• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.305-316
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• 2021

The objective is to recognize the better opportunities from targeted reveal advertising, to show a banner ad to the consumer of online who is most expected to obtain a preferred action like signing up for a newsletter or buying a product. Discovering the most excellent commercial impression, it means the chance to exhibit an advertisement to a consumer needs the capability to calculate the probability that the consumer who perceives the advertisement on the users browser will acquire an accomplishment, that is the consumer will convert. On the other hand, conversion possibility assessment is a demanding process since there is tremendous data growth across different information dimensions and the adaptation event occurs infrequently. Retailers and manufacturers extensively employ the retail services from internet as part of a multichannel distribution and promotion strategy. The rate at which web site visitors transfer to consumers is low for online retail, out coming in high customer acquisition expenses. Approximately 96 percent of web site users concluded exclusive of no shopper purchase[1].This category of conversion rate is collected from the advertising of social media sites and pages that dataset must be estimating and assessing with the concept of big data clustering, which is used to group the particular age group of people along with their behavior. This makes to identify the proper consumer of the production which leads to improve the profitability of the concern.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.241-248
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• 2021

The article reviews the social media as an Internet phenomenon, determines their place and level of popularity in the society, as a result of which the social networks are a resource with perspective pedagogical potential. The analysis of social media from the point of view of studying a foreign language and the possibility of their usage as a learning medium has been carried out. The most widespread and popular platforms have been considered and, based on their capabilities in teaching all types of speech activities, the "Instagram", "Twitter", and "Facebook" Internet resources have been selected as the subject of the research. The system of tasks of teaching all types of speech activities and showing the advantages of the "Instagram", "Twitter", and "Facebook" platforms has been proposed and briefly reviewed.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.305-311
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• 2022

This research aimed to provide a profound description of the practices of social media users in the Kingdom of Saudi Arabia (KSA), specifically the users of Facebook^® (FB) and Snapchat^® (SC), the reasons for these practices, decisions made, and the people involved. Such research would be of significant help to designers and policymakers of social media applications in understanding user practices when using social media applications and the reasons for such practices in the KSA. This better comprehension would be of significant help in improving current applications and creating new ones. According to the data analysis, there was a clear preference for SC over FB in the KSA. Most participants with SC accounts were described as very active users, accessing their accounts at least once a day compared to FB users. The users were led by this high preference for SC to create new words derived from the name of the application and use them in daily life. We showed our experience of carrying out a study in which the main objective was to collect factual empirical data from participants about their daily usage of social media applications while considering the unique cultural settings in the KSA. Mixed quantitative and qualitative methods were used to triangulate the data, increasing its trustworthiness and validity. Multiple perspectives were obtained using various data collection methods. Therefore, conclusions would not be confounded with limitations of any particular methodology or with conditions of any collection rounds. This research would constitute a valuable guide for researchers intending to use methods with male and female informants from different cultures, preparing them for potential challenges and suggesting possible solutions.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• 한국컴퓨터정보학회논문지
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• 제24권11호
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• pp.195-200
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• 2019

이 연구의 목적은 언론 보도 분석을 통해 우리나라에서 경비원에 대한 인식과 이미지를 살펴보고 이를 통해 경비원의 지위와 역할에 대해 살펴보는데 있다. 연구방법은 뉴스 빅데이터 분석이 가능한 빅카인즈를 통해 경비원에 대한 키워드 트랜드와 연관어 분석을 실시하였다. 민간경비의 시대적 구분에 따라 정착기, 성장기(양적), 성장기(질적)으로 구분하여 분석한 결과 범죄, 경비업, 최저임금, 갑질에 관련된 언론의 관심과 노출이 많았던 것으로 나타났지만 범죄예방의 주체가 아닌 범죄와 갑질의 피해자, 경비업무의 애매모함, 최저임금 근무자로 근무환경이 열악한 직업의 이미지로 비춰지는 것으로 나타났다. 앞으로 경비원의 이미지 제고를 위해 경비원의 지위와 업무영역을 확고히 하고 전문성을 높여야 할 것이다.

• 한국IT서비스학회지
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• 제23권2호
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• pp.13-29
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• 2024

Recently, with the rapid spread of mobile terminals such as smartphones and tablet PCs, social demand for mobile information security is increasing as new security issues that are difficult to predict as well as service evolution and lifestyle changes are raised. Smart terminals include smartphones, smart pads, chromebooks, laptops, etc. that provide various functions such as phone calls, text messages, Internet browsing, social media apps, games, and education. Along with the explosive spread of these smart terminals, they are naturally being used in our daily life and educational environment. In the mobile environment, behind the convenience of portability, there are more various security threats and vulnerabilities than in the general PC environment, and threats such as device loss, information leakage, and malicious codes exist, so it is necessary to take fundamental security measures at a higher level. In this study, we suggest ways to improve security by identifying trends in mobile smart information security and effectively responding to security threats to the mobile environment. In addition, it presents implications for various measures for effective class utilization along with correct security management methods and security measures related to the supply of smart devices that the Office of Education is promoting for schools at each level.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.97-104
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• 2022

In recent years, data privacy has become increasingly important. The goal of network cryptography is to protect data while it is being transmitted over the internet or a network. Social media and smartphone apps collect a lot of personal data which if exposed, might be damaging to privacy. As a result, sensitive data is exposed and data is shared without the data owner's consent. Personal Information is one of the concerns in data privacy. Protecting user data and sensitive information is the first step to keeping user data private. Many applications user data can be found on other websites. In this paper, we discuss the issue of privacy and suggest a mechanism for keeping user data hidden in other applications.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.67-80
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• 2023

Rice is an important food crop for most of the population in Nowadays, psychologists consider social media an important tool to examine mental disorders. Among these disorders, depression is one of the most common yet least cured disease Since abundant of writers having extensive followers express their feelings on social media and depression is significantly increasing, thus, exploring the literary text shared on social media may provide multidimensional features of depressive behaviors: (1) Background: Several studies observed that depressive data contains certain language styles and self-expressing pronouns, but current study provides the evidence that posts appearing with self-expressing pronouns and depressive language styles contain high emotional temperatures. Therefore, the main objective of this study is to examine the literary cyber writers' posts for discovering the symptomatic signs of depression. For this purpose, our research emphases on extracting the data from writers' public social media pages, blogs, and communities; (3) Results: To examine the emotional temperatures and sentences usage between depressive and not depressive groups, we employed the SentiStrength algorithm as a psycholinguistic method, TF-IDF and N-Gram for ranked phrases extraction, and Latent Dirichlet Allocation for topic modelling of the extracted phrases. The results unearth the strong connection between depression and negative emotional temperatures in writer's posts. Moreover, we used Naïve Bayes, Support Vector Machines, Random Forest, and Decision Tree algorithms to validate the classification of depressive and not depressive in terms of sentences, phrases and topics. The results reveal that comparing with others, Support Vectors Machines algorithm validates the classification while attaining highest 79% f-score; (4) Conclusions: Experimental results show that the proposed system outperformed for detection of depression trends in literary cyber writers using sentiment analysis.