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| 1 | Applied machine learning in greenhouse simulation;new application and analysis显示文摘Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot regions.The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it.So the main goal of this study is to select the best method between Artificial Neural Network(ANN)and Support Vector Machine(SVM)to estimate three different variables include inside air,soil and plant temperatures(Ta,Ts,Tp)and also energy exchange in a polyethylene greenhouse in Shahreza city,Isfahan province,Iran.The environmental factors which influencing all the inside temperatures such as outside air temperature,wind speed and outside solar radiation were collected as data samples.In this research,13 different training algorithms were used for ANN models(MLPRBF).Based on K-fold cross validation and Randomized Complete Block(RCB)methodology,the best model was selected.The results showed that the type of training algorithm and kernel function are very important factors in ANN(RBF and MLP)and SVM models performance,respectively.Comparing RBF,MLP and SVM models showed that the performance of RBF to predict Ta,Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 indices.The range of RMSE and MAPE factors for RBF model to predict Ta,Tp and Ts were between 0.07 and 0.12C and 0.28-0.50%,respectively.Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups.The performance of best model(RBF)to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy. | Morteza Taki Saman Abdanan Mehdizadeh Abbas Rohani Majid Rahnama Mostafa Rahmati-Joneidabad | 2018 | Information Processing in Agriculture2018，5，2： | 8 |
| 2 | Combining artificial neural network and multi-objective optimization to reduce a heavy-duty diesel engine emissions and fuel consumption显示文摘Nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) is well known for engine optimization problem. Artificial neural networks(ANNs) followed by multi-objective optimization including a NSGA-Ⅱ and strength pareto evolutionary algorithm(SPEA2) were used to optimize the operating parameters of a compression ignition(CI) heavy-duty diesel engine. First, a multi-layer perception(MLP) network was used for the ANN modeling and the back propagation algorithm was utilized as training algorithm. Then, two different multi-objective evolutionary algorithms were implemented to determine the optimal engine parameters. The objective of the present study is to decide which algorithm is preferable in terms of performance in engine emission and fuel consumption optimization problem. | Amir-Hasan Kakaee Pourya Rahnama Amin Paykani Behrooz Mashadi | 2015 | Journal of Central South University2015，22，11： | 3 |
| 3 | Replantation and transplantation following avulsion of two maxillary incisors显示文摘 | Swiatkowski W Rahnama M Tomaszewski T | 2007 | Dent Traumatol2007，23，1： | 1 |
| 4 | 查看详情显示文摘 | Rahnama M | | 0，，： | 1 |
| 5 | Chatter Suppression in Micro End Milling with Process Damping 显示文摘 | RAHNAMA R SAJJADI M PARK S S | 2009 | Journal of Materials Processing Technology2009，209，17： | 1 |
| 6 | The effect of NaCI on antioxidant enzyme activities in potato seedlings显示文摘 | Rahnama H Ebrahimzadeh H | 2005 | Biolo Plantarum2005，49，1： | 1 |
| 7 | Oxidative stress responses in physical education students during 8 weeks aerobic training显示文摘 | RAHNAMA N GAEINI A A HAMEDINIA M R | | 0，，01： | 1 |
| 8 | Seasonal Variation Of Aflatoxin M1 Contamination In Industrial And Traditional Iranian Dairy Products 显示文摘 | Fallah A A Rahnama M Jafari T | 2011 | Food Control2011，，： | 1 |
| 9 | Scrotal approach to both palpable and impalpable undescended testes:should it become our first choice显示文摘 | Callewaert PRH Rahnama'i MS Biallosterski BT | | 0，，： | 1 |
| 10 | Injury risk associated with playing actions during competitive soccer显示文摘 | RAHNAMA N REILLY T LEES A | 2002 | Br J Sport Med2002，36，： | 1 |
| 11 | Emission of mito- chondrial biophotons and their effect on electrical activity of membrane via microtubules显示文摘 | Rahnama M Tuszynski JA Bokkon I | 2011 | Joumal of Integrative Neu- roscience2011，10，1： | 1 |
| 12 | Part-per-trillion determination of chlorobenzenes in water using dispersive liquid–liquid microextraction combined gas chromatography–electron capture detection显示文摘 | Reyhaneh Rahnama Kozani Yaghoub Assadi Farzaneh Shemirani Mohammad-Reza Milani Hosseini Mohammad Reza Jamali | 2006 | Talanta2006，，2： | 1 |
| 13 | Seasonal variation of aflatoxin M 1 contamination in industrial and traditional Iranian dairy products显示文摘 | Aziz A. Fallah Mohammad Rahnama Tina Jafari S. Siavash Saei-Dehkordi | 2011 | Food Control2011，，10： | 1 |
| 14 | Part-per-trillion determination of chlorobenzenes in water using dispersive liquid-liquid microextraction combined gas chromatography-electron capture detection 显示文摘 | Rahnama Kozani Reyhaneh Yaghoub Assadi Farzaneh Shemirani | 2007 | Talanta2007，72，2： | 1 |
| 15 | Opposition- based differential evolufion显示文摘 | Rahnama S Tizhoosh H R Salama M M A | 2008 | IEEE Trans on Evolutionary Computation2008，12，1： | 1 |
| 16 | Part-per-trillion determination of chlorobenzenes in water using dispersive liquid -liquid microextraction combined gas chromatography-electron capture detec-tion显示文摘 | Rahnama Kozani R Assadi Y Shemirani F | 2007 | Talanta2007，72，2： | 1 |
| 17 | Opposition-based Differential Evolution显示文摘 | Rahnama S | 2008 | IEEE Transactions on Evolution Computation2008，12，1： | 1 |
| 18 | Effects of vita-min H supplementation on oxidative stress at rest and afterexercise to exhaustion in athletic students显示文摘 | Gaeini AA Rahnama N Hamedinia MR | 2006 | J Sports MedPhys Fitness2006，46，3： | 1 |
| 19 | Part-per-trillion determination of chlorobenzenes in water using dispersive liquid–liquid microextraction combined gas chromatography–electron capture detection显示文摘 | Reyhaneh Rahnama Kozani Yaghoub Assadi Farzaneh Shemirani Mohammad-Reza Milani Hosseini Mohammad Reza Jamali | 2006 | Talanta2006，，2： | 1 |
| 20 | Determination of tri-halomethanes in drinking water by dispersive liquid -liquid microextraction then gas chromatography with electron-capture detection显示文摘 | Rahnama Kozani R Assadi Y Shemirani F | 2007 | Chromatographia2007，66，12： | 1 |
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