dr. mohammad khansari

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 showed 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.

Within the field of data sharing, discussions surrounding privacy concerns and big data management are extensive. This study aimed to provide a comprehensive framework for health data sharing with the objective of creating value.

This study is a qualitative content analysis, which was conducted using a combination of written sources through a systematic review method, in conjunction with content derived from interviews with experts in information technology and healthcare within hospital and emergency settings. Grounded theory serves as the qualitative methodology, involving three coding phases: open, axial, and selective, facilitated by MAXQDA software.

Qualitative content analysis of the interviews revealed seven main (core) categories and 44 subcategories as driving factors in promoting healthcare data sharing. Simultaneously, inhibiting factors resulted in six main categories and 36 subcategories. The driving factors encompassed technology, education, patient management improvement, data utilization for various purposes, data-related considerations, legal and regulatory aspects, and health-related factors. Conversely, inhibiting factors encompassed security and privacy concerns, legal issues, external organizational influences, monitoring and control activities, financial considerations, and inter-organizational challenges.

This study has identified key driving and inhibiting factors that influence the sharing of healthcare data. These factors contribute to a more comprehensive understanding of the dynamics surrounding data sharing within the healthcare information system.

With the introduction of digital crypto currency, the worth of data has been obvious to everybody. Of course, this research is in the sphere of medicine. The goal of this research is to perform a comprehensive evaluation of studies in the area of health information system data sharing and secondary uses of health data, with the goal of generating value in multiple dimensions by sharing these data.

 The researchers used the intelligent Web of science and IEEE search engines to conduct a systematic evaluation of English-language keyword searches. Two tactics have been studied in this respect, one using keywords related to "health data value" and the other using terms linked to "health data sharing."

 Although several studies have suggested solutions for decreasing obstacles to health information system data sharing, they have often simply addressed the problem, according to one criticism of the papers examined. The sharing barrier has no remedy, and its relevance is simply emphasized.

 It was discovered that not all of the successful components of data sharing, particularly data from health information systems, were addressed in the evaluated studies.

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.

According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new objective function for the RPL routing protocol is proposed which addresses the characteristics of multimedia data in the routing process. In the objective function, node’s remaining energy and the remaining buffer capacity of nodes measures are combined using a weighted pair. In order to evaluate this method, input data is generated based on a video trace. Packet delivery ratio, network lifetime, nodes availability over the lifetime of the network, node energy distribution, and end-to-end delay are used to evaluate the proposed method. The evaluation results show that the proposed method increases the package delivery ratio compared to the standard RPL. This method also improves the lifetime of the nodes by distributing energy between the nodes in comparison with standard RPL and extends the node's availability over the lifetime of the network. Finally, it reduces the network congestion which led to a lower end-to-end delay.

Many investments in research and development have been made to stimulate the formation of cooperative relations and have significantly improved economic growth and the performance of the national innovation system. Numerous studies have already been conducted on relationships between members of the networks; however, there are gaps such as insufficient attention to government actors in these studies. The present study seeks to develop a model for strengthening relationships between the members of social capital theory-oriented collaborative networks of science and technology in Iran so that the nature of members of the network also considered in terms of their being public or private. It is a case study, and the members of the nanotechnology laboratory network are the statistical population. In the first step, factors that influence the strengthening of relationships between the members of the network were identified through a literature review (Related to social capital theory) and adjusted by the experts’ consensus. Then using the interpretive structural modeling, those factors were compared and prioritized within ternary compositions of private-private, private-public, and public-public. The results show that the factors of programs and policies of network management, the experience of previous relationships, and the duration of membership are three radical factors in different compositions of public-public, private-public, and private-private. Also, the factor of programs and policies of network members is a radical and influential one in the public-public composition.

The rapid growth of IT applications and providing more services on computer networks comes with security threats with malicious and business targets. One method to deal with network traffic analysis complexities is to analyze a summary of network data that is extracted from network connections. Netflow is a defacto standard for generating network flow data introduced by Cisco and integrated into Cisco switches and routers which produce flow records about underlying network traffic. In this paper, we use machine learning techniques to analyze Netflow data and classifying connections pertain to network attacks and do respective prevention measures after the classification. Machine learning algorithms including Naïve Bayes, SVM, and NBTree has been used to model different attacks based on network flow data. In the evaluation phase, KDDcup99 dataset used and related features to Netflow data are selected (7 features), and then, classification has been done with both original KDDcup99 features (41 features) and our selected Netflow features. Average classification accuracy for different 22 attack classes and one benign class shows that using just seven Netflow related features does not affect the accuracy obviously while the computation overhead is obviously decreased. Average detection accuracy for our selected features in different algorithms is 97% whereas, for the best case (i.e, SVM) with 41 features, the average accuracy is 99% which is not so much better than our less complex Netflow based method.

A major indicator of networks’ success is developing stable relationships and collaboration among the members. In fact, collaboration is the key to successful performance of members and the entire network. However, collaboration among the members of a network is a complex phenomenon requiring a comprehensive approach. A theory that can explain the relationships among the members of a network is Social Capital Theory. This research aims to develop a framework to analyze the relationships among the members of the formal collaborative networks of science and technology through the lens of Social Capital Theory. Using systematic review, 44 valid documents retrieved from WOS database, were selected, reviewed and codified using meta-synthesis approach. In the synthesis process, three main dimensions of relational, cognitive and structural and 24 sub-dimensions were revealed.

Recently, analysis of the vulnerable points of power grid as a vital national infrastructure network has received particular attention. Specifically, correction or prediction of vulnerable points can be effective in power grid crisis management. In most previous studies, prediction of the vulnerable points was based on the theory of network science and dealt with the power grid of no direction. In this paper, the power grid of a certain direction has been investigated and PageRank Weighted link analysis has been adopted to detect related vulnerable points. The major advantage of this method lays in shortening the calculation time and in yielding results that are in conformity with the Dispatching- Center's own computations. By modeling a fragment of the Iranian power grid at 400 and 230 KV, connectivity and vulnerable points are predicted. The obtained results following this innovative approach could be used by the power grid decision makers and managers to ameliorate the network management, security and development.

This study clarified the critical success factors (CSFs) that effect on adopting and implementing PACS and its applications in Iranian hospitals. We identified CSFs by literature review and interview by experts. Then examined its importance by T-test with 110 respondents. Kaiser-Meyer test and Varimax rotation are used for validity of data. Factor analysis is used for clustering. And the results are examined in 11hospitals who have implemented PACS. 20 of 23 CSFs, are distinguished important by T-test and clustered in 6 groups by Factor analysis. (1st) Ability to choose and purchase the appropriate PACS; (2nd) Being patient-centered and paying attention to patient satisfaction; were the most important CSFs. 77% questionnaires were completed by less than 2% miss data. The results are approved in 11 hospitals in Iran. This paper fulfils an identified need to study how PACS can be adopted in Iran's hospital by determining 6 CSFs. They can be applicable for policy makers and managers of other hospitals of Iran and some developing countries such as Iran to use of PACS as integrated IT technology.

Different aspects of Open Source Software (OSS) have been subject of many research in last decades. Among them many researchers have tried to adopt the pervasive literature of information systems success with this special kind of system development and its specific dimensions. On the other hand the question of success in the OSS development may cover all different aspects of OSS development and help managers and sponsors of OSS projects to evaluate and increase effectiveness of these projects. So drawing a full picture of related research may be beneficial in different ways. In this paper we try to make a systematic review of related literature in the field and specially pay attention to the measures of success, factors affecting the OSS success and research methods used in previous research. We discussed measures of success and determinants that affect success of OSS as well as methods used in related research and conclude with some points that may strengthen the quality of further work in the topic.

Intrusion Detection Systems (IDSs) are security tools widely used in computer networks. While they seem to be promising technologies, they pose some serious drawbacks: When utilized in large and high traffic networks, IDSs generate high volumes of low-level alerts which are hardly manageable. Accordingly, there emerged a recent track of security research, focused on alert correlation, which extracts useful and high-level alerts, and helps to make timely decisions when a security breach occurs. In this paper, we propose an alert correlation system consisting of two major components; first, we introduce an Attack Scenario Extraction Algorithm (ASEA), which mines the stream of alerts for attack scenarios. The ASEA has a relatively good performance, both in speed and memory consumption. Contrary to previous approaches, the ASEA combines both prior knowledge as well as statistical relationships. Second, we propose a Hidden Markov Model (HMM)-based correlation method of intrusion alerts, fired from different IDS sensors across an enterprise. We use HMM to predict the next attack class of the intruder, also known as plan recognition. This component has two advantages: Firstly, it does not require any usage or modeling of network topology, system vulnerabilities, and system configurations; Secondly, as we perform high-level prediction, the model is more robust against over-fitting. In contrast, other published plan-recognition methods try to predict exactly the next attacker action. We applied our system to DARPA 2000 intrusion detection scenario dataset. The ASEA experiment shows that it can extract attack strategies efficiently. We evaluated our plan-recognition component both with supervised and unsupervised learning techniques using DARPA 2000 dataset. To the best of our knowledge, this is the first unsupervised method in attack-plan recognition.