scholarly journals Predicting the Variables That Determine University (Re-)Entrance as a Career Development Using Support Vector Machines with Recursive Feature Elimination: The Case of South Korea

2020 ◽  
Vol 12 (18) ◽  
pp. 7365
Author(s):  
Taejung Park ◽  
Chayoung Kim
Keyword(s):  
High School ◽  
South Korea ◽  
High School Graduates ◽  
Recursive Feature Elimination ◽  
Future Research ◽  
Support Vector ◽  
University Choice ◽  
Social Prestige ◽  
Vector Machines ◽  
The University

The current study seeks to identify variables that affect the career decision-making of high school graduates with respect to the choice of university (re-)entrance in South Korea where education has great importance as a tool for self-cultivation and social prestige. For pattern recognition, we adopted a support vector machine with recursive feature elimination (SVM-RFE) with a big-data of survey of Korean college candidates. Based on the SVM-RFE analysis results, new enrollers were mostly affected by the mesosystems of interactions with parents, while re-enrollers were affected by the macrosystems of social awareness as well as individual estimates of talent and aptitude of individual systems. By predicting the variables that affect the high school graduates’ preparation for university re-entrance, some survey questions provide information on why they make the university choice based on interactions with their parents or acquaintances. Along with these empirical results, implications for future research are also presented.

Support Vector Machines and Affective Science

2020 ◽  
Vol 12 (4) ◽  
pp. 297-308
Author(s):  
Chris H. Miller ◽  
Matthew D. Sacchet ◽  
Ian H. Gotlib
Keyword(s):  
Support Vector Machines ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Data Driven ◽  
Future Research ◽  
Support Vector ◽  
Predictive Algorithm ◽  
Affective Science ◽  
Vector Machines ◽  
Potential Applications

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

Athletic Maturity and Preferred Leadership

1983 ◽  
Vol 5 (4) ◽  
pp. 371-380 ◽  
Author(s):  
P. Chelladurai ◽  
A.V. Carron
Keyword(s):  
Social Support ◽  
High School ◽  
Linear Trend ◽  
Future Research ◽  
Basketball Players ◽  
Wide Range ◽  
University Level ◽  
The University ◽  
Task Oriented ◽  
Direction Opposite

The purpose of the study was to determine if preferences of athletes for training and instruction (task-oriented) behavior and social support (relationship-oriented) behavior would vary with athletic maturity (operationalized in terms of level of competition). Basketball players from high school midget (n = 67), junior (n = 63), and senior (n = 63) divisions and university (n = 69) completed the “preferred leader behavior” version of the Leadership Scale for Sports. Trend analyses revealed the presence of a quadratic trend in preference for training and instruction which progressively decreased from high school midget, through junior to senior levels and increased at the university level; however, the direction of this trend was opposite to that predicted. A linear trend was obtained for social support which progressively increased from the high school midget level to the university level but, again, it was in a direction opposite than that predicted. It was noted that future research should incorporate both a wide range of competition levels and groups with markedly different levels of success in order to determine the interrelationship between leadership preference and athletic maturity. It was also noted, however, that sport as a social system may not afford athletes an opportunity to achieve athletic maturity.

scholarly journals Performance Analysis of Rough Set–Based Hybrid Classification Systems in the Case of Missing Values

2021 ◽  
Vol 11 (4) ◽  
pp. 307-318
Author(s):  
Robert K. Nowicki ◽  
Robert Seliga ◽  
Dariusz Żelasko ◽  
Yoichi Hayashi
Keyword(s):  
Performance Analysis ◽  
Rough Set ◽  
Missing Values ◽  
Rough Set Theory ◽  
Classification Systems ◽  
Support Vector ◽  
Vector Machines ◽  
Hybrid Solutions ◽  
Hybrid Classification ◽  
The University

Abstract The paper presents a performance analysis of a selected few rough set–based classification systems. They are hybrid solutions designed to process information with missing values. Rough set-–based classification systems combine various classification methods, such as support vector machines, k–nearest neighbour, fuzzy systems, and neural networks with the rough set theory. When all input values take the form of real numbers, and they are available, the structure of the classifier returns to a non–rough set version. The performance of the four systems has been analysed based on the classification results obtained for benchmark databases downloaded from the machine learning repository of the University of California at Irvine.

scholarly journals Hubungan antara Optimism dan Academic Adjustment Mahasiswa Semester Tiga Fakultas Psikologi di Universitas "X" Bandung

2018 ◽  
Vol 2 (1) ◽  
pp. 49-62
Author(s):  
Karla Nathania ◽  
Irene Prameswari Edwina
Keyword(s):  
High School ◽  
University Students ◽  
Academic Adjustment ◽  
Rank Correlation ◽  
Senior High School ◽  
Early Years ◽  
Future Research ◽  
Significant Finding ◽  
Academic Needs ◽  
The University

In the early years of university learning, university students required academic adjustment in regards to the differences between the learning demands and strategies between senior high school and university. Academic adjustment is a required process to fulfill academic needs appropriately. Schneider (1964), Aspinwal & Taylor (1992) found that students who are optimist were more likely to undergo the transition from senior high school to university with a lower level of stress. Seligman (2006) stated optimism as a way for individuals to explain and link an event that is perceived to be wonderful as personal, permanent, and pervasive. 129 students from the Faculty of Psychology participated in this research. The measures used based on Seligman’s theory weas Schneider academic adjustment. The validity of the measure was between 0.3-0.65 and the validity of the academic adjustment measure was between 0.3-0.62. The reliability of the optimism measure was between 0.17-0.64 and the reliability of the academic adjustment measure was 0.874. Based on the analysis of the data and the results of the Spearman Rank Correlation test, there was a quite significant finding on the relationship between optimism and academic adjustment. The aspect of permanence was found to have a stronger relationship with academic adjustment in comparison to the other two aspects of optimism. Future research suggested further research in understanding the role of optimism towards the academic adjustment of the university students of the Faculty of Psychology. The staffs of the faculty of psychology could utilised the results of this research to assemble an optimism and academic adjustment training for the recently enrolled university students.

scholarly journals FACTORS INFLUENCING THE UNIVERCITY CHOICE DECISIONS OF HIGH SCHOOL STUDENTS

2009 ◽  
Vol 12 (15) ◽  
pp. 87-102
Author(s):  
Qui Van Tran ◽  
Thi Hao Cao
Keyword(s):  
High School ◽  
High School Students ◽  
College Choice ◽  
Multiple Linear Regression Model ◽  
School Students ◽  
University Choice ◽  
School Year ◽  
Factors Influencing ◽  
Five Factors ◽  
The University

In reality, there are many high school students who do not determine exactly the career and the university which they want to attend. As the result of an investigate of Nguoi Lao Dong newspaper, over 60 percents of students admit that they had not have good vocational guidance when they registered to the university [1]. Therefore, a conceptual model of factors influencing students' college choice was developed to indentify the key factors and to evaluate the level of influence of these factors on high school students' university choice decisions. The result of 227 valid questionares from grade 12 students, school year 2008-2009 at 5 high schools at Quang Ngai province indicated 5 main factors influencing to the students' college choice including factors on future occupation opportunity; factors on information available; factors on student characteristics; factors on fixed college characteristisc and factors significant persons. The result of multiple linear regression model confirmed the relationship between these five factors above and the high school students' university choice decisions with the theories are supported at the statistically significant level of 0.05. And from this result, proposing motions to help families, schools and education organizations have practical approaches in order to well orient create good conditions for high school students to have the best university choices.

scholarly journals Identification of Molecular Biomarkers Associated With Stroke Progression Using WGCNA and SVM-RFE

2022 ◽  
Author(s):  
Si-tong Liu ◽  
You Zhang ◽  
Xin-gui Wu ◽  
Chang-xing Lu ◽  
Qi-Ping Hu
Keyword(s):  
Gene Expression ◽  
Inflammatory Response ◽  
Disease Progression ◽  
Signal Pathway ◽  
Clinical Deterioration ◽  
Functional Enrichment ◽  
Recursive Feature Elimination ◽  
Future Research ◽  
Support Vector ◽  
Hub Genes

Abstract Background: Stroke is the second most common cause of death worldwide and the leading cause of long-term severe disability with neurological impairment worsening within hours after stroke onset and being especially involved with motor function. So far, there are no established and reliable biomarkers to prognose stroke. Early detection of biomarkers that can prognose stroke is of great importance for clinical intervention and prevention of clinical deterioration of stroke.Methods: TGSE119121 dataset was retrieved from the Gene Expression Integrated Database (Gene Expression Omnibus, GEO) and weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules that could regulate disease progression. Moreover, functional enrichment analysis was conducted to study the biological functions of the key module genes. The GSE16561 dataset was further analyzed by the Support Vector Machines coupled with Recursive Feature Elimination (SVM-RFE )algorithm to identify the top genes regulating disease progression. The hub genes revealed by WGCNA were associated with disease progression using the receiver operating characteristic curve (ROC) analysis. Subsequently, functional enrichment of the hub genes was performed by deploying gene set variation analysis (GSVA). The changes at gene level were transformed into the changes at pathway level to identify the biological function of each sample. Finally, the expression level of the hub gene in the rat infarction model of MCAO was measured using RT-qPCR for validation. Results: WGCNA analysis revealed four hub genes: DEGS1, HSDL2, ST8SIA4 and STK3. The result of GSVA showed that the hub genes were involved in stroke progression by regulating the p53 signal pathway, the PI3K signal pathway, and the inflammatory response pathway. The results of RT-qPCR indicated that the expression of the four HUB genes was increased significantly in the rat model of MCAO.Conclusion: Several genes, such as DEGS, HSDL2, ST8SIA4 and STK3, were identified and associated with the progression of the disease. Moreover, it was hypothesized that these genes may be involved in the progression stroke by regulating the P53 signal, the PI3K signal, and the inflammatory response pathway, respectively. These genes have potential prognostic value and may serve as biomarkers for predicting stroke progression. The early identification of the patients at risk of progression is essential to prevent clinical deterioration and provide a reference for future research.

scholarly journals The Determinants of the Transition in South Korea from Vocational and General High School to Higher Education, Including a Gender Comparison

2020 ◽  
Vol 9 (4) ◽  
pp. 1 ◽  
Author(s):  
Seonkyung Choi
Keyword(s):  
Higher Education ◽  
High School ◽  
High School Students ◽  
South Korea ◽  
High School Graduates ◽  
Multinomial Logit Model ◽  
Teacher Assessment ◽  
Individual Characteristics ◽  
Vocational High School ◽  
School Students

This study examines the factors determining whether vocational and general high school students in South Korea subsequently graduate from university and, if so, whether from 2-year or 4-year courses, for the first time using a gender lens. High-quality official data from the Korean Education and Employment Panel (KEEP) is used in a multinomial logit model. The results show that coming from a vocational high school (compared to a general high school) is negatively correlated with going to university, especially to 4-year university. Among general high school graduates, the most important determinant of attending a 4-year rather than a 2-year university is the teacher assessment of the student’s performance; father’s education and income have no effect for either males or females. The results also show that vocational high school graduates’ university choice is determined by a combination of individual characteristics, including being male, and by having been at a vocational high school, whereas the choice between 2-year and 4-year university depends negatively on father’s education for males but not for females and on father’s income and the number of siblings for both genders. The income and sibling findings suggest that a possible policy implication might be to provide financial support to vocational high school graduates to enable them to attend higher education and to offset the negative effect of low paternal income.

scholarly journals Hierarchy-Based File Fragment Classification

2020 ◽  
Vol 2 (3) ◽  
pp. 216-232
Author(s):  
Manish Bhatt ◽  
Avdesh Mishra ◽  
Md Wasi Ul Kabir ◽  
S. E. Blake-Gatto ◽  
Rishav Rajendra ◽  
...  
Keyword(s):  
Cross Validation ◽  
Hierarchical Classification ◽  
Future Research ◽  
Support Vector ◽  
Challenging Problem ◽  
Fine Grain ◽  
Average Accuracy ◽  
Vector Machines ◽  
Essential Problem ◽  
Fold Cross Validation

File fragment classification is an essential problem in digital forensics. Although several attempts had been made to solve this challenging problem, a general solution has not been found. In this work, we propose a hierarchical machine-learning-based approach with optimized support vector machines (SVM) as the base classifiers for file fragment classification. This approach consists of more general classifiers at the top level and more specialized fine-grain classifiers at the lower levels of the hierarchy. We also propose a primitive taxonomy for file types that can be used to perform hierarchical classification. We evaluate our model with a dataset of 14 file types, with 1000 fragments measuring 512 bytes from each file type derived from a subset of the publicly available Digital Corpora, the govdocs1 corpus. Our experiment shows comparable results to the present literature, with an average accuracy of 67.78% and an F1-measure of 65% using 10-fold cross-validation. We then improve on the hierarchy and find better results, with an increase in the F1-measure of 1%. Finally, we make our assessment and observations, then conclude the paper by discussing the scope of future research.

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