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Articles by Shahin Rafiee
Total Records ( 4 ) for Shahin Rafiee
  Paria Sefeedpari , Shahin Rafiee , Asadollah Akram , Kwok-Wing Chau and Seyyed Hassan Pishgar Komleh
  This study focused on the capability of two artificial intelligent approaches, including Artificial Neural Networks (ANNs) and Multi-Layered Adaptive Neural Fuzzy Inference System (MLANFIS), as a prediction tool to model and forecast milk yield on the basis of energy consumption in dairy cattle farms of Iran. For this purpose, data was collected from 50 farms in Tehran province, Iran. For the purpose of gaining the best accurate ANFIS model, five energy inputs were clustered into two groups based on their energy share in total energy consumption and an ANFIS network was trained for each cluster. The results of statistical parameter evaluation showed that ANFIS 1 and ANFIS 2 from layer one were not as accurate as ANFIS 3 network (layer two) whereas, coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values were 0.75, 1256.72 and 0.129 for ANFIS 1 and 0.65, 1409.43 and 0.144 for ANFIS 2 and 0.93, 681.85 and 0.063 for ANFIS 3 network, respectively. These results were considerably better than ANNs model with R2, RMSE and MAPE calculated as 0.85, 1052.413 and 0.0702, respectively. Eventually, the outcomes revealed that multi-layered ANFIS contrasted to ANNs modeling could successfully predict the milk yield level accurately. Hence, it is recommended that the multi-layered ANFIS can potentially be applied as an alternative approach.
  Hamzeh Fathollahzadeh , Hossein Mobli , Ali Jafari , Shahin Rafiee and Ali Mohammadi
  Physical properties of apricot kernel are necessary for the design of equipments for processing, transportation, sorting and separating. In this paper the physical properties of apricot kernel have been evaluated as a function of moisture content varying from 3.19% to 17.46% (w.b.). With increasing in moisture content, kernel length, width, thickness, Geometric mean diameter and surface area increased; the sphericity varying from 59.79 % to 62.21%; mass, thousand grain mass, volume and true density increase from 0.380 to 0.448 (gr), 381.6 to 447.9 (gr), 0.442 to 0.463 (cm3) and 882.588 to 983.383 (kg/m3) respectively; The porosity and bulk density decreased from 52.68% to 51.33% and 471.6 to 406.8 (kg/m3) respectively; the coefficient of static friction on all surfaces increased as the moisture content increased; and the rupture strength in weakest direction (through length) decrease from 23.443 to 16.620 (N).
  Ali Mohammadi , Shahin Rafiee , Alireza Keyhani and Zahra Emam - Djomeh
  Drying behavior of kiwifruit slice was studied at 40, 50, 60, 70 and 80oC and at a constant air velocity of 1.5 m/s for constant sample thickness of 4 mm in a thin layer dryer. Sample weight, temperature and drying air velocity were measured during drying and drying curves were obtained for each experimental data. The curves were fitted to twelve different semi-theoretical and/or empirical thin-layer drying models to estimate a suitable model for drying of kiwifruit. Coefficients were evaluated by non-linear regression analysis. The models were compared based on their coefficient of determination (EF), root mean square error (RMSE) and reduced chi-square (x2). Midilli model had the highest value of EF (0.999319), the lowest RMSE (0.032536) and x2 (0.001119). The Midilli model was found to satisfactorily describe the drying behavior of kiwifruit.
  Elham Meisami-asl , Shahin Rafiee , Alireza Keyhani and Ahmad Tabatabaeefar
  Drying is one of the primary methods of food preservation. Determining coefficients used in drying models is essential to predict the drying behavior. The present study was conducted to compute drying characteristics of apple slices. Thin layer drying kinetics of apple slices (variety-Golab) was experimentally investigated in a convective dryer and the mathematical modeling was performed by using thin layer drying models in the literature. Drying characteristics of apple slices were determined using heated ambient air at temperatures between 40 and 80oC, velocities at 0.5 m/s and thickness of thin layer 2, 4, 6 mm. Beside the effects of drying air temperature, effects of slice thickness on the drying characteristics, drying time and quality of dried product were also determined. Drying curves obtained from the experimental data were fitted to twelve different thin layer drying models. All the models were compared according to three statistical parameters, i.e. Root Mean Square Error (RMSE), chi-square (X2) and modeling efficiency (EF). The results showed that increasing drying air temperature resulted to shorter drying times. Midilli model had the highest value of EF (0.999611), the lowest values of 0.031806 and 0.001088 for RMSE and X2 respectively. The Midilli model was found to be the best model for describing the drying curves of apples. The effects of drying air temperature and thickness on the drying constant and coefficient were also shown.
 
 
 
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