داده

Open Access Article

1 - طراحی اولین پایگاه داده کلمات دستنویس کردی برای سیستم های تشخیص تصویری کلمات
fatemeh daneshfar basir alagheband vahid sharafi

چکیده: یکی از اجزای زیربنایی سیستم های تشخیص تصویری کلمات پایگاه داده هاست. هر سیستمی که در این زمینه طراحی گردد لاجرم می بایست از یک نوع پایگاه داده ها استفاده کند. بدیهی است چون موضوع مورد مطالعه در این سیستم ها شکل نوشتاری زبان های مختلف میباشد پس برای هر زبان مشخص More

چکیده: یکی از اجزای زیربنایی سیستم های تشخیص تصویری کلمات پایگاه داده هاست. هر سیستمی که در این زمینه طراحی گردد لاجرم می بایست از یک نوع پایگاه داده ها استفاده کند. بدیهی است چون موضوع مورد مطالعه در این سیستم ها شکل نوشتاری زبان های مختلف میباشد پس برای هر زبان مشخص پایگاه داده بخصوصی لازم است. زبانی که این مقاله بر آن متمرکز شده کردی است و در این مقاله مراحل مختلف چگونگی طراحی اولین پایگاه داده دستنویس برای زبان کردی شرح داده شده است. از آنجا که تاکنون هیچ پایگاه داده ای مخصوص تشخیص تصویری کلمات، مربوط به زبان کردی طراحی نشده است بنابراین زمینه ای بکر و مستعد برای انجام تحقیق محسوب می گردد. همچنین با توجه به اینکه زبان کردی دارای دو رسم الخط مختلف لاتین و آرامی می باشد در این مقاله منحصرا به رسم الخط آرامی البته از نوع دستنویس آن پرداخته شده است. Manuscript profile

Open Access Article

2 - A method for clustering customers using RFM model and grey numbers in terms of uncertainty
azime mozafari

The purpose of this study is presentation a method for clustering bank customers based on RFM model in terms of uncertainty. According to the proposed framework in this study after determination the parameter values of the RFM model, including recently exchange (R), fre More

The purpose of this study is presentation a method for clustering bank customers based on RFM model in terms of uncertainty. According to the proposed framework in this study after determination the parameter values of the RFM model, including recently exchange (R), frequency exchange (F), and monetary value of the exchange (M), grey theory is used to eliminate the uncertainty and customers are segmented using a different approach. Thus, bank customers are clustered to three main segments called good, ordinary and bad customers. After cluster validation using Dunn index and Davis Bouldin index, properties of customers are detected in any of the segments. Finally, recommendations are offered to improve customer relationship management system. Manuscript profile

Open Access Article

3 - A proper method for the advertising email classification based on user’s profiles
rahim hazratgholizadeh Mohammad Fathian

In general, Spam is related to satisfy or not satisfy the client and isn’t related to the content of the client’s email. According to this definition, problems arise in the field of marketing and advertising for example, it is possible that some of the advertising email More

In general, Spam is related to satisfy or not satisfy the client and isn’t related to the content of the client’s email. According to this definition, problems arise in the field of marketing and advertising for example, it is possible that some of the advertising emails become spam for some users, and not spam for others. To deal with this problem, many researchers design an anti-spam based on personal profiles. Normally machine learning methods for spam classification with good accuracy are used. However, there isn’t a unique successful way based on Electronic Commerce approach. In this paper, at first were prepared a new profile that can lead to better simulations of user’s behavior. Then we gave this profile with advertising emails to students and collected their answers. In continue, were examined famous methods for email classification. Finally, comparing different methods by criteria of data mining standards, it can be shown that neural network method has the best accuracy for various data sets. Manuscript profile

Open Access Article

4 - Proposing a Model for Extracting Information from Textual Documents, Based on Text Mining in E-learning
Somayeh Ahari

As computer networks become the backbones of science and economy, enormous quantities documents become available. So, for extracting useful information from textual data, text mining techniques have been used. Text Mining has become an important research area that disco More

As computer networks become the backbones of science and economy, enormous quantities documents become available. So, for extracting useful information from textual data, text mining techniques have been used. Text Mining has become an important research area that discoveries unknown information, facts or new hypotheses by automatically extracting information from different written documents. Text mining aims at disclosing the concealed information by means of methods which on the one hand are able to cope with the large number of words and structures in natural language and on the other hand allow handling vagueness, uncertainty and fuzziness. Text mining, referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text that high-quality information is typically derived through the patterns and processes. Moreover, text mining, also known as text data mining or knowledge discovery from textual databases, refers to the process of extracting patterns or knowledge from text documents. In this research, a survey of text mining techniques and applications in e-learning has been presented. During these studies, relevant researches in the field of e-learning were classified. After classification of researches, related problems and solutions were extracted. In this paper, first, definition of text mining is presented. Then, the process of text mining and its applications in e-learning domain are described. Furthermore, text mining techniques are introduced, and each of these methods in the field of e-learning is considered. Finally, a model for the information extraction by text mining techniques in e-learning domain is proposed. Manuscript profile

Open Access Article

5 - Modified orthogonal chaotic colonial competition algorithm and its application in improving pattern recognition in multilayer perceptron neural network
Payman Moallem mehrdad sadeghi hariri MAHDI hashemi

20.1001.1.27170411.1397.10.35.1.7

Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthog More

Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthogonal Chaotic Colonial Competition (COICA), is proposed. In the policy of absorbing the proposed version, each colony seeks the space to move towards the colonizer through the definition of a new orthogonal vector. Also, the possibility of selecting powerful empires is defined through the boltzmann distribution function, and the selection operation is performed through the roulette wheel method. The proposed multilevel perceptron neural network (MLP) algorithm is used to classify standard datasets, including ionosphere and sonar. To evaluate the performance of this algorithm and to evaluate the generalizability of the trained neural network with the proposed version, the K-Fold cross-validation method has been used. The results obtained from the simulations confirm the reduction of network training error as well as the improved generalizability of the proposed algorithm. Manuscript profile

Open Access Article

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

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

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

Open Access Article

7 - A Combinational Model for Evaluating Organizational Readiness for Data Warehouse Implementation by Using Analytical Hierarchical Process
jafar bagherinejad zhinoos adibi

Enterprise Data Warehouse initiative is a high investment project. The adoption of Data Warehouse will be significantly different depending upon the level of readiness of an organization. Before implementation of Data Warehouse system in a firm, it is necessary to evalu More

Enterprise Data Warehouse initiative is a high investment project. The adoption of Data Warehouse will be significantly different depending upon the level of readiness of an organization. Before implementation of Data Warehouse system in a firm, it is necessary to evaluate the level of the readiness of firm. A successful Data Warehouse assessment model requires a deep understanding of opportunities, challenges and influential factors that a typical firm’s Data Warehouse (DW) may include. Actually, data warehouse system is one of Knowledge Management and Decision Support System tools. By this system, the distributed data throughout organizations could be collected, extracted and integrated and with knowledge discovery and data mining the latent data can be extracted and analyzed In this paper, after reviewing the relevant literature and a comparative analysis of assessment models for organizational readiness for implementation of Data warehouse system, a conceptual framework was designed and its validity was approved by test of hypothesis. Then, by using analytical hierarchical process technique and its expert choice software, criteria and sub-criteria of influential factors were assessed and weighted. The validity and effectiveness of the model including, six criteria and 23 sub-criteria with main influential factors named Information needs, Data structure , Organizational processes , Organizational factors ,Technical structure and Project management were approved by a field study and the relevant statistical analysis. Manuscript profile

Open Access Article

8 - Learning to Rank for the Persian Web Using the Layered Genetic Programming
Amir Hosein Keyhanipour

Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavio More

Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavior of the users embedded in their search sessions’ logs. On the other hand, machine-learning as a data-intensive process requires a large volume of data about users’ queries as well as Web documents. This situation has made the usage of L2R techniques questionable in the real-world applications. Recently, by the use of the click-through data model and based on the generation of click-through features, a novel approach is proposed, named as MGP-Rank. Using the layered genetic-programming model, MGP-Rank has achieved noticeable performance on the ranking of the English Web content. In this study, with respect to the specific characteristics of the Persian language, some suitable scenarios are presented for the generation of the click-through features. In this way, a customized version of the MGP-Rank is proposed of the Persian Web retrieval. The evaluation results of this algorithm on the dotIR dataset, indicate its considerable improvement in comparison with major ranking methods. The improvement of the performance is particularly more noticeable in the top part of the search results lists, which are most frequently visited by the Web users. Manuscript profile

Open Access Article

9 - Presenting a model for extracting information from text documents, based on text-mining in the field of e-learning
AhmadAgha kardan Mina Kaihani nejad

۱٬۵۸۱ / ۵٬۰۰۰ When computer networks became the mainstay of science and economics, a large amount of documentation became available. For this purpose, text mining methods are used to extract useful information. Text mining is an important research field in discovering More

۱٬۵۸۱ / ۵٬۰۰۰ When computer networks became the mainstay of science and economics, a large amount of documentation became available. For this purpose, text mining methods are used to extract useful information. Text mining is an important research field in discovering unknown information, hypotheses, and new facts by extracting information from various documents. Also, text mining is revealing hidden information using a method that shows the ability to deal with a large number of words and structures in natural language on the one hand, and allows the management of ambiguity and doubt on the other hand. In addition, text mining is defined as data mining of text, which is equivalent to text analysis and deals with the process of extracting information from text and extracting high quality information from patterns and processes. It is also known as text data mining or knowledge discovery from text databases and is defined as the process of extracting patterns or knowledge from text documents. The research method in this work is as follows: firstly, the research conducted in the field of text mining was investigated with an emphasis on its methods and applications in electronic education. During these studies, related researches were classified in the field of e-learning. After classifying the researches, issues and solutions related to the issues raised in those works were extracted. In this regard, in this article, the definition of text mining will be discussed first. Then the process of text mining and the fields of application of text mining in e-learning are examined. In the following, text mining methods are introduced and each of these methods is discussed in the field of electronic education. At the end, while deducing the important points of the conducted studies, a model for extracting information for the use of text mining methods in e-learning is proposed. Manuscript profile

Open Access Article

10 - Provide a method for customer segmentation using the RFM model in conditions of uncertainty
mohammadreza gholamian azime mozafari

20.1001.1.27170411.1395.8.30.4.6

The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values of RFM model indicators including e More

The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values of RFM model indicators including exchange novelty (R), number of exchanges (F) and monetary value of exchange (M) were extracted from the customer database and preprocessed. Given the breadth of the data, it is not possible to determine the exact number to determine whether the customer is good or bad; Therefore, to eliminate this uncertainty, the gray number theory was used, which considers the customer's situation as a range. In this way, using a different method, the bank's customers were segmented, which according to the results, customers were divided into three main sections or clusters as good, normal and bad customers. After validating the clusters using Don and Davis Boldin indicators, customer characteristics in each sector were identified and at the end, suggestions were made to improve the customer relationship management system. Manuscript profile

Open Access Article

11 - Providing a suitable method for categorizing promotional e-mails based on user profiles
Mohammad fathiyan rahim hazratgholizadeh

20.1001.1.27170411.1395.8.27.1.7

In general, the definition of spam is related to the consent or lack of consent of the recipient, not the content of the e-mail. According to this definition, problems arise in the classification of electronic mails in marketing and advertising. For example, it is possi More

In general, the definition of spam is related to the consent or lack of consent of the recipient, not the content of the e-mail. According to this definition, problems arise in the classification of electronic mails in marketing and advertising. For example, it is possible that some promotional e-mails are spam for some users and not spam for others. To deal with this problem, personal anti-spams are designed according to the profile and behavior of users. Usually, machine learning methods are used with good accuracy to classify spam. But in any case, there is no single successful method based on the point of view of e-commerce. In this article, first, a new profile is prepared to better simulate the behavior of users. Then this profile is presented to students along with emails and their responses are collected. In the following, well-known methods are tested for different data sets to categorize electronic mails. Finally, by comparing data mining evaluation criteria, neural network is determined as the best method with high accuracy. Manuscript profile

Open Access Article

12 - ۹۳ / ۵٬۰۰۰ Integration of data envelopment analysis model and decision tree in order to evaluate units based on information technology
Amir Amini علی رضا علی نژاد سمیه شفقی¬زاده

20.1001.1.27170411.1395.8.27.5.1

Every organization needs an evaluation system to measure this usefulness in order to know the performance and usefulness of its units, and this issue is more important for financial institutions, including companies based on information technology. Data envelopment anal More

Every organization needs an evaluation system to measure this usefulness in order to know the performance and usefulness of its units, and this issue is more important for financial institutions, including companies based on information technology. Data envelopment analysis is a non-parametric method for measuring the efficiency and productivity of decision making units (DMUs). On the other hand, data mining techniques allow DMUs to explore and discover meaningful information, which was previously hidden in large databases. This paper proposes a general framework combining data envelopment analysis with regression trees to evaluate the efficiency and productivity of DMUs. The result of the hybrid model is a set of rules that can be used by policy makers to discover the reasons for efficient and inefficient DMUs. As a case study using the proposed method to investigate the factors related to productivity, a sample including 18 branches of Iranian insurance in Tehran was selected and after modeling based on the advanced input-oriented LVM model with poor accessibility in data coverage analysis with Undesirable output was calculated and with the decision tree technique, rules are extracted to discover the reasons for productivity increase and productivity regression. Manuscript profile

Open Access Article

13 - Increasing the value of collected data and reducing energy consumption by using network coding and mobile sinks in wireless sensor networks
ehsan kharati

20.1001.1.27170414.1399.12.43.3.4

The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile More

The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile sinks, which increases the life of wireless sensor networks. Using network coding, this paper presents a Mixed Integer Linear Programming Model to determine the optimal multicast routing of source sensor nodes to mobile sinks in wireless sensor networks, which determines the time and location of sinks to collect maximum coded data and reduces the delay in sink movement and energy consumption. Solving this problem in polynomial time is not possible due to the involvement of various parameters and the constrained resources of wireless sensor networks. Therefore, several exploratory and greedy and fully distributed algorithms are proposed to determine the movement of sinks and their residence location based on maximizing the value of coded data and the type of data dead time. By simulating, the optimal method and the use of coding and proposed algorithms, reduce the runtime and energy consumption and increase the value of collected data and network lifetime than non-coding methods. Manuscript profile

Open Access Article

14 - An Improved Method for Detecting Phishing Websites Using Data Mining on Web Pages
mahdiye baharloo Alireza Yari

20.1001.1.27170414.1399.12.43.4.5

Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is More

Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is regarded as one of the important prerequisites in designing an accurate detection system. Therefore, in order to detect phishing features, a list of 30 features suggested by phishing websites was first prepared. Then, a two-stage feature reduction method based on feature selection and extraction were proposed to enhance the efficiency of phishing detection systems, which was able to reduce the number of features significantly. Finally, the performance of decision tree J48, random forest, naïve Bayes methods were evaluated{cke_protected_1}{cke_protected_2}{cke_protected_3}{cke_protected_4} on the reduced features. The results indicated that accuracy of the model created to determine the phishing websites by using the two-stage feature reduction based Wrapper and Principal Component Analysis (PCA) algorithm in the random forest method of 96.58%, which is a desirable outcome compared to other methods. Manuscript profile

Open Access Article

15 - An access control model for online social networks using user-to-user relationships
Mohamad Javad Piran mahmud deypir

20.1001.1.27170411.1399.12.45.1.1

With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the manag More

With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the management and privatization of access to their shared information by other users. In this article, using the concept of social graph, a new model of user access control was proposed to the user, which allows the expression of privacy policies and more accurate and professional access control in terms of pattern and depth of relationships between users in social networks. In this article, by using the regular index method, indirect relationships among users are examined and analyzed, and more precise policies than previous models are presented. The evaluation of the results showed that for 10 neighbors for each user, the probability accumulation of a qualified path for the first three counter loops is 1, 10.5 and 67.3%, respectively, and finally for the fourth counter it reaches 100%. As the defined counting characteristic increases, the average execution time of the proposed algorithm and previously proposed algorithms increases. However, for the higher limits of the counting characteristic, the proposed algorithm performs better than the previous ones. Manuscript profile

Open Access Article

16 - A Combination Method of DEA, DEMATEL and ANP for Evaluation of ERP Systems
amir amini alireza alinezhad

20.1001.1.27170411.1399.12.45.6.6

In this study Data envelopment analysis is used for the assessment of enterprise resources planning systems of 18 manufacturing companies, to determine whether the defined goals for ERP systems have been able to affect the performance after the implementation of the sys More

In this study Data envelopment analysis is used for the assessment of enterprise resources planning systems of 18 manufacturing companies, to determine whether the defined goals for ERP systems have been able to affect the performance after the implementation of the system. Considering the identification of effective factors in implementing ERP systems and using the previous researches, the performance evaluation criteria of this system were identified. Then, using experts’ views the most important input indicators were ranked by fuzzy DEMATEL and output indicators by ANP. In this ranking, time spent on implementation, the implementation infrastructure, training and user support were identified as top input indicators, and three indicators of productivity increase, proper resource management and user satisfaction were selected as top output indicators. Using selected indicators, the performance of ERP systems of selected companies was evaluated. The results of this research will be useful in identifying the strengths and weaknesses of companies compared to top ones and making their ERP system even better. Manuscript profile

Open Access Article

17 - Measuring Similarity for Directed Path in Geometric Data
Mohammad Farshi Zeinab Saeidi

20.1001.1.27170411.1399.12.45.12.2

We consider the following similarity problem concerning the Fréchet distance. A directed path π is given as input and a horizontal segment Q is defined at query time by the user. Our goal is to preprocess and save the directed path π into a data structure such that base More

We consider the following similarity problem concerning the Fréchet distance. A directed path π is given as input and a horizontal segment Q is defined at query time by the user. Our goal is to preprocess and save the directed path π into a data structure such that based on the information saved in the data structure, one sub-path of the directed path can be reported which Fréchet distance between the sub-path and the horizontal query segment Q is minimum between all possible sub-paths. To the best of our knowledge, no theoretical results have been reported for this problem. In this paper, the first heuristic algorithm is proposed. We only experimentally show the quality of the algorithm in several datasets due to no existing algorithm. Manuscript profile

Open Access Article

18 - A Decision Support System based on Rough sets for Enterprise Planning under uncertainty
سید امیرهادی مینوفام Hassan Rashidi

20.1001.1.27170414.1400.13.47.1.7

Increasing rate of novice technology in global marketing arises some challenges in the economic enterprise planning. One of the appropriate approaches to resolve these challenges is using rough set theory along with decision making. In this paper, a decision support sys More

Increasing rate of novice technology in global marketing arises some challenges in the economic enterprise planning. One of the appropriate approaches to resolve these challenges is using rough set theory along with decision making. In this paper, a decision support system with an algorithm based on rough set theory is provided. The proposed algorithm is implemented for a product line in one of the organizations under supervision of mining, industry and trade ministry. The variable effects on the enterpise aims are evaluated by analysing the strength and support criteria of rough sets. The rules are classeified as three different classes and 3 out of 12 have high reasonable averagewhie the last 3 have a relatively high violation probability. The other rules have heterogenious distribution and are not certain. The advantages of the proposed system are avoidance of enterprse capital wasting, prevention of errors due to data uncertainty, and high precision of decitions. The decision makers in the enterprise validated the increasment of simplicity and speeds of vital decision making by using the proposed system. Manuscript profile

Open Access Article

19 - Presenting a new conceptual model for the field of big data and analyzing the data-driven business in Iran based on the proposed model
Mojgan Farhoodi rezvan kalantari hormozi Hesam Zand Hesami

20.1001.1.27170414.1400.13.47.3.9

Forecasting a 10-fold increase in global data till 2025 by IDC indicates that the data journey for organizations has just begun. Collecting, storing, analyzing and using this valuable gold contributes to innovation in companies and organizations and leads businesses to More

Forecasting a 10-fold increase in global data till 2025 by IDC indicates that the data journey for organizations has just begun. Collecting, storing, analyzing and using this valuable gold contributes to innovation in companies and organizations and leads businesses to a competitive future. The purpose of this article is to examine the various contexts of value creation and big-data-driven business for startups that rely on data as their primary source of business. Therefore, in this regard, after examining the various dimensions of big data, a conceptual model of this field has been presented. This model helps companies to carry out their activities in this field with more awareness and focus. Also in the continuation of this article, the results of the survey of knowledge-based companies with data-driven businesses are presented. The results show that most of these companies are more focused on the two areas of data analysis and visualization. Also, the two areas of "Fintech" and "Media" have been more receptive to the technologies of these two areas. On the other hand, products related to "big data management and processing", which includes providing solutions and launching big-data processing and storage services, have the highest sales volume and a significant amount of activity of these companies on Content of news, social networks and information related to stock exchange transactions. Manuscript profile

Open Access Article

20 - An Intelligent Model for Multidimensional Personality Recognition of Users using Deep Learning Methods
Hossein Sadr fatemeh mohades deilami morteza tarkhan

20.1001.1.27170414.1400.13.47.6.2

Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opini More

Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opinions about a particular topic that are expressed in the form of text. Text published by users on social networks can represent their personality. Although machine learning based methods can be considered as a good choice for analyzing these data, there is also a remarkable need for deep learning based methods to overcome the complexity and dispersion of content and syntax of textual data during the training process. In this regard, the purpose of this paper is to employ deep learning based methods for personality recognition. Accordingly, the convolutional neural network is combined with the Adaboost algorithm to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter sizes using AdaBoost. The proposed model was conducted on Essays and YouTube datasets. Based on the empirical results, the proposed model presented superior performance compared to other existing models on both datasets. Manuscript profile

Open Access Article

21 - Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news
arefeh esmaili Saeed Farzi

20.1001.1.27170414.1400.13.47.9.5

For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has bec More

For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news. Manuscript profile

Open Access Article

22 - A framework for establishing a national data vault for Data Governance institution
Nader naghshineh fatima fahimnia hamidreza Ahmadian chashmi

20.1001.1.27170411.1400.13.49.2.7

the goal of this research is mainly presenting a framework for national data with the concentration on parameters respecting data governance in order to design an effective and comprehensive pattern for all spots interacting with national data. The author has adopted de More

the goal of this research is mainly presenting a framework for national data with the concentration on parameters respecting data governance in order to design an effective and comprehensive pattern for all spots interacting with national data. The author has adopted descriptive approach and mixed method for this research. In the first step, the articles regarding national data organization are extracted and subsequently accorded with the articles based on technology ecosystem design patterns, 10 key components are formed as main modules. Thereafter, for each module, indexes and sub-indexes are taken into account by considering articles and also taking advantages of interviews and Delphi method. by designing two questionnaires, strategy-management and technical-lawful oriented, total number of 22 indexes and 154 sub-indexes are collected. the research has the capacity of being a scientific reference for the national data vault. it is recommended that development of technical infrastructure and data governance patterns in national level accorded with indexes and sub-indexes counted in this research Manuscript profile

Open Access Article

23 - Investigating the Information and Communication Technology Deployment Impact on Energy Expenditures of Iranian households (A Provincial Approach)
Elham Hosseinzadeh َAmir Hossein Mozayani

20.1001.1.27170411.1400.13.49.3.8

Nowadays, investing in information and communication technology (ICT) is inevitable, because it affects various aspects of human life, including the economy. Due to the rapid growth of population, increasing energy demand, and limited energy resources, one of the bas More

Nowadays, investing in information and communication technology (ICT) is inevitable, because it affects various aspects of human life, including the economy. Due to the rapid growth of population, increasing energy demand, and limited energy resources, one of the basic measures to achieve sustainable development in countries, is optimization and reform of energy consumption structures. Given that the home sector is one of the main sectors of energy consumption, one of the effective approaches in reducing and managing household energy expenditures is to use ICT capabilities. In this regard, in this study, the effect of ICT expansion on energy consumption of urban households in Iran using the Panel Data method and GLS model during the period 2008-2015 and in the form of provincial data has been analyzed. The results indicate that in some models, a significant reducing effect of ICT on energy expenditure was observed. However, in most of the estimated models, there is no significant reducing effect of ICT on household energy expenditure. It seems that the main reasons for this are the subsidy structure governing energy prices, the low share of energy in total household consumption expenditures, the lack of proper consumption culture. Manuscript profile

Open Access Article

24 - Presenting the model for opinion mining at the document feature level for hotel users' reviews
ELHAM KHALAJJ shahriyar mohammadi

20.1001.1.27170411.1400.13.49.6.1

Nowadays, online review of user’s sentiments and opinions on the Internet is an important part of the process of people deciding whether to choose a product or use the services provided. Despite the Internet platform and easy access to blogs related to opinions in the More

Nowadays, online review of user’s sentiments and opinions on the Internet is an important part of the process of people deciding whether to choose a product or use the services provided. Despite the Internet platform and easy access to blogs related to opinions in the field of tourism and hotel industry, there are huge and rich sources of ideas in the form of text that people can use text mining methods to discover the opinions of. Due to the importance of user's sentiments and opinions in the industry, especially in the tourism and hotel industry, the topics of opinion research and analysis of emotions and exploration of texts written by users have been considered by those in charge. In this research, a new and combined method based on a common approach in sentiment analysis, the use of words to produce characteristics for classifying reviews is presented. Thus, the development of two methods of vocabulary construction, one using statistical methods and the other using genetic algorithm is presented. The above words are combined with the Vocabulary of public feeling and standard Liu Bing classification of prominent words to increase the accuracy of classification Manuscript profile

Open Access Article

25 - Presenting a novel solution to choose a proper database for storing big data in national network services
Mohammad Reza Ahmadi davood maleki ehsan arianyan

The increasing development of tools producing data in different services and the need to store the results of large-scale processing results produced from various activities in the national information network services and also the data produced by the private sector an More

The increasing development of tools producing data in different services and the need to store the results of large-scale processing results produced from various activities in the national information network services and also the data produced by the private sector and social networks, has made the migration to new databases solutions with appropriate features inevitable. With the expansion and change in the size and composition of data and the formation of big data, traditional practices and patterns do not meet new requirements. Therefore, the necessity of using information storage systems in new and scalable formats and models has become necessary. In this paper, the basic structural dimensions and different functions of both traditional databases and modern storage systems are reviewed and a new technical solution for migrating from traditional databases to modern databases is presented. Also, the basic features regarding the connection of traditional and modern databases for storing and processing data obtained from the comprehensive services of the national information network are presented and the parameters and capabilities of databases in the standard and Hadoop context are examined. In addition, as a practical example, a solution for combining traditional and modern databases has been presented, evaluated and compared using the BSC method. Moreover, it is shown that in different data sets with different data volumes, a combined use of both traditional and modern databases can be the most efficient solution. Manuscript profile

Open Access Article

26 - Open Data Accessing Policymaking in Iran, in the Aspect of Preserving Privacy and Personal Data Ownership
Behrooz Eliasi معصومه صادقی نسرین دسترنج Mehdi Hosseinpour Tahereh Mirsaeedghazi

20.1001.1.27170411.1401.14.51.5.1

Enhancing accessibility to open data ensures to promote the research, innovation, and extension of solutions confronting with complex social challenges in our country. Offered policies by OECD and other scientific associations, is an emphasis on this strategy. Certainly More

Enhancing accessibility to open data ensures to promote the research, innovation, and extension of solutions confronting with complex social challenges in our country. Offered policies by OECD and other scientific associations, is an emphasis on this strategy. Certainly, implementing the strategy needs stablishing governance systems, clarifying processes, and trustiness guarantee to research and business areas. The main part of valuable data resources is personal in nature and gathering, storage, and processing them in cybernet is an enormous source of earning for data-driven businesses. Including the main challenges in trustiness issue, are decision making on privacy policies and ownership. In this paper, considering the complexity in ownership concept for personal data ecosystem, challenges on offered policies like OECD reports will be negotiated to enhance open data. Also, shortages in E-trade and cybercrime rules in our country are briefly debated. Then, aiming to suggest an accessing policy to open data, referring to public sensitiveness to personal data, firstly the detailed conclusions of a field study including realizing criterias of goal and possibly policymaking will be extracted by Delphi method. This work shows the public awareness in this subject, even in an excellence target community, is not desirable. Moreover, trustiness in privacy for personal data, hoping to effective law performance on violations, is not satisfiable. Finally, with a field evaluation by FAHP method, policymaking options will be measured and analysed and strategic requirements for performing elected policy will be suggested. Manuscript profile

Open Access Article

27 - Identifying and ranking factors affecting the digital transformation strategy in Iran's road freight transportation industry focusing on the Internet of Things and data analytics
Mehran Ehteshami Mohammad Hasan Cheraghali Bita Tabrizian Maryam Teimourian sefidehkhan

This research has been done with the aim of identifying and ranking the factors affecting the digital transformation strategy in Iran's road freight transportation industry, focusing on the Internet of Things and data analytics. After reviewing the literature, semi-stru More

This research has been done with the aim of identifying and ranking the factors affecting the digital transformation strategy in Iran's road freight transportation industry, focusing on the Internet of Things and data analytics. After reviewing the literature, semi-structured interviews were conducted with 20 academic and road freight transportation industry experts in Iran, who were selected using the purposive sampling method and saturation principle. In the quantitative part, the opinions of 170 employees of this industry, who were selected based on Cochran's formula and stratified sampling method, were collected using a researcher-made questionnaire. Delphi technique, literature review and coding were used to analyze the data in the qualitative part. In the quantitative part, inferential statistics and SPSS and smartPLS software were used. Finally, 40 indicators were extracted in the form of 8 factors and ranking of indicators and affecting factors was done using factor analysis. The result of this research shows that the internal factors have the highest rank and software infrastructure, hardware infrastructure, economic, external factors, legal, cultural and penetration factor are in the next ranks respectively. Therefore, it is suggested that organizations consider their human resource empowerment program in line with the use of technology and digital tools. Manuscript profile

Open Access Article

28 - Anomaly and Intrusion Detection through Datamining and Feature Selection using PSO Algorithm
Fereidoon Rezaei Mohamad Ali Afshar Kazemi Mohammad Ali Keramati

Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, th More

Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, the individuals intrude the websites and virtual markets through cyberattacks and disrupt them. Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. In this article, by using data mining algorithms such as J48 and PSO, we were able to significantly improve the accuracy of detecting anomalies and attacks. Manuscript profile

Open Access Article

29 - Synthesizing an image dataset for text detection and recognition in images
Fatemeh Alimoradi Farzaneh Rahmani Leila Rabiei Mohammad Khansari Mojtaba Mazoochi

20.1001.1.27170411.1401.14.53.5.5

Text detection in images is one of the most important sources for image recognition. Although many researches have been conducted on text detection and recognition and end-to-end models (models that provide detection and recognition in a single model) based on deep lear More

Text detection in images is one of the most important sources for image recognition. Although many researches have been conducted on text detection and recognition and end-to-end models (models that provide detection and recognition in a single model) based on deep learning for languages such as English and Chinese, the main obstacle for developing such models for Persian language is the lack of a large training data set. In this paper, we design and build required tools for synthesizing a data set of scene text images with parameters such as color, size, font, and text rotation for Persian. These tools are used to generate a large still varied data set for training deep learning models. Due to considerations in synthesizing tools and resulted variety of texts, models do not depend on synthesis parameters and can be generalized. 7603 scene text images and 39660 cropped word images are synthesized as sample data set. The advantage of our method over real images is to synthesize any arbitrary number of images, without the need for manual annotations. As far as we know, this is the first open-source and large data set of scene text images for Persian language. Manuscript profile

Open Access Article

30 - Data-driven Marketing in Digital Businesses from Dynamic Capabilities View
Maede Amini vlashani ayoub mohamadian Seyed Mohammadbagher Jafari

20.1001.1.27170411.1401.14.53.15.5

Despite the enormous volume of data and the benefits it can bring to marketing activities, it is unclear how to use it in the literature, and very few studies have been conducted in this field. In this regard, this study uses dynamic capabilities view to identify the dy More

Despite the enormous volume of data and the benefits it can bring to marketing activities, it is unclear how to use it in the literature, and very few studies have been conducted in this field. In this regard, this study uses dynamic capabilities view to identify the dynamic capabilities of data-driven marketing to focus on data in the development of marketing strategies, make effective decisions, and improve efficiency in marketing processes and operations. This research has been carried out in a qualitative method utilizing the content analysis strategy and interviews with specialists. The subjects were 18 professionals in the field of data analytics and marketing. They were selected by the purposeful sampling method. This study provides data-driven marketing dynamic capabilities, including; Ability to absorb marketing data, aggregate and analyze marketing data, the ability to data-driven decision-making, the ability to improve the data-driven experience with the customer, data-driven innovation, networking, agility, and data-driven transformation. The results of this study can be a step towards developing the theory of dynamic capabilities in the field of marketing with a data-driven approach. Therefore, it can be used in training and creating new organizational capabilities to use big data in the marketing activities of organizations, to develop and improve data-driven products and services, and improve the customer experience Manuscript profile

Open Access Article

31 - Fuzzy Multicore Clustering of Big Data in the Hadoop Map Reduce Framework
Seyed Omid Azarkasb Seyed Hossein Khasteh Mostafa Amiri

20.1001.1.27170411.1402.15.55.5.6

A logical solution to consider the overlap of clusters is assigning a set of membership degrees to each data point. Fuzzy clustering, due to its reduced partitions and decreased search space, generally incurs lower computational overhead and easily handles ambiguous, no More

A logical solution to consider the overlap of clusters is assigning a set of membership degrees to each data point. Fuzzy clustering, due to its reduced partitions and decreased search space, generally incurs lower computational overhead and easily handles ambiguous, noisy, and outlier data. Thus, fuzzy clustering is considered an advanced clustering method. However, fuzzy clustering methods often struggle with non-linear data relationships. This paper proposes a method based on feasible ideas that utilizes multicore learning within the Hadoop map reduce framework to identify inseparable linear clusters in complex big data structures. The multicore learning model is capable of capturing complex relationships among data, while Hadoop enables us to interact with a logical cluster of processing and data storage nodes instead of interacting with individual operating systems and processors. In summary, the paper presents the modeling of non-linear data relationships using multicore learning, determination of appropriate values for fuzzy parameterization and feasibility, and the provision of an algorithm within the Hadoop map reduce model. The experiments were conducted on one of the commonly used datasets from the UCI Machine Learning Repository, as well as on the implemented CloudSim dataset simulator, and satisfactory results were obtained.According to published studies, the UCI Machine Learning Repository is suitable for regression and clustering purposes in analyzing large-scale datasets, while the CloudSim dataset is specifically designed for simulating cloud computing scenarios, calculating time delays, and task scheduling. Manuscript profile

Open Access Article

32 - The main components of evaluating the credibility of users according to organizational goals in the life cycle of big data
Sogand Dehghan shahriyar mohammadi rojiar pirmohamadiani

20.1001.1.27170411.1402.15.55.8.9

Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of inform More

Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of information validity. The accuracy, reliability and value of the information are clarified by these networks. For this purpose, it is possible to check the validity of information with the features of these networks at the three levels of user, content and event. Checking the user level is the most reliable level in this field, because a valid user usually publishes valid content. Despite the importance of this topic and the various researches conducted in this field, important components in the process of evaluating the validity of social network information have received less attention. Hence, this research identifies, collects and examines the related components with the narrative method that it does on 30 important and original articles in this field. Usually, the articles in this field are comparable from three dimensions to the description of credit analysis approaches, content topic detection, feature selection methods. Therefore, these dimensions have been investigated and divided. In the end, an initial framework was presented focusing on evaluating the credibility of users as information sources. This article is a suitable guide for calculating the amount of credit of users in the decision-making process. Manuscript profile

Open Access Article

33 - Predicting the workload of virtual machines in order to reduce energy consumption in cloud data centers using the combination of deep learning models
Zeinab Khodaverdian Hossein Sadr Mojdeh Nazari Soleimandarabi Seyed Ahmad Edalatpanah

20.1001.1.27170411.1402.15.55.9.0

Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted usi More

Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models. Manuscript profile

Open Access Article

34 - Design and implementation of a survival model for patients with melanoma based on data mining algorithms
farinaz sanaei Seyed Abdollah Amin Mousavi Abbas Toloie Eshlaghy ali rajabzadeh ghotri

10.61186/jict.44093.15.57.1

20.1001.1.27170411.1402.15.57.1.6

Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of More

Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival. Methodology: This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms. Findings: A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics. Conclusion: The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model. Manuscript profile

Open Access Article

35 - The framework of the national macro plan for transparency and information release based on the grounded theory method
Mahdi Azizi MehmanDoost Mohammad Reza Hosseini reza taghipour Mojtaba Mazoochi

10.61186/jict.44095.15.57.31

20.1001.1.27170411.1402.15.57.3.8

The purpose of this research is to present the framework of the national plan for transparency and information release. The research employs an integrated approach (qualitative and quantitative) and grounded theory as its research methodology. In the qualitative part، w More

The purpose of this research is to present the framework of the national plan for transparency and information release. The research employs an integrated approach (qualitative and quantitative) and grounded theory as its research methodology. In the qualitative part، with an in-depth and exploratory review of upstream laws and documents، models، theories، plans، and white papers of different countries related to transparency and information release، data analysis was done until theoretical saturation through three stages of open، axial، and selective coding. To acquire the dimensions، components، and subcomponents of this framework، 129 concepts were extracted from 620 primary codes، which were reduced to 593 secondary codes by removing the duplicated elements. Finally، 24 subcategories were placed under the five main components based on the paradigm model. In the quantitative section، the results of the analysis of the questionnaire indicated that، from a validity standpoint، the total value of the questionnaire، in different dimensions، was between 0.87 and 0.92، and the reliability coefficient was between 0.73 and 0.78. Based on data analysis، the establishment of a supranational management institution for transparency and information release، the precise determination of exceptions، network governance، demanding transparency، adherence to frameworks، maximum disclosure and support for legitimate disclosure، and the establishment of a data governance center are among the subcategories emphasized in this framework. Manuscript profile

Open Access Article

36 - Presenting a web recommender system for user nose pages using DBSCAN clustering algorithm and machine learning SVM method.
reza molaee fard Mohammad mosleh

20.1001.1.27170411.1402.15.57.6.1

Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can s More

Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN clustering algorithm to cluster data, and this algorithm obtained an efficiency score of 99%. Then, using the Page rank algorithm, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent. Manuscript profile

Open Access Article

37 - BIG DATA
Behshid Behkamal

The main purpose of linked data is to realize the semantic web and extract knowledge through linking the data available on the web. One of the obstacles to achieving this goal is the existence of problems and errors in the published data, which causes incorrect links an More

The main purpose of linked data is to realize the semantic web and extract knowledge through linking the data available on the web. One of the obstacles to achieving this goal is the existence of problems and errors in the published data, which causes incorrect links and as a result, invalid conclusions. Considering that the quality of the data has a direct effect on the success of the linked data project and the realization of the semantic web, it is better to evaluate the quality of each of the data sets in the early stages of publication. In this paper, a learning-based method for evaluating linked datasets is presented. For this purpose, first, the base quality model is selected and the quality features of the model are mapped to the field under study (which is the field of linked data in this article). Then, based on the mapping done, the important qualitative features in the study area are identified and described in detail by defining sub-features. In the third stage, based on past studies, the measurement metrics of each of the sub-features are extracted or defined. Then, measurement metrics should be implemented based on the type of data in the studied domain. In the next step, by selecting several data sets, the metric values are automatically calculated on the tested data sets. To use observational learning methods, it is necessary to evaluate the quality of data experimentally by experts. At this stage, the accuracy of each of the data sets is evaluated by experts, and based on the correlation study tests, the relationship between the quantitative values of the proposed metrics and the accuracy of the data is investigated. Then, by using learning methods, the effective metrics in the accuracy evaluation that have an acceptable predictability are identified. In the end, using learning methods, a quality prediction model based on the proposed criteria is presented. The results of the evaluations showed that the proposed method is scalable, efficient and applicable in addition to being automatic. Manuscript profile

Open Access Article

38 - Providing a New Solution in Selecting Suitable Databases for Storing Big Data in the National Information Network
Mohammad Reza Ahmadi davood maleki ehsan arianyan

The development of infrastructure and applications, especially public services in the form of cloud computing, traditional models of database services and their storage methods have faced sever limitations and challenges. The increasing development of data service produ More

The development of infrastructure and applications, especially public services in the form of cloud computing, traditional models of database services and their storage methods have faced sever limitations and challenges. The increasing development of data service productive tools and the need to store the results of large-scale processing resulting from various activities in the national network of information and data produced by the private sector and pervasive social networks has made the process of migrating to new databases with appropriate features inevitable. With the expansion and change in the size and composition of data and the formation of big data, traditional practices and patterns do not meet new needs. Therefore, it is necessary to use data storage systems in new and scalable formats and models. This paper reviews the essential solution regarding the structural dimensions and different functions of traditional databases and modern storage systems and technical solutions for migrating from traditional databases to modern ones suitable for big data. Also, the basic features regarding the connection of traditional and modern databases for storing and processing data obtained from the national information network are presented and the parameters and capabilities of databases in the standard platform context and Hadoop context are examined. As a practical example, a combination of traditional and modern databases using the balanced scorecard method is presented as well as evaluated and compared. Manuscript profile

Open Access Article

39 - Identifying and Ranking Factors Affecting the Digital Transformation Strategy in Iran's Road Freight Transportation Industry Focusing on the Internet of Things and Data Analytics
Mehran Ehteshami Mohammad Hasan Cheraghali Bita Tabrizian Maryam Teimourian sefidehkhan

This research has been done with the aim of identifying and ranking the factors affecting the digital transformation strategy in Iran's road freight transportation industry, focusing on the Internet of Things and data analytics. After reviewing the literature, semi-stru More

This research has been done with the aim of identifying and ranking the factors affecting the digital transformation strategy in Iran's road freight transportation industry, focusing on the Internet of Things and data analytics. After reviewing the literature, semi-structured interviews were conducted with 20 academic and road freight transportation industry experts in Iran, who were selected using the purposive sampling method and saturation principle. In the quantitative part, the opinions of 170 employees of this industry, who were selected based on Cochran's formula and stratified sampling method, were collected using a researcher-made questionnaire. Delphi technique, literature review and coding were used to analyze the data in the qualitative part. In the quantitative part, inferential statistics and SPSS and smartPLS software were used. Finally, 40 indicators were extracted in the form of 8 factors and ranking of indicators and affecting factors was done using factor analysis. The result of this research shows that the internal factors have the highest rank and software infrastructure, hardware infrastructure, economic, external factors, legal, cultural and penetration factor are in the next ranks respectively. Therefore, it is suggested that organizations consider their human resource empowerment program in line with the use of technology and digital tools. Manuscript profile

Open Access Article

40 - Anomaly and Intrusion Detection Through Data Mining and Feature Selection using PSO Algorithm
Fereidoon Rezaei Mohamad Ali Afshar Kazemi Mohammad Ali Keramati

Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, th More

Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, the individuals intrude the websites and virtual markets through cyberattacks and disrupt them. Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. In this article, by using data mining algorithms such as Bayesian, Multilayer Perceptron, CFS, Best First, J48 and PSO, we were able to increase the accuracy of detecting anomalies and attacks to 0.996 and the error rate to 0.004. Manuscript profile

Open Access Article

41 - Explanation of Standardization Role in the Proposed Solutions for Privacy Protection in Health Data
batool mehrshad mohammad mehraeen Mohammad Khansari saeed mortazavi

Due to the importance of data sharing in the digital era and the two main considerations related to it that are; standardization and privacy protection, this article aims to answer a critical question that is, does standardization play a role in the proposed solutions f More

Due to the importance of data sharing in the digital era and the two main considerations related to it that are; standardization and privacy protection, this article aims to answer a critical question that is, does standardization play a role in the proposed solutions for health data privacy protection? The present study is a systematic review conducted by searching databases such as Web of Science, PubMed, ScienceDirect, Springer, Magiran and SID and by applying a time limit filter. After applying the criteria for inclusion and exclusion and evaluating the results, relevant studies were selected. Articles addressing standardization and privacy protection in health data have been analyzed by taking 5 indicators into account. The need for standardization and its role to preserve privacy in health data have also been explained by examining the findings and discussing various laws related to privacy in the health field and its relationship with standardization. After the investigation, our study reveals that due to the technical structure of the fourth and fifth generation of health care, which has facilitated standardization, privacy protection can also be achieved through standardization. Finally, directions for future research on this topic are also suggested. The results of this research shows that the fourth- and fifth-generation health care systems that are technology-oriented; are formed based on standards, and these standards provide the possibility of their evaluation. Thus if laws related to health data privacy protection are developed based on standards, they will have a high execution guarantee. This further highlights the critical role of standard development organizations in this field. Manuscript profile

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