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>

scholarly journals Pulse Waveform Classification Using Support Vector Machine with Gaussian Time Warp Edit Distance Kernel

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Danbing Jia ◽  
Dongyu Zhang ◽  
Naimin Li
Keyword(s):  
Quantitative Research ◽  
Edit Distance ◽  
Difficult Problem ◽  
Average Error ◽  
Support Vector ◽  
Pulse Waveform ◽  
Vector Machines ◽  
Processing Techniques ◽  
Gaussian Time

Advances in signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis. However, because of the inevitable intraclass variations of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. Utilizing the new elastic metric, that is, time wrap edit distance (TWED), this paper proposes to address the problem under the support vector machines (SVM) framework by using the Gaussian TWED kernel function. The proposed method, SVM with GTWED kernel (GTWED-SVM), is evaluated on a dataset including 2470 pulse waveforms of five distinct patterns. The experimental results show that the proposed method achieves a lower average error rate than current pulse waveform classification methods.

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.

scholarly journals Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

2019 ◽  
Vol 8 (6) ◽  
pp. 3697-3706
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Kidney Disease ◽  
Support Vector ◽  
Final Conclusion ◽  
Classification Algorithms ◽  
Major Objective ◽  
Vector Machines ◽  
Improve Accuracy ◽  
Processing Techniques

Preventing Chronic Kidney Disease has become one of the most intriguing task to the healthcare society. The major objective of this paper is to deal mainly with different classification algorithms namely NaiveBayes, Multi Layer Perceptron and Support Vector Machine. The work analyzes the Chronic Kidney Disease dataset taken from the machine learning repository of UCI. Pre-processing techniques such as missing value replacement, unsupervised discretization and normalization are applied to the Chronic Kidney Disease dataset to improve accuracy. Accuracy and time are the taken as the experimental outcomes of the classification models. The final conclusion states that Support Vector Machine implements much superior than all the other classification methods.

A Novel Background Subtraction Method Using Multiclass Support Vector Machine

2014 ◽  
Vol 701-702 ◽  
pp. 265-269
Author(s):  
Shao Na Zhou ◽  
Shao Rui Xu ◽  
Hua Xiao
Keyword(s):  
Support Vector Machine ◽  
Background Subtraction ◽  
Visual Surveillance ◽  
Support Vector ◽  
Subtraction Method ◽  
New Approach ◽  
Vector Machines ◽  
Multiclass Support Vector Machine ◽  
Multiclass Support Vector Machines

Background subtraction, where the foreground is segmented from the background, is the first step of data analysis and processing in automated visual surveillance. Aiming to solve the problems associated with dynamic, multi-modal background, we explore a new approach which can handle the unconstrained environment. Based on multiclass support vector machines, a new MSVM is proposed for the classification of the background and the foreground. The simulation indicates our proposed algorithm is feasible.

TOWARDS UNDERSTANDING AUTISM RISK FACTORS: A CLASSIFICATION OF BRAIN IMAGES WITH SUPPORT VECTOR MACHINES

2013 ◽  
Vol 07 (02) ◽  
pp. 205-213
Author(s):  
COLLEEN PAM CHEN ◽  
CHRISTOPHER LEE KEOWN ◽  
RALPH-AXEL MÜLLER
Keyword(s):  
Risk Factors ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Support Vector ◽  
Brain Images ◽  
Vector Machines ◽  
Neuroimaging Data

We demonstrate the use of support vector machine methods to classify autism neuroimaging data collected from multiple sites.

scholarly journals The Sparse Learning of The Support Vector Machine

2021 ◽  
Vol 2078 (1) ◽  
pp. 012006
Author(s):  
Lipeng Cui ◽  
Jie Shen ◽  
Song Yao
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Sparse Learning ◽  
Support Vector ◽  
Research Directions ◽  
Sparse Model ◽  
Vector Machines ◽  
Fitting Problem

Abstract The sparse model plays an important role in many aeras, such as in the machine learning, image processing and signal processing. The sparse model has the ability of variable selection, so they can solve the over-fitting problem. The sparse model can be introduced into the field of support vector machine in order to get classification of the labels and sparsity of the variables simultaneously. This paper summarizes various sparse support vector machines. Finally, we revealed the research directions of the sparse support vector machines in the future.

Detection of Amaranthus palmeri sp. Seedlings in Vegetable Farms Using Genetic Algorithm Optimized Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 267-271 ◽  
Author(s):  
W.K Wong ◽  
Muralindran Mariappan ◽  
Ali Chekima ◽  
Manimehala Nadarajan ◽  
Brendan Khoo
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Fine Tuning ◽  
Support Vector ◽  
Amaranthus Palmeri ◽  
Weed Species ◽  
Classification Rate ◽  
Vector Machines

This research is a part of a larger research scope to recognise individual weed species for weed scouting and spot weeding. Support Vector Machines are used to classify the presence of specified weeds(Amaranthus palmeri )by analysing the shape of the weeds. Weed leaves are extracted using image dilation and erosion methods. Several shape feature types were proposed and a total of 59 features were used as the feature pool. The feature selection and fine tuning of the Support Vector Machine are performed using Genetic Algorithm. The outcome is a generalised classifier that enables classification of weed leaves with an average of 90.5% classification rate.

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
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