An Indirect Symmetrical Phase Shift Transformer (ISPST) represents both electrically connected and magnetically coupled circuits, which makes it unique compared to a power transformer. Effective differentiation between transformer inrush current and internal fault current is necessary to avoid incorrect differential relay tripping. This research proposes a system that uses a Chebyshev Neural Network ((ChNN)) as a core classifier to distinguish such internal faults. For simulations, we used PSCAD/EMTDC software. Internal faults and inrush have been simulated in various ways using various ISPST parameters. A large, simulated dataset is used, and performance is recorded against different sized ISPSTs. We observed an overall accuracy greater than 99%. The (ChNN) classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters.

This paper presents the application of competitive Hopfield Neural Network ((ChNN)) for medical images segmentation. Our proposed approach consists of two steps: 1) translating segmentation of the given medical image into an optimization problem, and 2) solving this problem by a version of Hopfield Network known as (ChNN). Segmentation is considered as a clustering problem and its validity criterion is based on both intraset distance (IAD) and interset distance (IED). The algorithm proposed in this paper is based on gray level features only. This leads to near optimal solutions if both IAD and IED are considered at the same time. If only one of these distances is considered, the result of segmentation process by (ChNN) will be far from optimal solution and incorrect even for very simple cases. Furthermore, sometimes the algorithm receives at unacceptable states. Both these problems may be solved by contributing both IAD and IED distances in the segmentation (optimization) process. The performance of the proposed algorithm is tested on both phantom and real medical images. The promising results and the robustness of algorithm to system noises show near optimal solutions.

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.

In this paper, a method is proposed for the control of a quadrotor based on sliding mode control by using Chebyshev Neural Networks. The proposed approach is a combination of the sliding mode controller and the Chebyshev Neural Network approximator that the Neural Network weights are tuned in real-time by using robust adaptive techniques. In this research, the dynamic model of the quadrotor is divided into two subsystems for the purpose of the position and orientation tracking control: a fully-actuated subsystem and an underactuated subsystem. For the former, the sliding surfaces are designed by using one state variable, and for the latter, the sliding manifolds are defined by a linear combination of two state variables. In this paper, the system stability is analyzed by Lyapunov theory-based techniques and the accuracy of the controller performance will be illustrated by the simulation results.

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.