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18 results on '"Gundoshmian, Tarahom Mesri"'

2. Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

Author

Ardabili, Sina, Mosavi, Amir, Mahmoudi, Asghar, Gundoshmian, Tarahom Mesri, Nosratabadi, Saeed, and Varkonyi-Koczy, Annamaria R.

Subjects
Economics - General Economics
Abstract

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.

Published
2020
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3. Performance Analysis of Combine Harvester using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

Author

Nadai, Laszlo, Imre, Felde, Ardabili, Sina, Gundoshmian, Tarahom Mesri, Gergo, Pinter, and Mosavi, Amir

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Statistics - Machine Learning, 68T05
Abstract

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy., Comment: 6 pages, 6 figures

Published
2020

4. Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology

Author

Gundoshmian, Tarahom Mesri, Ardabili, Sina, Mosavi, Amir, Várkonyi-Kóczy, Annamária R., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Várkonyi-Kóczy, Annamária R., editor

Published
2020
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12. Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology

Author

Gundoshmian, Tarahom Mesri, Ardabili, Sina, Mosavi, Amir, and Várkonyi-Kóczy, Annamária R.

Subjects
artificial_intelligence_robotics
Abstract

Automated controlling the harvesting systems can significantly increase the efficiency of the agricultural practices and prevent food wastes. Modeling and improvement of the combine harvester can increase the overall performance. Machine learning methods provide the opportunity of advanced modeling for accurate prediction of the highest performance of the machine. In this study, the modeling of combine harvesting id performed using radial basis function (RBF) and the hybrid machine learning method of adaptive neuro-fuzzy inference system (ANFIS) to predict various variables of the combine harvester for the optimal performance. Response surface methodology (RSM) is also used to optimize the models. The comparative study shows that the ANFIS method outperforms the RBF method.

Published
2019

17. Energy use pattern in production of sugar beet in western Azerbaijan province of Iran.

Author

Shahgholi, Gholamhossein, Gundoshmian, Tarahom Mesri, Molaie, Faezeh, and Eskandari, Orooj

Subjects
*SUGAR beets, *NONRENEWABLE natural resources, *AGRICULTURAL research, *AGRICULTURAL productivity
Abstract

With regard to the limitation of energy resources, especially non-renewable sources and the increasing trend of energy consumption in agriculture, energy management in this sector is important. The purpose of this study was to assess energy productivity, input and net energy output, and output-input energy ratio of sugar beet production in western Azerbaijan province of Iran. To achieve these objectives, statistical data about cultivation area, sugar beet yield in 2010 were acquired from the agricultural research center of west Azerbaijan province. Also, data about cultivation methods, implements and machinery in use were obtained from sugar beet farmers by questionnaire. According to the results, total energy consumption in sugar beet production was 52268.72 MJ ha-1, output energy was 722400 MJ ha-1, energy output-input ratio was 13.8, net energy was 670131.28 MJ ha-1 and energy productivity was 0.82 kg MJ-1. The major energy consumers were chemical fertilizers with 34% of total input energy, irrigation (22%), implements and irrigation equipment manufacturing (12.84%) and spraying (6.98%), respectively. Approximately 29.58% of total input energy used in sugar beet production was direct energy and the remaining of 70.42% was indirect. [ABSTRACT FROM AUTHOR]

Published
2018

18. Energy efficiency improvement for broiler production using non-parametric techniques.

Author

Ebrahimi, Sajjad, Gundoshmian, Tarahom Mesri, Abdi, Reza, and Jadidi, Mehri Raei

Subjects
*ENERGY consumption, *BROILER chickens, *SUSTAINABILITY, *LINEAR programming
Abstract

The goal of this study was to evaluate the sustainability and efficiency of broiler production with regard to energy consumption in Ardabil province, Iran. To reach the goal, linear programming model and Data Envelopment Analysis (DEA) were employed. Data were collected from the farmers using a face-to-face questionnaire performed in September- December 2014 period. The DEA application results showed that the average values of technical, pure technical and scale efficiency scores of producers were 0.949, 0.988 and 0.960, respectively. Also, energy saving target ratio for broiler production was calculated as 8.33%, indicating that by following the recommendations resulted from this study, about 12316.85 MJ/(1000 bird) of total input energy could be saved while holding the constant level of broiler production. The results of linear programming model revealed that by using of optimum energy, producers could increase average yield by 17.6%. Also the results indicated that the existing productivity level could be achieved even by reducing the existing energy use level by 13.89%. Diesel fuel, natural gas and electricity energy inputs had the highest potential for saving energy in two methods; so, if inefficient producers would pay more attention towards these sources, they would considerably improve their energy productivity. [ABSTRACT FROM AUTHOR]

Published
2016

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