• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.745-748
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• 2014

SentiWordNet is an important lexical resource supporting sentiment analysis in opinion mining applications. In this paper, we propose a novel approach to construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated from WordNet in which each synset has numerical scores to indicate its opinion polarities. Many previous studies obtained these scores by applying a machine learning method to WordNet. However, Vietnamese WordNet is not available unfortunately by the time of this paper. Therefore, we propose a method to construct VSWN from a Vietnamese dictionary, not from WordNet. We show the effectiveness of the proposed method by generating a VSWN with 39,561 synsets automatically. The method is experimentally tested with 266 synsets with aspect of positivity and negativity. It attains a competitive result compared with English SentiWordNet that is 0.066 and 0.052 differences for positivity and negativity sets respectively.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.909-918
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• 2020

This study, not only investigates the important role of lifelong learning in shaping young people's knowledge and in maximizing their potential, but also aims to shed light on the influencing factors of lifelong learning of young people in Vietnam. The author applied STATA and SPSS to analyze quantitative data collected from questionnaires with 332 respondents aged between 19 years old and 24 years old. Based on a holistic review of literature, this study concludes that four driver factors affect young people's lifelong learning ability, comprising: organizational culture, motivation, human resource development, and domestic private type of enterprise. The results emphasize the positivity of organizational culture, human resource development, and the nature of work, especially organizational culture and human resource development, which are dominant reasons for young people to maintain lifelong learning. The relationship between demographics and lifelong learning was tested and it indicated that male has a stronger interest in learning than female. The result of the study also shows the impact of different types of business sectors on employees' learning intentions. It points out that the domestic private type of enterprise is the most effective factor that has a positive relationship with the lifelong learning of the individual.

• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.402-408
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• 2020

Purpose: To understand the students' level of recognition of online classes in the Department of Dental Technology and to provide the basic data for designing online classes based on the dental technology course. Methods: A survey was conducted among the students of the dental technology department. The collected data was analyzed with the SPSS ver. 25.0 program. To ensure a reliable verification, the α=0.05 significance level was used. The t-test and analysis of variance were also performed. Results: The students' level of recognition of online classes in the Department of Dental Technology is shown in the rate of recognition for video-based classes for both the theory and experiments. Students displayed high positivity with the video-based learning as it is repeated learning that is not affected by the limitations of time. In addition, video-based learning is highly beneficial in terms of convenience, satisfaction, and achievement for learning. Conclusion: Based on the results, video-based learning is a highly positive learning type for students. It was also recommended that the Department of Dental Technology should offer a post-COVID-19 online class to include the blended methods of a face-to-face class and video-based learning.

• Biomedical Science Letters
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• v.25 no.1
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• pp.99-106
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• 2019

Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish ${\beta}$-Amyloid ($A{\beta}$) positive from $A{\beta}$ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). $^{18}F$-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for $A{\beta}$ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for $A{\beta}$ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify $^{18}F$-Florbetaben amyloid brain PET image for $A{\beta}$ positivity using PCA-SVM model, with no additional effects on GMM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1903-1908
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• 2008

The present time, their affect on modern teaching-learning methodology, especially that of cooperative learning is most noticeable through E-learning, ICT(Information Communication Technology), application of Computer, and the Internet. This paper evaluate and analyze a cooperation with computer which affects to the achievement degree for studying. Especially, this paper analyzes the degree and duration of computer usage, and then analyzes the ripple effects on the individuals positivity and participation. And, this paper estimate the different of students' scholastics attainment according to sex, environment and computer usage ability.

• Journal of the Korean Society of Radiology
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• v.84 no.3
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• pp.638-652
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• 2023

Purpose To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity. Results The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes. Conclusion The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.61-68
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• 2022

The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word's coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.

• Dementia and Neurocognitive Disorders
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• v.22 no.2
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• pp.61-68
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• 2023

Background and Purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.261-268
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• 2021

After developing literacy education programs using coaching techniques, applying them to literacy learners, in order to see how they have an impact on improving learning achievement, we selected 13 senior literacy learners in U city and chose qualitative research method based on in-depth interviews, observation journals, and learning materials. Literature education programs using coaching techniques are a process-oriented model consisting of four stages of mind-opening, introducing positivity, strengthening learning competence and assistance, confidence and persistence. You can find the results as following. Firstly, communication between teachers and learners was expanded in the first stage, and secondly, self-directed learning ability was strengthened in the second stage by forming a positive mind. Thirdly, the results of utilizing the three-stage balanced literacy teaching method and interaction teaching method resulted in confidence in reading and writing, leading to an increase in self-efficacy. Fourthly, the fourth stage showed the results of improving learning achievement, which overcame the fear of learning with active praise and continuous encouragement and implied hope for higher courses. As a result of the above-mentioned research, I think literacy education programs using coaching techniques can be useful as an educational method for learners in the field of literacy education.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.221-232
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• 2019

This study aimed at finding out special education teachers' failure and success factors in teacher recruitment examinations. Total of 24 special education teachers participated in the semi-structured interview and 12 separate semantic themes were extracted via continuous comparative analysis on the interview contents. The findings were the following. First, the identified factors for the failures on the examinations were merely following what others do, failure-causing learning strategies, unconditional memorization, ineffective study groups, anxiety and lack of confidence, and lack self-management issue. Second, the identified factors for the success on the examinations were my style of study habits, success-causing learning strategies, balance of understanding and memorization, effective study groups, positivity, and strong self routine. The research proposes several practical applications to prepare the exam regarding this results.