• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• Earthquakes and Structures
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• v.10 no.3
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• pp.563-588
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• 2016

In this study, solution models are proposed to obtain code-compatible ground motion record sets which can be used for both uni-directional and bi-directional dynamic analyses. Besides scaled, unscaled ground motion record sets are obtained to show the utility and efficiency of the solution models. For scaled ground motion sets the proposed model is based on hybrid HS-Solver which integrates heuristic harmony search (HS) algorithm with the spreadsheet Solver add-in. For unscaled ground motion sets HS based solution model is proposed. Design spectra defined in Eurocode-8 for different soil types are selected as target spectra. The European Strong Motion Database is used to get ground motion record sets. Also, a sensitivity analysis is conducted to evaluate the effect of different HS solution parameters on the solution accuracy. Results show that the proposed solution models can be regarded as efficient ways to develop scaled and unscaled ground motion sets compatible with code-based design spectra.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• Microbiology and Biotechnology Letters
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• v.50 no.4
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• pp.477-487
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• 2022

Probiotics provide a symbiotic relationship and beneficial effects by balancing the human intestinal microbiota. The relationships between microbiota changes and various diseases may predict health abnormalities and diseases. Treatment with vitamins and probiotics is one therapeutic approach. To evaluate the effect of probiotics on vitamin absorption, we chose Lactobacillus rhamnosus CBT-LR5 treatment, which has resistance to vitamin C-inducible toxicity, with vitamins in high-fat diet (HFD)-induced obesity models. CBT-LR5 affected the absorption of micronutrients, such as ionic minerals and water-soluble vitamins. An increase in vitamin C absorption by CBT-LR5 enhanced the antioxidant response in HFD-induced obesity models. Increased vitamin B absorption by CBT-LR5 regulated lipid metabolism in HFD-induced obesity models. These favorable effects of CBT-LR5 on the absorption of vitamins should be investigated as candidate therapeutic target treatments for metabolic diseases.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.15-34
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• 2006

This paper introduces an empirical modeling technique. This technique uses a set of sample results which are collected from a few small scale simulations. Empirical models are developed by applying a couple of statistical estimation techniques to these samples. We built two types of models for cache miss rates in Symmetric Multiprocessor systems. One is for the changes of input data set size while the specification of target system is fixed. The other is for the changes of the number of processors in target system while the input data set size is fixed. To develop accurate models, we built individual model for every kind of cache misses for each shared data structure in a program. The final model is then obtained by integrating them. Besides, combined use of Least Mean Squares and Robust Estimations enhances the quality of models by minimizing the distortion due to outliers. Empirical modeling technique produces extremely accurate models without analysis on sample data. In addition, since only snail scale simulations are necessary, once a set of samples can be collected, empirical method can be adopted in any research areas. In 17 cases among 24 trials, empirical models present extremely low prediction errors below $1\%$. In the remaining cases, the accuracy is excellent, as well. The models sustain high quality even when the behavioral characteristics of programs are irregular and the number of samples are barely enough.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1063-1073
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• 2015

In the long-term care insurance (LTCI) system, National Health Insurance Service (NHIS) provide counseling services for beneficiaries and their family caregivers, which help them use LTC services appropriately. The purpose of this study was to develop a Target Selection Model for the Counseling Services based on needs of beneficiaries and their family caregivers. To develope models, we used data set of total 2,000 beneficiaries and family caregivers who have used the long-term care services in their home in March 2013 and completed questionnaires. The Target Selection Model was established through various data-mining models such as logistic regression, gradient boosting, Lasso, decision-tree model, Ensemble, and Neural network. Lasso model was selected as the final model because of the stability, high performance and availability. Our results might improve the satisfaction and the efficiency for the NHIS counseling services.

• Phonetics and Speech Sciences
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• v.6 no.4
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• pp.27-36
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• 2014

This study provided longitudinal examination of the Chinese learners' acquisition of Korean vowels. Specifically, I examined the Chinese learners' Korean monophthongs /i, e, ɨ, ${\Lambda}$, a, u, o/ that were created at the time of 1 month and 12 months, tried to verify empirically how they learn by dealing with their mother tongue, and Korean vowels through dealing with pattern of the Perceptual Assimilation Model (henceforth PAM) of Best (Best, 1993; 1994; Best & Tyler, 2007) and the Speech Learning Model (henceforth SLM) of Flege (Flege, 1987; Bohn & Flege, 1992, Flege, 1995). As a result, most of the present results are shown to be similarly explained by the PAM and SLM, and the only discrepancy between these two models is found in the 'similar' category of sounds between the learners' native language and the target language. Specifically, the acquisition pattern of /u/ and /o/ in Korean is well accounted for the PAM, but not in the SLM. The SLM did not explain why the Chinese learners had difficulty in acquiring the Korean vowel /u/, because according to the SLM, the vowel /u/ in Chinese (the native language) is matched either to the vowel /u/ or /o/ in Korean (the target language). Namely, there is only a one-to-one matching relationship between the native language and the target language. In contrast, the Chinese learners' difficulty for the Korean vowel /u/ is well accounted for in the PAM in that the Chinese vowel /u/ is matched to the vowel pair /o, u/ in Korean, not the single vowel, /o/ or /u/.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.27-34
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• 2023

Purpose: The aim of this study was to clarify the influence of training with a different kind of lesion on the performance of a target model. Materials and Methods: A total of 310 patients(211 men, 99 women; average age, 47.9±16.1 years) were selected and their panoramic images were used in this study. We created a source model using panoramic radiographs including mandibular radiolucent cyst-like lesions (radicular cyst, dentigerous cyst, odontogenic keratocyst, and ameloblastoma). The model was simulatively transferred and trained on images of Stafne's bone cavity. A learning model was created using a customized DetectNet built in the Digits version 5.0 (NVIDIA, Santa Clara, CA). Two machines(Machines A and B) with identical specifications were used to simulate transfer learning. A source model was created from the data consisting of ameloblastoma, odontogenic keratocyst, dentigerous cyst, and radicular cyst in Machine A. Thereafter, it was transferred to Machine B and trained on additional data of Stafne's bone cavity to create target models. To investigate the effect of the number of cases, we created several target models with different numbers of Stafne's bone cavity cases. Results: When the Stafne's bone cavity data were added to the training, both the detection and classification performances for this pathology improved. Even for lesions other than Stafne's bone cavity, the detection sensitivities tended to increase with the increase in the number of Stafne's bone cavities. Conclusion: This study showed that using different lesions for transfer learning improves the performance of the model.

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.31-44
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• 2014

In the present study, a few of recently developed geostatistical models are comparatively studied. The models are two-point statistics based sequential indicator simulation (SISIM) and generalized coupled Markov chain (GCMC), multi-point statistics single normal equation simulation (SNESIM), and object based model of FLUVSIM (fluvial simulation) that predicts structures of target object from the provided geometric information. Out of the models, SNESIM and FLUVSIM require additional information other than conditioning data such as training map and geometry, respectively, which generally claim demanding additional resources. For the comparative studies, three-dimensional fluvial reservoir model is developed considering the genetic information and the samples, as input data for the models, are acquired by mimicking realistic sampling (i.e. random sampling). For SNESIM and FLUVSIM, additional training map and the geometry data are synthesized based on the same information used for the objective model. For the comparisons of the predictabilities of the models, two different measures are employed. In the first measure, the ensemble probability maps of the models are developed from multiple realizations, which are compared in depth to the objective model. In the second measure, the developed realizations are converted to hydrogeologic properties and the groundwater flow simulation results are compared to that of the objective model. From the comparisons, it is found that the predictability of GCMC outperforms the other models in terms of the first measure. On the other hand, in terms of the second measure, the both predictabilities of GCMC and SNESIM are outstanding out of the considered models. The excellences of GCMC model in the comparisons may attribute to the incorporations of directional non-stationarity and the non-linear prediction structure. From the results, it is concluded that the various geostatistical models need to be comprehensively considered and comparatively analyzed for appropriate characterizations.