Interactive Data Selection

2014 ◽  
pp. 141-171
Author(s):  
Adam Aspin
Keyword(s):  
Data Selection ◽  
Interactive Data

scholarly journals Intensive Care Data — I: Computer Controlled Analysis

1979 ◽  
Vol 7 (4) ◽  
pp. 322-328 ◽  
Author(s):  
K. L. Austin ◽  
G. D. Phillips
Keyword(s):  
Social Sciences ◽  
Intensive Care ◽  
Clinical Data ◽  
Data Entry ◽  
Data Selection ◽  
Data Input ◽  
Controlled System ◽  
The Social ◽  
Computer Controlled ◽  
Interactive Data

A flexible, simple computer-controlled system for analysis of administrative and clinical data in intensive care is described. The system uses an interactive data input programme to facilitate data entry, an editor to correct errors, and the Statistical Package for the Social Sciences (SPSS) for data selection, retrieval and analysis. The system has the following features: data input is simple; large amounts of data can be handled rapidly and accurately; old data is readily available for comparison with new data and can be included in the present analysis; available statistical packages remove the need for a programmer; complex analyses are possible. The system has been successfully used for two years to provide administrative and clinical data which is pertinent to patient care.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

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.

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

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.

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

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.

ANALISIS KOMPARATIF TUTURAN BAHASA INDONESIA PADA MAHASISWA UNIVERSITAS IQRA BURU

2020 ◽  
Author(s):  
HARZIKO
Keyword(s):  
Historical Development ◽  
Qualitative Approach ◽  
Data Selection ◽  
Communication Tool ◽  
Comparative Linguistics ◽  
Data Presentation ◽  
Vocal System ◽  
The Difference ◽  
Bahasa Indonesia ◽  
Over Time

Language is a tool used to convey ideas, ideas, feelings, and thoughts to others. Language, which is an arbiter sound symbol system that is used by members of one community to work together, interact, and identify themselves. In other words, language can be a means to convey intent or purpose so that the desired thing is achieved. As a communication tool, language develops over time. Therefore, language will never die if its speakers still care and love for the language comparative linguistics as a study or study of language which includes the comparison of cognate languages or the historical development of a language. This research aims to elucidate the use of Indonesian language at students of Iqra Buru University with Comparative Analysis. The method used in this research is descriptive with a qualitative approach. Data collected by observation, interview, and documentation techniques. Sources of data in this study were 5 speakers 5 speakers language Analyzing the data by means of data selection, data classification, and data presentation. The results of the study stated that there were phonological differences, namely the Jamee vowel system contained / ɛ / and / ɔ /, while the vocal system was / a / and the consonant system was Jamee / ɣ / or / R /, while the language / r /, then the difference pronunciations include single vowels, for example makɛn, bɔRa in Jamee and eating, bara in Language used by iqra Rush university students. Keywords

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