Background: Abortion is an important and controversial issue and one of the important reasons for the mortality of pregnant women worldwide. This study aimed to predict the risk factors of abortion in pregnant women using artificial neural network, wavelet neural network, and adaptive neural fuzzy inference system. Materials and Methods: The study is an analytical-comparative modeling and data of 4437 pregnant women from the Ravansar Non-Communicable Disease (RaNCD) cohort study from 2014 to 2016 was used. First, six variables were chosen through the genetic algorithm approach, then artificial neural network (ANN), wavelet neural network (WNN), and adaptive neural fuzzy inference system (ANFIS) were run. Finally, the performance of the models was compared based on the evaluation criteria. All analyses were done in MATLAB R2019b software. Results: ANN with RMSE of 0. 019 showed better performance than ANFIS and WNN with 0. 42 and 1. 445, respectively. Further, the accuracy, sensitivity, and specificity in ANN were 100%, 99%, and 100%, while in WNN, they were 76. 2%, 76. 4%, and 66. 7%. However, when the researchers used three selected variables, the accuracy, sensitivity, and specificity as well as RMSE in ANFIS were 100%, 100% 100%, and 0,100%, 99%, 100%, and 0. 021 in ANN,and finally 76. 2%, 76. 4%, 38. 5%, and 1. 553 in WNN. Conclusion: The models with six input variables indicated that the artificial neural network has a better performance than the other two models, but based on the three variables, the fuzzy neural inference system performed better than the other two models.

Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on deep learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale modified and hybrid deep neural network. The proposed preprocessing method enriches the network to accept and encode genes at any length in a new mapping method. The proposed multi-scale deep neural network uses a combination of an embedding layer, a modified CNN, and an LSTM network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid multi-scale CNN-LSTM network.

Estimation of evapotranspiration is essential for planning, designing and managing irrigation and drainage schemes, as well as water resources management. In this research, artificial neural networks, neural network wavelet model, multivariate regression and Hargreaves' empirical method were used to estimate reference evapotranspiration in order to determine the best model in terms of efficiency with respect to the existing data. The daily data of two meteorological stations of Shahrekord and Farrokhshahr airport in the dry and cold zones of Shahrekord during the period 2013-2004 was used; these included the minimum and maximum temperature, the average nominal humidity, wind speed at 2 meters height and sunshine hours. %75 of the data were validated, and %25 of the data was used for testing the models. Designed network is a predictive neural network with an active sigmoid tangent function hidden in the layer. In the next step, different wavelets including Haar, db and Sym were applied on the data and the neural network-wavelet was designed. To evaluate the models, the method was used by the Penman-Montith Fao and for all four methods, RMSE, MAE and R statistical indices were calculated and ranked. The results showed that the wave-let-neural network with the db5 wavelet had a better performance than other wavelets, as well as the artificial neural network, multivariate regression and the Hargreaves method. The results of wavelet network modelling with the db5 wavelet in the Farrokhshahr station were calculated to be 0. 2668, 0. 2067 and 0. 998, respectively; at the airport station, these were equal to 0. 2138, 0. 14 and 0. 9989, respectively. The results, therefore, showed that the neural network-wavelet performance was more accurate than the other models studied in this study.