Computational intelligence techniques have a great potential to solve different computational problems in engineering sciences. In this paper, modeling and simulation of down hole drilling motor using the computational intelligence methods such as artificial neural network (ANN), radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) is presented. Experimental data are used to train and test the proposed models. The results of the proposed models are compared with the experimental data. The predicated values are found to be in a good agreement with the experimental values. Also, they are very faster than the experimental measurement method. These compact models can reduce the computational time while keeping the accuracy of physics-based model and allow the fast and accurate system level simulation and modeling of industrial packages. Finally, using the proposed ANN model, which is the best proposed model, an equation to describe the nonlinear behavior of down hole drilling motor is introduced.

In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. True measurement of this parameter, carried out by laboratory measurements, is very expensive. Therefore, many researchers have attempted to find rapid and accurate alternative ways to predict this parameter (Bhatt and Helle 2002, Rezaee, Jafari et al. 2006, Hamada and Elshafei 2009, Al-Anazi and Gates 2010, Bjørlykke and Jahren 2012, Wang, Wang et al. 2013, Zerrouki, Aifa et al. 2014). Intelligent methods such as artificial neural networks (ANN) and swarm intelligence (SI) are robust tools for estimation of this parameter. Review of the literature shows that many intelligent methods for prediction of porosity have been suggested by the past researchers. In the research documented here, ANN optimized by simulated annealing algorithm (SAA), is investigated for its capability to predict porosity from log data. 

Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Porosity is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, the application of soft computing methods for data analysis called ANN optimized by SAA to estimate porosity is demonstrated. The simulated annealing algorithm was used for initial weighting of the parameters in the artificial neural network. The developed methodology was examined using real field data (Marun reservoir, Iran). 

In this paper, hybrid ANN was SAA utilized to build a prediction model for the estimation of the porosity from available data, using MATLAB environment. A dataset that includes 1356 data points was employed in current study, while 1085 data points (80%) were utilized for constructing the model and the remainder data points were utilized for assessment of degree of accuracy and robustness. The training and testing procedures of ANN-SAA model were conducted from scratch for the mentioned five datasets. The obtained mean squared error (MSE), root mean squared error (RMSE) and correlation coefficient (R) values for training datasets indicate the capability of learning the structure of data samples, whereas the results of testing dataset reveal the generalization potential and the robustness of the system modeling method. The correlations between measured and predicted values of porosity for training and testing phases are shown in Figs. 1 and 2. Also, a comparison between predicted values of porosity and measured values for data sets at training and testing phases is shown in Figs. 3 and 4.   Figure 1. Correlation between measured and predicted values of porosity for training data   Figure 2. Correlation between measured and predicted values of porosity for testing data   Figure 3. Comparison between measured and predicted values of porosity for training data   Figure 4. Comparison between measured and predicted values of porosity for testing data   As shown in Figs. 3 and 4, the results of the ANN optimized by SAA model in comparison with actual data show a good precision of the ANN optimized by SAA model.

A quantitative formulation between conventional well logs (available in all wells) and porosity eliminates the aforementioned problems and makes it possible to perform geophysical and geomechanical studies. Due to significance of calling for porosity knowledge, several researchers attempted to determine porosity through empirical correlations and/or traditional intelligent systems. Nonetheless, the quest for highest precision possible demands looking for high accuracy methods. In this study, hybrid ANN with SAA was employed in order to respond this demand. ANN-SAA model was used to formulate conventional well log data. The results indicated ANN optimized by SAA performed acceptably and it was capable of mining hidden knowledge about porosity from conventional well logs.

IntroductionMineral exploration is a process by which it is decided whether or not continuing explorations at the end of each stage t will be cost-effective or not. This decision is dependent upon many factors including technical factors, economic, social and other related factors. All new methods used in mineral exploration are meant to make this decision making more simplified. In recent years, advanced computational intelligence methods for modeling along with many other disciplines of science, including the science of mineral exploration have been used. Although the results of the application of these methods show a good performance, it is essential to determine the mineral potential in terms of geology, mineralogy, petrology and other factors for a final decision. The purpose of this paper is to provide a comprehensive set of mineral exploration research and different applications of computational intelligence techniques in this respect during the last decades.
Materials and methodsArtificial neural network and its application in mineral exploration Artificial neural network (ANN) is a series of communications between the units or nodes that try to function like neurons of the human brain (Jorjani et al., 2008). The network processing capability of communication between the units and the weights connection originates or comes from learning or are predetermined (Monjezi and Dehghani, 2008). The ANN method has been applied in different branches of mining exploration in the last decades (Brown et al., 2000; Leite and de Souza Filho, 2009; Porwal et al., 2003).
Support vector machines (SVM) and its application in mineral exploration SVM uses a set of examples with known class of information to build a linear hyperplane separating samples of different classes. This initial dataset is known as a training set and every sample within it is characterized by features upon which the classification is based (Smirnoff et al., 2008). The SVM classifier is a new method that has been applied in mining exploration in recent years, for example for separating alterations in initial stages of mining exploration (Abbaszadeh et al., 2013).
Neuro-fuzzy methods and its application in mineral exploration
The base of fuzzy logic is to make flexible borders between different samples. By applying this method with other methods, we can improve their performance. The adaptive neuro-fuzzy inference system (ANFIS) is one of the useful approaches in this branch of intelligent methods in mining exploration. For example, we can note the use of this approach in mineral mapping (Porwal et al., 2004).
Hybrid computational intelligence methods and its application in mineral exploration
In order to improve the performance of intelligence methods, often a hybrid form of these methods and optimization algorithms is a fit option. For example, Genetic Algorithm (GA), Ant Colony Optimization and Particle Swarm Optimization (PSO) have been applied with ANN and SVM in research studies. For example, (Chatterjee et al., 2008) applied a genetic algorithm-based ANN for ore grade estimation.
ConclusionsEarth sciences in general and more specifically mineral explorations have always been a part of science that encompasses all the factors involved due to their complexity and the factors that influence them thereby making the solution very difficult or almost impossible to solve. Because of the difficulty of accurate measurement parameters and boundaries, in recent years, researchers have been trying to use modeling in order to simplify natural disasters for better evaluation. One of the models that has received a lot of attention in recent years is modeling with of computational intelligent methods. The appropriate results show the usefulness of these methods.

Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of the decisions that generally has direct and non-strict relationship with the quality of decisions. This is the most important reason that why endeavor for improving the forecasting accuracy has never been stopped in the literature. Electricity demand forecasting is one of the most challenging areas forecasting and important factors in the management of energy systems and economic performance. Determining the level of electricity demand is essential for careful planning and implementation of the necessary policies. For this reason electricity demand forecasting is important for financial and operational managers of electricity distribution. The unique feature of the electricity which makes it more difficult forecasting in comparison with other commodity is the impossibility of storing it in order to use in the future. In other words, the production and consumption of electricity should be taken simultaneously. It has caused to create a high level of complexity and ambiguity in electricity markets data. Computational intelligence and soft computing approaches are among the most precise and useful methods for modeling the complexity and uncertainty in data. In this paper a soft intelligent method by combining mentioned methods is proposed in order to electricity demand forecasting. The main idea of the proposed model is to simultaneously use advantages of these models in modeling complex and ambiguous systems. Empirical results indicate that proposed model can achieve more accurate results rather than its component (Seasonal auto-regressive Integrated Moving Average models, artificial neural network) and also other current single forecasting methods such as classic regression, Seasonal Auto-Regressive Integrated Moving Average-fuzzy models and support vector machine

Nowadays, electricity load forecasting, as one of the most important areas, plays a crucial role in the economic process. What separates electricity from other commodities is the impossibility of storing it on a large scale and cost-effective construction of new power generation and distribution plants. Also, the existence of seasonality, nonlinear complexity, and ambiguity pattern in electricity data set makes it more difficult to forecast by using the traditional methods. Therefore, new models, computational intelligence and soft computing tools and combining models are the most accurate and widely used methods for modeling the complexity and uncertainty in the data set. In this paper, a parallel optimal hybrid model using computational intelligence tools and soft computations is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of the individual models in the modeling of complex systems in a structure and elimination of  the limitations of them, simultaneously. The experimental results indicate that the proposed hybrid model has a higher performance accuracy in comparison to iterative suboptimal hybrid models and its computational cost is lower than the other hybrid models; also, the proposed model can achieve more accurate results, as compared with its component and some other seasonal hybrid models.

More than two decades ago, the balanced scorecard method was proposed to control and monitor the strategy of organizations. The most important outcome of this method is the strategy map. The causal relations among strategic goals (SGs) are established in this map which can help managers in decision making process. To have a precise strategy map, it is necessary to estimate the strengths of each causal relation correctly. So, the estimation of causal coefficients has attracted research interest in forming strategy maps. In this way, DEMATEL and Delphi are two well-known methods that are based on the experts’ opinion. However, these opinions are not exact in the complex business fields; so, the computational intelligence (CI) algorithms have been employed for more precise estimation of causality coefficients. In this study, the relations among SGs and their coefficients have been provided by the experts of a banking institution as the input of the proposed method. The main purpose of this study is to improve the precision of causal coefficients using a CI-based algorithm. For this purpose, the strategy map is decomposed into multiple fuzzy cognitive maps (FCMs) and then, the gravitational search algorithm (GSA) is employed for FCM training. In this way, two objective functions are used for determining the optimal value of causality coefficients. The first objective function is employed for reducing error in the prediction of SG realization percentages. The second objective function keeps causal coefficients in the intervals determined by the experts. Experimental results show that the total error of proposed model is lower than the expert-based model. In addition, GSA performs better than the following algorithms in finding the global optimum point in this real-world case study: particle swarm optimization and ant colony optimization.

Nowadays, saving time and economy of each country requires proper planning, decision making, and rational forecasts in different areas. One of the most well-known areas that has received a lot of attention is electricity forecasting. The features of the electricity which makes it distinguished from other commodities are the impossibility of storing it and the existence of seasonality and nonlinear and ambiguity pattern in electricity data set. These features of the electricity makes it more difficult to forecast using traditional methods. Therefore, in this paper, a parallel optimal hybrid model using seasonal linear and nonlinear methods is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of individual models in the modeling of complex systems in a structure, simultaneously. Experimental results indicate that in this method due to the use of a direct weighting method, the computational cost of modeling it is significantly lower than other parallel hybrid methods.

Employees’ intelligence is one of the crucial factors in staffing and job enrichment. An optimal functioning of market rules is in turn determined by the quality of the sociological environment, by its cultural and moral values. Identify the relationship between personality characteristics and ethical principle has effect on organizational effectiveness. This research is a descriptive survey. This paper aims to explain and clarify the inter-relationships and the interaction effects among business ethics and employees’ intelligence in international companies in Iran. Research variables were identified by interview with experts and review research literature. Based on key factors and relations derived from the literature and our semi structured interviews, our Hypotheses were formulated. The survey sample population consisted of 397 randomly taken employees who work in international companies in Iran, according to a cluster random sampling skim. Data accumulated through questionnaires were run through the Cronbach alpha reliability test. The structure and content of our questionnaire were discussed with the experts in mentioned semi-structured interviews and some modifications were made to justify the validity of our measuring instrument. An important part of the results revealed business ethics relates directly with cultural intelligence (r=0.55) and emotional intelligence (r=0.63(but business ethics doesn’t relate with logical intelligence. Also, Logical intelligence does not relate with cultural and emotional intelligence. Our study results indicated that although there is a positive relationship between cultural intelligence and emotional intelligence, logical intelligence does not positively relate to cultural and emotional intelligence. It means that each employee with logical intelligence does not necessarily have cultural and emotional intelligence.

Recent advances in wireless electronic and communications provide us the ability to build small, economical sensors with low power consumption and many diverse applications. Limited energy capacity of sensors is a huge challenge that will affect these networks. Clustering has been used as a well-known method to handle this challenge. To find appropriate location of cluster's heads, imperialist competitive algorithm as an emerging topic in computational intelligence has been used. Cluster's heads are connected in a three-level model so that cluster heads with low energy capacity and far from station are considered as level three indirectly communicating with base station. This eventually increases lifetime of wireless sensor networks.

The main objective of this paper is to use a computational intelligence algorithm for preparing a mapping map that categorizes different patterns of identification of infected areas and changes in radiation pollution. In this paper, the use of the fuzzy inference system has been proposed to determine the degree of radiation contamination in the regions. The study uses ultra-high resolution spectrometry data to detect uranium. The research area includes well-known uranium deposits, including LambaPur-Peddagattu, Chitrial and Koppunuru. The high-resolution Spectrometry data collected for uranium exploration was used to estimate the average absorption rate in the air due to the distribution of females (potassium per cent and uranium and thorium per million) in these areas. Mamdanichr('39')s Fuzzy Inference System has also been used to determine the amount of radiation contamination in each region. The results showed that the efficiency of this method was 76% accurate for the detection of three levels of radiation contamination (no radiation contamination, low radiation exposure, medium radiation and high radiation pollution) and 89% for the overall identification of contaminated areas from non-polluted areas.