• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.253-256
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• 2017

This paper deals with the implementation of speech disorder detection system based on machine learning classification. Problems with speech are a common early symptom of a stroke or other brain injuries. Therefore, detection of speech disorder may lead to correction and fast medical treatment of strokes or cerebrovascular accidents. The speech disorder system can be implemented by extracting features from the input speech and classifying the features using machine learning algorithms. Ten machine learning algorithms with various scaling methods were used to discriminate speech disorder from normal speech. The detection system was evaluated by the TORGO database which contains dysarthric speech collected from speakers with either cerebral palsy or amyotrophic lateral sclerosis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.653-658
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• 2015

Cloud service is one of major challenges in IT industries. In cloud environment, service providers predict dynamic user demands and provision resources to guarantee the QoS to cloud users. The conventional prediction models guarantee the QoS to cloud user, but don't guarantee profit of service providers. In this paper, we propose a new resource allocation mechanism using Q-learning algorithm to provide the QoS to cloud user and guarantee profit of service providers. To evaluate the performance of our mechanism, we compare the total expense and the VM provisioning delay with the conventional techniques with real data.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.14 no.2
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• pp.27-40
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• 2003

In the society of lifelong learning, a school library has to play an important educational role in improving self-directed learning ability that school education is seeking. First of all, This study makes it clear that the goal of school library use education is to develop self-directed learning ability. It carries out a research into the present condition of school library use education which is an important educational role of a school library. Finally this study proposes some improvement plans in school library use education, as mentioned below : 1. Building an administration system of school library media center. 2. Re-establishing information literacy as culture education 3. Team teaching by a curriculum based approach

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.555-560
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• 2006

In contrast with the earlier remote education that simply recorded off-line classes, modern remote education emphasizes on offering additional functions that could maximize learning efficiency. Usage of such multimedia information as the texts, graphics, sounds, animations is considered fundamental element in offering the additional functions. This paper designs and implements an encoder/decoder that could accommodate the multimedia information with emphasis on demonstrating execution files. Instructors can demonstrate my type of execution files or application data files, and the remote learners can freely try running the corresponding execution files by themselves. Consequently, a high-level of learning efficiency can be achieved by the proposed encoder/decoder.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.294-300
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• 2021

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Journal of the Korean Society of School Health
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• v.36 no.2
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• pp.21-30
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• 2023

Purpose: The study aimed to identify factors that affect college students' learning immersion in non-face-to-face remote classes. Methods: During COVID-19, a survey was conducted on 140 college students who were taking non-face-to-face remote courses at universities located in Seoul, Gyeonggi-do, and Chungcheong-do, Korea. Data were analyzed using the Pearson correlation coefficients, Independent t-test, ANOVA, and Hierarchial stepwise multiple regression with SPSS (Windows version 27.0). Results: In the study, the most important variable influencing learning immersion was the student's self-efficacy, followed by instructor presence, class participation, lecture satisfaction, and credits. Conclusion: Instructors who teach major courses at college need to develop and apply ways to enhance learners' self-efficacy and class content that can boost learners' motivation in order to maximize learners' learning immersion. In order to facilitate learners' access to online media and maintain their interest in remote classes, passionate efforts need to be made by active instructors.

• 한국정보교육학회:학술대회논문집
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• 2010.08a
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• pp.127-134
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• 2010

In the information oriented society, a good lesson helps learners to develop self-learning ability. To give a good lesson, teachers should teach with details in learning contents that learners can understand easily, not with abstract and generalized concepts in the textbook. And they also should find the effective instructional media to give students the learning contents. Since the newspaper carries the most current and up-to-date source of information about various fields, if it is used as the teaching material in the classroom, it can help students to learn useful and practical knowledge. Therefore, newspapers are so-called 'living textbook'. NIE which enables students to enhance their ability to utilize the information and to learn wisdom about life, is already actively applied to the educational activities. And e-learning system and the electronic newspaper that make learning happen to anyone anytime anywhere, appears with the advancements in information technology and changing educational paradigm. This changed circumstance make the field and the way of NIE enlarge to e-NIE. e-NIE is the educational media that helps the learners to learn the way of learning. But the study about e-NIE through designing a teaching plan systematically has not been made completely. Therefore, this research aims to find out the characteristics of e-NIE and the effective way of designing a teaching plan that fits the characteristics of e-NIE.

• Smart Media Journal
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• v.9 no.4
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• pp.26-35
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• 2020

Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.