable uses. However, during treatment processes, contaminants such as suspended solids would lead to clogging in storm water filters, especially those with high infiltration rates. There are several parameters that affect clogging of filters, and a major challenge is to understand the parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required to accurately predict clogging which would contribute to the development of filtration technologies and in predictive maintenance. This research employs the use of Artificial Neural Network (ANN) model to predict clogging performance of stormwater filters under different operational conditions using experimental data from previous work. A single hidden layer ANN model with 19 hidden layer neurons was developed in this preliminary work.
  • Description: IEEE International Symposium on Industrial Electronics

Evaluation of dump slope stability of a coal mine using artificial neural network

- Rahul, Manoj, Rai, Rajesh, Shrivastva, B.K.

  • Authors: Rahul, Manoj , Rai, Rajesh , Shrivastva, B.K.
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 1, no. 3-4 (2015), p. 69-77
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  • Description: Dump slope stability is critical in the design and operation of any open pit mines having significant impacts on safety and on the economics of an open pit project. The issues related to the stability of dumps is catching attention worldwide from quite some time, which is quite important for the safe working in and around monstrous structures as well as restricted availability of land. In the present paper, artificial neural network (ANN) has been used to calculate the factor of safety of dump slope of a coal mine. A three-layer, feed-forward back-propagation neural network having optimum hidden neurons is used to model. The input parameters are dump slope geometry, geotechnical properties and hydrological conditions have been used to evaluate the stability of dump slope. Six input parameters and one output parameters have been trained using various algorithms. New dump slope data sets have been used for the validation and comparison of the factor of safety of dump slope. Factor of safety has also been determined using numerical modeling technique. Results were compared between factor of safety, calculated from the numerical modeling tool and predicted value of factor of safety by ANN. It was found a very closer agreement between the predicted and calculated values of factor of safety for the dump slope with ANN over numerical modelling.

Backbreak prediction in the Chadormalu iron mine using artificial neural network

- Monjezi, Masoud, Ahmadi, Zabiholla, Yazdian-Varjani, Ali, Khandelwal, Manoj

  • Authors: Monjezi, Masoud , Ahmadi, Zabiholla , Yazdian-Varjani, Ali , Khandelwal, Manoj
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 23, no. 3-4 (2013), p. 1101-1107
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  • Description: Backbreak is one of the unfavorable blasting results, which can be defined as the unwanted rock breakage behind the last row of blast holes. Blast pattern parameters, like stemming, burden, delay timing, stiffness ratio (bench height/burden) and rock mass conditions (e.g., geo-mechanical properties and joints), are effective in backbreak intensity. Till date, with the exception of some qualitative guidelines, no specific method has been developed for predicting the phenomenon. In this paper, an effort has been made to apply artificial neural networks (ANNs) for predicting backbreak in the blasting operation of the Chadormalu iron mine (Iran). Number of ANN models with different hidden layers and neurons were tried, and it was found that a network with architecture 10-7-7-1 is the optimum model. A comparative study also approved the superiority of the ANN modeling over the conventional regression analysis. Mean square error (MSE), variance account for (VAF) and coefficient of determination (R 2) between measured and predicted backbreak for the ANN model were calculated and found 89.46 %, 0.714 and 90.02 %, respectively. Also, for the regression model, MSE, VAF and R 2 were computed and found 66.93 %, 1.46 and 68.10 %, respectively. Sensitivity analysis was also carried out to find out the influence of each input parameter on backbreak results, and it was revealed that burden is the most influencing parameter on the backbreak, whereas water content is the least effective parameter in this regard. © 2012 Springer-Verlag London Limited.
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