scholarly journals Sistem Identifikasi Usia Manusia pada Citra Panoramic Radiograph Gigi Molar Pertama

2021 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
BANYU BIRU ◽  
HILMAN FAUZI ◽  
FAHMI OSCANDAR
Keyword(s):  
Decision Tree ◽  
Processing System ◽  
Panoramic Radiograph ◽  
Large Object ◽  
Human Teeth ◽  
Dental Identification ◽  
Structuring Element ◽  
Tree Methods ◽  
Identification Processes ◽  
Radiograph Image

ABSTRAKOdontologi forensik merupakan sebuah cabang ilmu forensik yang melakukan proses identifikasi berdasarkan gigi. Gigi merupakan salah satu bagian tubuh manusia paling kuat kuat. Dalam masa pertumbuhan, gigi manusia mengalami degeneratif pada usia tertentu, sehingga gigi dapat menjadi media dalam proses identifikasi usia. Pada penelitian ini, dirancang sistem pengolahan citra yang dapat mendeteksi usia manusia pada citra radiograf panoramik gigi. Sistem ini menggunakan metode Binary Large Object dan Decision Tree. Berdasarkan hasil pengujian, sistem dapat mendeteksi usia berdasarkan citra gigi molar pertama dengan tingkat akurasi lebih dari 80%, pada saat menggunakan parameter structuring element jenis Disk dengan jari-jari 4 piksel, ciri area dan rasio pulpa, serta jenis algoritma pada decision tree yaitu curvature dengan jumlah 50 percabangan.Kata kunci: citra radiograf panoramik, pulpa gigi, molar pertama, decision tree, binary large object ABSTRACTForensic odontology is a branch of forensic science that carries out dental identification processes. Teeth are one of the strongest parts of the human body In the period of growth, human teeth degenerative at a certain age, so that teeth can be a medium in the process of age identification. In this study, an image processing system was designed that could detect human age on dental panoramic radiographs. This system using the Binary Large Object and Decision Tree methods. Based on the test results, the system can detect age based on the image of the first molar with an accuracy level of more than 80%, when using a Disk type structuring element parameter with a radius of 4 pixels, the area and pulp ratio features, and the type of algorithm in the decision tree, namely curvature with the number of 50 branches.Keywords: panoramic radiograph image, teeth pulp, first molar, decision tree,binary large object

scholarly journals Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

2021 ◽  
Author(s):  
Xu Wang ◽  
Hongyang Gu ◽  
Tianyang Wang ◽  
Wei Zhang ◽  
Aihua Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Structural Characteristics ◽  
Proposed Model ◽  
The Hierarchical Structure ◽  
Hierarchical Decision ◽  
Tree Methods ◽  
New Perspective ◽  
Simple Combination

AbstractThe fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

Comparisons of decision tree methods using water data

2016 ◽  
Vol 46 (4) ◽  
pp. 2924-2934 ◽  
Author(s):  
Muhammad Azam ◽  
Muhammad Aslam ◽  
Khushnoor Khan ◽  
Anwar Mughal ◽  
Awais Inayat
Keyword(s):  
Decision Tree ◽  
Tree Methods ◽  
Decision Tree Methods

An Approach for ECG Characterization and Classification Using the Combination of Wavelet Transform and Decision Tree Methods

2012 ◽  
Vol 1 (3) ◽  
pp. 72-81
Author(s):  
Faiza Charfi ◽  
Ali Kraiem
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
High Reliability ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signals ◽  
Interaction Detection ◽  
Fast Independent Component Analysis ◽  
Tree Methods ◽  
Decision Tree Methods

A new automated approach for Electrocardiogram (ECG) arrhythmias characterization and classification with the combination of Wavelet transform and Decision tree classification is presented. The approach is based on two key steps. In the first step, the authors adopt the wavelet transform to extract the ECG signals wavelet coefficients as first features and utilize the combination of Principal Component Analysis (PCA) and Fast Independent Component Analysis (FastICA) to transform the first features into uncorrelated and mutually independent new features. In the second step, they utilize some decision tree methods currently in use: C4.5, Improved C4.5, CHAID (Chi - Square Automatic Interaction Detection) and Improved CHAID for the classification of ECG signals, which are taken, from the MIT-BIH database, including normal subjects and subjects affected by arrhythmia. The authors’ results suggest the high reliability and high classification accuracy of C4.5 algorithm with the bootstrap aggregation.

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