Exploratory Simulation Drilling Environment Design Based on Interactive Data Generation Method

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.88 ◽

2017 ◽

Vol 7 (7) ◽

pp. 8

Author(s):

Mona E. Elbashier ◽

Suhaib Alameen ◽

Caroline Edward Ayad ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Size Distribution ◽

Texture Analysis ◽

Classification Accuracy ◽

Ct Images ◽

Diabetic Patients ◽

Gray Level ◽

Grey Level ◽

Run Length ◽

Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

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Environment Design in Space: Lunar Base and the IS...

10.2514/6.iac-05-e5.2.02 ◽

2005 ◽

Author(s):

Ayako Ono

Keyword(s):

Environment Design

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Query Optimization: Fund Data Generation Applying NonClustered Indexing and MapReduced Data Cube Numerosity Reduction Method

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/1991.12020 ◽

2020 ◽

Vol 9 (1.1 S I) ◽

pp. 102-109

Author(s):

Mercy Burawis

Keyword(s):

Query Optimization ◽

Reduction Method ◽

Data Cube ◽

Data Generation

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Efektivitas Model Problem Based Learning (PBL) Berbantuan CD Pembelajaran, CD Interaktif Terhadap Prestasi Belajar Matematika

Prosiding Seminar Nasional Pendidikan KALUNI ◽

10.30998/prokaluni.v2i0.2 ◽

2019 ◽

Vol 2 ◽

Author(s):

Ek Ajeng Rahmi Pinahayu ◽

Aulia Ar Rakhman Awaludin ◽

Purni Munah Hartuti

Keyword(s):

Learning Outcomes ◽

Mathematics Learning ◽

Interactive Media ◽

Problem Based Learning ◽

Learning Achievement ◽

Interactive Data ◽

Variant Analysis ◽

Academic Year ◽

Cluster Random Sampling ◽

Better Than

The study aims to determine the effectiveness of mathematics learning achievement for students who are subject to the Problem Based Learning model assisted by CD Learning and CD Interactive as well as Conventional learning models on flat-build material. The population in this study were seventh grade students of SMP Negeri 223 Jakarta Timur in the academic year 2017/2018. By using selected cluster random sampling techniques VII 3 as the control class, VII 6 and VII 7 respectively as the experimental class PBL models assisted CD learning and assisted PBL models CD Interactive. Data were obtained by documentation and tests, then analyzed by Normality, Homogeneity, One-way Variant Analysis (ANOVA), Two-party and One-party Similarity Test. The results showed that there were significant differences between mathematics learning achievement of students who were taught by learning using PBL models assisted by CD Interactive, PBL models assisted by CD learning and conventional learning. Learning achievement of students who get learning using CD Interactive media is better than students who get conventional learning. Likewise, the learning achievements of students who get learning using CD Learning, media are better than students who get conventional learning. In addition, there were no significant differences between the mathematics learning outcomes of students who were taught using PBL models assisted by CD Interactive and students using PBL models assisted by CD Learning.

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