• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.153-160
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• 2024

Information technology plays an important role in healthcare. The cloud has several applications in the fields of education, social media and medicine. But the advantage of the cloud for medical reasons is very appropriate, especially given the large volume of data generated by healthcare organizations. As in increasingly health organizations adopting towards electronic health records in the cloud which can be accessed around the world for various health issues regarding references, healthcare educational research and etc. Cloud computing has many advantages, such as "flexibility, cost and energy savings, resource sharing and rapid deployment". However, despite the significant benefits of using the cloud computing for health IT, data security, privacy, reliability, integration and portability are some of the main challenges and obstacles for its implementation. Health data are highly confidential records that should not be made available to unauthorized persons to protect the security of patient information. In this paper, we discuss the privacy and security requirement of EHS as well as privacy and security issues of EHS and also focus on a comprehensive review of the current and existing literature on Electronic health that uses a variety of approaches and procedures to handle security and privacy issues. The strengths and weaknesses of some of these methods were mentioned. The significance of security issues in the cloud computing environment is a challenge.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.123-127
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• 2024

Cloud computing services has gained increasing popularity in recent years for supporting various on demand and scalable services for IT consumers where there is a need of less investment towards infrastructure. While storage architecture of cloud enjoys a more robust and fault-tolerant cloud computing network, such architecture also poses a number of security challenges especially when applied in applications related to social networks, Financial transactions, etc. First, as data are stored and maintained by individual virtual machines so Cloud resources are prone to hijacked. Such attacks allow attackers to create, modify and delete machine images, and change administrative passwords and settings successfully. hence, it is significantly harder to ensure data security. Second, Due to dynamic and shared nature of the Cloud, data may be compromised in many ways. Last but not least, Service hijacking may lead to redirect client to an illegitimate website. User accounts and service instances could in turn make a new base for attackers. To address the above challenges, we propose in this paper a distributed data access control scheme that is able to fulfil fine-grained access control over cloud data and is resilient against strong attacks such as compromise and user colluding. The proposed framework exploits a novel cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it for cloud computing with respect to security requirements

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.133-141
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• 2015

Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user's current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user's recent emotion or current situation by analyzing user's social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user's current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.390-394
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• 2009

The semantic web-based social network is highly useful in a variety of areas. In this paper we make diverse analyses of the FOAF-based social network, and propose an expert recommendation system. This system presents useful method of ontology-based social network using SparQL, RDFS inference, and visualization tools. Then we apply it to real social network in order to make various analyses of centrality, small world, scale free, etc. Moreover, our system suggests method for analysis of an expert on specific field. We expect such method to be utilized in multifarious areas - marketing, group administration, knowledge management system, and so on.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.1-5
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• 2011

Everyday, technology is evolved in many different disciplines. Computer and smart devices revolution take part of the evolved technology that continuously promising new features. Moreover, social networks services recently become widely popular, which most people in the world become a social-network-fond. In addition to the revolution of the evolved technology and social networks services, ubiquitousness is taking significant part in our daily lives. Although, there are many e-learning systems already existed, which use Internet technology along with a Web technology to provide education in various ways, in despite of that, there is no such existing system exploits the usefulness of smart devices along with the legacy of the online social networks besides the power of the ubiquitous computing technology. Therefore, we propose a smart device application, which fills the gap that has been missing in the recent contemporary era. It is an application that runs on smart devices particularly Smartphone devices; we call our system “Smart Device based Social E-learning System(SDES)”. We have preliminary implemented our system on Android OS. In this paper, we intentionally propose the system in order to ease the way people learn, to provide interactive accessibility in our system, and to utilize the advanced technology more wisely.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.20-27
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• 2013

In recently years, with the growth of social media and the development of mobile devices, the data have been significantly increased. MapReduce is an emerging programming model that processes large amount of data. However, since MapReduce evenly places the data in the dedicated distributed computing environment, it is not suitable to the non-dedicated distributed computing environment. The data replacement algorithms were proposed for performance optimization of MapReduce in the non-dedicated distributed computing environments. However, they spend much time for date replacement and cause the network load for unnecessary data transmission. In this paper, we propose an efficient data replacement algorithm for the performance optimization of MapReduce in the non-dedicated distributed computing environments. The proposed scheme computes the ratio of data blocks in the nodes based on the node availability model and reduces the network load by transmitting the data blocks considering the data placement. Our experimental results show that the proposed scheme outperforms the existing scheme.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Journal of Internet Computing and Services
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• v.16 no.4
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• pp.93-99
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• 2015

The purpose of this study was to explore the reasons why college students use Facebook and the ways in which they feel of social presence while using Facebook. In fact, the study aimed to specify the links between motivations for using SNS and a sense of 'being together'. The findings of this study suggest that major motivations of SNS use were social interaction, entertainment, self-presentation, and information-seeking. Specifically, results from a survey of 280 respondents revealed that college students used Facebook to seek maintenance and connection with friends, to express themselves, to get information about school activities, and to be entertained. These findings are consistent with the existing literature regarding SNS as a primary channel to maintain the existing social relationships among college students. The study also found that all motivation factors, except information-seeking factor, were positively and significantly related to social presence. The strongest correlations were between social presence and Facebook use for social interaction and entertainment needs. It seemed that users who seek social interaction and entertainment needs are more likely to feel a sense of being with others while users who seek to get information are less likely to feel a sense of social presence. These findings implied that to some extent, a sense of social presence occurs in the context of Facebook and that the experience of social presence depends on what college students seek from Facebook use. In addition, the results showed a positive relationship between Facebook use and social presence; the more college students use Facebook, the more they are likely to experience sense of social presence. Given that Facebook provides college students with a place where they can share thought and feelings among friends, it can be concluded that Facebook contributes to the sense of belonging among users. And such feeling may enhance a sense of presence with others while using Facebook. These findings suggest that uses and gratifications researchers should consider the concept of social presence as an important variable in explaining what audience members do with media.

• Journal of Information Processing Systems
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• v.4 no.2
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• pp.61-66
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• 2008

In recent years, numerous studies have been attempted to exploit ontology in the area of ubiquitous computing. Especially, some kinds of ontologies written in OWL are proposed for major issues in ubiquitous computing such like context-awareness. OWL is recommended by W3C as a descriptive language for representing ontology with rich vocabularies. However, developers struggle to design ontology using OWL, because of the complex syntax of OWL. The research for OWL visualization aims to overcome this problem, but most of the existing approaches unfortunately do not provide efficient interface to visualize OWL ontology. Moreover, as the size of ontology grows bigger, each class and relation are difficult to represent on the editing window due to the small size limitation of screen. In this paper, we present OWL visualization scheme that supports class information in detail. This scheme is based on concept of social network, and we implement OWL visualization plug-in on $Prot{\acute{e}}g{\acute{e}}$ that is the most famous ontology editor.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.83-90
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• 2012

With the rapid growth of internet infrastructure, World Wide Web is evolving recently into various services such as cloud computing, social network services. It simply go beyond the sharing of information. It started to provide new services such as E-business, remote control or management, providing virtual services, and recently it is evolving into new services such as cloud computing and social network services. These kinds of communications through World Wide Web have been interested in and have developed user-centric customized services rather than providing provider-centric informations. In these environments, it is very important to check and analyze the user requests to a website. Especially, estimating user preferences is most important. For these reasons, analyzing web logs is being done, however, it has limitations that the most of data to analyze are based on page unit statistics. Therefore, it is not enough to evaluate user preferences only by statistics of specific page. Because recent main contents of web page design are being made of media files such as image files, and of dynamic pages utilizing the techniques of CSS, Div, iFrame etc. In this paper, large log analyzer was designed and executed to analyze web server log to estimate web contents preferences of users. With mapreduce which is based on Hadoop, large logs were analyzed and web contents preferences of media files such as image files, sounds and videos were estimated.