Adaptive Neuro-Fuzzy Inference System (ANFIS) and genetic algorithm-artificial neural network (GA-ANN) were used for modeling of the hot-air drying kinetics of papaw slices. The ANFIS and GA-ANN were fed with 3 inputs of drying time (0-320 min), drying temperature (40, 50 and 60 °C) and slice thickness (3, 5 and 7 mm) for prediction of moisture ratio (MR). The triangular membership functions (MFs) were applied and 27 rules were provided for the ANFIS designing. The developed ANFIS predictions were relatively similar to the experimental data (R2=0.9967 and RMSE=0.0161). The optimized GA-ANN, which included 7 hidden neurons, predicted the MR with a good precision (R2=0.9936 and RMSE=0.0220). The effective diffusivity for papaw slices was within the range of 6.93 ×10-10 to 1.50×10-9 m2/s over the temperature range. The activation energy was found to be 32.5 kJ/mol indicating the effect of temperature on diffusivity.

Estimating the xanthate decomposition percentage has a crucial role in the treatment of xanthate contaminated wastewaters and in the improvement of the flotation process performance. In this research, the modeling of xanthate decomposition percentage was performed using the least squares regression method and the Adaptive Neuro-Fuzzy Inference System (ANFIS). A multi-variable regression equation and the ANFIS models with various types and numbers of membership functions (MFs) were constructed, trained, and tested for the estimation of xanthate decomposition percentage. The statistical indices such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) were used to evaluate the performance of various models. The lowest values of RMSE and MAPE and the closest value of R2 to unity were determined for the ANFIS model with the triangular membership function and the number of input MFs 9 9 9 (0. 766906, 3. 553509 and 0. 998793). This indicates that ANFIS is a powerful method in the estimation of xanthate decomposition percentage. The performance of new-adopted ANFIS data modeling was significantly better than the conventional least squares regression method.

In recent years, using fuzzy sets theory in modeling of complex and uncertain hydrological phenomena has attracted research workers. For this reason, in this research for river flow forecasting, we have used models of FIS and ANFIS which are based on fuzzy logic. Data of daily flow discharges were provided from Lighvanchay watershed for 6 years. For considering the randomness of data, return points test was used. Then correlogram of data was employed to determine the input optimum models and finally 5 models of discharge forecasting designed based on previous days' discharge. The results showed that ANFIS was more precise and less disperse (RMSE=0.0234) with compare to FIS (RMSE=0.1982). The ANFIS was also more precise in peak discharges simulation than FIS.

Rainfall-runoff process is physical phenomena that their investigation is very difficult due to effectiveness of different parameters. Various methods have so far introduced to analyze these phenomena. This study has been aimed to investigate performance of wavelet-Adaptive Neuro-Fuzzy Inference System (wavelet-ANFIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for simulation of rainfall-runoff process involved with snow water equivalent (SWE) in Latyan watershed located in Tehran province. For this reason, 92 MODIS images have provided by NASA website during three water years 2003-2005, snow cover area in all images has been extracted and finally SWE values have been calculated for the mentioned years. Also, the rainfall, temperature and discharge data for the mentioned years is available which has been used for modeling. The results showed that wavelet-ANFIS with rainfall, temperature and discharge inputs and 1-day delay these inputs with root mean (RMSE) of 0.006 and coefficient of determination (R2) of 0.97 had more effeciency than ANFIS by grid partitioning with rainfall, temperature and discharge inputs with RMSE of 0.059 and R2 of 0.62 and ANFIS by subtractive clustering with rainfall, temperature and discharge inputs with RMSE of 0.059 and R2 of 0.65. The results Also showed that SWE involvement causes to increase the accuracy of models.

One of the most significant threats of a national economy is the bankruptcy of its firms. Assessment of bankruptcy provides valuable information on which governments, investors and shareholders can base their financial decisions in order to prevent possible losses. The aim of this study was to model bankruptcy by using Adaptive Neuro Fuzzy Inference System (ANFIS). Statistical society for performing of this research is companies which were listed at Tehran Stock Exchange since 2001 up to 2010 and according to article 141 of commercial code, including 40 bankrupt companies and 40 non bankrupt companies. These companies were divided randomly in three sets: train set for creating model, test set and check set for validating model. financial ratios of the companies in the year before bankruptcy were considered as input variables ANFIS. The result of this study points out that percentage of success predictions one year before bankruptcy is 83.75. Finally, according to this study, the ANFIS selection is helpful to predict the financial distress situation for companies which were listed at Tehran Stock Exchange.

Introduction: With regards to the importance of early prognosis of coronary artery diseases, in recent years the use of the latest artificial intelligence and data mining findings is considered to assist physicians. The purpose of this study was to increase the precision and prediction speed for the results of angiography by using a combination of fuzzy Inference Systems and particle swarm optimization algorithm. Materials & Methods: A new System consisting of a combination of fuzzy Inferences and particle swarm optimization algorithm was proposed and simulated by MATLAB software R2015a (8. 5. 0. 197613). The samples consisted of 152 patients who were randomly selected from those undergone coronary artery angiographies in Kowsar Hospital of Shiraz, Iran, in August 2013. The data were then analyzed by Excel 2010 and the essential parameters of the proposed System were extracted. Findings: The data were then randomly divided into 20 groups for training and testing. These groups were selected randomly in a manner that 85% of the data were used for training and 15% for testing, and each group was simulated individually. The results of the simulation after 20 rounds of simulation with different training and testing data in System performance indicators displayed that the average of sensitivity, specificity, precision, and accuracy was 0. 8422, 0. 9192, 0. 8554, and 0. 8888, respectively, and it was equal to 1 in the most optimal situations. Discussion & Conclusions: High performance indicators prove that the proposed System has a satisfactory performance in predicting the results of angiography and classifying them into two classes of normal and patient. In fact, in this study, prediction speed and precision were improved by using the proposed System, which was based on Neuro-Fuzzy Inference System in combination with particle swarm optimization meta-heuristic algorithm.