scholarly journals Rice Plant Disease Detection using Twin Support Vector Machine (TSVM)

2019 ◽  
Vol 7 ◽  
pp. 61-69
Author(s):  
Bikash Chawal ◽  
Sanjeev Prasad Panday
Keyword(s):  
Support Vector Machine ◽  
Binary Data ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Prototype System ◽  
Crop Disease ◽  
Processing Techniques ◽  
Disease Epidemics

Crop disease epidemics can cause severe losses and affect agricultural products and food security especially in south Asian countries and Nepal where rice is enjoyed as a staple throughout the year. To achieve automatic diagnosis of crop disease the proposed system aims to develop a prototype system for detection of the paddy disease. Image recognition of the disease would be conducted based on Image Processing techniques to enhance the quality of the image and Twin Support Vector Machine (TSVM) technique to classify the paddy disease. The methodology involves image acquisition, pre-processing, analysis and classification of the paddy disease. All the paddy sample images will be passed through the RGB calculation before it proceeds to the binary conversion. If the sample is in the range of normal paddy RGB, then it is automatically classify as normal. Then, all the segmented paddy disease sample will be converted into the binary data in data base before proceed through the TSVM for training and testing. The proposed system is targeted to achieve better recognition results.

Classification of Edge Distortion of Tooth Profile Image Based on Improved Twin Support Vector Machine

2020 ◽  
Vol 49 (10) ◽  
pp. 1015002-1015002
Author(s):  
孙禾 He SUN ◽  
赵文珍 Wen-zhen ZHAO ◽  
赵文辉 Wen-hui ZHAO ◽  
段振云 Zhen-yun DUAN
Keyword(s):  
Support Vector Machine ◽  
Tooth Profile ◽  
Twin Support Vector Machine ◽  
Support Vector

scholarly journals Phishing Website Classification using Least Square Twin Support Vector Machine

2019 ◽  
Vol 9 (1) ◽  
pp. 2063-2068
Keyword(s):  
Support Vector Machine ◽  
Cyber Security ◽  
Classification Accuracy ◽  
Personal Information ◽  
Classification Problem ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Security Issue

Phishing is one among the luring procedures used by phishing attackers in the means to abuse the personal details of clients. Phishing is earnest cyber security issue that includes facsimileing legitimate website to apostatize online users so as to purloin their personal information. Phishing can be viewed as special type of classification problem where the classifier is built from substantial number of website's features. It is required to identify the best features for improving classifiers accuracy. This study, highlights on the important features of websites that are used to classify the phishing website and form the legitimate ones by presenting a scheme Decision Tree Least Square Twin Support Vector Machine (DT-LST-SVM) for the classification of phishing website. UCI public domain benchmark website phishing dataset was used to conduct the experiment on the proposed classifier with different kernel function and calculate the classification accuracy of the classifiers. Computational results show that DT-LST-SVM scheme yield the better classification accuracy with phishing websites classification dataset

scholarly journals Personality Prediction Based on Iris Position Classification Using Support Vector Machines

2018 ◽  
Vol 9 (3) ◽  
pp. 667 ◽  
Author(s):  
Sofea Ramli ◽  
Sharifalillah Nordin
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Representational System ◽  
Kernel Parameter ◽  
Vector Machines ◽  
Efficient Alternative ◽  
Training Activities ◽  
Personality Prediction ◽  
Processing Techniques

<p>Predicting personality generally involves personal interpretations of a person which makes the current methods for personality prediction process less adequate, timely and tedious. Thus, a simple yet efficient alternative method is proposed in this project for detecting iris positions which are used in Neuro-Linguistic Programming as clues for the human internal representational system and mental activity. This study set out to determine several positions of the iris of a person based on the Eye Accessing Cues. The design and the development of a complete system will be undertaken as for the users to use as a medium to predict their personality based on their iris position. Several pre-processing techniques were executed to each of the data before run into the testing and training activities for accuracy gaining. The algorithm used for classification of the positions is Support Vector Machine which by taking rectangle crop of an eye with 9000 pixels as inputs. Radial Basis Function is used for the kernel parameter of the proposed method. The classification will then map into the type of a person with the lists of his personality based on Visual, Auditory and Kinaesthetic theory. The result of the classification of the iris positions is currently 84.9% accurate which in the future might be increased by tuning several other parameters that consisted in Support Vector Machine.</p>

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification

Support Vector Machine Based Sub-Classification of Arrhythmia Using ECG Signal

2020 ◽  
Author(s):  
Mohit Singh Dhaka ◽  
Poras Khetarpal ◽  
Neeraj Kumar
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Ecg Signal
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