scholarly journals Student Placement Prediction Model: A Data Mining Perspective for Outcome-Based Education System

2019 ◽  
Vol 8 (3) ◽  
pp. 2497-2507
Keyword(s):  
Data Mining ◽  
Performance Measures ◽  
Education System ◽  
Large Scale ◽  
Educational Institution ◽  
Vital Role ◽  
Past Performance ◽  
Student Placement ◽  
Outcome Based Education ◽  
Mining Methods

Campus placement plays a vital role in every educational institution in helping students to achieve their goals. Data mining classification can be used as a useful tool for extracting the associated information from the large scale student dataset. Data mining methods have been used broadly in the area of the education system which involves various methods and approach for discovering knowledge. In this paper, a predictive model is designed which can predict the category of placements (dream companies, super dream companies and mass recruiter companies) in which students are eligible by considering their past performance in academics and other curricular activities. The model will also suggest further skills required for future recruitments which may help the students for placement preparation. The paper also provides real-time experimental results and findings along with performance measures used for model validation which helps in achieving the milestone of outcome-based education (OBE) in educational institutes as it is given utmost importance in present scenario to ensure better placement prospects in students, which would in turn help the students for carrier building

scholarly journals Implementing a Dual Education System at the Mining and Metallurgical Enterprises (Example of «Metinvest» LLC)

2021 ◽  
Vol 10 (525) ◽  
pp. 76-81
Author(s):  
M. Y. Shkurat ◽  
◽  
A. O. Zavydovska ◽  
Keyword(s):  
Education System ◽  
Large Scale ◽  
Energy Transport ◽  
Educational Institution ◽  
Pilot Project ◽  
Machine Building ◽  
Ministry Of Education ◽  
State Technical ◽  
Dual Form ◽  
Almost All

The article is aimed at studying the dual education system at the mining and metallurgical enterprises (on the example of «Metinvest» LLC), highlighting the main trends and significance. As a result of the study, the process of introduction of dual education on the territory of Ukraine is considered, the main advantages of obtaining the dual form of education (ODFE) are determined. The main aspects of carrying out the large-scale pilot project on training specialists in the dual form of education with the support of the Foundation named after Frederick Ebert and the Ministry of Education and Science of Ukraine are highlighted. The list of enterprises participating in the pilot project on the implementation of ODFE in Ukraine is analyzed, comprised of the employers conducting various types of economic activity and representing almost all regions of our country. In particular, these are enterprises of various forms of ownership, design bureaus, banks, enterprises of food industry, machine-building, agriculture, energy, transport, metallurgy, IT sector, etc. It is noted that metallurgical enterprises subordinated to «Metinvest Holding» LLC were among the first to take the initiative to introduce a dual system. The actions of the principal partner of «Metinvest Holding» LLC, the State Higher Educational Institution of the Azov State Technical University, on the implementation of the ODFE on the territory of Ukraine, are indicated and determined. The activities of «Metinvest Politekhnika», a non-governmental mining and metallurgical university, and its impact on the development of the dual education system in Ukraine are researched.

STUDY OF ADAPTATION FACTORS OF STUDENTS OF 2000S FOR TRAINING IN HIGHER EDUCATIONAL INSTITUTION BY DATA MINING METHODS

2015 ◽  
Vol 10 (6) ◽  
pp. 49-56
Author(s):  
Шуметов ◽  
Vadim Shumetov ◽  
Лясковская ◽  
Olga Lyaskovskaya
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
University Students ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Educational Institution ◽  
Mining Methods ◽  
Gender Factor ◽  
University Training ◽  
Main Factor

The article contains the results of studying the factors of students’ adaptation to the conditions of university training with one of the most effective technologies Data Mining - the method of analysis of variance. The results of modeling factors of adaptation of students of peripheral universities to the new method of Data Mining, implemented by the procedure of generalized linear model analysis of the data packet of Social Sciences SPSS Base are presented. The empirical base of modeling is the result of the poll of university students in Orel, Bryansk, Kursk and Belgorod. It is shown that in 2000s the main factor determining the adaptation of students to training in university, was the course of training and the gender factor, the faculty of training was less significant factor.

scholarly journals Fungsi Pesantren Dan Tuntutan Perubahan Sosial Pendidikan

2016 ◽  
Vol 21 (2) ◽  
pp. 281-292
Author(s):  
Afga Sidiq Rifai
Keyword(s):  
Social Change ◽  
Education System ◽  
Life Skills ◽  
Educational Institution ◽  
Real Life ◽  
Vital Role ◽  
Education History ◽  
Cognitive Intelligence

Abstract: The development of education is no longer oriented cognitive intelligence alone, but already demanded how to prepare outputs ready to face real life the increasing number of unemployment in Indonesia is because our education has not provided supplies of life, so we need to follow the steps to improve existing social change. pesantren as an educational institution native Indonesia, as well as the oldest in the Indonesian education system could be a pioneer dorm life skills-based education History shows boarding a vital role in influencing this nation. Pesantren movement once the center of religious, educational, social, cultural, and political. Now this function ditutntut schools to be able to prepare graduates who are ready to plunge in the community, so pesantren must be entered on the functioning of the economy. Keywords: Schools, Economy, Social change, Education.

scholarly journals Revealing microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation

2019 ◽  
Author(s):  
Shion Hosoda ◽  
Suguru Nishijima ◽  
Tsukasa Fukunaga ◽  
Masahira Hattori ◽  
Michiaki Hamada
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Gut Microbiome ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Mining Methods ◽  
Gut Metagenome ◽  
Dirichlet Allocation

AbstractRecent research has revealed that there are various microbial species in the human gut microbiome. To clarify the structure of the human gut microbiome, many data mining methods have been applied to microbial composition data. Cluster analysis, one of the key data mining methods that have been used in human gut microbiome research, can classify the human gut microbiome into three clusters, called enterotypes. The human gut microbiome has been suggested to be composed of the microbial assemblages or groups of co-occurring microbes, and one human gut microbiome can contain several microbial assemblages. However, cluster analysis can cluster samples into groups without capturing minor assemblages. In addition, a reliable method of assemblage detection has not been established, and little is known about the distributions of microbial assemblages at a population-level scale. Accordingly, the purpose of this study was to clarify the microbial assemblages in the human gut microbiome. In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method, which was first proposed for the classification of documents in natural language processing. We applied LDA to a large-scale human gut metagenome dataset and found that a four-assemblage LDA model can represent relationships between enterotypes and assemblages with high interpretability. This model indicates that each individual tends to have several assemblages, and each of three assemblages corresponded to each enterotype. However, the C-assemblage can exist in all enterotypes. Interestingly, the dominant genera of the C-assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Finally, we revealed that genera mainly appearing in the same assemblage were correlated to each other. We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA, a powerful method for detection of microbial assemblages. This approach has the potential to reveal the structure of the human gut microbiome.

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

THE MODEL FOR THE ANALYSIS OF PHARMACOEPIDEMIOLOGICAL DATA BASED ON DATA MINING METHODS

2017 ◽  
Vol 19 (10) ◽  
pp. 289-298
Author(s):  
I.M. Burykin ◽  
◽  
G.N. Aleeva ◽  
R.Kh. Khafizianova ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Mining Methods

Rethinking the notion of the high-performing education system: A Daoist response

2021 ◽  
pp. 174549992098369
Author(s):  
Charlene Tan
Keyword(s):  
Education System ◽  
Large Scale ◽  
Alternative Interpretation ◽  
Educational Systems ◽  
Education Systems ◽  
High Performing ◽  
Mutual Learning ◽  
System A ◽  
High Performers ◽  
Major Implication

This article challenges the dominant notion of the ‘high-performing education system’ and offers an alternative interpretation from a Daoist perspective. The paper highlights two salient characteristics of such a system: its ability to outperform other education systems in international large-scale assessments; and its status as a positive or negative ‘reference society’. It is contended that external standards are applied and imposed on educational systems across the globe, judging a system to be high- or low- performing, and consequently worthy of emulation or deserving of criticism. Three cardinal Daoist principles that are drawn from the Zhuangzi are expounded: a rejection of an external and oppressive dao (way); the emptying of one’s heart-mind; and an ethics of difference. A major implication is a celebration of a plurality of high performers and reference societies, each unique in its own dao but converging on mutual learning and appreciation.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals A modular microfluidic system based on a multilayered configuration to generate large-scale perfusable microvascular networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tao Yue ◽  
Da Zhao ◽  
Duc T. T. Phan ◽  
Xiaolin Wang ◽  
Joshua Jonghyun Park ◽  
...  
Keyword(s):  
Large Scale ◽  
Circulatory System ◽  
Vertical Direction ◽  
Microfluidic System ◽  
Vital Role ◽  
Microvascular Networks ◽  
Biological Studies ◽  
Wide Range ◽  
High Flexibility

AbstractThe vascular network of the circulatory system plays a vital role in maintaining homeostasis in the human body. In this paper, a novel modular microfluidic system with a vertical two-layered configuration is developed to generate large-scale perfused microvascular networks in vitro. The two-layer polydimethylsiloxane (PDMS) configuration allows the tissue chambers and medium channels not only to be designed and fabricated independently but also to be aligned and bonded accordingly. This method can produce a modular microfluidic system that has high flexibility and scalability to design an integrated platform with multiple perfused vascularized tissues with high densities. The medium channel was designed with a rhombic shape and fabricated to be semiclosed to form a capillary burst valve in the vertical direction, serving as the interface between the medium channels and tissue chambers. Angiogenesis and anastomosis at the vertical interface were successfully achieved by using different combinations of tissue chambers and medium channels. Various large-scale microvascular networks were generated and quantified in terms of vessel length and density. Minimal leakage of the perfused 70-kDa FITC-dextran confirmed the lumenization of the microvascular networks and the formation of tight vertical interconnections between the microvascular networks and medium channels in different structural layers. This platform enables the culturing of interconnected, large-scale perfused vascularized tissue networks with high density and scalability for a wide range of multiorgan-on-a-chip applications, including basic biological studies and drug screening.

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