artificial neural network

The purpose of this research is to identify the drivers of team effectiveness in Khuzestan Steel Company through artificial neural network. This research is based on the purpose of applied-development research, based on the data collection method, it is a descriptive-survey research, and in terms of data type, it is a quantitative research approach. The statistical population of research is 1020 employees and managers of Khuzestan Steel Company, of which 360 employees were selected as a sample using a simple and purposeful random sampling method. The results showed that the artificial neural network, with a high percentage of accuracy (in identifying indicators that increase effectiveness: that is, 97.3 in the training section and 95.8 in the testing section, also in identifying indicators that do not increase effectiveness: that is, 96.5% in the training section and 92.9% in the testing section), has been able to work correctly and predict that this indicates the very high efficiency and sensitivity of this system. Also, out of the 125 indicators presented, 65 active independent indicators were successful in predicting team effectiveness, which this method was able to identify by defining zero and one variables. Artificial neural network in the data analysis led us to the conclusion that team learning, group orientation and agile mindset indicators are the most important drivers in team effectiveness, respectively, and there is a need for a deep study on these dimensions.

Predicting the total stock index is a challenging task, due to the complexity of stock market variables and the lack of management and the occurrence of problems in critical situations, it is very difficult to develop an efficient model for predicting the total stock index. One of the important tools used for investment decisions is forecasting techniques, which are an integral part of the decision-making and control process. On the other hand, the accuracy of forecasting is directly related to decision risk. One of the well-known and new methods for predicting the total stock index is the method of using artificial neural networks. this research is applied in terms of purpose and descriptive based in terms of research method. It is analytical-mathematical in terms of survey and survey method. The statistical population of this research is the total index of TEPIX from 1369 to 1399. In this research, the tool that has been used to measure the desired variables is the documents and statistics of TEPIX and to analyze the data of this research, descriptive statistics and inferential statistics as well as multi-layer artificial neural network. Perceptron has been used. The results of this study show the confirmation of high accuracy of forecasting the total index of TEPIX compared to other estimation methods provided by the model, which has the power to predict the total index up to 1.7% error and also confirms the adherence to the index of TEPIX. A non-linear process is another result of this research.

In order to control the supply and demand of energy and the correct planning in directing the consumption, the monthly gas consumption of the country's industries was investigated with artificial neural network and genetic algorithm in this research for 1402. The country's population information, industrial producer price index, gross domestic product at a fixed price of 90 and gas consumption of the country's industries were investigated as influential variables. The results showed that the best artificial neural network combined with the genetic algorithm is a network with a mutation rate of 0.5, an intersection rate of 0.5, the number of repetitions of 150, and the initial population size of 150. The combination of artificial neural network and genetic algorithm showed that in the spring season there was a total of 2957.7 million barrels of crude oil equivalent, in the summer this figure increased to 3502.6, in the autumn to 4329.9 and in the winter with a growth of 8.15 percent. It will reach 4683 million barrels of crude oil equivalent

Sustainable development is defined as development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. It encompasses economic, social, and environmental dimensions that must be considered simultaneously. With the increasing importance of sustainable development, many companies worldwide are motivated, either proactively or reactively, to collect their used products. In such circumstances, establishing a reverse logistics network based on sustainable development is essential. The decision to outsource logistics has gained significance due to the need to avoid fixed costs, heavy investment, and achieve economic advantages, with many companies recognizing the potential benefits of high-quality logistics services.

This research presents a mixed integer programming model for planning reverse logistics outsourcing in the assembly cycle of the automotive industry, focusing on a cost-oriented objective function. The research scope includes the assembly cycle of production lines, specifically prioritizing high-volume car manufacturers (light vehicles), and focuses on the Saipa Automotive Industrial Group, including the Ryan Saipa Leasing Group. The research period spans from 1389 to 1398 in the Iranian calendar. Variables such as non-commercial receivables, total assets, operating profit, net profit, and market value were evaluated using MATLAB software based on published statistics from Saipa.

The research findings indicate that among the variables of non-commercial receivables, total assets, operating profit, net profit, and market value, net profit to operating profit and sales (operating income) are of significant importance. The highest amount of non-commercial receivables for Saipa occurred in 1398 during the summer, while the highest total assets were recorded in 1392 during the summer. The highest operating profit was observed in 1398 during the winter, and the highest net profit was in 1390 during the spring. The degree of data convergence was calculated in the regression charts of sales (operating income) to operating profit and net profit to operating profit for the years 1389-1398. The degree of data convergence in the regression chart of sales to operating profit based on the conceptual model in 1398 was 0.9895, and for net profit to operating profit in 1398, it was 0.9961. The regression rate for the conceptual model in the test phase was 0.79, and in the overall processing stage, it was also 0.79. The histogram error rate was calculated for all three stages of learning, validation, and testing, with an error rate of 0.002375, which is acceptable due to its proximity to zero. Comparing these results with other studies shows an improvement in the regression and error rate in the analysis of the objective function.

Based on the calculated weight of the criteria in two-way assembly line balancing issues, it can be concluded that the decision team pays special attention to strategic issues in addition to production issues. The production rate of the line, which is the inverse of the production cycle time, affects the company's market share in the long term and increases its market share.

This study was conducted to predicte the resistance properties of concrete with different types of neural networks. The studied data was collected from the database of 127 mixing plans. The input data included the age of concrete in day, the amount of coarse grain, fine grain, cement, water and concrete plasticizer. The target data included compressive strength.

In this research, an attempt has been made to make models for different projects by statistical study of laboratory samples of concrete in order to have a suitable prediction for estimating the resistance properties of concrete. The use of artificial intelligence as a modern method has a special place in engineering sciences. In this research, the data used were first normalized and then the desired data were trained using the Lorenberg Marquardt algorithm.

The evaluation criteria of artificial neural network models were obtained using evaluation and error and the results showed that the use of 10 hidden layers had the highest correlation coefficient and the lowest error. The structure of this network was multi-layered perceptron.

Originality/Value:

 The results showed that for the constructed neural network, the value of correlation coefficient, mean root, error square and mean absolute error of the artificial neural network were 0.94 and 1.9, respectively.

Predicting the results of the current quality improvement activities is one of the concerns of the officers of the armed forces officer universities. Taking advantage of the capabilities of the artificial neural network and taking into account the aforementioned concern, the present research has presented a model to predict the process of the results of the effective factors in the military educational activities of one of the officers' universities.

The statistical population is all the young officers under training in the combat camp in Kohestan University under study (from 2018 to 2022). In the first step, the data related to the factors and parameters of the training-educational activities of the military camp in the mountains were collected for the mentioned five-year time period. In the next step, pre-processing of the data was done and using the proposed IRNN algorithm and its coding in Python, the appropriate model was built and its validation was done.

By using the built model and the available data, it was predicted the quality of the training performance of the combat camp.

Originality: 

The results showed that with the continuation of the current policies, all the factors will have an upward trend except for the factor of "updating the level of knowledge and skills of the teaching staff". Therefore, proper planning should be done in order to update the level of knowledge and skills of the educational staff of specialized educational activities

The aim of this research was to find the most important indicators in creating job stress of country's air transport industry employees during the Corona era. This research is developmental-applicative in terms of purpose, in terms of data collection method, it is a descriptive survey type research and in terms of data collected type, it is quantitative. The statistical population of research was made up of 1420 working employees in headquarters department of Iran's air transport industry, of which 312 were selected as a sample by simple random sampling. The data collection tool of this research was a researcher-made questionnaire consisting of 126 specialized questions in line with the research variables, which were designed based on the five-choice Likert scale. The results showed that the dimensions of work environment, age and experience are important factors in creating job stress among air transport industry employees during the covid-19 pandemic, and from the number of 126 introduced indicators, 43 active independent indicators have succeeded in predicting job stress behavior and are important. Therefore, it is suggested to the managers of the air transport industry to take effective measures to reduce the psychological pressure of employees by strengthening the field of training and specialized preparation and to try to provide safe working conditions for employees with organizational support.

In this research, the intelligent model of preventive maintenance and repairs based on artificial neural network methodology - fuzzy logic with the help of artificial intelligence environment of MATLAB software based on the structure of Falcon's five-layer model of artificial neural networks is presented, the research method is based on systems thinking. After determining the most important factors affecting preventive maintenance and repairs with the help of a questionnaire and based on a dataset of 2,000 samples of data and reports of the Director General of Textile and Clothing Industries of the Ministry of Safety during the years 1396 to 1401 (in the form of six and a half years) and validity Data evaluation by the maintenance and repair experts of 240 industrial units, a smart model was designed, which after the implementation of the model in Borujerd textile factories as the place of implementation of the plan can be claimed if (If); Five "technology" factors have values of 0.9129; Good condition (upper bound of good membership function), "Employees" has values of 0.9239; good condition (upper bound of good membership function), "working environment" has values of 0.8859; relatively good (lower limit of the membership function), "quality" has values of 0.9999; Perfect condition (highest function), "strategy" has values of 0.9999; good status (upper limit) in preventive maintenance and repairs, then: the status of the output variable of the research, i.e."Optimization of preventive maintenance and repairs performance (Y)" will be at its fifth level, i.e. very good, equal to 0.882.

The main goal of the current research is to predict the ethical purchase intention of sustainable products in the circular business model through the behavior of customers/tourists using artificial neural network (ANN). The statistical population of the present study are customers/tourists who have used the products/services of Gargop restaurant. Sampling was done based on Morgan's table and 384 people were examined through a questionnaire. The results indicate that as much as the input variables include attitude (willingness to pay more money, attitude towards circular business models), perceived value (social value, functional value and value) and awareness (awareness of Ethical product/service and brand awareness) have a higher value, it is expected that the ethical purchase intention will be stronger. The present study by examining the behavioral factors of customers to the collection of literature related to circular business models and also Sustainable development helps.

The purpose of this study is “to design a model for evaluating the performance of educational centers of one of the government organizations in the country using artificial neural network”. This is an evaluation study to evaluate the performance of public organizational educational centers. The information required for this research was collected through parallel information channels such as using the documents of educational centers under the organization and referring to their documents while maintaining the classification level. The statistical population of this study was five educational centers, one of the government organizations that hold educational courses for about 10 thousand personnel between 2013 and 2020; Based on the opinion of experts and the results of related studies, the inputs and outputs of the research were selected and determined. In order to reduce the input and output variables, the structural equation modeling method - partial least squares were used. In order to train the MLP bilayer neural network, the training method was used. After the teaching of neural network. The performance of neural network was examined through test patterns. The value (mean square error) of the MSE corresponds to 13 equal test patterns and 74/7413, which indicated the high accuracy of the trained network. Finally, the performance of the educational centers was ranked based on the analyzed data.

Finding innovative solutions to satisfy consumers has always been a concern of businesses. Accordingly, this study seeks to investigate the effect of gamification on consumer shopping behavior using the artificial neural network, in the tourist offices of the Iranian western city of Kermanshah.

This study is developmental and applicable research. The semi-experimental method (pre-test/post-test) was used with experimental and control groups. The statistical population of this study included the customers of Kermanshah tourist offices with one registered trip in the first quarter of the Iranian calendar year of 1400 (2020-2021). Each of the control and experimental groups consisted of 20 participants. Kim’s consumer behavior standard questionnaire was used to gather the required data. SPSS software was used to analyze descriptive statistics and MATLAB software was used to design an artificial neural network. The artificial neural network was constructed using the radial basis functions. The used data set was divided into 70 percent for training and 30 percent for testing.

The results of descriptive statistics showed that nine participants in both control and experimental groups were women and 11 were men. In the experimental group, eight participants were single and 12 were married. Seven participants in the control group were single and 13 were married. The average ages of the experimental group and control group were 41.8 years and 39.6 years, respectively. The average number of made trips in the year 1400 for the experimental group, and control group were 3.6 and 3.55, respectively. The achieved results also showed that the average scores of all three dimensions of willingness to attend, word of mouth and purchase intention were increased in the experimental group after applying gamification. These values were increased from 6.5 to 13.15, 8.95 to 17.6, and 7.55 to 12.9, respectively. While in the control group, the average scores in all dimensions remained almost constant. The effect of gamification on changing consumer buying behavior was approved by these results. Furthermore, the behavior of a typical person in experimental or control groups (with or without gamification) was predicted using RBF artificial neural network. The obtained results from the designed neural network showed that all of the predictions using the proposed artificial neural network were predicted correctly. Also, the network was able to classify all of the outputs correctly based on the defined inputs. The network had an acceptable error rate.

Based on the obtained results from the artificial neural network, prediction of customer behavior was done correctly. It should be noted that the gamification activities such as completing the puzzle which was tested in this study should be used accurately in businesses. This result will be useful for businesses, marketers, sales managers, market management analysts, and researchers interested in this field.