scholarly journals An Approach for Predicting the Shape and Size of a Buried Basic Object on Surface Ground Penetrating Radar System

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Nana Rachmana Syambas
Keyword(s):  
Decision Tree ◽  
Ground Penetrating Radar ◽  
Rectangular Cross Section ◽  
Training Model ◽  
Training Data ◽  
Buried Objects ◽  
Basic Object ◽  
Decision Tree Method ◽  
Ground Penetrating ◽  
Tree Method

Surface ground-penetrating radar (GPR) is one of the radar technology that is widely used in many applications. It is nondestructive remote sensing method to detect underground buried objects. However, the output target is only hyperbolic representation. This research develops a system to identify a buried object on surface GPR based on decision tree method. GPR data of many basic objects (with circular, triangular, and rectangular cross-section) are classified and extracted to generate data training model as a unique template for each type of basic object. The pattern of object under test will be known by comparing its data with the training data using a decision tree method. A simple powerful algorithm to extract feature parameters of object which is based on linear extrapolation is proposed. The result showed that tested buried basic objects can be correctly predicted and the developed system works properly.

scholarly journals Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
D. Mudali ◽  
L. K. Teune ◽  
R. J. Renken ◽  
K. L. Leenders ◽  
J. B. T. M. Roerdink
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Fdg Pet ◽  
Imaging Techniques ◽  
Principal Component ◽  
Training Data ◽  
Brain Diseases ◽  
Parkinsonian Syndromes ◽  
Decision Tree Method ◽  
Tree Method

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

scholarly journals ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5

2018 ◽  
Vol 2 (2) ◽  
pp. 178
Author(s):  
Tri Sutrisno ◽  
Stefanny Claudia
Keyword(s):  
Information Technology ◽  
Decision Tree ◽  
Training Data ◽  
Prediction System ◽  
Tree Model ◽  
Average Accuracy ◽  
C4.5 Algorithm ◽  
Academic Grades ◽  
Decision Tree Method ◽  
Tree Method

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.

scholarly journals Decision Tree Method Using for Fetal State Classification from Cardiotography Data

2020 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
Md Zannatul Arif ◽  
Rahate Ahmed ◽  
Umma Habiba Sadia ◽  
Mst Shanta Islam Tultul ◽  
Rocky Chakma
Keyword(s):  
Heart Rate ◽  
Decision Tree ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Classification Tree ◽  
Training Data ◽  
Single Node ◽  
Data Set ◽  
Decision Tree Method ◽  
Tree Method

The motive of the investigation is analyzing the categorization of fetal state code from the Cardiographic data set based on decision tree method. Cardiotocography is one of the important tools for monitoring heart rate, and this technique is widely used worldwide. Cardiotocography is applied for diagnosing pregnancy and checking fetal heart rate state condition until before delivery. This classification is necessary to predict fetal heart rate situation which is belonging. In this paper, we are using three input attributes of training data set quoted by LB, AC, and FM to categorize as normal, suspect or pathological where NSPF variable is used as a response variable. After drawing necessary analysis into three variables we get the 19 nodes of classification tree and also we have measured every single node according to statistic, criterion, weights, and values. The Cardiotocography Dataset applied in this study is received from UCI Machine Learning Repository. The dataset contains 2126 observation instances with 22 attributes. In this experiment, the highest accuracy is 98.7%. Overall, the experimental results proved the viability of Classification and Regression Trees and its potential for further predictions.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited. 

scholarly journals Klasifikasi Penentuan Jenis Obat Menggunakan Algoritma Decision Tree

2021 ◽  
Vol 7 (3) ◽  
pp. 53-60
Author(s):  
Rika Nursyahfitri ◽  
Alfanda Novebrian Maharadja ◽  
Riva Arsyad Farissa ◽  
Yuyun Umaidah
Keyword(s):  
Decision Tree ◽  
Medical Records ◽  
Training Data ◽  
Accuracy Rate ◽  
Data Set ◽  
Drug Determination ◽  
Decision Tree Method ◽  
The Relationship ◽  
Tree Method

Classification is a technique that can be used for prediction, where the predicted value is a label. The classification of drug determination aims to predict the type of drug that is accurate for patients with the dataset that has been obtained. The data used in this study are data from the patient's medical records based on the symptoms of the disease but the type of medicine is not yet known. The data set used comes from kaggle.com which is then presented in the form of a decision tree with a mathematical model. To complete this research, a classification method is used in data mining, namely the decision tree. The decision tree method is used to find the relationship between a number of candidate variables, so that it becomes a classification target variable by dividing the data into 70% data testing and 30% training data. The results obtained from this study are in the form of rules and an accuracy rate of 96.36% as well as the recall and precision values ​​of each type of drug using a multiclass configuration matrix.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree

2013 ◽  
Vol 774-776 ◽  
pp. 1757-1761
Author(s):  
Bing Xiang Liu ◽  
Xu Dong Wu ◽  
Ying Xi Li ◽  
Xie Wei Wang
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Bp Neural Network ◽  
Cable Television ◽  
Model Building ◽  
Television System ◽  
Marketing Model ◽  
Television Service ◽  
Decision Tree Method ◽  
Tree Method

This paper takes more than four hundred records of some cable television system for example, makes data mining according to users data record, uses BP neural network and decision tree method respectively to have model building and finds the best model fits for users to order press service. The results of the experiment validate the methods feasibility and validity.

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

2011 ◽  
Vol 403-408 ◽  
pp. 1804-1807
Author(s):  
Ning Zhao ◽  
Shao Hua Dong ◽  
Qing Tian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Arc Welding ◽  
Data Cleaning ◽  
Data Extraction ◽  
Electric Arc ◽  
Welded Tube ◽  
Electric Arc Welding ◽  
Decision Tree Method ◽  
Tree Method

In order to optimize electric- arc welding （ERW） welded tube scheduling , the paper introduces data cleaning, data extraction and transformation in detail and defines the datasets of sample attribute, which is based on analysis of production process of ERW welded tube. Furthermore, Decision-Tree method is adopted to achieve data mining and summarize scheduling rules which are validated by an example.

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