Objective Hedging the risk caused by price volatility using options relies on an accurate and appropriate valuation of those options. Therefore, the purpose of this research is to value the options traded on the Tehran Stock Exchange using Modular Neural Networks. The study will also compare the performance of these Modular Neural Networks with the most renowned options valuation models, namely the Black-Scholes-Merton model and the multi-layer perceptron Neural Network model.   Methods For this research, data on call options traded on the Tehran Stock Exchange from March 2018 to March 2022 were utilized. Initially, after removing outlier data, 80% of the dataset was designated as training data, while the remaining 20% was set aside as test data. To facilitate a comparison of results obtained from different models, these two subsets of data remained constant throughout the research. In this study, the theoretical prices generated by each model were compared with the market prices traded on the Tehran Stock Exchange using MSPE, RMSPE, and MAPE statistical criteria. To calculate the prediction error for the Black-Scholes-Merton model, the theoretical price of options was first obtained using its pricing formula. Subsequently, the theoretical prices derived from the Black-Scholes-Merton equation were compared with their corresponding market prices. In the Neural Network models, option prices were predicted using Python and its machine learning algorithms. Finally, the predicted prices from the models were compared with the market prices of the same options. To assess the significant differences between each model and the others, the Paired Sample Test of the mean percentage of errors was employed.   Results This research showed that, from the perspective of the RMSPE criterion, the developed Neural Network model with implied volatility has the lowest error and has the best performance in valuing call options across all monetary positions and periods compared to other investigated models. However, the performance of the developed multi-layer perceptron Neural Network model with implied volatility has been slightly better than that of its Modular counterpart. Following this, the Neural Networks developed with historical volatility, the Neural Networks with discrete data, the Black-Scholes and Merton model, and the Modular Neural Network model proposed by Gradoevich et al. (2009) have been the most accurate, respectively. From the perspective of the MAPE criterion, the developed Neural Network model with implied volatility has performed the best; however, among all the Neural Network models, the multi-layer perceptron Neural Network has outperformed the Modular model.   Conclusion Modular Neural Network models can outperform the Black-Scholes and Merton models. Incorporating implied volatility enhances the performance of Neural Networks in options valuation. However, when considering the RMSPE criterion, Modular Neural Networks trained with historical volatility perform better than multi-layer perceptron Neural Networks. In contrast, for models using implied volatility, the Modular Neural Network does not achieve better performance than the multi-layer perceptron Neural Network. Overall, Neural Networks utilizing implied volatility—whether in Modular or multi-layer perceptron configurations—exhibit superior performance in long-term periods and in ITM (in-the-money) moneyness situations.

Flood routing, one of the most complex issues in hydraulic open channel science and river engineering is investigated. There are different methods in the field of flood routing which are divided broad and focus groups. Today, a new technique using evolutionary artificial Neural Network model based on artificial intelligence is widely used in various fields, especially water engineering works. In this study the flood routing in Maroon river, Khyrabad-Pol Felor reach using MNN, FF, JEN, and RBF evolutionary artificial Neural Network models Were Studied. Including cases where a new method is a model using linear cross correlation between input and output time series. Using genetic algorithm models the structure of artificial Neural Networks in terms of number of layers, number of nodes in the hidden layer, the type of Network learning algorithm and transfer function were optimized. Results show that the method of cross correlation. and the output models compared with actual values show that the MNN selected model has capabilities, flexibility and greater accuracy in forecasting and flood routing in the river than the statistical model and other models of artificial Neural Network determining the number and time delay. Effective input data were effecative, while the number and time delay input data were also effective.

Previous works on speech recognition utilizing Neural Networks have often relied on either recognition through segmentation or mapping of the representation trajectories to the phoneme space. Here, information could be missed due to the method of border labeling techniques. Recent works have indicated that firstly, phonetic borders and transitions would have a good potential to be recognized as acoustic units, and secondly, recognition of the fast transitions by Neural Networks, as fixed cues in time, results in high performance detection and recognition of those events. This approach was manifested through recognition of basic units formed from the VC and CV borders in Farsi (Persian) spoken language. Analysis of the resulting errors has indicated certain discrepancies amongst the theoretical linguistic points of view and implementation outcome.

The mian goal of this paper is to design a controller for nonlinear MIMO systems. It is no convenient to control these systems by classical control methods due to their interaction and nonlinearity. This paper proposed one of the appropriate solutions by using fuzzy Neural Networks. Using Neural Networks for control of MIMO systems has led to some difficulties such as high computation cost, high training period and disruptive effects of unnecessary inputs. To cope with such nonlinear control problems, research has been underway on control using a special Local Modular Fuzzy Neural Network (LMFNN) based entirely on measured inputs and outputs. This Network described by using mathematical relations, and its internal stucture is shown based on block diagram. Then the proposed controller based on this Network with appropriate structure and learning algorithm is designed. This controller applied to control nonlinear MIMO systems. Experimental results are used to show the feasibility and effectiveness of the proposed controller.

Summary One of the most important aspects of mineral deposit exploration is depth estimation values of the mineral masses. Gravity method is used widespread for detection of mineral deposits. A new approach is presented in order to interpret residual gravity anomalies due to simple geometrically shaped bodies such as horizontal cylinder, vertical cylinder, and sphere. This approach is mainly based on using feed forward Modular Neural Network (MNN) inversion for estimating the shape factor, depth, and the amplitude coefficient. The sigmoid function has been used as the activation function in the MNN inversion. The new approach has been tested first on synthetic data from different models using only one well-trained Network. The results of this approach show that the parameter values estimated by the Modular inversion are almost identical to the true parameters. Furthermore, noise analysis has been made. The inversion of noisy data produces satisfactory results for the data up to 5% of random noise. The reliability of this approach is demonstrated for real gravity field anomalies taken over a chromite deposit near Sabzevar City, Khorasan Province, Iran. Introduction Forward modeling plays an important role in gravity data interpretation. Gravity data interpretation aims mainly to estimate the depth and location of the causative target. It is known that the gravity data interpretation is non-unique where different subsurface causative targets may yield the same gravity response (anomaly); however, a priori information about the geometry of the causative target may lead to a unique solution (Roy et al., 2000; Aboud et al., 2004). Neural Networks (NNs) provide means to build mathematical models that relate input data to desired output data. The Neural Networks do not know the physics of the forward problem; they have only catalogs of the input/output pairs of the forward mapping that have been fed to it. In this paper, MNN inversion is used mainly to compute the depth and the shape factor of the causative target from a gravity anomaly. NNs can offer a unique solution, especially for noisy data, when acknowledge of a task is not available or unknown nonlinearity between input and output may exist (Bhatt and Helle, 2002; Al-Garni, 2010). Methodology and Approaches NNs can be considered as universal approximation which can approximate any function in terms of its variables. Generally, a NN is fed by a training set of a group of examples from which it learns to estimate the mapping function described by the example patterns. NNs algorithms may be divided into two main groups, which are supervised (associative) learning and unsupervised (self-organization) learning. The supervised learning is based on desired outputs. During the training, the NN tries to match the outputs with the desired values. In unsupervised learning, the method is not given any target value where the desired output of the Network is unknown. During the training, the Network performs some kind of data compression such as dimensionality reduction or clustering. The NN inversion that has been used for training is based on the MNN architecture. A MNN is characterized by a series of independent NNs moderated by some intermediary. Each independent NN serves as a module (local expert) and operates on separate inputs to accomplish some subtask of the task that the Network wishes to implement (Azam, 2000). The outputs of the modules are mediated by an integrated unit called gating Network, which does not permit to feed information back to the modules. Results and Conclusions NN inversion of gravity data over simple geometric shaped bodies such as sphere, horizontal cylinder, and vertical cylinder has been investigated in this paper. MNN inversion has been used in order to obtain three parameters: shape factor, depth, and amplitude coefficient. This approach has been tested first on synthetic data using only one welltrained Network, and then, on a field example taken from Sabzevar area, Iran. The results show the upper and bottom depths of the ore body are about 8 m and 32 m, respectively.

In this paper, Modular Neural Network (MNN) inversion has been applied for the parameters approximation of the gravity anomaly causative target. The trained Neural Network is used for estimating the amplitude coefficient and depths to the top and bottom of a finite vertical cylinder source. The results of the applied Neural Network method are compared with the results of the least-squares standard deviation method. The inverse modeling has been tested first on synthetic gravity data. The synthetic data are infected with random noise to evaluate the effect of noise on performance of the methods. Both methods show satisfactory results, with and without random noise. The MNN and least squares standard deviation approaches have been applied to two real gravity data due to two salt domes from Iran and USA, where the results comparison shows good agreement with each other. The computed standard errors indicate the generated gravity response of the estimated parameters from MNN has better conformity with the observed gravity anomaly than the generated gravity response from the least squares method. The results of the MNN inversion show the top and bottom depths of the salt dome situated in Iran are about 24. 5 m and 63. 8 m and for the salt dome situated in USA are about 1451 m and 9263 m, respectively.

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.