scholarly journals Klasifikasi Gambar Gigitan Ular Menggunakan Regionprops dan Algoritma Decision Tree

2020 ◽  
Vol 1 (2) ◽  
pp. 69
Author(s):  
Yoga Widi Pamungkas ◽  
Adiwijaya Adiwijaya ◽  
Dody Qori Utama
Keyword(s):  
Feature Extraction ◽  
Decision Tree ◽  
Significant Influence ◽  
Snake Bite ◽  
Decision Tree Algorithm ◽  
Bite Wound ◽  
Know How ◽  
Classification Of Images ◽  
Bite Wounds

Indonesia has a high biodiversity of snakes. Snake species that exist throughout Indonesia, consisting of venomous and non-venomous snakes. One of the dangers that can be posed by snakes is the bite of several types of deadly snakes. Snake bite cases recorded in Indonesia are quite high with not a few fatalities. Most of the deaths caused by snakebite occur due to errors in the handling procedure for the bite wound. This problem can be overcome one of them if we know how to classify snake bite wounds, whether venomous or non-venomous. In this study, a classification system for snake bite wound image was built using Regionprops feature extraction and Decision Tree algorithm. Snake bite images are classified as either venomous or non-venomous without knowing the kind of the snake. In Regionprops several features are used to help the process of feature extraction, including the number of centroids, area, distance, and eccentricity. Evaluation of the model that was built was found that the parameters of the number of centroids and the distance between centroids had the most significant influence in helping the classification of images of snakebite wounds with an accuracy of 97.14%, precision 92.85%, recall 91.42%, and F1 score 92.06%.

scholarly journals Classification of diabetes disease using decision tree algorithm (C4.5)

2021 ◽  
Vol 1869 (1) ◽  
pp. 012082
Author(s):  
B A C Permana ◽  
R Ahmad ◽  
H Bahtiar ◽  
A Sudianto ◽  
I Gunawan
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Feature Extraction and Classification of Images Based on Corner Invariant Moments

2013 ◽  
Vol 475-476 ◽  
pp. 374-378
Author(s):  
Xue Ming Zhai ◽  
Dong Ya Zhang ◽  
Yu Jia Zhai ◽  
Ruo Chen Li ◽  
De Wen Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Image Feature ◽  
Support Vector ◽  
System Management ◽  
Image Feature Extraction ◽  
Invariant Moments ◽  
Harris Corner ◽  
Classification Of Images

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.

An improved diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model

Sensor Review ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Deepika Kishor Nagthane ◽  
Archana M. Rajurkar
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Feature Selection ◽  
Decision Tree ◽  
Association Rule ◽  
Content Type ◽  
Tree Classifier ◽  
Mammogram Images ◽  
Diagnosis Technique

PurposeOne of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.Design/methodology/approachThe proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based ona priorialgorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.FindingsThe performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.Originality/valueThis paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

Classification of cow behavior using 3-DOF accelerometer and decision tree algorithm

2016 ◽  
Author(s):  
Phung Cong Phi Khanh ◽  
Nguyen Dinh Chinh ◽  
Trinh Thi Cham ◽  
Pham Thi Vui ◽  
Tran Duc Tan
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

scholarly journals Electronic Glove for Troops to Interpret Hand and Arm Gestures in Close Combat Scenario

2020 ◽  
Vol 9 (4) ◽  
pp. 1478-1484
Keyword(s):  
Decision Tree ◽  
Verbal Communication ◽  
Lookup Table ◽  
Line Of Sight ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Percent Accuracy ◽  
The Military

In a close combat situation several types of non-verbal communication are available. However these signals have limits of range and reliability, particularly when line of sight is disrupted. This paper proposes the system for troops to interpret hand and arm military gestures applicable in close combat scenario. In the proposed system, signals are transmitted through secured Bluetooth connections and interpreted at the receiver end. k-NN algorithm, Lookup Table (LuT) and Decision Tree algorithm are used to determine the exact classification of the gestures. This paper presents a system keeping only one fellow trooper in picture and reported 94.6 percent accuracy of the military gestures interpretation.

Application of Decision Tree Algorithm in Lumber Hierarchies

2012 ◽  
Vol 466-467 ◽  
pp. 308-313
Author(s):  
Dan Guo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Inductive Reasoning ◽  
Decision Tree Algorithm ◽  
Discrete Function ◽  
Tree Algorithm ◽  
Id3 Algorithm ◽  
Noise Data ◽  
Construction Model

The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decision tree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.

scholarly journals CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 335-338 ◽  
Author(s):  
H. Sahu ◽  
D. Haldar ◽  
A. Danodia ◽  
S. Kumar
Keyword(s):  
Maximum Likelihood ◽  
Random Forest ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Random Forest Algorithm ◽  
Synthetic Aperture Radar Data ◽  
Tree Algorithm ◽  
Maximum Likelihood Classifier ◽  
Sar Data

<p><strong>Abstract.</strong> A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different classifiers that are maximum likelihood classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum likelihood classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47<span class="thinspace"></span>%, 0.47<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>% and 25.5<span class="thinspace"></span>% respectively in all the classification algorithm but root mean square error for maximum likelihood classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum likelihood classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum likelihood classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.</p>

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