Tumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI images. Images of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an Artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s.

Recent studies have shown significant success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, as different models must be built independently for each pair of image domains. To overcome this limitation, we propose a model based on generative adversarial networks (GAN), which is a new and scalable approach that can perform image-to-image translation for multiple domains using only a single model. The integrated GAN model architecture allows simultaneous training of multiple datasets with different domains in one network. This leads to the superior quality of images translated by GAN compared to existing models, as well as the new ability to flexibly translate an input image to any desired domain. We experimentally demonstrate the effectiveness of our approach in facial feature transfer and facial expression composite tasks.

Objective The aim of this study was to propose an efficient algorithm to predict antimicrobial peptides using Artificial Intelligence Algorithms. Materials and methods In this study, an updated AMP and non-AMP data set including physico-chemical characteristics at the level of amino acids and protein sequence in three animal species and humans was extracted. After data exploration and data pre-processing steps, four methods Supervised learning including Decision Tree, Random Forest, Naive Bayes and SVM on the AMP dataset with 10-fold cross-validation to build models and predict the AMP label using the evaluation criteria of specificity, sensitivity, rate Accuracy, precision, recall, F1 score and area under the rock curve (AUC) were evaluated. Results In this study, using an up-to-date dataset, a machine learning model has been successfully trained to predict antimicrobial peptides. A comprehensive set of features has been subjected to feature selection to identify key features of antimicrobial peptides. After selecting the feature, among the different generated models, the model based on the RF model classifier showed the best performance with Accuracy (95 percent), Precision (96 percent), Recall (95 percent), F1 Score (95 percent). the four classification of Algorithms, Random Forest algorithm and SVM are the most accurate. The Decision Tree classification algorithm had the least accuracy. Conclusions According to the obtained results, the proposed RF model has a better performance than other models for AMP prediction. This model predicted some peptides as peptides with antimicrobial properties. This predictive approach can be useful in extracting AMPs with antimicrobial properties from AMP libraries in useful clinical applications before moving on to experimental studies. On the other hand, several features in the final selection properties indicate that these features are critical determinants of peptide properties and should be considered in the development of models to predict peptide activity. The executable code is available in the attached file.

In this paper a simple and effective expert system to predict random data fluctuation in short-term period is established. Evaluation process includes introducing Fourier series, Markov chain model prediction and comparison (Gray) combined with the model prediction Gray- Fourier- Markov that the mixed results, to create an expert system predicted with Artificial Intelligence, made this model to predict the effectiveness of random fluctuation in most data management programs to increase. The outcome of this study introduced Artificial Intelligence Algorithms that help detect that the computer environment to create a system that experts predict the short-term and unstable situation happens correctly and accurately predict. To test the effectiveness of the algorithm presented studies (Chen Tzay len,2008), and predicted data of tourism demand for Iran model is used. Results for the two countries show output model has high accuracy.

Introduction Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et. al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of  the most important factors (David and Nicholas, 1994). Sand areas are one of the desert  landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et. al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding  and  interaction with the environment and ultimately its sustainable management  and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert Algorithms and Artificial Intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two Algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed. Materials and Methods The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and Artificial Intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two Algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output Artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices. Results and Discussion In evaluating the results of modeling Algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.

Pore pressure is one of the most important reservoir-drilling parameters and knowledge of this pressure is essential for drilling costs, well safety and prevention of potential hazards. Research has shown that experimental equations have good performance accuracy only for certain regions. Most of these experimental equations have been compiled and developed based on a limited data set. Therefore, these correlations are valid in the range of changes in the parameters of those fields and are not valid for other areas. Therefore, Artificial intelligent methods have given way to empirical equations. In this study, 2827 data related to three wells from one of the oil fields located in the southwest of Iran have been used. The input variables used in this paper to predict the pore pressure include 9 variables that have been selected using the feature selection method. In this study, 4 Artificial Intelligence Algorithms include,random forest algorithm, support vector regression algorithm, Artificial neural network algorithm and decision tree algorithm have been used to predict the pore pressure. After reviewing the results, it was found that the performance accuracy of the decision tree algorithm is higher than the other three Algorithms (performance accuracy for the entire data set including R2 = 0. 9985 and RMSE = 14. 460 psi). Among the advantages of this algorithm compared to other Algorithms are the best results without the need for statistical knowledge, separation of unnecessary data, short time to prepare data and reduction of relative error by finding the main node of the decision maker and analyzing it. Therefore, it can be concluded that with the development of this technique, it is possible to have high performance accuracy for a small amount of data from each field.

Introduction: Water quality is one of the most important factors in healthy living and human life. In this sense, some of the water quality parameters should be controlled in maintaining the human health and welfare. In today’, s industrial world, most of the global natural water sources, including those in Iran, contain impurities such as the TDS. Numerous factors that are includes include cations such as sodium ion (Na+), potassium ion (K+), calcium ion (Ca+2), and magnesium ion (Mg+2) and anions such as chloride ion (Cl-) and bicarbonate ion (HCO3-) with sulphate ion (SO42-) affect the concentration of these parameters in natural water systems. The total dissolved solids (TDS) is one of its most important factors,Many water resources development programs will be implemented to identify these factors. Accurate prediction of water quality parameters is a basic need for water quality management, human health, public consumption and household consumption. In the last decades, Artificial Intelligence (AI) techniques have become viable and popular due to their advantages, and have been widely developed in solving a variety of environmental engineering and water quality engineering problems. Methods: For the estimation of water quality parameters (WQPs), Singh et al. (2011) utilized the clustering method, or support vector clustering (SVC), to optimize surface water quality monitoring in the city of Lucknow, India. The overall view of the water quality index of their study area revealed that most of the study area come under highly to very highly polluted zones. Tan et al. (2012) predicted phosphorus values in China with the least square support vector regression (LSSVR) method. They compared the efficiency of the LSSVR method with neural networks of the radial basis function (RBF) and back-propagation (BP). Experimental results showed that the small sample case with noise, LSSVM method was better than multi-layer BP and RBF neural network and is able to better meet the requirements of water quality prediction. Liu et al. (2013) addressed WQPs prediction in aquaculture employing the GP and real-value genetic algorithm-SVM (RGA-SVR). They used the GA to modify the coefficients of the SVR method. The results showed the superiority of the RGA-SVR algorithm over other methods based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Ghavidel and Montaseri (2014) employed ANN, GEP, and ANFIS with grid partition as well as ANFIS with subtractive clustering (ANFIS-SC) to predict TDS values of the Zarinehroud basin, Iran. A comparison was made between the above AI approaches, and the results demonstrated the superiority of GEP over the other intelligent models. Abyane (2014) compared Artificial neural network (ANN) with multivariate linear regression (MLR) for prediction of BOD and COD in the wastewater treatment plant. In their study, ANN could predict BOD and COD parameters with higher precision than MLR. Results: Due to complex characteristics of time series WQPs, a standalone model can hardly satisfy the estimation accuracy requirements. Therefore, the hybrid models combined with different single models will be an effective way to improve the WQPs estimation accuracy. This study proposes a new and accurate hybrid model for predicting WQP (i. e., TDS) using ions at Varand and Garmrood, two hydrometric stations of Tajan basin, Iran. The proposed WQP estimating framework was developed based on the combination of a data pre-processing Algorithms (i. e., EEMD) with two AI-based models that was not addressed by the literature related to the WQPs modelling. Acceptance and reliability of proposed hybridized and standalone models (e. g., Artificial neural networks (ANN), EEMD-ANN, support vector machine (SVM) and EEMD-SVM) using five performance criteria and visual diagrams were evaluated. Comparison of results between independent and hybrid models showed that EEMD data pre-processing algorithm can increase the performance of the hybrid SVM model for estimating the TDS quality parameter in both training and testing stages at both considered hydrometric stations. For example, the EEMD-SVM model with RMSE = 20. 23 for the training phase and RMSE = 27. 29 for the test phase at Varand station and RMSE = 45. 26 for the training phase and RMSE = 40. 06 for the test phase at Garmrood station has performed better than other hybrid and standalone models. In general, the proposed hybridized model of support vector machines based on EEMD data pre-processing algorithm can be proposed as a superior model to decision makers for planning and management in the field of river water quality detection and determination.