• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.257-266
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• 2023

The aim of this study is to use media big data to understand the characteristics and changes in technology that prevents wandering and missing for dementia patients as well as supports safe walking since 1990 until recently. BigKinds as a media big data was used to conduct an analysis in two stages. In the results, first, the media reports began to be reported in the early 2000s, and it increased after 2014. Second, regarding to the characteristics of changes in technology and device utilization, there has been a change to advanced technology that combines AI and IoT, focusing on GPS. Drone has recently increased in media report, however problems of personal information security need to be resolved. Third, technology development focused on location identification by police and guardians. Based on the results, technology development and community cooperation for dementia patient were discussed.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.5
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• pp.329-339
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• 2019

Recently, the advancement of information and communication technology(ICT) is expanding the connectivity through Internet of Things(IoT), and the media of connection is also expanding from wire/cable transmission to broadband wireless communication, which has significantly improved mobility. This hyperconnectivity has become a key element of the fourth industrial revolution, whereas the supervisory control network of purification plants in korea is operated as a communication network separated from the outside, thereby lagging in terms of connectivity. This is considered the best way to ensure security, and thus there is hardly any consideration of establishing alternatives to operate an efficient and stable communication network. Moreover, security for management of a commercialized communication network and network management solution may be accompanied by immense costs, making it more difficult to make new attempts. Therefore, to improve the conditions for the current supervisory control network of purification plants, this study developed a industrial security L2 switch that supports modbus TCP(Transmission Control Protocol) communication and encryption function of the transmission section. As a result, the communication security performance improved significantly, and the cost for implementing the network management system using Historical Trend and information of HMI(Human Machine Interface) could be reduced by approximately KRW 200 million. The results of this study may be applied to systems for gas, electricity and social safety nets that are infrastructure communication networks that are similar to purification plants.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.127-140
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• 2015

In this study, we aim to analyze the relationship between the centrality in the social network and the sentiment of medial web forum users. In recent, many people use online resources to obtain health and wellness information especially social media resources. In the medial web forum, people give and receive informational supports and emotional supports and this interaction forms the social network. We analyze the social network, derive node characteristics in terms of centrality and compare the centrality index and the sentiment score derived from users' messages. We found that as more people express their emotion, they possess higher central position in the network. Further, people who express positive emotion in their messages have higher central position in the network than people who have negative emotion. This study will help to identify influentials of emotional supports to others and finally to control the depression of Alzheimer's disease patients and their related ones.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• The Journal of Information Systems
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• v.25 no.2
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• pp.111-129
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• 2016

Purpose Today, world-wide societies share their daily life and many communities exchange their information through the explosive developed SNS and the social media systems. However, many SNS fatigue related factors forced the discontinuance of SNS. This paper is aim to examine effect of SNS feature and negative emotion to figure out the reason of SNS discontinuance. This is verifying the effect of maintenance of SNS, security concern and psychological concern on SNS fatigue and the effect of upward/lateral comparison on negative emotion. Moreover, the effect of SNS fatigue and negative concern on the SNS discontinuance intention was examined. Design/methodology/approach This research used to the survey method to test its hypotheses and the survey population is Facebook SNS users. A software tool called AMOS 18 is used to analyze the structural equation model. Findings The results showed that maintenance of SNS, security concern and psychological concern had a positive effect on SNS fatigue respectively and upward/lateral comparison did on negative emotion. In addition, SNS fatigue and negative emotion had significant effect on discontinuance intention.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.79-86
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• 2021

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.297-307
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• 2021

In the vibrant environment, documentation and managing systems are maintained autonomously through education foundations, book materials and libraries at the same time as information are not voluntarily accessible in a centralized location. At the moment Libraries are providing online resources and services for education activities. Moreover, libraries are applying outlets of social media such as Facebook as well as Instagrams to preview their services and procedures. Librarians with the assistance of promising tools and technology like analytics software are capable to accumulate more online information, analyse them for incorporating worth to their services. Thus Libraries can employ big data to construct enhanced decisions concerning collection developments, updating public spaces and tracking the purpose of library book materials. Big data is being produced due to library digitations and this has forced restrictions to academicians, researchers and policy creator's efforts in enhancing the quality and effectiveness. Accordingly, helping the library clients with research articles and book materials that are in line with the users interest is a big challenge and dispute based on Taibah university in Saudi Arabia. The issues of this domain brings the numerous sources of data from various institutions and sources into single place in real time which can be time consuming. The most important aim is to reduce the time that lapses among the authentic book reading and searching the specific study material.