Early Detection and Continuous Quantization of Plant Disease Using Template Matching and Support Vector Machine Algorithms

2013 ◽  
Author(s):  
Rong Zhou ◽  
Shun'ichi Kaneko ◽  
Fumio Tanaka ◽  
Miyuki Kayamori ◽  
Motoshige Shimizu
Keyword(s):  
Support Vector Machine ◽  
Early Detection ◽  
Template Matching ◽  
Plant Disease ◽  
Support Vector

scholarly journals Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 667
Author(s):  
Wismaji Sadewo ◽  
Zuherman Rustam ◽  
Hamidah Hamidah ◽  
Alifah Roudhoh Chusmarsyah
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Support Vector Machine ◽  
Early Detection ◽  
Feature Space ◽  
Kernel Functions ◽  
Twin Support Vector Machine ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

scholarly journals Sistem Pengenalan Wajah 3D Menggunakan ICP dan SVM

2019 ◽  
Vol 6 (6) ◽  
pp. 601
Author(s):  
Ledya Novamizanti ◽  
Nadya Viana De Lima ◽  
Eko Susatio
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Template Matching ◽  
Confusion Matrix ◽  
Iterative Closest Point ◽  
Reference Image ◽  
Support Vector ◽  
Identification System ◽  
3D Face ◽  
3 Dimensional

<p>Pengenalan wajah merupakan salah satu teknologi biometrik yang banyak diaplikasikan terutama pada sistem keamanan. Sistem absensi dengan wajah, mengenali pelaku tindak kriminal dengan CCTV adalah beberapa aplikasi dari pengenalan wajah. Efisiensi dan akurasi menjadi faktor utama pengenalan wajah banyak diaplikasikan. Pada penelitian ini, sistem identifikasi<em> </em>diimplementasikan dalam bentuk pengenalan wajah 3 dimensi berbasis <em>t</em><em>emplate </em><em>m</em><em>atching </em>menggunakan metode<em> Iterative Closest Point</em> (ICP) dan klasifikasi <em>Support Vector Machine</em> (SVM). <em>Iterative Closest Point</em> (ICP) memberikan informasi dimensi dengan meminimalisasi kesalahan antara titik-titik dalam satu tampilan dan titik terdekatnya agar template wajah 3D yang dibuat sesuai dengan citra referensi. Sedangkan SVM adalah adalah metode klasifikasi dengan menentukan kelas citra berdasarkan informasi yang diperoleh dari proses ektraksi ciri.<em> </em>Hasil akhir dari penelitian ini adalah suatu aplikasi yang mampu melakukan identifikasi pengenalan pola wajah 3D. Berdasarkan <em>c</em><em>onfusion </em><em>m</em><em>atrix</em>, diperoleh bahwa sistem ini bekerja dengan <em>p</em><em>recision</em> 97,30%, <em>r</em><em>ecall</em> 100,00%, <em>a</em><em>ccuracy </em>97,56% pada pengambilan <em>frame</em> citra sebanyak 48, iterasi ke 49, partisi 12, dan menggunakan SVM tipe OAA.</p><p><em><strong>Abstract</strong></em></p><p><em>Face recognition is a biometric technology that is widely applied especially in the security system. Attendance systems with faces, recognizing criminals with CCTV are some of the applications of face recognition. Efficiency and accuracy are the main factors that face recognition is widely applied. In this study, the identification system was implemented in the form of 3-dimensional face recognition based on template matching using the Iterative Closest Point (ICP) method and Support Vector Machine (SVM) classification. Iterative Closest Point (ICP) provides dimensional information by minimizing errors between points in one view and the closest point so that 3D face templates are made in accordance with the reference image. Whereas SVM is a classification method by determining the image class based on information obtained from the extraction of features. The final result of this study is an application that is able to identify 3D face pattern recognition. Based on the confusion matrix, found that this system works with 97.30% precision, recall 100.00%, 97.56% accuracy in image frame capture as much as 48 iterations to 49, the partition 12, and using the SVM-type OAA.</em></p><p><em><strong><br /></strong></em></p>

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