• International Journal of Knowledge Content Development & Technology
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• v.14 no.3
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• pp.59-76
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• 2024

The prominent role accorded to social media in the academic community for research, teaching and learning revolves around its significance among users. Social media offers a platform for individuals to engage with and share perceptions relating to different disciplines. This current research was conducted to investigate the level of awareness and frequency of social media technology use among postgraduate students of Library and Information Science in Nigerian universities. The descriptive survey design was used for the study. Structured questionnaires were used to collect data from 919 library and information science (LIS) postgraduate students in the universities. In all, 742 copies out of the 919 distributed were returned and found usable, thereby making the return rate to be 81%. Data collected were analysed using mean and standard deviation. The study revealed that the LIS postgraduate students frequently use social media such as Wikipedia (x=3.94>3.50), Instagram (x=3.86>3.50), Facebook (x=3.85>3.50), Zoom ($\overline{x}$=3.78>3.50), LinkedIn (x=3.69>3.50), YouTube ($\overline{x}$=3.54>3.50), Twitter (x=3.52>3.50). The study established that students use social media tools for their personal, professional and research activities. The study also found that the level of awareness and use of social media by the students was high. The study recommended that the use of social media should be incorporated into the LIS curriculum including training sessions for the students on how to use the media effectively.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.309-316
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• 2022

This study is a study on the YouTube e-learning advanced service plan in the non-face-to-face era. The trends in education change were examined through literature research and prior research, and improvement measures were suggested through online surveys and in-depth interviews. As for the research method, the first online survey was conducted based on the Honeycomb model and the Likert 5-point scale targeting 90 MZ generation who have experience learning on YouTube for a total of 14 days from October 15 to 28, 2021. A second in-depth interview was conducted with 6 people who answered that the frequency of learning through YouTube is high. As a result of the experiment, users thought that there was an improvement point according to the purpose of learning, and they were able to derive elements that felt a problem in common. In addition, I proposed a new YouTube learning platform through additional questions. Through this study, it is expected that YouTube e-learning service reference materials can be used to respond to the post-non-face-to-face era.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• The Journal of Korean Academic Society of Nursing Education
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• v.4 no.2
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• pp.204-219
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• 1998

The use of instructional media in nursing education was investigated using a descriptive research methodology. Data were collected from 199 professors teaching in the areas of Fundamental Nursing (48 subjects), Adult Nursing(56 subjects), Pediatric Nursing(49 subjects), and Community Health Nursing(46 subjects). 120 professors were from 3-year college of nursing and 79 professors were from 4-year college of nursing, Data were analyzed using descriptive statistics (mean, standard deviation), and ANOVA with SPSS $PC^+$ program. The results are as follows. 1) The general attitude of the subjects toward the use of instructional media was positive(mean : 3.75). However, from the ANOVA result the subjects from 4-year college of nursing had more positive attitudes in the areas of media utilization and supply system. Most subjects had high interest in the areas of effectiveness of the media, and media literacy in using instructional media. 2) OHP(mean was 3.76) and VTR(mean was 3.36) were the most used instructional media in nursing education. These media were efficiently supplied by the school. However, other media like CD-ROM, Opaque Projector, and LCD or beam Projector were not sufficiently provided by the school. 3) The main reasons to use instructional media were that the use of media is effective to raise students' attention and learning motivation. 4) Insufficiency of the media and environment to use media were the causes of the lack of using instructional media. 5) The use of PC communication of internet, LCD or beam Projector, and Computer Graphics was low. The reasons were that the subjects did not have enough knowledge and skills to use these media, and there was lack of media or environment. 6) In general, environment to use media of the 3-year college of nursing was worse than that of 4-year college of nursing. However, there was no significant differences between the two groups in the use of media related to their position, and subject. On the basis of the study results professors teaching nursing have positive attitudes to use instructional media but the lack of media supply or facility to use media limited the use of instructional media.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.163-170
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• 2002

Previous buffer cache schemes for continuous media file system only exploited the sequentiality of continuous media accesses and didn't consider looping references. However, in some video applications like foreign language learning, users mark the scene as loop area and then application automatically playbacks the scene several times. In this paper, we propose a novel buffer cache scheme for continuous media file system that sequential and looping references exist together. Proposed scheme increases the cache hit ratio by detecting reference pattern of files and appling an appropriate replacement policy to each file.

• The Mathematical Education
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• v.40 no.2
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• pp.265-289
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• 2001

The elementary and middle school curriculum in Korea has been modified periodically to reach today's 7th national curriculum. Although the intent of each new curriculum was to improve education, lack of proper preparation for teachers and students has not made the new curriculums as effective as it could be. Goodlad et al.(1979) suggested that curriculum should encompass all practices including not only knowledge but all the elements of the curriculum and experiences of the student and teachers. The purpose of this paper is to investigate the actual practices of the current curriculum with focus on the use of instructional media in mathematics teaching and learning. A nationwide curriculum survey was carried out with the Goodlad's curriculum inquiry model as the framework. The result shows that elementary and secondary mathematics teachers used textbook manual (for teachers) and practice books most frequently for their class preparation. In addition to these, mathematics teachers also used manipulatives, visual aids, computers, internet, and calculators in a decreasing order. In general, many mathematics teachers did not use much instructional media in their classes and said that there are not enough effective instructional media to use. However, the teachers have positive attitude toward the educational media that they have used. In this study, we analyzed the survey data regarding educational tools, their use and effects to support the development of a new curriculum model in mathematics for a knowledge-based society.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.117-122
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• 2022

This paper proposes a method for evaluating the work of manufacturing workers using MediaPipe as a risk factor for musculoskeletal diseases. Recently, musculoskeletal disorders (MSDs) caused by repeated working attitudes in industrial sites have emerged as one of the biggest problems in the industrial health field while increasing public interest. The Korea Occupational Safety and Health Agency presents tools such as NIOSH Lifting Equations (NIOSH), OWAS (Ovako Working-posture Analysis System), Rapid Upper Limb Assessment (RULA), and Rapid Entertainment Assessment (REBA) as ways to quantitatively calculate the risk of musculoskeletal diseases that can occur due to workers' repeated working attitudes. To compensate for these shortcomings, the system proposed in this study obtains the position of the joint by estimating the posture of the worker using the posture estimation learning model of MediaPipe. The position of the joint is calculated using inverse kinetics to obtain an angle and substitute it into the REBA equation to calculate the load level of the working posture. The calculated result was compared to the expert's image-based REBA evaluation result, and if there was a result with a large error, feedback was conducted with the expert again.