saeed rouhani

Recently, network analysis has become one of the most popular and practical areas in data science. This technology, by analyzing complex data and identifying communicative patterns among different elements, delves into a deeper understanding of the structure, behavior, and interactions within networks and complex systems. Despite the critical importance of network analysis in data science and social research, particularly in the field of human resources, there remains a lack of comprehensive studies on this topic. In particular, domestic market data have not been fully studied and discussed. This lack of attention misses opportunities for deeper understanding and presenting innovative solutions to future challenges. Therefore, researchers need to employ modern approaches to investigate the challenges in this field and propose new solutions. This study introduces an innovative model that utilizes multimodal network analysis to enhance the understanding of labor market communications and predict its future developments.

To design the proposed network, the required primary data were collected from international classifications and the initial network was drawn based on them. Then, to ensure that the network matches the current labor market conditions, data from one of the domestic job search websites were also extracted and the network was updated accordingly. The Jaccard index was employed to quantify the connections between network elements, while the algorithms of preferential attachment, Adamic-Adar, and common neighbors were utilized for network validation. The Dijkstra algorithm was used to calculate the shortest path in the network and the term frequency-inverse document frequency metric was used for ranking.

This research sought to present a new method for designing a multimodal labor market network and described how it was updated and diversified to match the dynamic changes of the labor market. With the final network obtained, the connections between labor market elements (jobs and skills) were examined, and a method for quantifying these connections was presented. Additionally, a method for calculating the most efficient job transitions within this network was outlined, along with an approach for ranking skills according to various job levels. Finally, the network was validated with three link prediction algorithms. The results indicate that the use of the preferential attachment algorithm will be the best option for predicting the future of this network.

The innovative model presented in this research offers a powerful tool for network design and analysis in understanding the labor market. It not only provides a comprehensive overview of the current and future state of the domestic labor market but also offers practical solutions for addressing future challenges. In particular, discovering communicative patterns and predicting emerging trends enables better and faster adaptation to labor market changes, which in turn leads to the development of sustainable job opportunities and economic growth. Through a deep analysis of existing data and predicting potential developments, this model can help labor market managers, policymakers, and social analysts design more effective strategies to enhance labor market capacities and optimize human resources. Ultimately, the results demonstrated that innovative models based on network analysis can open new horizons for predicting and managing future developments.

Product pricing in the current business world is one of the main concerns of companies and organizations active in the field of production, and the rapid and significant progress of information technology and software has also become a reason for pricing in the field of software to become a serious challenge. In this regard, this research has been carried out to provide a hierarchical pricing framework for enterprise software as a service. For this purpose, this research was carried out using a mixed method (qualitative-quantitative), extracting 28 indicators of the desired pattern in the qualitative part using the method of meta-composition and content analysis. The pattern in the quantitative part was obtained using the Interpretive Structural Modeling (ISM) method. The statistical population in the quantitative section included 16 research experts. The finding of this research is the achievement of a five-level model where the most influential indicators of this model at the fifth level were order compatibility and software performance value and quality.

Technical and fundamental analysis are the two principal methods for studying financial markets. However, access to internet and social media helps investors make better decisions. Social media has turned into a source of information for investors. Surowiecki (2005) found social media can predict better than individuals, known as the Wisdom of the Crowd. In this study, we tried to evaluate the wisdom of the crowd’s potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Persian users' opinions on Tehran Stock Exchange stocks were gathered for 8 months and classified as buying, sell, or neutral. During the research period, people mentioned 823 stocks and 52 stocks, which had over 100 recommendations, were chosen. Prediction model accuracy was increased for 19 stocks. While, for 33 stocks were not more accurate with the wisdom of the crowds and social media features. It is important to note that investors apply critical thinking. The wisdom of the crowd can be one input to the decision-making process, along with other related factors. The wisdom of the crowd provides an opportunity to access vast and diverse information. Getting opinions from various people can provide valuable insights into economics and investment preferences. The wisdom of the crowd can help reveal the flow of money. The combination of the wisdom of the crowd, fundamental, and technical analysis can be a useful tool for traders in detecting capital flow and profitable opportunities.

Two predominant methods for analyzing financial markets have been technical and fundamental analysis. However, the emergence of the Internet has altered the trading landscape. The availability of Internet and social media access plays a moderating role in information asymmetry, resulting in investors making informed decisions. Social media has turned into a source of information for investors. Through diverse communication channels on social media, investors articulate their perspectives on whether to buy or sell a stock. According to Surowiecki, the collective opinions gathered through social media frequently offer better predictions than individual opinions, a phenomenon referred to as the Wisdom of the Crowd. The wisdom of the crowd stands as an essential measure within social networks, with its potential to reduce errors and lessen information-gathering costs. In this study, we tried to evaluate the wisdom of the crowd's potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Users’ opinions in Persian about the Tehran Stock Exchange (TSE) stocks were collected from SAHMETO for eight months. The Support Vector Machine classified them into buy, sell, and neutral classes. During the research period, people mentioned 823 stocks, and 52 stocks with over 100 signals were chosen. The results of the study show that although the model presented has achieved an acceptable level of accuracy, correlations between the actual and predicted values exceeded 90%. The accuracy metrics of the proposed model compared to the base model were not improved.

In order to facilitate processes and provide innovative services, Most of organizations pay special attention to using data center services to host new platforms, services and tools to manage and run their systems. In the field of data center cybersecurity, all aspects need to be considered to identify the causes and then solve the problems. Therefore, in this study, we seek to identify and provide management codes that consider all internal and external aspects of cyber security. In this research, using NIST cybersecurity framework and existing localized framework for Identify management codes of Comprehensive framework for data center cybersecurity by systematic literature review (SLR), 1831 documents was extracted by searching the Science direct, Scopus and ISC articles. After necessary examinations, 63 of them were identified as related documents. They were analyzed and coded using the Meta- synthesis method. Finally, the extracted features were classified into 5 main category and 23 sub categories and 108 codes, which were related to data center cyber security, i.e. Identification, protection, detection, response and retrieval.

Blockchain is believed to have the potential to digitally transform and disrupt many industry sectors such as finance, supply chain, healthcare, marketing, and entertainment. However, there are obstacles and challenges with the wide-spread applications of this technology. The special issue of “The business value of Blockchain, challenges, and perspectives” at the Journal of Information Technology Management (JITM) aims to cover this timely and hot research topic of Blockchain, the challenges it faces, and the value it brings to the different industry sectors. We received numerous submissions; nevertheless, after the initial screening and two rounds of the peer review process, ten papers have been finally accepted for publication. Accepted papers can be categorized into four categories as Blockchain for the public sector, Blockchain for marketing and advertising, Other Blockchain applications, and Blockchain platforms.

Increasing advances in emerging technologies in all areas have led to profound changes at the community level. Legal and judicial systems have also undergone technological changes. They are looking for a digitally capable system in which judicial services and, consequently, justice is available to all citizens. The judicial system in our country is facing some challenges, including procrastination, which despite the use of various systems in the judicial system of the country has not yet been resolved. Benefiting from digital transformation as a solution to challenges as well as the potential to create new opportunities will be effective in providing better judicial services. This article aims to design a conceptual framework for digital judicial transformation to create a systematic perspective on the transformation of the judiciary.

After studying the background of the subject and comparative study and content analysis of international research, the initial framework was designed. Then, during the two phases of the focus group, the initial framework was validated. The first focus group consisted of six legal experts and the second group consisted of seven experts in the field of law and information technology.

Finally, the framework was designed with seven main sections, which include enablers (including two layers of infrastructure components and technological components), applications (including challenges, drivers and opportunities), judicial ecosystem (including two Layer of internal and external stakeholders), digital transformation governance, digital transformation roadmap (including three layers of vision, goals and plans) and digital transformation journey (including five stages).

In order to create digital judicial transformation, a systematic perspective must be created that, in addition to hard dimensions such as technologies, also considers soft dimensions such as culture, skills and laws. Consider different types of stakeholders and use the benefits and incentives of technologies to solve challenges. There should also be a clear vision and specific goals and plans in this regard.

Microblogging, like Twitter, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiments, beliefs and opinions on various topics. Users express themselves freely with varying levels of uncertainty, which makes exploiting microblogs as a source of data a tedious task requiring this aspect to be taken into consideration. Here we talk about the uncertainty expressed in microblogs not the uncertainty relative to the claimed information factuality. This aspect that we approach has received little attention in the context of microblogging, whereas it is important to know with which degree of uncertainty the users intend to provide information. The research works carrying out the retrieval of information or investigation in microblogs, are particularly concerned by this subject. In this paper we present a state of the art on the identification of uncertainty in microblogs with the aim of identifying this issue and describing the current knowledge through the study of similar or related work. We mainly constated that, to adapt to the characteristics of social media, it is necessary to identify the uncertainty based on the contextual uncertain semantics rather than the traditional cue-phrases, and considering multiple sub-classes could provide more information for research on handing uncertainty in social media texts.

Market manipulation remains the biggest concern of investors in today’s securities market. The development of technologies and complex trading algorithms seems to facilitate stock market manipulation and make it inevitable for regulators to use Deep Learning models to prevent manipulation. In this research, a Denoising GAN-based model has been designed. The proposed model (GAN-DAE4) consists of a three-layer encoder along with a 2-dimension encoder as the discriminator and a three-layer decoder as the generator. First, using statistical methods such as sequence, skewness, and kurtosis tests and some unsupervised learning methods such as Contextual Anomaly Detection (CAD) and some visual and graphical methods, the manipulated stocks have been detected in the Tehran Stock Exchange from 2015 to 2020; then GAN-DAE4 and some supervised deep learning models have been applied to the prepared data set. The results show that GAN-DAE4 outperformed other deep learning models (with F2-measure 73.71%) such as Decision Tree (C4.5), Random Forest, Neural Network, and Logistic Regression.

Increasing advances in emerging technologies in all areas have led to profound changes at the community level. Legal and judicial systems have also undergone technological changes. They are looking for a digitally capable system in which judicial services and, consequently, justice is available to all citizens. The judicial system in our country is facing some challenges, including procrastination, which despite the use of various systems in the judicial system of the country has not yet been resolved. Benefiting from digital transformation as a solution to challenges as well as the potential to create new opportunities will be effective in providing better judicial services. This article aims to design a conceptual framework for digital judicial transformation to create a systematic perspective on the transformation of the judiciary.

After studying the background of the subject and comparative study and content analysis of international research, the initial framework was designed. Then, during the two phases of the focus group, the initial framework was validated. The first focus group consisted of six legal experts and the second group consisted of seven experts in the field of law and information technology.

Finally, the framework was designed with seven main sections, which include enablers (including two layers of infrastructure components and technological components), applications (including challenges, drivers and opportunities), judicial ecosystem (including two Layer of internal and external stakeholders), digital transformation governance, digital transformation roadmap (including three layers of vision, goals and plans) and digital transformation journey (including five stages).

In order to create digital judicial transformation, a systematic perspective must be created that, in addition to hard dimensions such as technologies, also considers soft dimensions such as culture, skills and laws. Consider different types of stakeholders and use the benefits and incentives of technologies to solve challenges. There should also be a clear vision and specific goals and plans in this regard.

As more and more data are generated day by day, the applicability of internet of things (IoT) devices becomes inevitable. The analysis of such data can have many benefits for the organizations and societies. Since previous research has not addressed the identification and prioritization of these applications, the purpose of this study is to identify and rank big IoT data analyses.

This article was divided into two sections. In part 1, the researchers used the meta-synthesis method to identify the applications; and in part 2, the multivariable method was used to prioritize the applications. Moreover, TELOS feasibility (Technical, Economic, Legal, Operational, & Scheduling) and AHP were used to weight and rank the criteria. Then, the applications were ranked based on the experts’ opinions through Vikor’s method.

In this research, the meta-synthesis method has been used to identify the applications of big IoT data analyses. In this meta-synthesis method, 490 articles were initially identified and after eliminating conference papers, 257 articles were selected to initiate the meta-synthesis process. Finally, 51 articles were selected and as a result, 256 sub-applications were identified which were categorized into 114 main categories, 16 industries, and 7 analytic applications. It is also noteworthy that the diagnostic application within the health and transportation industries (with 102 & 100 applications, respectively), as well as the monitoring application within the health, transportation, and agriculture industries were reported to have the highest functioning. The most identified applications in industry-analysis belong to transport-diagnostic (32 applications), health-diagnostic (29 applications), health-monitoring (26 applications), agriculture-monitoring (25 applications), and transport-monitoring (20 applications). In the prioritization step, after calculating the weights based on the experts’ opinions and hierarchical analysis, the applications of transportation and health industries were ranked using TELOS feasibility as well as the experts’ ratings and the Vikor’s method. According to the experts’ opinions and TELOS feasibility criteria, the predictive applications in the transportation industry and the automation applications in the health industry have received the highest priorities.

According to the research findings, big IoT data analysis is mostly used in the transportation and health industrieswhere the predictive applications in the transportation industry and the automation applications in the health industry have been regarded as a priority. Based on these results, the two health and transportation industries and their priority applications are proposed for the companies that want to work in this area. Due to differences in prioritization of the applications in the two transportation and health industries, the justifications for the two industries are different as well.

One of the important topics in this field is Customer Lifetime value(CLV) that means how much profit a customer generates in his lifetime for a company. The main goal of this research is presenting a model for clustering and predicting customer lifetime value and customer evaluation in IRAN National Center for Numbering Goods and Services. In this research, 74,385 records of members at a specified time interval were used (from 2011 to 2017). Members are classified by CRISP methodology, resulting in the presentation of a model for predicting them. At first, members are classified into 7 clusters by RFM and K-Means. Next, each cluster is rated by CLV. Next, hidden patterns are discovered inside the data and various segments of members are then predicted through classification algorithms. Finally, the algorithms are evaluated and the final report is prepared. The results of this study exhibit the member’s behavior in each cluster on the organization’s services and membership or extended subscription and also future customer behavior are unveiled. This research helps the managers to come up with marketing strategies to keep loyal members and attract or remove inactive members by analyzing the clusters.

One of today's major research trends in the field of information systems is the discovery of implicit knowledge hidden in dataset that is currently being produced at high speed, large volumes and with a wide variety of formats. Data with such features is called big data. Extracting, processing, and visualizing the huge amount of data, today has become one of the concerns of data science scholars. The impact of big data on information analysis can be traced to four different parts. The first part is data extraction and processing, the second part is data analysis, the third part is data storage, and finally the visualization of the data. In the field of big data processing, in various studies, different categories have been presented. For example, in the studies of Hashim et al., big data processing is divided into two categories. These two types are: batch and real time. These two categories of processing, which nowadays are standard in any comprehensive big data solution, also have been introduced in Abawajy studies: batch processing is related to offline processing, and real-time processing is usually used to analyze the streaming data without any need to storage of data on disk. As data flows from various sources, the data is analyzed and processed real time, for immediate insight. As today's world is rapidly changing and survival in today's competitive world requires instant decision-making based on flows of data, streaming data analysis is becoming increasingly important. On the other hand, one of the great valuable sources of streaming data is the data generated by social networks’ users such as Twitter. Social networks data sources are very rich sources for analysis as they come from the opinions and opinions of their users. As discussed earlier, and since previous studies such as Flash's studies have focused more on batch analysis (offline data), this study has attempted to investigate a variety of tools and infrastructures related to big streaming data, and finally design a real-time dashboard based on Twitter social network streaming data. The following article addresses two research questions: 1) How to design and implement a real-time dashboard based on social networks data? 2) Which different configurations are best suited for real-time dashboard analysis and visualization? In other words, the purpose of this article is to provide a solution for extracting and visualizing Twitter's social network streaming data by deleting databases, as an examples of big data real time analysis. In this research, we used Twitter streaming data as an input, Apache Storm as a processing platform and D3.js as a visualization tool. Finally, the designed dashboard was evaluated using Design of Experiment method and other statistical tests in various types of Apache Storm configurations and eventually it was proved that the dashboard is real time with an average response time for 1 minute and 30 seconds.

Identification and evaluation of internal factors related to the education management system in the Red Crescent Society of the Islamic Republic of Iran The purpose of this study was to identify and assess the current status of internal factors related to the educational management system in the Red Crescent Society of the Islamic Republic of Iran. The research method is based on the purpose is applied and in terms of collecting data is blended-exploratory. In this regard, in the qualitative section, the most important internal factors associated with the educational management system were identified through semi-structured interviews with 14 key Experts of the organization that include: "Goals and educational programs, human resources, educational resources, educational content, educational processes, teaching methods, educational infrastructure". In the next stage, a researcher-made questionnaire was prepared in a descriptive-analytical manner based on the identified factors. In this way, the attitudes of staff were examined about the status of the identified factors.The results of quantitative analysis show that among the seven factors, "goals and educational programs, educational content, educational processes, teaching methods, educational infrastructure in a relatively desirable, and the factors of human resources and financial resources in the field of education are in an unfavorable situation.

In today's competitive environment, one of the new tools in the field ofinformation technology is business or organizational dashboards that are as abackup in the process of strategic management of organizations. The aim ofthis study is building a prototype of a hospital dashboard on the principles andguidelines of dashboards and evaluating it based on End UserComputing Satisfaction (EUCS). The prototype of a simple dashboard was prepared for evaluation, using experimental research through a questionnaire according to the end-user computing satisfaction model. The test results to compare the average of research variables consisted of satisfaction of the dashboard format, being up to date, ease of use, accuracy and content with average status showed a significant difference between the average of all variables and their average value. Results concluded that among the variables, the format which has had the highest satisfaction rate and accuracy has had the lowest levels of satisfaction among users. Also, the level of users’ satisfaction of all factors of the dashboard is equal.

Providing that data mining has been an effective solution of improving the efficiency and the effectiveness of the retail industry, this industry has been the subject of data mining science due to the nature of its data. In this study, the prediction of customer behavior in the retail industry of Fast Moving Consumer Goods is aimed at increasing the quantity and quality of sales in the study of Golpakhsh Avval Co.
The present study is applied in terms of purpose, using data survey to collect data. The research is based on the CRISP-DM process, which uses the RFMCL clustering model, regression classification and regression techniques as well. Eventually, a collaborative recommendation method has been applied for recommendation.
The result is a forecasting model recommended to the best customers goods that they have not bought on a particular date and to a certain amount, so that, the order-based sale is changed to hot sale method. The final solution involves three sub models of customer clustering, sale forecasting and a recommendation system. The five variables model –with MSE/Range accuracy of 2.24% – is solved for recommendation of sales amount.
By implementing the developed recommender system in Golpakhsh Avval Co., the proactive production master plan would be possible to execute. In addition, the marketing approach could be transformed from visiting sales to hot sales in the future which provides considerable savings in shipping and personnel costs.

In the world of the training, online training is introduced as a modern model of training services. Cloud computing is a modern technology which is provided software, infrastructure and platform as internet services in IT world. So in this research, the impact of cloud computing on e-learning with quantative / prohebationary method was recommended. Both servers with cloud computing and web based technology, with particular parameters by means of Apache benchmark service was measured. SPSS software was used as data analysis to represent the hypothesis testing. The final consequence which was resulted from comparing two servers throughout three different tests named as: evaluation of accessibility to the first page of moodle, accessibility to the login file system and accessibility to the academic file, by means of four parameters such as: response time, throughput, scalability and accessibility, showed that cloud computing server is better than the web based server.

Regarding the complexity of IT service management (ITSM), the purpose of this study is to provide a mathematical model for evaluating and selecting ITSM solution. Through this research, a new classification of functional and non-functional requirements as well as a fuzzy multi-objective model is presented. The functional requirements derived from the ITIL processes generally include: strategic requirements, design, transfer, operation, and continuous service improvement. In addition, three requirements for availability, capacity and occurrence were carefully examined. a total of 64 criteria for assessing ITSM systems are provided. The developed models with fuzzy approach also include 5 objective and 7 constraints that were implemented with fuzzy multi-objective Technics. The proposed model was used to evaluate and select the software in the IT service provider company. The proposed model not only helps the organization to choose the best supplier, but also helps identify the supplier's weaknesses, reduce decision-making time, and increase the accuracy and ease of decision-making. The results of the methods that used to solved the model show that the MOPSO algorithm has the best result than the other three ones. Respectively NSGA2, Goal programming and epsilon constraint were ranked in the following order.

In recent years, organizations have shown special attention toward green IT seeking to benefit from new information technologies with the lowest cost, highest efficiency and lowest environmental destructive effects. Calculating green IT maturity of organizations is one of the methods that can help organizations’ stability in green IT planning. Employing such an assessment along with knowing the green IT maturity level can provide chance of comparing an organization with other organizations, similar industries and Global standards. It can be considered as a criterion for investment actions in order to upgrade their status in this working area. In previous studies, various criteria were mentioned for evaluating green IT that a number of criteria are qualitative and fuzzy in nature and confidently measuring and evaluating them has certain difficulties. Therefore in this research a fuzzy expert system is designed intending to calculate the green IT maturity level of organizations. The main criteria of system include Data Centers, Office Environment, Work Practice, Procurement and Corporate Citizenship. The designed system has been tested for its accuracy and the obtained results and have proven its performance’s validity and reliability. Furthermore, this system has been employed for calculating the green IT maturity level in an Iranian organization.

Nowadays, in this industrial modern world, the incidence of chronic diseases has been significantly increased. Gestational diabetes mellitus is one of the major health problems that if not treated, it will cause serious complications for mother and her child. The purpose of this research was to find ways for determining the risk of gestational diabetes mellitus and making early diagnosis to prevent it in the initial stages of pregnancy.
This applied-survey research used two approaches of neural network and decision tree in experimental analysis of data and prediction. The extracted data were normalized and analyzed through Matlab software.
The results showed that data-based method is effective in improving the accuracy of prediction and has good performance in discovering implied knowledge and diagnosis of hidden relationships among data. In both methods, decision errors were acceptable and very close to each other.
Based on the obtained results, data mining methods can be used in health centers for less familiar diseases in order to achieve on-time diagnosis, patient management and to decrease treatment costs.

One of the new tools in the field of information technology is business or organizational dashboards that are as a backup in the process of strategic management of organizations. The aim of this study was to design a prototype of a hospital dashboard based on the principles and guidelines of organizational dashboards and evaluating it based on End-user Computing Satisfaction (EUCS) model.
In this experimental study, the prototype of a dashboard was prepared. Then, using survey method, a questionnaire consisted of 16 questions related to the five independent variables of EUCS model (content, accuracy, format, ease of use and timeliness) was designed and evaluated. The validity of the questionnaire was determined (α > 0.7). The designed dashboard was given to the study samples (n= 36) selected based on Morgan table and through target-based sampling from the study population (n= 40). Data were gathered using the questionnaire and analyzed.
Comparison of mean of study variables, including satisfaction of the dashboard format, timeliness, ease of use, accuracy and content, with average status showed a significant difference between mean of all variables and their average values.
The variables of format and accuracy had respectively the highest and lowest satisfaction rates among users. Users’ satisfaction level of all dashboard factors was equal showing acceptable satisfaction rate of the designed dashboard based on the principles and guidelines of organizational dashboards.

The implementation of smart schools has significantly progressed in current times due to the execution of intelligent systems. School administrators are also seeking the implementation of smart schools so that they can improve their educational process efficiency. The purpose of this research was to design a system recommending smartening mechanisms for use at the current level, and provide recommendations for improving the quality of schools.
This is a design science and survey research. The surveyed population consisted of experts in implementing smart schools in the country. Based on convenience accidental sampling method, 32 experts were elected. In this study, previous works on effective factors for the implementation of smart schools were reviewed and categorized. Using the e-learning Maturity Model and Capability Maturity Model, some questions were prepared and accordingly, the decision tree was drawn in the identified areas. For proper assessment of performance of the recommender system, a QUIS-based questionnaire was developed and expert's opinions were collected through it. For greater certainty and assessment of the face and content validity, the relevant opinions were used. The questionnaire’s reliability was calculated using Cronbach's alpha coefficient (92%). Data analysis was performed using SPSS version 21 and descriptive statistics (mean and SD) as well as inferential statistics (Kolmogorov–Smirnov and Pearson correlation coefficient tests).
The results showed that this system had great potential for improving the implementation quality of smart schools such that the weighted average grades rose above the mean (3.95 to 4.187 of 5) in the assessment.
With regard to the required training criteria, a model was presented and an expert system was designed to recommend mechanisms for implementing smart schools. Finally, this recommender system was evaluated.

This research attempted to evaluate Business Intelligence criteria accelerating further development of Core Banking System with creating meaningful analysis, decision support environment and optimizing the investments. By reviewing the related literature, Business Intelligence criteria were determined and the importance and priority of each criterion specified on the basis of questionnaire and doing Friedman and Binomial tests. This research presented an evaluation model based on the fuzzy multi-criteria decision making (Fuzzy TOPSIS) method. In the fuzzy-based method, weight of each criterion and results of assignable intelligence were described using linguistic terms, which can also be expressed as triangular fuzzy numbers. According to the findings, Risk Management System ranked as the first module with the largest distance from the negative ideal which indicated that this system had appropriate Business Intelligence capabilities to fortify decision support environment.

Nowadays, with the development of organizations and complexity of business processes, the management and control of such organizations are much more important than before. Therefore, having a determined prospect toward Business Processes Management (BPM) is significant. Since the BPM systems are electronic systems and they are used for supporting, monitoring and managing the processes and other activities in the organization, therefore for preventing misemployment of valuable resources in applying BPM systems and the protection of organization against possible risk of system application, it is necessary to be aware of the critical factors before taking any action. Accordingly, this research paper is conducted to present critical success factors for the implementation of BPM systems in Iran. In this regard, 34 critical factors were identified and presented to the experts through a questionnaire and the answers were analyzed using exploratory factor analysis method. The statistical population consists of managers, experts, suppliers, and developers of information systems having at least 3 years of experience in BPM systems. The designed questionnaires were distributed among the statistical sample and 128 completed questionnaires were returned and analyzed. Finally, 27 key factors categorized into 5 main groups as the following priorities: managerial, organizational, procedural, human-related, and technical. The result of this research can assist the suppliers and the users of BPM systems either in successful application process or confrontation with possible troubles.

The advancement of technology and its application in various industries make the organization looking for use of key technologies such as ERP. The number of failures occur in ERP implementation in various industries including Iranian industries. The attention of many researchers has focused on identifinig critical success factors or critical failure factors. Hence، these factors should be prioritized properly. On the other، these factors have mutual effects usually focus and prioritize the critical success factors. The critical failure factors is a farce regardless of the mutual effects. This paper has been tried using the Delphi method to identify and classify critical failure factors of ERP Implementation in Iranian Industries. Then 37 factors were classified in 8 groups. Mutual effects of these factors are measured utilizes Gray DEMATEL method and prioritize has been conducted each factor and sub-factors.