• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• Steel and Composite Structures
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• v.49 no.1
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• pp.65-79
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• 2023

This paper presents a development of empirical evaluations, which can be used to evaluate the damage of fiber-reinforced concrete composites (FRC) wall subjected to close-in blast loads. For this development, a combined application of numerical simulation and machine learning approaches are employed. First, finite element modeling of FRC wall under blast loading is developed and verified using experimental data. Numerical analyses are then carried out to investigate the dynamic behavior of the FRC wall under blast loading. In addition, a data set of 384 samples on the damage of FRC wall due to blast loads is then produced in order to develop machine learning models. Second, three robust machine learning models of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) are employed to propose empirical evaluations for predicting the damage of FRC wall. The proposed empirical evaluations are very useful for practical evaluation and design of FRC wall subjected to blast loads.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.109-121
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• 2003

The point of this research is the use of the high resolution satellite image for local government's new address system, as well as spatially field investigation support and base map error finding. Most local governments use scale 1/1,000 and 1/5,000 digital map for base map and field investigation. But field investigator's knowledge insufficiency and the lack of base map's currency make things too difficult from the beginning of the project. As the way of solving this problem, this research offers the use of the high resolution satellite image in new address system with cadence data of digital base map. Until now satellite image is not suitable for our situation because it has low resolution. But this problem was solved for 1m space resolution satellite image and it is being applied wider and wider. Now vector data and Raster data are integrated for complimenting of each weak point. In this study the use of the high resolution satellite image in new address system is expected to improve the quality of the results and reduce the expenses. In addition the satellite image can use local government's fundamental data.

• Journal of Plant Biotechnology
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• v.35 no.4
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• pp.275-280
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• 2008

One of the limitation for Agrobacterium-mediated transformation via organogenesis from cotyledon explants routinely in cucumber is the production of chimeric plants. To overcome the limitation, Agrobacterium-mediated transformation system via somatic embryogenesis from hypocotyl explants of cucumber (c.v., Eunsung) on the selection medium with paromomycin as antibiotics was developed. The hypocotyl explants were inoculated with Agrobacterium tumefaciens strain EHA101 carrying binary vector pPTN290; then were subsequently cultured on the following media: co-cultivation medium for 2 days, selection medium for $5{\times}14$ days, and regeneration medium. The T-DNA of the vector (pPTN290) carried two cassettes, Ubi promoter-gus gene as reporter and 35S promoter-nptll gene conferring resistance to paromomycin as selectable agent. The confirmation of stable transformation and the efficiency of transformation was based on the resistance to paromomycin indicated by the growth of putative transgenic calli on selection medium amended with 100mg/L paromomycin, and GUS gene expression. Forty eight clones (5.2%) with GUS gene expressed of 56 callus clones with resistance to paromomycin were independently obtained from 928 explants inoculated. Of 48 clones, transgenic plants were only regenerated from 5 clones (0.5%) at low frequency. The histochemical GUS assay in the transgenic seeds ($T_1$) also revealed that the gus gene was successfully integrated and segregated into each genome of transgenic cucumber.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.169-186
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• 2018

The social influence of the elderly population will accelerate in a rapidly aging society. The purpose of this study is to establish a methodology for extracting vulnerable districts of the welfare of the aged through machine learning(ML), artificial neural network(ANN) and geospatial analysis. In order to establish the direction of analysis, this progressed after an interview with volunteers who over 65-year old people, public officer and the manager of the aged welfare facility. The indicators are the geographic distance capacity, elderly welfare enjoyment, officially assessed land price and mobile communication based on old people activities where 500 m vector areal unit within 15 minutes in Yongin-city, Gyeonggi-do. As a result, the prediction accuracy of 83.2% in the support vector machine(SVM) of ML using the RBF kernel algorithm was obtained in simulation. Furthermore, the correlation result(0.63) was derived from ANN using backpropagation algorithm. A geographically weighted regression(GWR) was also performed to analyze spatial autocorrelation within variables. As a result of this analysis, the coefficient of determination was 70.1%, which showed good explanatory power. Moran's I and Getis-Ord Gi coefficients are analyzed to investigate spatially outlier as well as distribution patterns. This study can be used to solve the welfare imbalance of the aged considering the local conditions of the government recently.

• Journal of Plant Biotechnology
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• v.34 no.4
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• pp.285-291
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• 2007

This study describes conditions for the mass production of activation-tagged mutant hairy root lines of ginseng by cocultivation with Agrobacterium rhizogenes. Because it is not currently possible to produce progeny from transgenic ginseng, a loss-of-function approach for functional genomics cannot be appliable to this species. A gain-of-function approach is alternatively the choice and hairy root production by cocultivation of A. rhizogenes would be most practical to obtain a large number of mutants. Various sources of explants were subjected to genetic transformation with various strains of A. rhizogenes harboring the activation-tagging vector pKH01 to determine optimum conditions for the highest frequency of hairy root formation on explants. Petiole explants cocultivated with A. rhizogenes R1000 produced hairy roots at a frequency of 85.9% after 4 weeks of culture. Conditions for maximum growth or branching rate of hairy roots were also investigated by using various culture media. Petiole explants cultured on half strength Schenk and Hildebrandt medium produced vigorously growing branched roots at a rate of 2.6 after 4 weeks of culture. A total of 1,989 lines of hairy root mutants were established in this study. These hairy root lines will be useful to determine functions of genes for biosynthesis of ginsenosides.

• Management & Information Systems Review
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• v.38 no.1
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• pp.141-153
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• 2019

This study attempts to analyze the role of price discovery and the dynamic interdependence between KOSPI200 Index and KODEX Leverage(KODEX inverse), which are Korea's representative ETFs, using the vector error correction model. For the empirical analysis, one minute data of KODEX leverage, KODEX inverse and KOSPI200 index from April 10, 2018 to July 10, 2018 were used. The main results of the empirical analysis are as follows. First, between KODEX Leverage and KOSPI200 index, we found evidence that KODEX leverage plays a dominant role in price discovery. In addition, the KOSPI200 index is superior to price discovery between KODEX inverse and KOSPI200 index. Second, the KOSPI200 index has a relatively strong dependence on KODEX leverage, which is consistent with the KODEX leverage index playing a dominant role in price discovery compared to the KOSPI200 index. On the other hand, KOSPI200 index has a dependency on KODEX inverse index, but it is weaker than KODEX leverage index. These results are expected to be useful information for investors in capital markets.