درخت حوزه‌های تخصصی

Letter to the Editor

The widespread use of E-commerce websites has drastically increased the need for automatic recommendation systems with machine learning. In recent years, many ML-based recommenders and analysers have been built; however, their scope is limited to using a single filtering technique and processing with clustering-based predictions. This paper aims to provide a systematic year-wise survey and evolution of these existing recommenders and analysers in specific deep learning-based hybrid filtering categories using movie datasets. They are compared to others based on their problem analysis, learning factors, data sets, performance, and limitations. Most contributions are found with collaborative filtering using user or item similarity and deep learning for the IMDB datasets. In this direction, this paper introduces a new and efficient Hybrid Filtering based Recommendation System using Deep Learning (HFRS-DL), which includes multiple layers and stages to provide a better solution for generating recommendations.

The rapid emergence and evolution of technology have greatly impacted the way people live their lives. The internet has become a vital part of our daily lives. E-commerce is a type of technology development that enables customers to buy and sell products online. It is a unique form of transaction that connects people from all around the world. Today, many consumers shop for products online and present their products along with their specifications. This is becoming more prevalent. This increases the number of consumers online, which can result in a drop in growth. This is one of the main factors that a company uses to measure its success. Growing business success is revealed with retained customers. Satisfied consumers are the assets for a growing business. The study investigates the factors that influence people's decisions when it comes to buying items online. It shows that the experiences they have while shopping online can affect their decisions. The following statistical tools were applied for this study: percentage analysis, mean score with rank correlation, and t-Test. The results reveal that the factors quality, cost, product variety, uniqueness, and safety payments were highlighted as important indicators of performance and that the companies that do online businesses had to take care of their main goal as per the proposal.

The purpose of this article, extracted from the thesis, was to present the model of organizational culture based on normative and individual variables in the managers of the education staff of Tehran province. In terms of field position, and in terms of nature, it is among the foundation data research. The statistical community of the qualitative part included management experts using the principle of saturation and targeted non-random sampling method, and in the quantitative part, it also included the director general, deputies, and heads of departments of the general administration. and managers and vice presidents of education districts of Tehran province were selected as the sample size. Presentation of the organizational culture model based on normative and individual variables is considered a central category in the paradigm model of the research and other concepts in the form of categories of causal, contextual, and intervening conditions. Strategies and outcomes were included in the model and 10 categories with 57 indicators were introduced in the normative dimension and 10 categories were introduced in the individual dimension with 61 indicators. The evaluation findings based on Shannon's entropy method show; maliciousness and suspicion towards each other Self-esteem, sense of ownership over the organization, enjoyment of work, self-motivation, improvement of job skills and knowledge, personal development, and attention to positive points and job achievements, innovative decisions, and accountability are of the highest importance in realizing organizational culture.

The main purpose of this research is to identify and analyze the effective factors of establishing a knowledge-based city in less developed areas. The research method is mixed (qualitative-quantitative). To carry out this research, in addition to studying documents, the ground theory method with MAXQDA12 software was used to identify the effective factors. The statistical population in this research was all experts in the field of public management, urban management, economics, managers of knowledge-based companies, and managers of Sistan and Baluchestan governorate and Zahedan municipality, which was conducted after conducting 17 theoretical saturation interviews. Finally, open, central, and selective codes were extracted and in the quantitative part, Dematal technique was used to determine the effectiveness of the effective dimensions on the establishment of a knowledge-based city. According to the semi-structured interviews, 6 dimensions (cause, main phenomenon, contextual, interveners, strategies, consequences) and 15 components (core codes), and 69 indicators (open code) were extracted for the establishment of the knowledge-based city. The Finding showed that dimensions of development, skill and expertise, education and technology, infrastructure, internationalization, institutional factor, urban knowledge development, knowledge management, foreign policies, laws and regulations, cultural-native, knowledge-based city development, policies, domestic, cultural conflict, and urban geography, in order from high impact to low impact, as well as dimensions of urban geography, cultural conflict, internal policies, knowledge-based city development, cultural-native, laws and regulations, foreign policies, knowledge management, knowledge development urban, institutional factor, internationalization, infrastructural, education and technology, skill and expertise and development are respectively from high effectiveness to low effectiveness.

The article is devoted to the modern development of high technologies and computer technology greatly enhanced the development of automated banking systems of banking sector organizations and allowed the synthesis of information and communication technologies for their formation. The main purpose of the article is to select the main indicators for assessing the level of financial security of the banking system of the state and identify promising areas of its development using forecasting models. In the process of research such analytical functions have been used: polynomial, exponential, power and logarithmic. The authors believe that the information and analytical provision of the financial security of the bank is an information provision that combines, on the one hand, information work, that is, ways, means and methods of collecting the necessary information, and on the other - analytical work, which includes forms and methods of information analysis and processing, which ensures an objective assessment of the situation and the adoption of a balanced management decision. As a result, forecast models were built for each of the indicators and also, it has been found that most indicators of the banking system of Ukraine in 2021-2023 will remain at “unsatisfactory” and “critical” levels. In conclusions it was proposed to introduce measures that would be aimed at improving the reliability and stability of the banking system of Ukraine.

The Covid-19 outbreak has had a severe effect on the world economy. The company's business operations and profitability are damaged during the covid 19 outbreak. This deterioration is not only threatening the company’s survival position but also destroy the investor’s investment return. Therefore, it is vital to establish an effective early prediction technical method to foresee a corporate distress by a Pro-technical measurement to enhance the corporate sustainability. This study applies Altman Z-Score Model to as a Pro-Technology technique to the financial distress prediction of Malaysia’s Government Linked Plantation Companies (GLC-P) over a period of 10 years starting from 2012 to 2021. The significant contribution of the study is that the Z-Score Model provides an advanced indication tool regarding the financial stability of the respective GLC-P companies. The findings indicate that Financial Distress Prediction was dependent via in-time application of leverage, liquidity, activity, and profitability to the Altman Z-Score Model. Profitability and leverage were found to be superior prediction tool to financial distress.

Hospitals are the most important part of the healthcare system. Statistics show that a significant portion of health budgets are allocated to hospitals. The continuous impact of information technology on hospitals’ performance has led to perfect competition. Accordingly, this study aimed to evaluate the quality indicators of hospital services considering information technology using a hybrid approach of the Kano model, Analytical Hierarchy Process (AHP), and Quality Function Deployment (QFD). In this regard, based on related studies, a total of 18 needs were recognized to evaluate the service quality of a hospital. The statistical population of the study consisted of patients of the hospital and due to the difficulty of access to the patient, a limited sample of 50 patients was selected. After collecting data, the identified needs were classified into three categories called basic, functional, and motivational using the Kano model, and 7 needs were set as basic needs. Then, using the AHP technique, the importance of the basic needs was calculated and considered as the input of the QFD model in the next phase. After providing some solutions based on the literature to meet these 7 needs, solutions were ranked and prioritized using the QFD model. Since the organization had limited resources, the Pareto technique was used to respond to 20% of these strategies and achieve 80% satisfaction. The results of the study showed that the hospitals can achieve 80% satisfaction by implementing the strategies of “holding ethics training courses online” and “creating team spirit and using health information technology in the hospital”, respectively.

One of the important factors in making a profit through financial markets is a quick and correct response to market events, which is possible only by examining all aspects of the market. Today, to solve this challenge, the use of trading algo-rithms has become inevitable and can be considered as transactions made by computers that these transactions are controlled and reviewed through algorithms. Depending on their type and purpose, these algorithms examine different aspects and, according to the strategies defined for them, make decisions and signal by order registration. These trading methods are growing rapidly in the world, espe-cially in strong and developed financial markets. Proper implementation of algo-rithmic transactions reduces transaction costs and increases the accuracy of inves-tors in their investments. One of the most widely used of these strategies is the trend-following strategy, which is welcomed by many traders. This strategy can be implemented in different ways and through different trading tools. In the pre-sent study, five types of them were examined and implemented on one of the most traded symbols of the Tehran Stock Exchange. The purpose of this study is to implement some of the popular strategies in algorithmic trading along with the introduction of algorithmic trading, its strategies in the Iranian stock market, which includes the study of its advantages and disadvantages. The present study is a cross-sectional retrospective and field survey in terms of applied purpose and in terms of data collection.

Traditional approaches applied in supply chain management consider only the physical logistic operations and ignore the financial aspects of the chain. In this study, a mathematical model has been developed to address the supply chain network design problem with a value-based management approach. This model integrates both operations and financial aspects to maximize the value created and measured by shareholder value analysis (SVA) as an objective function. The results attributed to the developed model and the basic model are compared. The results indicate that creating more value for the company and its shareholders is achievable with appropriate financial decisions. To validate and show the applicability of the proposed model, it was solved by GAMS software with data provided by literature. Finally, sensitivity analyses on financial parameters were performed to evaluate the results. The results clearly reveal the improvement of using the new approach and convince managers to take advantage of the proposed approach.

The present study is aimed to present a model of earnings transparency with an artificial intelligence approach in companies listed on the Tehran Stock Exchange (TSE). For this purpose, the data of 167 companies during the years 2011 to 2018 were used to test the research hypotheses. Variable selection test performed using Lasso's artificial intelligence algorithm showed that among the criteria of the audit committee's independence management system, the non-executive managers ratio, gender diversity and among the performance criteria, the ratio of cash holding in the company, operating profit margin and accounts receivable ratio had the highest effect to explain the earnings transparency of companies and also to predict the earnings transparency of the companies in the next year, the LARS algorithm method was used. The results of prediction showed the high power of Lars artificial intelligence algorithm to predict the earnings transparency of the companies listed on Tse. Keywords: Earnings Transparency, Corporate governance and performance criteria, Artificial Intelligence Approach

Due to overlap between stock markets and financial markets, this study was an attempt to examine the herding behavior in the Iranian stock market and the crude oil, foreign exchange and gold markets. For this purpose, in this research, monthly data between 2011 and 2020 for Tehran Stock Foreign exchange were used. The results of the study based on two criteria explaining herding behavior indicate the existence of herding behavior of the stock market and crude oil, gold and foreign exchange markets. The results also show that it has had different ef-fects on herding behavior in different periods. This issue has also been different in increasing and decreasing market periods. Therefore, gold is introduced as an important asset that influences herding behavior. Also, during the decreasing period of the stock market, herding behavior is not affected by the exchange and crude oil market, and in this period, the behavior of investors and investment risks in the stock market can be predicted without considering the exchange and crude oil market.

Trying to retain the employees as the main capital of an organization seems highly substantial for the survival of any organizations. There are several influential factors to retain them and prevent them from leaving. One of these factors is favoritism in the organizations. The study examines the effects of favoritism on employee’s intention to leave in government organization with the mediating roles of burnout and alienation. Having reviewed the literature and in light of “Adams' Equity Theory”, the data was collected using a standard questionnaire from a total sample of 379 employees. The data was then analyzed using the Structural Equation Modeling (SEM) technique using AMOS software. The results of this study revealed that favoritism, which was investigated with two variables namely nepotism and cronyism, would have a positive and significant effect on the staff leave. Furthermore, the mediating role of burnout and alienation for the impact of nepotism and cronyism on employee leave was confirmed. This study serves as a reference for favoritism management in Iranian government organization. The study provides several theoretical as well as practical implications.

Proving helpful in an efficient management of cash in order to reach optimal cash and clearly explain relevant optimization policies, this study examined the adjustment speed of cash holding using asymmetric models. The sample consisted of 117 firms listed in Tehran Stock Exchange and their financial details over the 2009-2018 period. Once the optimal level for cash holding was identified, asymmetric models such as the dummy variable approach, the quadratic model, and the threshold regression model were employed to test the adjustment speed of cash holding. The results revealed that cash-rich firms are moving toward optimal cash at a greater speed than cash-poor firms. In addition, the results from the quadratic model showed a non-linear, skewing effect of the cash holding adjustment speed in terms of the different cash levels. Therefore, there is an optimal level of cash holding that enables firms to deviate from the cash target. Should firms fall outside the optimal cash range, cash adjustment will occur at a greater speed, and it will be both partial and asymmetric.

In the field of corporate tax behavior, corporate governance mechanisms are effective for shaping and monitoring the behavior of managers. The board, which is responsible for allocating resources, improving performance and increasing shareholder wealth, plays a central role in choosing a tax management strategy. Hence, companies with different governance structures may adopt different types of tax management. Therefore, it can be argued that corporate governance mechanisms can affect corporate tax activities. In order to achieve the weight of the identified criteria, the network analysis process method was used. The purpose of this method was to structure the decision-making process, according to a scenario affected by multiple independent factors. In this method, there can be a relationship between all levels, while in the method of hierarchical analysis process, this relationship could only be from high levels to low levels. The method of the network analysis process, such as the hierarchical analysis process, calculates the inconsistency of all judgments by the conformity rate, and the calculated inconsistency rate must be less than 10%. The compatibility coefficient of the final matrix shows the validity of the questionnaire. In order to analyze the data, Super Decision 1.6.0 software developed by Thomas L. Saati, the inventor of this method, as well as MS Excel 2019 software were used. There is considerable evidence that increasing tax requirements can increase tax compliance, but typically some types of tax offenses are used as a tool to control the phenomenon of tax default.