Regression

Open Access Article

1 - Relation of emotional intelligence and trends of e-learning in organizations (Case study: Bank employees Alborz Province)
Somayeh Ahari

Today the world is moving toward a virtual space. Whereas the business, health, education, and many of the other activities would devote time and money in the past, by appearing the virtual space most of them have suitable function today. Educating is an inseparable par More

Today the world is moving toward a virtual space. Whereas the business, health, education, and many of the other activities would devote time and money in the past, by appearing the virtual space most of them have suitable function today. Educating is an inseparable part of human which is entered to electronic world as individuals and organizations are gradually moving towards this type of educating. In this research, the relationship between emotional intelligence and trends of e-learning in organizations has been studied. The purpose of this study is applied and the research method is descriptive- survey. The questionnaire method was used to collect data. The Cronbach’s Alpha coefficient of the first questionnaire was obtained 0.76 and the second questionnaire was 0.83 which confirm the validity of the questionnaires. The content validity to test questions was used and for this purpose, experts, academics and experts were used. The population of this research is all the Alborz Province Bank Employees which 80 individuals were selected as sample size and sampling is done randomly. To analyze data the Spearman correlation coefficient and multiple regression was used. Finally all the theories proved. Self-motivation and self-awareness variables were determined as predictor variables which can entry into the ultimate regression equation to describe the tendency of employees to accept changes in their electronic education. Manuscript profile

Open Access Article

2 - Integrating data envelopment analysis and decision tree models In order to evaluate information technology-based units
Amir Amini

In order to evaluate the performance and desirability of the activities of its units each organization needs an evaluation system to assess this desirability and it is more important for financial institutions, including information technology-based companies. Data enve More

In order to evaluate the performance and desirability of the activities of its units each organization needs an evaluation system to assess this desirability and it is more important for financial institutions, including information technology-based companies. Data envelopment analysis (DEA) is a non-parametric method to measure the effectiveness and efficiency of decision-making units (DMUs). On the other hand, data mining technique allows DMUs to explore and discover meaningful information, which had previously been hidden in large databases. . This paper presents a general framework for combining DEA and regression tree for evaluating the effectiveness and efficiency of the DMUs. Resulting hybrid model is a set of rules that can be used by policy makers to discover reasons behind efficient and inefficient DMUs. Using the proposed method for examining factors related to productivity, a sample of 18 branches of Iran insurance in Tehran was elected as a case study. After modeling based on advanced model the input oriented LVM model with weak disposability in data envelopment analysis was calculated using undesirable output, and by use of decision tree technique deals with extracting and discovering the rules for the cause of increased productivity and reduced productivity. Manuscript profile

Open Access Article

3 - Integrating Data Envelopment Analysis and Decision Tree Models in Order to Evaluate Information Technology-Based Units
Amir Amini ali alinezhad somaye shafaghizade

In order to evaluate the performance and desirability of the activities of its units each organization needs an evaluation system to assess this desirability and it is more important for financial institutions, including information technology-based companies. Data enve More

In order to evaluate the performance and desirability of the activities of its units each organization needs an evaluation system to assess this desirability and it is more important for financial institutions, including information technology-based companies. Data envelopment analysis (DEA) is a non-parametric method to measure the effectiveness and efficiency of decision-making units (DMUs). On the other hand, data mining technique allows DMUs to explore and discover meaningful information, which had previously been hidden in large databases. . This paper presents a general framework for combining DEA and regression tree for evaluating the effectiveness and efficiency of the DMUs. Resulting hybrid model is a set of rules that can be used by policy makers to discover reasons behind efficient and inefficient DMUs. Using the proposed method for examining factors related to productivity, a sample of 18 branches of Iran insurance in Tehran was elected as a case study. After modeling based on advanced model the input oriented LVM model with weak disposability in data envelopment analysis was calculated using undesirable output, and by use of decision tree technique deals with extracting and discovering the rules for the cause of increased productivity and reduced productivity. Manuscript profile

Open Access Article

4 - Urban Growth and Its Influencing Factors

The urbanization process and urban growth in different parts of the world result from the interconnected interactions between factors and various socio-economic, political, technological, geographical, cultural, global and local issues. Accordingly, identifying the driv More

The urbanization process and urban growth in different parts of the world result from the interconnected interactions between factors and various socio-economic, political, technological, geographical, cultural, global and local issues. Accordingly, identifying the driving and shaping factors of the growth of cities is vital for urban planning and sustainable development, especially in developing countries, which is used as a means of predicting future trends, controlling and guiding the growth of the city, organizing the future development of the city and eventually efficiently and purposefully managing the city. The main objective of this study is to identify the driving and shaping factors of the biophysical and socio-economic growth of Tehran metropolis. In order to achieve this goal, five main stages are taken into consideration, including the production of land use maps, land cover to study the land use spatial-temporal changes, land cover in Tehran and its surrounding areas using satellite images, identifying driving factors of urban growth using logistic regression, using a factor ecology approach to investigate the human factors effective on Tehran landscape, calculating spatial metrics for quantization of the structure and characteristics of the landscape pattern in Tehran metropolis using Fragstatas software and investigating the relationship between urban growth pattern and social areas using multivariate regression. The results indicate that the distance from the roads and the commercial center of Tehran in the period 2000 to 2014 is the most significant biophysical factor shaping the growth pattern of Tehran metropolis, and among the obtained five socioeconomic components, the population density and employment components have the most effect on urban growth pattern in Tehran metropolis. Manuscript profile

Open Access Article

5 - بررسی کارایی مدل هیبریدی هالت-وینترز موجکی (WHW)در شبیه¬سازی تراز سطح ایستابی آبخوان ساحلی ارومیه
Mohammad Nakhaei Farshad Alijani Ali Mirarabi HamidReza Nasseri

For management and planning valuable groundwater resources, it is very important to predict groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for More

For management and planning valuable groundwater resources, it is very important to predict groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for groundwater forecasting. A monthly data set of 16 years consisting of groundwater level fluctuations was used in two observation wells of Urmieh coastal aquifer. In the WHW, the dataset was converted into several sub-dataset with different time scales. Then, the sub-series were used in the HW model as inputs. Subsequently, the performance of the WHW model was compared with ARIMA, HW, and SARIMA as linear models and neural network models (ANN) and Support Vector Regression (SVR) as nonlinear models. The results showed that the NSE and RMSE values of the WHW model were upgraded up to 30% and 60% respectively, in comparison with linear models. The WHW hybrid model also has the same performance compared to nonlinear models. This research reflects that if there are multiple seasonal fluctuations in the groundwater time series, the performance of the WHW model compared with linear models will be more accurate. Manuscript profile

Open Access Article

6 - Estimating the LNAPL level elevation in oil-contaminated aquifer by using of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)
فاطمه ابراهیمی Mohammad Nakhaei HamidReza Nasseri

One of the main concerns in the aquifers adjacent to oil facilities is the leakage of LNAPLs. Since remediation processes costly and time consuming, so the first step in these systems is determining design goals. Often the most important goal of these systems is to maxi More

One of the main concerns in the aquifers adjacent to oil facilities is the leakage of LNAPLs. Since remediation processes costly and time consuming, so the first step in these systems is determining design goals. Often the most important goal of these systems is to maximize pollutant removal and minimize the cost. Identifying the thickness of LNAPL and its fluctuations can determine the type of recovery method and thus can be effective on the amount of removal and the cost of the implementation. In this study, three methods of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and multivariate linear regression (MLR) were used to estimate and predict the LNAPL level. Input variables are groundwater level elevation and discharge rate of LNAPL and the output variable is the LNAPL level elevation. The results of the three models were analyzed by statistical parameters and it was determined that GEP technique has better results and could be used successfully in predicting LNAPL level fluctuations in recovery processes. Also, the GEP model provides an equation for predicting the LNAPL level that can be used in the field to predict the elevation of the LNAPL level. Manuscript profile

Open Access Article

7 - A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
hadi shakibian Jalaledin Nasiri

10.52547/jist.19789.10.40.259

An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from c More

An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average. Manuscript profile

Open Access Article

8 - Identifying Effective Critical Success Factors in Electronic Supply Chain Management (Case study: Small and Medium-sized Enterprises)
morteza jabale Sajad Ranjkesh Mohammad Reza Ghiasy Hossein Ali Hassan pour

10.7508/jstpi.2016.03.004

Electronic supply chain management (e-SCM) is one of the competitive industrial fields of the country. Availability of Internet network and information in supply chain management, gives the capability and competitiveness to small and medium-sized enterprises (SMEs) and More

Electronic supply chain management (e-SCM) is one of the competitive industrial fields of the country. Availability of Internet network and information in supply chain management, gives the capability and competitiveness to small and medium-sized enterprises (SMEs) and also helps them for resilience in today`s competitions. Electronic supply chain management plays an important role in improving supply chain factors, information authenticity, reducing bullwhip effect and delivering products to the customers in right time and right place with high speed and accuracy. Attending to these factors can be helpful in making suitable decisions to promote enterprise by managers. The goals of this paper is to identifying effective critical success factors in electronic supply chain management and survey their effects on e-SCM implementation in small and medium-sized enterprises. To achieve this goal, thirteen questions has been developed and a multi-variable linear regression technique in SPSS used for analyzing questionnaire. A Multi regression analyses a dependent variable, which linearly relates to some independent controlled variables. Independent variables Xk are critical sub-factors for main factors and dependent variable Y indicate implementation of electronic supply chain management. Result analysis shows that four critical success factors are the most important factors playing role in e-SCM implementation in small and medium-sized enterprises, which are supporting of capacity of supplier, organizational scale, compatibility of e-SCM and CEO’s creative level. Management by focusing on four factors will make to improvement and forward upgrades in future. Manuscript profile

Open Access Article

9 - Identifying factors affecting bancassurance desks efficiency, Case study: Bank MELLAT and MA Insurance
حمیدرضا نورعلیزاده علی بنیادی نایینی Mohsen Sadeghi

A majority of studies around efficiency assessment just estimate it. Indeed, they don’t address the roots of inefficiency of DMUs. These shortcomings could be resulted from silo approaches conducted by researchers. Our research aims address this gap. We propose a two-st More

A majority of studies around efficiency assessment just estimate it. Indeed, they don’t address the roots of inefficiency of DMUs. These shortcomings could be resulted from silo approaches conducted by researchers. Our research aims address this gap. We propose a two-stage procedure in which first, the efficiency of bancassurance desks are estimated by DEA (Data Envelopment Analysis) and then, roots of inefficiency are identified by a survey designed based on enabler section of European Foundation for Quality Management model (EFQM) and conducted among bancassurance desks employees. This study is applied in terms of purpose, and mathematical as well as statistical in terms of data analysis. The population of this study consisted of all bancassurance desks of BANK MELLAT in 2015. After efficient and inefficient branches of bancassurance have been estimated and ranked, in order to identify factors affecting efficiency, a questionnaire designed according with criteria of EFQM. Reliability of questionnaire was tested by Cronbach's alpha coefficient, and normal distribution of data was tested by using the Kolmogorov-Smirnov test. The effects of variables (factors) on efficiency were evaluated via regression analysis, then obtained answers were tested for research questions. The results of ranking the impact of factors on the efficiency of bancassurance desks showed that employees, partnerships and resources, products and services, strategy, and leadership are the efficient factors, respectively. Manuscript profile

Open Access Article

10 - Development of Multivariate Regression Relationship Between Factors Affecting Unemployment Rate
roya soltani mahnaz ebrahimi sadrabadi Ali Mohammad Kimiagari

In this research, the multi-variable linear regression relationship has been developed among the important factors influencing unemployment rate. The seasonal data is from 1394 to 1394, which is compiled from reliable economic data bases of country. Independent variable More

In this research, the multi-variable linear regression relationship has been developed among the important factors influencing unemployment rate. The seasonal data is from 1394 to 1394, which is compiled from reliable economic data bases of country. Independent variables are: net foreign assets of the banking system (billion rials), net debt of the public sector to the banking system (billion rials), liquidity in terms of its constituent parts (billion rials), rate of dollar (rials), economic participation rate, average inflation rate, The average annual interest rate on state-owned banks, the percentage of jobseekers (65-15). The results indicate that there is a negative and significant relationship between unemployment rate and average inflation rate and economic participation rate, while the net debt of the public sector has had a positive and significant relationship with the banking system and unemployment rate. The greatest negative effects on unemployment rate are the rate of economic participation and the greatest positive impact on the unemployment rate is the net debt of the public sector to the banking system. Manuscript profile

Open Access Article

11 - Assessing the effect of macroeconomic shocks on systemic risk of the banking system using the SVAR model in Iran
ali ostadhashemi seyed jalal sharif ali Souri

In this study, we have used the total capital market index (TEDPIX) as an index of the real sector of the economy and the index of banks and credit financial institutions as an index that explains the developments of the banking system. Also, oil revenues, exchange rate More

In this study, we have used the total capital market index (TEDPIX) as an index of the real sector of the economy and the index of banks and credit financial institutions as an index that explains the developments of the banking system. Also, oil revenues, exchange rate uncertainty, tax revenues, liquidity, nominal interest rates, inflation uncertainty and GDP have been used as macroeconomic variables in the research period (1370-1396). To estimate the systemic risk of the banking system, the quarterly data of the banks' index is used and the value at risk of the return of the seasonal data of the index is estimated using an exponential GARCH model. In order to model the interaction of macroeconomic variables and systemic risk of the banking system, an unrestricted vector autoregression (VAR) model was estimated and then using instantaneous impact functions and based on Chulsky analysis, systemic risk response to other variables was investigated and analyzed. In order to identify the channels of impact of economic shocks on the systemic risk of the banking system, based on the structures of the Iranian economy, a structural vector autoregression (SVAR) model was specified and then the instantaneous impact functions were extracted and the effect of macro variable shocks on the systemic risk of the banking system was investigated. Also, the effect of systemic risk of the banking system on macroeconomic variables was investigated and analyzed using instantaneous impact functions. Finally, the interaction model of macroeconomic variables and systemic risk of the banking system was approved using the vector autoregressive model. Manuscript profile

Open Access Article

12 - Online Signature Verification in Stationary Wavelet Transform Domain
M. Valizadeh E. Kabir

In this paper, an online signature verification method using extended regression in stationary wavelet domain is presented. To calculate the similarity between two signatures by extended regression, we should equalize the time length of the corresponding signals in two More

In this paper, an online signature verification method using extended regression in stationary wavelet domain is presented. To calculate the similarity between two signatures by extended regression, we should equalize the time length of the corresponding signals in two signatures. Using all points of the signals to equalize their time length will decrease the difference between a genuine signature and its forgery. Here a new approach based on the extreme points warping of the signals is presented. This approach equalizes the time length of two signals without degrading the differences between them. Also we calculated the similarity of signatures by using the details of the signals in stationary wavelet transform, SWT, domain, which showed very good results. The proposed system was tested on SVC2004 signature database. The results were compared with the results of participant teams in the first international signature verification competition. We have gained EER=6% for skilled forgery signatures. Comparing the result, it shows that we stand in the second rank between all the participants. This system has no verification error for random forgery signatures and stands in the first rank. Our experimental results show that using SWT domain instead of time domain decreases the verification error rate by 35%. Manuscript profile

Open Access Article

13 - Correction of the Load Tracking Method in Transmission Pricing Considering Correlation Coefficients
M. T. Ameli M. Ansari

Transmission pricing has become one of the important issues of the power industry with the power industry´s restructuring. The pricing should be done fairly to achieve a healthy competitive environment. The load tracking method will be discussed in this paper. For this More

Transmission pricing has become one of the important issues of the power industry with the power industry´s restructuring. The pricing should be done fairly to achieve a healthy competitive environment. The load tracking method will be discussed in this paper. For this purpose, various operating points were made around the nominal operating point at first, using statistical data. After that, the network load flow is being calculated for each operating point and the linear regression and correlation coefficients between each producer/consumer’s generation/consumption and the following power of each line, were being achieved and the players' share of the transmission cost is being calculated by combining this coefficients. The participation coefficients were being calculated at the end for the 39 Bus IEEE test system and its results will be compared with previous methods. The Comparison of the results show that the reviewed methods cover the transmission costs, but the determined share for each player using this paper’s method, has a greater proportion with the amount of the transmission network usage. Manuscript profile

Open Access Article

14 - A Self-Learning Single Image Super-Resolution by Considering Consistency in Adjacent Pixels
M. Habibi A. Ahmadyfard H. hassanpour

In this paper, we propose a self-learning single image super-resolution. In our proposed method, adjacent pixels information in smooth area is used. Low and high-resolution pyramids are built by applying up-sampling and down-sampling techniques on input image, as traini More

In this paper, we propose a self-learning single image super-resolution. In our proposed method, adjacent pixels information in smooth area is used. Low and high-resolution pyramids are built by applying up-sampling and down-sampling techniques on input image, as training data. In training phase, we apply support vector regression (SVR) to model the relationship between the pair of low and high-resolution images. For each patch in the low-resolution image, sparse representation is extracted as a feature vector. In this paper, in order to reduce the edge blurring effects, we first separate edge pixels from non-edge pixels. In the smooth area, because of the similar colors around the each pixel, the center pixel value is determined by considering the reconstructed adjacent pixels. Experimental results show that the proposed method is quantitatively and qualitatively outperform the competitive super-resolution approaches. Manuscript profile

Open Access Article

15 - Evaluation of trend of rainfall and temperature changes and their effects on meteorological drought in Kermanshah province
Maryam Teymouri Yeganeh Liela Teymouri Yeganeh

Climate change is one of the natural features of the atmospheric cycle, which results in anomalies or fluctuations in the process of meteorological parameters such as rainfall and temperature. Also, drought is one of the weather and climate disasters, including catastro More

Climate change is one of the natural features of the atmospheric cycle, which results in anomalies or fluctuations in the process of meteorological parameters such as rainfall and temperature. Also, drought is one of the weather and climate disasters, including catastrophic events. It alternates with floods and causes significant damage each year. Lack of rainfall has different effects on groundwater, soil moisture and river flow. For this reason, the study of changes in precipitation and temperature has always been the focus of researchers in various sciences, including natural resources and the environment. In this study, using the data of Kermanshah Meteorological Organization related to 30 years of rainfall, average minimum temperature and average maximum temperature in three stations of Kermanshah, Islamabad West and Sarpol-e Zahab to assess the severity of drought each year by DIC software Using standard precipitation index (SPI) and examining the trend of temperature changes using two non-parametric Mann-Kendall tests, Sensitimator and also linear regression. In order to study the drought trend during the 30-year period, statistical software was used and the results showed that during the 30-year period, all three stations are in near normal condition. Also, the results of temperature changes using the mentioned tests indicate the increasing trend of temperature and this trend is significant at the level of 99% using two non-parametric Mann-Kendall tests. Manuscript profile

Open Access Article

16 - Breast Cancer Classification Approaches - A Comparative Analysis
Mohan Kumar Sunil Kumar Khatri Masoud Mohammadian

10.52547/jist.33395.11.41.1

Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods More

Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods for predicting cancer based on proteomic datasets and peptides have been published. The cells turns into cancerous cells because of various reasons and get spread very quickly while detrimental to normal cells. In this regard, identifying specific immunity signs for a range of cancers has recently gained a lot of interest. Accurately categorizing and compartmentalizing the breast cancer subtype is a vital job. Computerized systems built on artificial intelligence can substantially save time and reduce inaccuracy. Several strategies for predicting cancer utilizing proteomic datasets and peptides have been reported in the literature in recent years.It is critical to classify and categorize breast cancer treatments correctly. It's possible to save time while simultaneously minimizing the likelihood of mistakes using machine learning and artificial intelligence approaches. Using the Wisconsin Breast Cancer Diagnostic dataset, this study evaluates the performance of various classification methods, including SVC, ETC, KNN, LR, and RF (random forest). Breast cancer can be detected and diagnosed using a variety of measurements of data (which are discussed in detail in the article) (WBCD). The goal is to determine how well each algorithm performs in terms of precision, recall, and accuracy. The variation of each classification threshold has been tested on various algorithms and SVM turned out to be very promising. Manuscript profile

Open Access Article

17 - Porosity modeling in Azadegan oil field: a comparative study of Bayesian theory of data fusion, multi layer neural network, and multiple linear regression techniques
عطیه مظاهری طرئی حسین معماریان بهزاد تخم چی بهزاد مشیری

Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for More

Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for almost all wells, log data is available. Usually these logs are used to estimate porosity. The porosity value obtained from this method is influenced by factors such as temperature, pressure, fluid type, and amount of hydrocarbons in shale formations. Thus it is slightly different from the exact value of porosity. Thus, estimates are prone to error and uncertainty. One of the best and yet most practical ways to reduce the amount of uncertainty in measurement is using various sources and data fusion techniques. The main benefit of these techniques is that they increase confidence and reduce risk and error in decision making. In this paper, in order to determine porosity values, data from four wells located in Azadegan oil field are used. First, multilayer neural network and multiple linear regressions are used to estimate the values and then the results of these techniques are compared with a data fusion method (Bayesian theory). To check if it would be possible to generalize these three methods on other data, the porosity parameter of another independent well in this field is also estimated by using these techniques. Number of input variables to estimate porosity in both the neural network and the multiple linear regressions methods is 7, and in the data fusion technique, a maximum of 7 input variables is used. Finally, by comparing the results of the three methods, it is concluded that the data fusion technique (Bayesian theory) is a considerably more accurate technique than multilayer neural network, and multiple linear regression, when it comes to porosity value estimation; Such that the results are correlated with the ground truth greater than 90%. Manuscript profile

Open Access Article

18 - Estimation of formation water saturation using cluster analysis, piecewise nonlinear regression and Monte Carlo simulation in a carbonate reservoir, south-west Iran
Hadi Fattahi zahra Varmazyari Mostafa Yosefi rad

Estimation of formation water saturation (Sw) using log data is an important approach in the oil exploration and characterization of a hydrocarbon reservoir. Therefore, it seems that the proper prediction/simulation of Sw is essential. The first objective of this study More

Estimation of formation water saturation (Sw) using log data is an important approach in the oil exploration and characterization of a hydrocarbon reservoir. Therefore, it seems that the proper prediction/simulation of Sw is essential. The first objective of this study was to develop a predictive model for Sw estimation based on hybrid cluster analysis with piecewise nonlinear regression, and after that, using the developed model, Sw was simulated by the Monte Carlo simulation (MCS). In order to achieve objectives of this study, a group of 909 data points was used for model construction and 302 data points were employed for assessment of model. The obtained results of MCS modeling indicated that this approach is capable of simulating Sw ranges with a good level of accuracy. The mean of simulated Sw by MCS was obtained as 0.28 m, while this value was achieved as 0.29 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that RHOB is the most influential parameter on Sw among all model inputs. It is noticeable that the proposed hybrid cluster analysis with piecewise nonlinear regression and MCS models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended. Manuscript profile

Open Access Article

19 - Modeling for Predicting Domestic Demands for Recreational Tourism in Tehran
ebtehal zandi

Recreational tourism is an important form of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts. This paper tried to propose models for predicting effective variables on predicting domestic demands for r More

Recreational tourism is an important form of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts. This paper tried to propose models for predicting effective variables on predicting domestic demands for recreational tourism in Tehran. The study used the monthly information between 2001 and 2015. The independent variable was the number of domestic recreational tourists in Tehran, and the dependent variables were selected based on Delphi and Fuzzy DEMATEL techniques. The model framework was a combination of regression, the fuzzy neural network, and SVR algorithm. The combinations of these methods helped measure prediction errors and compare methods. Results showed that the proposed hybrid approach of regression and Adaptive Neuro-Fuzzy Inference System (ANFIS) could have a better prediction compared to other methods for predicting domestic recreational tourism. Manuscript profile

Open Access Article

20 - Relationship between Quran learning and sense of well-being
مانک ییلاق بیگی

Objective(s): The present research has proceeded to Ghoran learning classes with happiness. That gold is which one of relation ways kind with Ghoran is suitable prognosis for happiness. Methods: Information were completed by Oxford happiness questionnaire (2001) and More

Objective(s): The present research has proceeded to Ghoran learning classes with happiness. That gold is which one of relation ways kind with Ghoran is suitable prognosis for happiness. Methods: Information were completed by Oxford happiness questionnaire (2001) and population questions. They analyzed with Pierson correlation index, Spirman and multistage regression with stepwise method. so, People have participated 382 persons. They had shared in Ghoran class in the Rasht city and they were selected by multi stage cluster sampling method Result: Education , income and duration of time learning had positive and meaning relation with happiness. Age had negative and meaning relation with happiness. Conclusion: Results indicated that sharing in Ghoran learning classes on basis of time have meaning relation with happiness. Meaning relation have not between kinds Ghoran learning with happiness. Manuscript profile

Open Access Article

21 - Exploring the Factors Involved in Increasing Attractiveness of Tourist Destinations
Ali Amiri mehrshad toulabi nejad Monirah Ghofran

To support tourism development, it is necessary to consider the factors that help increase the appeal of tourist destinations, trying to portray a positive image of the destinations in the tourists’ minds. Therefore, this applied study sought to examine the factors invo More

To support tourism development, it is necessary to consider the factors that help increase the appeal of tourist destinations, trying to portray a positive image of the destinations in the tourists’ minds. Therefore, this applied study sought to examine the factors involved in increasing the appeal of tourist destinations in Poldokhtar County, Lorestan province, Iran, using a descriptive-analytic approach. The required data were collected through the administration of the researcher-developed questionnaire. The population of the study comprised fifty tourism experts and specialists who were selected through purposive sampling. Moreover, regression and binary logistics models (LM-Newton-Marcard-Rafson) were used to find answers to the research questions. The results of the study indicated that the diversity of job opportunities created by tourism products, cultural authenticity/natural assets, and tourism infrastructure were the most important factors involved in increasing the appeal of tourist attractions in Poldokhtar County. On the micro-structure level, the results suggested that the diversity of income-generating opportunities created by the development of tourism products, promotion of indigenous-local arts and handicrafts, the establishment of conference halls/ resorts to satisfy tourists, preservation of cultural/ historical and ancient attractions of Poldokhtar county, the development and improvement of tourism transport infrastructure, and the promotion and preservation of indigenous-local traditions were the most important indicators which could help increase the appeal of tourist attractions in Poldokhtar. Manuscript profile

Open Access Article

22 - Test case Selection based on Test-Driven Development
Zohreh Mafi mirian mirian

Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular and short cod More

Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular and short code, as well as increasing the quality, productivity and reliability, and the possibility of regression testing due to the creation of a comprehensive set of unit tests. The large number of unit test cases produced in this method is considered as a strong point in order to increase the reliability of the code, however, the repeated execution of test cases increases the duration of the regression testing in this method. The purpose of this article is to present an algorithm for selecting test cases to reduce the time of the regression test in TDD method. So far, various ideas have been proposed to select test cases and reduce the regression test time. Most of these ideas are based on programming language and software production methods. The idea presented in this article is based on the program difference method and the nature of the TDD method. In this method, meaningful semantic and structural connections are created between unit tests and code blocks, and the test case selection is done based on these relationships. Manuscript profile

Open Access Article

23 - Regression Test Time Reduction in Test Driven Development
Zohreh Mafi mirian mirian

Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular, and short co More

Test-Driven Development (TDD) is one of the test-first software production methods in which the production of each component of the code begins with writing the test case. This method has been noticed due to many advantages, including the readable, regular, and short code, as well as increasing quality, productivity, and reliability. The large number of unit test cases produced in this method is considered as an advantage (increases the reliability of the code), however, the repeated execution of test cases increases the regression test time. The purpose of this article is to present an algorithm for selecting test cases to reduce the time of the regression test in the TDD method. So far, various ideas have been proposed to select test cases. Most of these ideas are based on programming language and software production methods. The idea presented in this article is based on the program difference method and the nature of the TDD method, also a tool is written as a plugin in Java Eclipse. The provided tool consists of five main components: 1) Version Manager, 2) Code Segmentation, 3) Code Change Detection (in each version compared to the previous version), 4) Semantic Connection Creation (between unit tests and code blocks), and finally 5) Test Cases Selection. Manuscript profile

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