پیش بینی

Predicting the performance of government Exchange-Traded Funds (ETFs) in the Tehran Stock Exchange holds a significant position due to their importance in macroeconomic decision-making and financial markets. This study aims to develop a model for predicting the returns of these funds using advanced machine learning algorithms, such as Support Vector Machines (SVM) and Linear Regression. The primary objective is to enhance economic and investment strategies at the governmental and institutional levels through accurate return analysis of these funds.

The study is applied in terms of purpose and descriptive-analytical in terms of methodology. Data for government ETFs in the Tehran Stock Exchange, covering the four years (2020–2023), were collected from the Tehran Stock Exchange Technology and Information Center (FIP Iran). Data analysis was conducted using two machine learning algorithms (SVM and Linear Regression) in R software. Due to the differences in asset composition, the ETFs "Palaayesh-e-Yekam" and "Daaraay-e-Yekam" were selected as the primary samples.

The results indicated that SVM and Linear Regression algorithms could predict the returns of government ETFs with high accuracy. Specifically, the "Palaayesh-e-Yekam" ETF, due to its less diverse asset composition and the inefficient Tehran market, exhibited lower prediction errors. The comparison of algorithm performance showed that both methods provided satisfactory accuracy and are suitable for future forecasting in this domain.

Originality/Value: 

By employing advanced machine learning algorithms, this research presents a practical model for predicting the returns of government ETFs in the Tehran Stock Exchange. The findings can be utilized in economic and investment decision-making at the governmental and institutional levels.

One of the new ways of identifying and planning to deal with future crises is futures research. One of the techniques of futures research is “environmental scanning” (ES). It is a type of recording and obtaining information through exploration and search that is used to understand environmental changes and developments. This research aims to provide a localized model of the ES tailored to scanning urban crises for the Tehran municipality. 

Participants were 12 experts who had a background in research or work in the field of ES and in Tehran municipality. Semi-structured interviews and focused group discussions were used to collect data. After reviewing the literature and existing models (n=13), the features of these models were extracted and ranked in terms of compliance with the missions of the Tehran Municipality based on the opinions of experts. 

Bradley–Terry (1997)’s ES model ranked first with 114 points. The final ES process model had three sections: input, analysis, and output. The input section of the system included the departments responsible for ES. The analysis section was responsible for the ES system management. The output section was responsible for implementation and operational action.

The ES model provided in this study can help the officials of Tehran municipality and other organizations in Iran with scanning urban crises.

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.

protecting employees, especially in cyber space, is essential. Enemies are trying to influence the beliefs of employees by using the capabilities of cyber space. Therefore, the aim of the current research is to analyze the factors affecting the increase of optimism of police employees towards security in cyber space.

The current research was conducted in terms of practical purpose, in terms of the nature of data, qualitative and in terms of data-based research method. The research tool is semi-structured interview. Using grounded theory, the data obtained from interviews with 10 police officers, experts of Jame Amin University and experts and trustees in the field of security and inspection were conducted during three stages of open, central and selective coding. The purposeful sampling method is non-random and it was also used to obtain validity from the point of view of experts up to theoretical saturation.

open codes of 34 concepts and 4 categories that include the characteristics of commanders and security ranks, environment and police facilities and equipment, security programs, police staff. "Attracting trust and the desire to increase the level of awareness of employees" was identified as the selected code.

Causal conditions: young human resources that encourage commitment, expertise, mobility and dynamism. Mediation conditions: the desire and effort of the organization to have knowledgeable, insightful and developed employees. Background conditions: The religion of Islam, which places a special value on being immune from crime and sin for humans. Implications: Increasing employees' optimism towards the cyber protection system. The core category (selected code) is "attracting trust and the desire to increase the level of awareness of employees" in cyber security. This category covers all other categories. The employees participating in this research explain the desire to increase the awareness level of employees in cyber protection as the main factor in increasing optimism.

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

Waterfall failures are one of the most important challenges and risks of this modern type of business, which can cause damage to other parts of the system in a chain manner and lead to the collapse of the entire system. The aim of this research is to investigate various factors related to waterfall failures in cloud business models and to introduce appropriate approaches for predicting, preventing and managing these types of failures. In this research, the literature review method using natural language processing was used to collect information and analyze the topic. The sources used included scientific articles in the field of information technology and business from 2015 to 2024 from IEEE Xplore, Google Scholar, and arXiv datasets, of which 23 articles were analyzed with natural language processing methods. The use of advanced NLP techniques allows for a more accurate assessment and deeper analysis of the factors associated with cascading failures. Also, rooting for errors and developing a practical roadmap for their better management allows businesses to facilitate improvement and productivity in their cloud environment and take a more sustainable path. These steps not only help to increase the quality and efficiency of cloud services, but also greatly reduce the costs and time required to fix problems. Various factors related to waterfall failures in cloud business models have been investigated in detail. These factors include technical problems, management problems and marketing problems. Also, different approaches have been introduced to predict, prevent and manage these types of failures.

Maritime logistics, as one of the pillars of global trade, faces challenges such as port congestion and environmental impacts. This paper investigates the use of green electric energy to power vessels outside port terminals through a method known as offshore cold ironing. This study develops an advanced machine learning approach, a hybrid stacking ensemble model, to forecast electricity demand. The focus of this research is on a maritime port, and it evaluates various machine learning models within a parallel stacking approach, demonstrating their effectiveness in forecasting the energy consumption of vessels outside the port compared to individual models. The aim of this research is to enhance sustainability in maritime energy consumption by examining the efficient transfer of renewable energy in offshore scenarios. This includes eliminating the use of fossil fuels by vessels and transitioning to renewable energy sources through accurate electricity demand forecasting. The results show that the MLP-GB ensemble model outperforms other individual or ensemble models in achieving lower root mean squared error, highlighting its potential for accurate forecasting. The findings of this research point to the critical role of accurate forecasting in promoting the replacement of maritime energy sources and contributing to global efforts to combat climate change and reduce greenhouse gas emissions in the maritime sector.

This study examines the prediction of volatility through jump risk in Exchange Traded Funds (ETFs) on the Tehran Stock Exchange, aiming to play a crucial role in the pricing options of financial assets through volatility forecasting mechanisms.

The study employs three main families of heterogeneous autoregressive (HAR) models for volatility forecasting, incorporating jumps within an econometric framework by estimating heterogeneous autoregressive models. By comparing various models, the study utilizes high-frequency data-based realized volatility to provide precise measurements. The data set includes daily and 15-minute interval data collected from six equity and fixed-income funds over the financial period from 2020 to 2022.

The results indicate that among the funds, the predictive power of volatility is higher in equity funds. Additionally, the findings reveal that the second-order heterogeneous autoregressive model with jumps (HAR-J) is the most effective heterogeneous autoregressive model for modeling and forecasting realized volatility, as determined by the mean squared error and quasi-likelihood criteria. Furthermore, strong evidence supports that second-power variation-based models outperform their counterparts in predicting realized volatility, offering more accurate forecasts and better volatility estimations than second-order heterogeneous autoregressive models.

This study provides insights into the differences among heterogeneous autoregressive models, specifically within the heterogeneous autoregressive model group, so that its results can be used by analysts and fund managers for improved financial decision-making.

Earthquakes are natural disasters that can cause high damage. Prediction of earthquakes is necessary to reduce damages, develop resistant infrastructure, and prevent the loss of life in human societies. This research aims to predict the magnitude of possible earthquakes in the Shahrood district of Khalkhal County using artificial neural networks (ANN). 

A multi-layer perceptron neural network was used to predict earthquake magnitude. 

The results showed that in the fault areas, especially the Kalur fault, earthquakes with a magnitude of 1-3 on the Richter scale are more likely to occur (70%). The likelihood of earthquakes with a magnitude of 4-6 on the Richter scale is moderate (26%) and the earthquakes with a magnitude of 7-10 on the Richter scale have a very low probability (4%).

In future studies, it is recommended to use machine learning models to better and more accurately predict earthquakes in the studied area.

Undoubtedly, to achieve goals in a specific field, an organization requires its employees to perform specific activities proficiently. This can be achieved if employees believe in their abilities, set challenging goals, and double their efforts to attain success, highlighting the importance of self-efficacy. Additionally, for full employee engagement, organizations must cultivate a positive and satisfactory mindset, facilitating effective communication with work, thereby emphasizing the necessity of work engagement. Moreover, organizations should instill optimism in employees, fostering the belief that their efforts will yield positive outcomes, underscoring the importance of optimism among employees. Given that low job performance is a primary concern for organizations and encompasses various performance domains, this research aims to explore factors influencing high job performance and provide managerial solutions to enhance employee job performance. Thus, this study investigates the impact of self-efficacy and optimism on job performance with work engagement as a mediating factor.

Design/ methodology/ approach:

 This study adopts a practical approach with survey methodology. The statistical population comprises all experts employed in the Deputy of Management Development and Resources of the Ministry of Economic Affairs and Finance, totaling 200 individuals. Simple random sampling was utilized, and Cochran's formula determined a sample size of 80 individuals. Data were collected through questionnaires assessing self-efficacy (Scheier et al., 1982), optimism (Carver et al., 2010), work engagement (Rich et al., 2010), and job performance (Patterson, 1990). Data analysis employed Structural Equation Modeling (SEM) with the partial least squares method using Smart-PLS 3 software.Research

Results confirm the validity of the research model, indicating that self-efficacy and optimism positively influence work engagement, which, in turn, impacts job performance. Work engagement also serves as a mediating factor in the relationships between self-efficacy and job performance, as well as between optimism and job performance.

Limitations & Consequences:  

One limitation of this research is its focus on a specific organization, limiting generalizability to other contexts. Therefore, conducting similar studies in diverse organizational settings is recommended. Additionally, reliance on questionnaires to measure attitudes poses another limitation.

Practical Consequences: 

The findings of this pioneering research can guide managerial actions towards employees, enhancing human resource effectiveness and aiding managers in addressing HR challenges within organizations. Thus, understanding employee morale and organizational conditions is crucial for improving job performance.

Innovation or value of the Article:

 Unlike previous studies that explored only a subset of the variables examined in this research, this study simultaneously investigates four variables, addressing theoretical and practical gaps in previous literature.

Based on the competition and product variety in the automotive industry, auto makers require to achieve capability to respond properly to customers and their competitors. The special position of after-sales service in automotive industry and also maintain the existing customers and attract the new ones, makes the prediction and measurement of customer satisfaction as a must in this industry.

In this paper, using prediction approach in futures studies, has made us firstly to identify the influential factors on customer satisfaction. Then the customer satisfaction level has been predicted and analyzed in after-sales service of dealers in an automotive manufacturer.

The statistical population includes the dealers of Saipayadak Co. The statistical sample includes 14486 of after-sales service dealers in the period of April 2017 to June 2017. Independent variables, after extracting through literature review, were finalized using brainstorming and fishbone diagrams. Statistical analysis and prediction was performed using stepwise regression method with coding in RStudio software.

By using data mining method, the customer satisfaction score in after-sales service of dealers in Saipayadak Co. has been predicted with 80% accuracy.

By knowing the customer satisfaction level, auto makers can define quality improvement projects and move toward to competitiveness desirably.

As a key and influential organizational issue to utilize the employees’ intellectual power and thoughts, their participation has become a competitive advantage. Given the considerable achievements of the positivism perspective, such as positive organizational behavior in the organization, the present research was aimed at designing the model of attracting employees’ participation in the organization with an emphasis on positive organizational behavior. The data was collected in the first stage through interviews with18 of experts and scholars in the management field (academic) were chosen by a purposive sampling method and with using Delphi method and in the second stage through a questionnaire.the statistical population of this research included 305 employees working for the Vice-Chancellor for Resource Management and Development of Shahid Beheshti University of Medical Sciences, and the statistical sample was calculated equal to 170 individuals through using Cochran’s formula. The fitting of the research model was done using confirmatory factor analysis and structural equations and using SmartPLS3 software. The results revealed a significant relationship between structural factors, behavioral factors, positive organizational behavior factors, environmental factors, and attracting employees’ participation. Furthermore, employees’ participation affects their job satisfaction, organizational commitment, and empowerment. According to the results of this research is suggested shahid Beheshti University of Medical Sciences can do things such as creating a suggestion system, reducing centralism in the organization, adjusting the formality of the organization, sharing information, creating motivation in employees, creating a supportive organizational atmosphere, strengthening the positive attitude of managers towards participation, developing employees' self-efficacy, increasing hope and development Optimistic style in the work environment should be used to attract the employees’ participation

The objective of this research is to predict and monitor the ton-kilometers and waybill on the Iran's roads to find abnormal Behavior. In this study, data was collected from monthly observations over a period of 6 years (1395 to 1400) by the Iran Road Maintenance and Transportation Organization, categorized by province. Different machine learning techniques, deep learning, and time series methods were employed to predict ton-kilometers, and the results were monitored for abnormal behavior following an increase in interest rates and taxes. For model implementation, a dataset of 72 records of ton-kilometers and 72 records of issued waybill, collected from 32 provinces over six years of road transportation, was utilized. Initially, four different prediction methods, including random forest, LSTM neural network, ARIMA, and ETS, were extensively examined. The empirical results indicate that the random forest outperforms the other models. this study employs the statistical quality control tool, the z-score, to detect outliers and abnormal behavior in the data. The empirical findings reveal that out of the 32 provinces, three provinces exhibit abnormal behavior, and one of them is attributed to factors other than an increase in interest rates and transportation taxes.

The present research is concerned with modeling natural gas consumption and its prediction in the domestic sector of Golestan Province. The studied statistical population includes the urban subscribers of 14 cities of the province in the household sector in three units: city, urban household and citizen, and the study period covers the years 2014-2022 (96 months). The panel data regression model and R software were used in modeling and forecasting. According to the results, weather variables, income and consumption of the previous period had a greater influence on the urban consumption model. In addition, in the household consumption model, the weather variables, the number of holidays, and the household's consumption before and after the period have a greater influence. In the citizen consumption model, weather variables, the number of holidays, the consumption of the previous period and the gas access index have a greater influence. Furthermore, the forecasts of the three models studied are 24 future periods in the city and citizen consumption models and 1 future period for the urban household consumption model.

The study analyses two psychological dimension's affecting the success of tourism entrepreneurs. The purpose of this study is to investigate the role of optimism and resilience components with respect to the mediating variable of job satisfaction on the success of tourism entrepreneurs in Mazandaran province. The research method is descriptive-analytical. Therefore, by collecting questionnaires from entrepreneurs in different sectors of tourism, including ecotourism resorts, tourist guides, forest eco-camps and recreational complexes in, this issue was studied in the field. The collected data were processed 2019 based on Spss 16 and Smart pls 3 software. The results show that the optimism and resilience variables can explain 47.3% of the variance of the job satisfaction variable and the job satisfaction variable can explain 38% of the success variable. Finally, it can be concluded that resilience and optimism through job satisfaction lead to the success of tourism entrepreneurs.

Life insurance has a very low adoption rate in Iran, mainly due to policy surrender. This research aims to analyze the individual characteristics and insurance contract features that influence the surrendering of term life insurance policies.

The study utilizes a pilot database of 35,171 policy-holders and pensioners registered by an Iranian insurance company in 2021. Data mining, deep learning, and neural network algorithms are used for analysis due to their high accuracy in prediction:

The model demonstrates desirable performance based on evaluation metrics with a 74 percent accuracy in predicting both types of surrendered and non-surrendered insurance policies. The model performs better in predicting non-surrendered insurance policies more attention is given to interpreting those results. Despite imbalanced data, the model still performs well. In the dataset, surrendered policies make up only 3 percent of the total, leading to bias towards predicting the majority class. Nonetheless, the model accurately predicts and categorizes most surrendered policies, covering 59 percent of the total 244 cases.

The results indicate that certain demographic characteristics, such as age, female gender, health surcharge, and accident risk rate, as well as specific contract characteristics, including policy term, time since start date, longer premium payment methods, higher annual increase in capital and premium, fewer covered risks, and lower benefits, are negatively correlated with policy surrender. Furthermore, the results suggest that if the insured person is the policy surrender themselves, the probability of surrender is minimized. On the other hand, if the insured person is someone else, especially distant relatives, the probability of surrender increases.

The main sources of income and assets are important for any organization. With this view, companies have started to do more to maintain health. Since in many companies the cost of acquiring a new customer is much higher than actual customer satisfaction, customer churn has become the main area of evaluation for these companies. Client-facing companies, including those active in the technology industry, are facing a major challenge due to customer attrition. With the rapid development of the telecommunications industry, dropout prediction becomes one of the main activities in gaining a competitive advantage in the market. Predicting customer churn allows operators a period of time to remediate and implement a series of preventative measures before customers migrate to other operators. In this research, a decision support system for predicting and estimating the churn of customers of Kurdistan Telecommunication Company (with 52,900 subscribers) with different data-mining and machine methods (including simple linear regression (SLR), multiple linear regression (MLR). Polynomial regression. (PR), logistic regression, artificial neural networks, Adabust and random forest) are presented. The results of the evaluations carried out on the data set of the Kurdistan Province Telecommunication Company, the high performance of artificial neural network methods with 99.9% accuracy, Adabust with 99.9% accuracy, 100% accuracy and random forest It shows 100% with accuracy.

In times of crisis, much can be done but to guide and control it. The most important action at this time is to make the right decisions. In the absence of correct information and proper planning, we will see new crises in the heart of the previous crisis, which constantly with hasty and ill-considered decisions not only aggravates the situation, but also the crisis. Creates new ones that are not even recognizable. The present study, citing library and field research sources, seeks to answer the question, "What are the constructive indicators of the role of people in the stage of predicting security crisis management?" The research method used in this research is a mixed method (qualitative and quantitative). Accordingly, first in the library studies section and by referring to reliable sources and interviewing experts, the most important components and indicators were extracted and according to their nature and role, these indicators were classified. Then, by preparing a written interview plan with 17 experts from the statistical community, experts in the field of security crisis management in intelligence organizations, relevant officials and interview experts, the items, components and indicators have been studied. As a result of this research, it was determined that people in the stage of predicting security crisis management with the following indicators: by informing and accompanying the government and intelligence services, accompanying the people, recognizing the crisis and identifying crisis centers, guiding thoughts in cyberspace and the future Crisis research and media enlightenment are very influential.

In today's world, innovation is a very important issue for all industries, including the defense industry, because innovations are needed to support defense power. Therefore, the present study aimed to investigate the impact of business strategies on innovative performance in order to achieve effective performance in companies under the defense industry. The present research is applied in terms of purpose and descriptive-survey in terms of research method. The statistical population of this study includes middle managers and top managers of 82 companies under the Ministry of Defense who were selected by simple random sampling for the research sample. The main data collection tool was a researcher-made questionnaire and its reliability was obtained by calculating Cronbach's alpha formula equal to 0.87. In order to analyze the obtained data, structural equation modeling using partial least squares method was used by SMARTPLS statistical software version 2. The results indicate that there is a significant relationship between business strategy variables and innovative performance and the variables of predictability, control and flexibility of business strategies are effective in promoting innovative performance. Also, the fit indicators of the model indicate that the proposed model has a suitable fit.