Design and Implementation of Intelligent System to Detect Malicious Facebook Posts Using Support Vector Machine (SVM)

2018 ◽  
pp. 17-24 ◽  
Author(s):  
Sasikumar Gurumurthy ◽  
C. Sushama ◽  
M. Ramu ◽  
K. Sai Nikhitha
Keyword(s):  
Support Vector Machine ◽  
Intelligent System ◽  
Support Vector ◽  
Design And Implementation

Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

2014 ◽  
Vol 493 ◽  
pp. 337-342 ◽  
Author(s):  
Achmad Widodo ◽  
I. Haryanto ◽  
T. Prahasto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Intelligent System ◽  
Feature Space ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Standard Data ◽  
Rolling Element

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.

Design and Implementation of a Cognitive Tool to Detect Malicious Images Using the Smart Phone

2014 ◽  
Vol 6 (2) ◽  
pp. 30-40 ◽  
Author(s):  
Hiroyuki Nishiyama ◽  
Fumio Mizoguchi
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
User Interface ◽  
Service System ◽  
Smart Phone ◽  
Image Data ◽  
Support Vector ◽  
Cognitive Tool ◽  
Design And Implementation ◽  
Learning Data

In this study, the authors design a cognitive tool to detect malicious images using a smart phone. This tool can learn shot images taken with the camera of a smart phone and automatically classify the new image as a malicious image in the smart phone. To develop the learning and classifier tool, the authors implement an image analysis function and a learning and classifier function using a support vector machine (SVM) with the smart phone. With this tool, the user can collect image data with the camera of a smart phone, create learning data, and classify the new image data according to the learning data in the smart phone. In this study, the authors apply this tool to a user interface of a cosmetics recommendation service system and demonstrate its effectiveness by in reducing the load of the diagnosis server in this service and improving the user service.

Design and implementation of a support vector machine using an optoelectronic matrix-vector multiplier

2007 ◽  
Author(s):  
J. Gimeno ◽  
H. Lamela ◽  
M. Jiménez ◽  
M. González ◽  
M. Ruiz-Llata
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Design And Implementation ◽  
Matrix Vector

A soft computing approach for diabetes disease classification

2016 ◽  
Vol 24 (4) ◽  
pp. 379-393 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Othman Bin Ibrahim ◽  
Abbas Mardani ◽  
Ali Ahani ◽  
Ahmad Jusoh
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intelligent System ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Support Vector

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

scholarly journals An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
E. Chatzimichail ◽  
E. Paraskakis ◽  
M. Sitzimi ◽  
A. Rigas
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Outcome Prediction ◽  
Intelligent System ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Support Vector ◽  
Asthma Outcome

Objectives. In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority.Methods. The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation.Results. The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results.Conclusions. This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.

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