• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.188-192
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• 2021

In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent's education level, parent's jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students' performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student's data.

• Computers and Concrete
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• 제16권4호
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• pp.569-585
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• 2015

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.

• Genomics & Informatics
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• 제18권4호
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• pp.39.1-39.8
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• 2020

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals' memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.

• 한국생활과학회지
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• 제24권6호
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• pp.887-901
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• 2015

This study aims to identify limitations and pending problems after reviewing the overall policies and status of rural Newtown projects, and to seek solutions to its problems. This study targeted the villages of 5 districts, which were developed as rural new-towns after 2009 and included the basic status and progress of the pilot districts. This study conducted a literature review to examine the basic status and progress of the pilot districts, and based on this, analyzed the demand prediction, site selection, project implementation, and housing and amenity facilities of the pilot districts. The study methods included literature reviews, on-site surveys, interviews with village representatives, and consultations with experts. According to the analysis results, a low occupancy rate of the Newtown project districts is because the prediction of occupancy demand was incorrectly completed before implementing the projects. Also, the eligibility for occupancy, such as age restriction and mandatory farming was too strict. Other problems included an absence of income generation support policies for rural returnees, a housing supply policy in disregard of agricultural characteristics, and a lack of understanding of maintenance of communal space, etc.

• 한국과학교육학회지
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• 제22권5호
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• pp.944-956
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• 2002

The purpose of this study was to investigate students' conceptions of the simple electric circuit using a battery and a bulb. 19 fourth grade students from a rural elementary school in Korea participated in this study. Data on the children's understandings of electric circuit were collected through three sources; prediction tests, drawing tests and individual interviews. The prediction tests were paper and pencil tests composed of 10 problems, predicting whether bulbs in 10 simple circuit diagrams would light. For each prediction, the children were asked to provide a written explanation of their thinking. The drawing tests consisted of 6 problems. One was to draw the inside of the bulb base, and the others were to make the wire connections between a battery and a bulb in the diagrams, to light the bulb. The interviews were conducted with seven children who showed differing degrees of understanding. No student was aware of the wire connections inside the bulb base. Many students stated whether the bulb would light or not, according to the tip of the bulb contacting the positive battery terminal and an end of wire contacting the negative battery terminal. Most of them thought that the tip of the bulb should contact the positive battery terminal, so that the bulb would light. In short, students did not use a scientific conception of electric current to predict and explain the electric circuit.

• Corrosion Science and Technology
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• 제23권4호
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• pp.324-333
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• 2024

Atmospheric corrosion poses a significant threat to durability of metallic materials and safety of structures, making precise prediction of corrosion rates crucial in industrial and engineering settings. Understanding the exact rate of corrosion is essential. However, accurate inclusion of various environmental factors that can influence atmospheric corrosion in the calculation of corrosion rate is a complex challenge. This study introduces a physics-based model that incorporates electrochemical methods and considers active surface area affected by surface contaminants to estimate atmospheric corrosion rate of carbon steel. The model can evaluate corrosion levels using key factors such as chloride deposition rate, relative humidity, and the presence of surface particles. By integrating these considerations, this model moves beyond empirical estimations, providing a more stable prediction of corrosion rate that is less susceptible to environmental variations. This model provides a robust tool for defense applications, offering precise insights into the dynamics of atmospheric corrosion that could enhance the maintenance and safety of weapon systems.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2005년도 추계학술발표대회 및 정기총회
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• pp.15-18
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• 2005

A ribonucleic acid (RNA) is one of the two types of nucleic acids found in living organisms. An RNA molecule represents a long chain of monomers called nucleotides. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Non-coding RNA genes produce transcripts that exert their function without ever producing proteins. Predicting the secondary structure of non-coding RNAs is very important for understanding their functions. We focus on Nussinov's algorithm as useful techniques for predicting RNA secondary structures. We introduce a new traceback matrix and scoring table to improve above algorithm. And the improved prediction algorithm provides better levels of performance than the originals.

• Korean Journal of Acupuncture
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• 제33권1호
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• pp.18-32
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• 2016

Objectives : This study aims to analyze the co-occurrence of pathological symptoms and corresponding acupoints as documented by the comprehensive acupuncture and moxibustion records in the classical texts of Far East traditional medicine as an aid to a more efficient understanding of the tacit treatment principles of ancient physicians. Methods : The Classic of Nourishing Life with Acupuncture and Moxibustion(Zhenjiu Zisheng Jing; hereinafter ZZJ) was selected as the primary reference book for the analysis. The pathology-acupoint co-occurrence analysis was performed by applying 4 values of vector space measures(weighted Euclidean distance, Euclidean distance, $Cram\acute{e}r^{\prime}s$ V and Canberra distance), which measure the distance between the observed and expected co-occurrence counts, and 3 values of probabilistic measures(association strength, Fisher's exact test and Jaccard similarity), which measure the probability of observed co-occurrences. Results : The treatment records contained in ZZJ were preprocessed, which yielded 4162 pathology-acupoint sets. Co-occurrence was performed applying 7 different analysis variables, followed by a prediction simulation. The prediction simulation results revealed the Weighted Euclidean distance had the highest prediction rate with 24.32%, followed by Canberra distance(23.14%) and association strength(21.29%). Conclusions : The weighted Euclidean distance among the vector space measures and the association strength among the probabilistic measures were verified to be the most efficient analysis methods in analyzing the correlation between acupoints and pathologies found in the classical medical texts.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 한국자동차공학회논문집
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• 제5권2호
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• pp.136-148
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• 1997

The present study has been carried out to develop a computational procedure for the analysis of the off-design performance in centrifugal compressors with vaneless diffusers by integrating empirical loss models and analytical equations. Losses in centrifugal compressors stem from a number of sources and their exact calculation is not yet possible. This study investigates several modeling schemes and shows that a fairly good prediction can be achieved by a proper selection of the most important flow parameters resulting form a meanline one-dimensional analysis. The performance maps for compressors are calculated and compared with measured performance maps. The off-design performance characteristics in terms of the pressure ratio vs. mass flow produced have generally correct forms. However, no universal means have been found to predict accurately the onset of surge. The prediction method developed through this study can serve as a tool to ensure good matching between parts and it can assist the understanding of the operational characteristics of general purpose centrifugal compressors.