• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Molecules and Cells
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• v.45 no.7
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• pp.465-478
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• 2022

MicroRNAs (miRNAs) are a class of small non-coding RNAs that regulate the expression of target messenger RNA (mRNA) complementary to the 3' untranslated region (UTR) at the post-transcriptional level. Hsa-miR-422a, which is commonly known as miRNA derived from transposable element (MDTE), was derived from short interspersed nuclear element (SINE). Through expression analysis, hsa-miR-422a was found to be highly expressed in both the small intestine and liver of crab-eating monkey. AT-Rich Interaction Domain 5 B (ARID5B) was selected as the target gene of hsa-miR-422a, which has two binding sites in both the exon and 3'UTR of ARID5B. To identify the interaction between hsa-miR-422a and ARID5B, a dual luciferase assay was conducted in HepG2 cell line. The luciferase activity of cells treated with the hsa-miR-422a mimic was upregulated and inversely downregulated when both the hsa-miR-422a mimic and inhibitor were administered. Nuclear factor erythroid-2 (NF-E2) was selected as the core transcription factor (TF) via feed forward loop analysis. The luciferase expression was downregulated when both the hsa-miR-422a mimic and siRNA of NF-E2 were treated, compared to the treatment of the hsa-miR-422a mimic alone. The present study suggests that hsa-miR-422a derived from SINE could bind to the exon region as well as the 3'UTR of ARID5B. Additionally, hsa-miR-422a was found to share binding sites in ARID5B with several TFs, including NF-E2. The hsa-miR-422a might thus interact with TF to regulate the expression of ARID5B, as demonstrated experimentally. Altogether, hsa-miR-422a acts as a super enhancer miRNA of ARID5B by collaborating with TF and NF-E2.

• Journal of Creative Information Culture
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• v.7 no.4
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• pp.243-255
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• 2021

The purpose of this study is to investigate the effect of software education based on physical computing on the CT ability of elementary school students. To this end, previous studies related to physical computing software education and software education in the 2015 revised curriculum were analyzed. In addition, COBL was selected among many physical computing tools on the market in consideration of the level and characteristics of learners in the school to conduct the study, and 'Professor Lee Jae-ho's AI Maker Coding with COBL' was used as the textbook. This study was conducted for 10 sessions on 135 students in 6 classes in 6th grade of H Elementary School located in Pyeongtaek, Gyeong gi-do. The results of this study are as follows. First, it was confirmed that physical computing software education linked to real life was effective in improving the CT ability of elementary school students. Second, the change in competency of CT ability by sector improved evenly from 8 to 30 points in the pre-score and post-score of computing thinking ability. Third, in this study, it was confirmed that 87% of students were very positive as a result of a survey of satisfaction with classes after real-life physical computing software education. We hope that follow-up studies will help select various regions across cities and rural areas, and prove that real-life physical computing software education for various learner members, including large and small schools, will help elementary school students improve their CT ability.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Korean Association of Arts Management
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• no.57
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• pp.5-32
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• 2021

The purpose of this study is to find out what kind of experience docent programs provide to visitors in museums by means of Giorgi's phenomenological method. In-depth interview was conducted with 6 visitors who had experienced firsthand. As a result of the coding based upon Giorgi's method, it was divided into 6 categories and 21 subcategories, and the following results were obtained. First, the reason that the subjects of the study participated in the docent program was due to factors such as information, coincidence, induction of companions, and habits. Second, from participating in the docent guide, they felt that the docent led them to actively visit the exhibition, get the educational effect, and generate interest and curiosity. Third, looking at the reaction after participating in the docent guide, in addition to the positive influence, the docent's reading-like explanation and the problem of the microphone facility were negative experiences. Through this study, it was confirmed that there were many visitors who recognized that the docent guide was helpful in viewing the exhibition and experienced positive reactions. In addition, in the evaluation of the commentary of docent, there was a difference of views between art-related majors and non-majors. In addition, as a result of analyzing the participants' experiences according to Holt's frame of experiential consumption, it was found that the docent experience was a comprehensive consumption behavior appearing in all four fields.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.189-199
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• 2023

This study aimed to identify the development direction of Arduino-based boards relating to artificial intelligence based on social awareness identified using big data analytical methods. For the purpose, big data were extracted through the Textom website, focusing on keywords that included 'Arduino + artificial intelligence' and 'Arduino + AI', and these data were refined and analyzed using the Textom website and the UNICET program. In this study, big data analyses, including frequency analysis, TF-IDF analysis, Degree Centrality analysis, N-gram analysis, and CONCOR analysis, were performed. The analyses' results confirmed that keywords relating to education and coding education, keywords relating to making and experience based on Arduino, and keywords relating to programs were the main keywords used in Arduino- and artificial intelligence-related Internet documents, and clusters were formed based on these keywords confirmed. The social awareness of Arduino and artificial intelligence was evaluated, and the direction of board development was identified based on this social awareness. This study is meaningful in that it identified various factors of board development based on the general public's social awareness, which was evaluated using a big data analysis method. This study may serve as a point of reference for future researchers or developers wishing to understand user needs using big data analysis methods.

• Journal of The Korean Association of Information Education
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• v.26 no.3
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• pp.197-207
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• 2022

In this paper, we develop a machine learning based block code generation and recommendation model for the purpose of reducing cognitive load of learners during coding education that learns the learner's block that has been made in the block programming environment using natural processing model and fine-tuning and then generates and recommends the selectable blocks for the next step. To develop the model, the training dataset was produced by pre-processing 50 block codes that were on the popular block programming language web site 'Entry'. Also, after dividing the pre-processed blocks into training dataset, verification dataset and test dataset, we developed a model that generates block codes based on LSTM, Seq2Seq, and GPT-2 model. In the results of the performance evaluation of the developed model, GPT-2 showed a higher performance than the LSTM and Seq2Seq model in the BLEU and ROUGE scores which measure sentence similarity. The data results generated through the GPT-2 model, show that the performance was relatively similar in the BLEU and ROUGE scores except for the case where the number of blocks was 1 or 17.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.291-303
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• 2021

The purpose of this study is to identify the effects of laughter therapy on depression and sleep wake disorders among the elderly in residential facilities using a systemic review and meta-analysis. Twelve databases were searched. Two researchers independently performed the selection of the studies, data coding and assessment. The risk of bias was assessed using risk of bias (RoB) and risk of bias assessment tool for non-randomized studies (RoBANs). To estimate the effect size, meta-analysis of the studies was performed using R version 4.04. Out of the 1,122 retrieved articles, one randomized controlled trial (RCT) and eleven non-randomized controlled trials (non-RCTs) were selected for analysis. The overall effect size of eleven studies on depression was determined to be -1.04 (95% Cl: -1.53~-0.54, p<.001). There were statistically significant in the effect of below ten sessions and the effect of below 400 minutes'and 400 to 1000 minutes'interventions on depression. The overall effect size of five studies on sleep wake disorders was 0.83 (95% Cl: -0.26~1.93, p=.136), which was not statistically significant. There was statistically significant in the effect of below 300 minutes'interventions on sleep wake disorders. Laughter therapy was an effective non-pharmacological intervention to reduce depression among the elderly in residential facilities. The findings also suggest that guidelines for laughter therapy need to be developed considering the number of sessions and a duration of intervention to reduce depression and sleep wake disorders of the elderly in residential facilities.

• The Plant Pathology Journal
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• v.38 no.5
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• pp.541-549
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• 2022

Potato late blight caused by Phytophthora infestans is a destructive disease in Korea. To elucidate the genomic variation of the mitochondrial (mt) genome, we assembled its complete mt genome and compared its sequence among different haplotypes. The mt genome sequences of four Korean P. infestans isolates were revealed by Illumina HiSeq. The size of the circular mt genome of the four major genotypes, KR_1_A1, KR_2_A2, SIB-1, and US-11, was 39,872, 39,836, 39,872, and 39,840 bp, respectively. All genotypes contained the same 61 genes in the same order, comprising two RNA-encoding genes, 16 ribosomal genes, 25 transfer RNA, 17 genes encoding electron transport and ATP synthesis, 11 open reading frames of unknown function, and one protein import-related gene, tatC. The coding region comprised 91% of the genome, and GC content was 22.3%. The haplotypes were further analyzed based on sequence polymorphism at two hypervariable regions (HVRi), carrying a 2 kb insertion/deletion sequence, and HVRii, carrying 36 bp variable number tandem repeats (VNTRs). All four genotypes carried the 2 kb insertion/deletion sequence in HVRi, whereas HVRii had two VNTRs in KR_1_A1 and SIB-1 but three VNTRs in US-11 and KR_2_A2. Minimal spanning network and phylogenetic analysis based on 5,814 bp of mtDNA sequences from five loci, KR_1_A1 and SIB-1 were classified as IIa-6 haplotype, and isolates KR_1_A2 and US-11 as haplotypes IIa-5 and IIb-2, respectively. mtDNA sequences of KR_1_A1 and SIB-1 shared 100% sequence identity, and both were 99.9% similar to those of KR_2_A2 and US-11.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.452-459
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• 2022

One of the important factors among the smart farm factors is environmental measurement. This study tried to design an environmental measurement monitoring system through Bluetooth wireless communication with LoRa using the open source programs Arduino, App Inventor, and Node Red. This system consists of Arduino, LoRa shield, temperature and humidity sensor (SHT10), and carbon dioxide sensor (K30). The environmental measurement system is configured as a system that allows the sensor to collect environmental data and transmit it to the user through wireless communication to conveniently monitor the farm environment. As libraries used in the Arduino program, LoRa.h, Sensirion.h, LiquidCrystal_I2C.h and K30_I2C.h were used. When receiving environmental data from the sensor at regular intervals, coding using average value was used for data stabilization. An Android-based app was developed using Node Red and App Inventor program as the user interface. It can be seen that the environmental data for the sensor is well collected with the screen output to the serial screen of Arduino, the screen of the smartphone, and the user interface of Node Red. Through these open source-based platforms and programs will be applied to various agricultural applications.