Cry as the only way of communication of babies with the surrounding environment can be happened for many reasons such as diseases, suffocation, hunger, cold and heat feeling, pain and etc. So, the analysis and detection of its source are very important for parents and health care providers. So the present study designed with the aim to test the performance of Neural Networks in the identification of the source of babies crying. The present study combines the Genetic Algorithm and Artificial Neural Network with (Linear Predictive Coding) LPC and MFCC (Mel-Frequency Cepstral Coefficients) to classify the babies crying. The results of this study indicate the superiority of the proposed method compared to the other previous methods. This method could achieve the highest accuracy in the classification of newborns crying among the previous studies. Developing methods for classification audio signal analysis are promising and can be effectively applied in different areas such as babies crying.

In this study, a 4×4 square Network structure made of titanium has been optimized under tensile force using relevant parameters. The Network structure is a quadrilateral structure with a side length of L and ϴ, and the fracture order of the walls has been compared using MATLAB software and simulation with Abaqus software, and the results of the fracture order of the structure match each other. In the present study, the objective function in optimization is to increase energy absorption and minimize the maximum stress, and the effect of parameters such as side lengths and various angles in this structure has been investigated. 100 different cases have been obtained for values of L and ϴ with output of area under the curve (energy absorbed) and maximum stress and strain using MATLAB software. With input data (L and ϴ) and output data (energy absorbed and maximum stress), a Neural Network has been trained and a regression model has been used in the Neural Network to achieve a prediction accuracy of over 99%, which is a high level of accuracy. The relationship function between input and output of the Neural Network has been obtained using MATLAB software, and the optimization of this 4×4 Network structure has been carried out using the Genetic Algorithm. The objective function in this study is to increase energy absorption and minimize the maximum stress so that the Network structure has the highest strength considering the examined parameters.

In the rubber industry, the process of designing rubber compound is of great importance due to the impact on product specifications. The good performance of this process is a competitive advantage for manufacturers in this industry. The process of designing a rubber compound includes a set of activities related to selecting the best amount of raw materials to prepare a composition with the desired physical and mechanical properties. Currently, the most common method for designing a rubber compound is the experimental method based on trial and errors. This method is time consuming and expensive. In addition, the obtained combination is not necessarily the best combination. To improve the performance of the rubber compound we need to design the desired process, this research presented using a combination of Artificial Neural Network and Genetic Algorithm, with an approach to reduce time and cost, while increasing accuracy. In this method, the behavior of the rubber compound was modeled with Artificial Neural Network. Then, using Genetic Algorithm as a quick search technique.The optimal values of the four raw materials such as carbon, sulfur, oil and accelerator; in order to determine the specified value of the two characteristics .abrasion and rubber modulus at 300% elasticity at the lowest price. To evaluate the method, several samples of rubber compound designed with two method. The results showed that the Artificial Neural Network model has the ability to predict the two characteristics of abrasion and modulus based on the four mentioned raw materials in the trained range with high accuracy. In addition, average results for Genetic Algorithm, is a price of 17% less and a design accuracy of 84.5% more than experimental method. The design speed with this method is 454 times higher than the experimental design speed. Based on the results, by designing the rubber compound with the integration of Artificial intelligence and Genetic Algorithms has a better performance than the experimental method.

This article aims to apply the Genetic Algorithm (GA) technique to estimate natural gas demand in Iran in terms of the socio-economic indicators. GA demand estimation models are developed in two forms (exponential and linear) and applied to estimate the natural gas demand values based on population, gross domestic product (GDP), import and export data. Two different scenarios are designed for future forecasting of each input variable (Regression Methods and Artificial Neural Networks). Natural gas consumption in Iran from 1981 to 2005 is considered as the case for the present study. The available data are partly used for finding the optimal, or near optimal, values of the weighting parameters (1981-1999) and partly for testing the models (2000–2005).Validations of the models show that GA models are in high agreement with the observed data (i.e. MAPE errors for GAlinear and GAexponential were 4.13% and 3.89%, respectively). Natural gas demand in Iran is forecasted up to year 2030.

The main objective of this research was to model an industrial hydrogen plant based on steam methane reforming using Artificial Neural Network (ANN). Two different ANN Networks were developed for prediction of hydrogen production rate and corresponding energy consumption using 20 operating parameters as inputs of both Networks. The obtained ANN resuls indicated a very close compatibility with average absolute error, average relative error, and probable error of 2.14, 1.21, and 2.9 for hydrogen production, 0.37, 0.84 and 0.55 for energy consumption, respectively. Based on sensitivity analysis, temperature of synthesized gas from reformer was identified as the most important parameter influencing the hydrogen production, and energy consumption was affected the most by the tail gas flow rate. After ANN modeling, Genetic Algorithm (GA) was used to optimize plant operating conditions. In this regard, plant gross profit was considered as objective function and GA optimization resulted in the profit of $42.56/h which is 25% higher than actual average profit.

Rutting is one of the major deteriorations of asphalt pavement, significantly impacts road safety and service quality. Prediction models are necessary to prevent and control the damage caused by this deterioration in the pavement management system. In this study, using the Artificial Neural Network Algorithm, models have been developed to predict the amount of rutting deterioration using the long-term pavement performance (LTPP) database. These models have been developed for wet freeze, dry freeze, and dry no-freeze climates. Since proper accuracy and simplicity are the most important features of a prediction model, using the NSGA ІІ-MLP multi-objective optimization method, the more important variables in predicting rutting deterioration are identified and selected as the model input. Then, using traffic, climatic and structural variables selected from the Genetic Algorithm, rutting deterioration prediction models were developed. The coefficient of determination and the mean squared error for the model made in the wet freeze zone and the model of dry freeze and dry no freeze zones are equal to 0. 96, 2.05, 0.94 and 3.45, respectively. Also, by performing sensitivity analysis, the effect of input data of each model on rutting deterioration was determined. The cumulative maximum and minimum daily temperature difference per year, pavement age, asphalt layer thickness, annual equivalent single axle loads, and bitumen penetration are the most impactful variables that have the greatest impact on rutting deterioration.

Coagulation process is of vital importance for achieving good performance in water pre-treatment units. It usually implements optimum operating conditions that results in highest turbidity removal. The choice, dosage, pH and rate of mixing of coagulant and coagulant-aid are the variables that define optimum operating conditions. In this research, coagulation process of Fajr petrochemical company has been studied. Several jar tests are conducted to determine performance of Alum, Ferric chloride and Poly aluminum chloride as coagulants and anionic poly electrolyte and wheat starch as coagulant-aids at different pH and mixing rates.Optimum operating conditions data obtained in jar tests are used in developing a Neural Network model. This model allows operators to have an estimation of the operating conditions. The success of water pre-treatment depends on fixed feed water quality without frequent need to run jar tests. The predicted result of this Network for operating parameters, presented the high consistency with operating data of industrial unit. The maximum relative error for prediction of turbidity is 0.6 % and for total hardness is 2.3 %.The number of neurons of Neural Network hidden layer is optimized and the developed model is validated and tested using a fraction of data that was not utilized in Network training. The estimated operating condition for a given feed water quality is implemented in practice and the result of pre-treated water quality matched the expected quality.

This paper proposes a hybrid approach based on the Artificial Neural Network and Genetic Algorithm to optimize surface roughness at the abrasive water jet (AWJ) cutting of glass material. At first, Artificial Neural Network (ANN) was developed in order to model and predict surface roughness by considering the controllable cutting parameters such as water pressure, abrasive flow rate, jet traverse rate and stand of distance. Then the results of the Neural Network were compared with corresponding experimental tests. According to the obtained results, it was shown that the ANN model is able to present a predictive model of the process in order to estimate the surface roughness successfully. After that, ANN model was combined by Genetic Algorithm to obtain suitable machining parameters yield to minimal surface roughness. Finally, obtained results showed that, utilized hybrid technique in this paper was employed properly for optimizing AWJ cutting process.