Leak Detection in Transportation Pipelines Using Feature Extraction and KNN Classification

2009 ◽  
Author(s):  
Sima Valizadeh ◽  
Behzad Moshiri ◽  
Karim Salahshoor
Keyword(s):  
Feature Extraction ◽  
Leak Detection ◽  
Knn Classification

scholarly journals Analisis Perbandingan KNN dengan SVM untuk Klasifikasi Penyakit Diabetes Retinopati berdasarkan Citra Eksudat dan Mikroaneurisma

2015 ◽  
Vol 3 (1) ◽  
pp. 75
Author(s):  
SUCI AULIA ◽  
SUGONDO HADIYOSO ◽  
DADAN NUR RAMADAN
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Test Data ◽  
Processing Method ◽  
Keywords Diabetic Retinopathy ◽  
Wavelet Method ◽  
Knn Classification ◽  
Normal Class

ABSTRAKPenelitian mengenai pengklasifikasian tingkat keparahan penyakit Diabetes Retinopati berbasis image processing masih hangat dibicarakan, citra yang biasa digunakan untuk mendeteksi jenis penyakit ini adalah citra optik disk, mikroaneurisma, eksudat, dan hemorrhages yang berasal dari citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma SVM dengan KNN untuk klasifikasi penyakit diabetes retinopati (mild, moderate, severe) berdasarkan citra eksudat dan microaneurisma. Untuk proses ekstraksi ciri digunakan metode wavelet  pada masing-masing kedua metode tersebut. Pada penelitian ini digunakan 160 data uji, masing-masing 40 citra untuk kelas normal, kelas mild, kelas moderate, kelas saviere. Tingkat akurasi yang diperoleh dengan menggunakan metode KNN lebih tinggi dibandingkan SVM, yaitu 65 % dan 62%. Klasifikasi dengan algoritma KNN diperoleh hasil terbaik dengan parameter K=9 cityblock. Sedangkan klasifikasi dengan metode SVM diperoleh hasil terbaik dengan parameter One Agains All.Kata kunci: Diabetic Retinopathy, KNN , SVM, Wavelet. ABSTRACT Research based on severity classification of the disease diabetic retinopathy by using image processing method is still hotly debated, the image is used to detect the type of this disease is an optical image of the disk, microaneurysm, exudates, and bleeding of the image of the fundus. This study was performed to compare SVM method with KNN method for classification of diabetic retinopathy disease (mild, moderate, severe) based on exudate and microaneurysm image. For feature extraction uses wavelet method, and each of the two methods. This study made use of 160 test data, each of 40 images for normal class, mild class, moderate class, severe class. The accuracy obtained by KNN higher than SVM, with 65% and 62%. KNN classification method achieved the best results with the parameters K = 9, cityblock. While the classification with SVM method obtained the best results with parameters One agains all .Keywords: Diabetic Retinopathy, KNN, SVM, Wavelet.

scholarly journals Feature Extraction in Pressure Signals for Leak Detection in Water Networks

2014 ◽  
Vol 70 ◽  
pp. 688-697 ◽  
Author(s):  
M.M. Gamboa-Medina ◽  
L.F. Ribeiro Reis ◽  
R. Capobianco Guido
Keyword(s):  
Feature Extraction ◽  
Leak Detection ◽  
Water Networks

scholarly journals Paddy Seed Classification and Identifying Varieties using Random Assessment Classification

2019 ◽  
Vol 9 (2) ◽  
pp. 2682-2685
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Research Work ◽  
Classification Algorithm ◽  
Experimental Results ◽  
Classification Algorithms ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Knn Classification

The current research work focuses in developing an accurate and efficient classification and feature extraction algorithm for paddy seed image analysis. The paddy images that are preprocessed by applying hybrid mediangaustransform algorithms were segmented using Paddysegmatch algorithm. The resultant image’s features are extracted by applying the proposed enhanced rapid SURF feature extraction including various features of image. Later, the paddy seeds are classified to form different categories by applying the proposed Random Assessment Classification algorithm. Experimental results on Paddy seed realtime image analysis database show that the proposed method performs better classification accuracy compared with SVM and KNN classification algorithms.

scholarly journals HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway

2020 ◽  
Vol 14 (3) ◽  
pp. 231
Author(s):  
Firnanda Al Islama Achyunda Putra ◽  
Fitri Utaminingrum ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Feature Extraction ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Knn Classifier ◽  
Knn Classification ◽  
Testing Data ◽  
Main Components

Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.

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