Prediction of tractor repair and maintenance costs using Artificial Neural Network

Rohani, Abbas; Abbaspour‐Fard, Mohammad Hossein; Abdolahpour, Shamsolla

doi:10.1016/j.eswa.2011.01.118

Cited by 68 publications

(28 citation statements)

References 5 publications

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“…It has three layers, namely input, hidden and output layers (Gholipoor et al , ). Two of the most widely used indices to determine the validity of the model are the mean squared error (MSE) and the coefficient of determination ( R 2 ) between the predicted and actual values (Keskin & Taylan, ; Rohani et al , ).…”

Section: Introductionmentioning

confidence: 99%

Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network

2015

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In arid and semi-arid regions of the world, including Iran, soil salinity is one of the major abiotic stresses. One of the ways to achieve high performance in these areas is to use salt-tolerant varieties of wheat. Iran is known as one of the places where the D-genome originated and evolved. In order to evaluate the salt tolerance of Iranian genotypes based on the eight indices using analysis of variance, regression and an artificial neural network (ANN), 41 Iranian wheat varieties (Trticum aestivum L.) were planted in a randomised complete block design with three replications under two saline irrigation conditions, 0.631 and 11.8 dS m −1 , in Kerman, Iran. Significant differences between the varieties were observed, and the significant two-way interaction of environment × varieties in combined analysis and non-significant correlation, 0.07, between the yield in two environments (yield in non-stress conditions, Yp, and yield in stress conditions, Ys) indicates the existence of genetic variation among varieties and the different responses of the varieties in both the environments. The indices of tolerance, geometric mean product (GMP), mean product (MP), harmonic mean (HM) and stress tolerance index (STI) were calculated based on grain yield evidence of positive significant correlation with Yp and Ys. Based on the ANN results, yield stability index (YSI), MP, GMP and STI were the best indices to predict salinity-tolerant varieties. The varieties selected based on these indices, such as Bolani, Sistan, Ofogh, Pishtaz, Karchia and Arg, produced high yield in both the environments. These results show that bread wheat originating from Iran has salt tolerance potential and can also be used in studies related to salinity tolerance mechanisms.Assessment of salinity indices to identify Iranian wheat varieties S.Z. Ravari et al.

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Section: Introductionmentioning

confidence: 99%

Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network

2015

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“…Nebot et al, 2012), agricultural crop yield prediction (Dahikar et al, 2014), agricultural economics (Chen, 2005;Rohani et al, 2011), data analysis tool in precision agriculture (Irmak et al, 2006).…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

Demır

Eski

Kuş

et al. 2017

Not Bot Horti Agrobo

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The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, surface and projected area, geometric mean diameter and sphericity were calculated tridimensional in lab conditions. Then, prediction of these parameters was carried out using NNs. The research was divided into two parts; experimental investigation and simulation analysis with NNs. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) structures were employed to estimate physical parameters of the pumpkin seeds. The Root Mean Squared Error (RMSE) was 0.6875 for BPNN and 0.0025 for RBNN structures. The RBNN structure was superior in prediction and could be used as an alternative approach to conventional methods.

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“…In this study, first, the data were split into three subsets: a training set (about 2/4 of all data, 52%), a test set (1/4 of all data, 24%), and a check set (1/4 of all data, 24%). There is no reasonable generalized rule to specify the size of training data for a proper training; nevertheless, the training sample should include all ranges of the available data (Rohani, Abbaspour‐Fard, & Abdolahpour, ). The training set can be altered if the operation of the model does not satisfy the expectations (Zhang & Fuh, ).…”

Section: Methodsmentioning

confidence: 99%

Prediction of bruise volume propagation of pear during the storage using soft computing methods

Razavi

Golmohammadi

Sedghi

et al. 2019

Food Science & Nutrition

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Bruises occur under both static and dynamic loadings when the imposed stress on fruit goes over the failure stress of the fruit tissue. Bruise damage is the main reason for fruit quality loss. In this study, the potential of artificial neural network (ANN), adaptive neuro‐fuzzy inference system (ANFIS), and multiple regression (MR) techniques to predict bruise volume propagation of pears during the storage time was evaluated. For this purpose, at first, the radius of curvature at loading region was obtained. Samples were divided into five groups and subjected to five force levels. Then, they were kept under storage conditions and at 7‐time intervals after loading tests, bruise volume was calculated using magnetic resonance imaging (MRI) and image processing techniques. Force, storage time, and radius of curvature at loading region were employed as input variables, and bruise volume (BV) was considered as output in the developed models. Multilayer perceptron (MLP) artificial neural network with three layers that includes an input layer (three neurons), two hidden layers (two and nine neurons), and one output layer was used. For the evaluation of models, three criteria (RMSE, VAF, and R2) were calculated. ANN and MR gave the highest and lowest correlation between predicted and actual values, respectively. These results indicate that the ANN techniques can be used to predict pear bruising propagation in storage time.

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Prediction of tractor repair and maintenance costs using Artificial Neural Network

Cited by 68 publications

References 5 publications

Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network

Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

Prediction of bruise volume propagation of pear during the storage using soft computing methods

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