A Novel Approach to Detect Hardware Malware Using Hamming Weight Model and One Class Support Vector Machine

2019 ◽  
pp. 159-172
Author(s):  
P. Saravanan ◽  
B. M. Mehtre
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Hamming Weight ◽  
Novel Approach ◽  
Weight Model

Membership Calculation Based on Dimension Hierarchical Division

2013 ◽  
Vol 475-476 ◽  
pp. 312-317
Author(s):  
Ping Zhou ◽  
Jin Lei Wang ◽  
Xian Kai Chen ◽  
Guan Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Bottom Up ◽  
Generalization Ability ◽  
Clustering Technique ◽  
Novel Approach ◽  
Training Samples ◽  
Noise Sample

Since dataset usually contain noises, it is very helpful to find out and remove the noise in a preprocessing step. Fuzzy membership can measure a samples weight. The weight should be smaller for noise sample but bigger for important sample. Therefore, appropriate sample memberships are vital. The article proposed a novel approach, Membership Calculate based on Hierarchical Division (MCHD), to calculate the membership of training samples. MCHD uses the conception of dimension similarity, which develop a bottom-up clustering technique to calculate the sample membership iteratively. The experiment indicates that MCHD can effectively detect noise and removes them from the dataset. Fuzzy support vector machine based on MCHD outperforms most of approaches published recently and hold the better generalization ability to handle the noise.

scholarly journals Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Kun Zhang ◽  
Minrui Fei ◽  
Xin Li ◽  
Huiyu Zhou
Keyword(s):  
Support Vector Machine ◽  
Mean Shift ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Square Estimation ◽  
Unstructured Environments ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

scholarly journals Improving Accuracy of Quasars' Photometric Redshift Estimation by Integration of KNN and SVM

2015 ◽  
Vol 11 (A29A) ◽  
pp. 209-209
Author(s):  
Bo Han ◽  
Hongpeng Ding ◽  
Yanxia Zhang ◽  
Yongheng Zhao
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Unsolved Problem ◽  
Support Vector ◽  
Catastrophic Failure ◽  
K Nearest Neighbor ◽  
Photometric Redshift ◽  
Novel Approach ◽  
Improving Accuracy ◽  
Fusion Approach

AbstractCatastrophic failure is an unsolved problem existing in the most photometric redshift estimation approaches for a long history. In this study, we propose a novel approach by integration of k-nearest-neighbor (KNN) and support vector machine (SVM) methods together. Experiments based on the quasar sample from SDSS show that the fusion approach can significantly mitigate catastrophic failure and improve the accuracy of photometric redshift estimation.

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

2020 ◽  
Vol 20 (04) ◽  
pp. 2050035
Author(s):  
Sumit Dhariwal ◽  
Sellappan Palaniappan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Error Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Image Normalization ◽  
Novel Approach ◽  
Low Levels ◽  
To Come

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

scholarly journals A Novel Approach to Arcing Faults Characterization Using Multivariable Analysis and Support Vector Machine

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2126 ◽  
Author(s):  
John Morales ◽  
Eduardo Muñoz ◽  
Eduardo Orduña ◽  
Gina Idarraga-Ospina
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
Transmission Line ◽  
Transmission Lines ◽  
Multivariable Analysis ◽  
Support Vector ◽  
Committee Report ◽  
Fault Type ◽  
Novel Approach ◽  
Convolution Process

Based on the Institute of Electrical and Electronics Engineers (IEEE) Standard C37.104-2012 Power Systems Relaying Committee report, topics related to auto-reclosing in transmission lines have been considered as an imperative benefit for electric power systems. An important issue in reclosing, when performed correctly, is identifying the fault type, i.e., permanent or temporary, which keeps the faulted transmission line in service as long as possible. In this paper, a multivariable analysis was used to classify signals as permanent and temporary faults. Thus, by using a simple convolution process among the mother functions called eigenvectors and the fault signals from a single end, a dimensionality reduction was determined. In this manner, the feature classifier based on the support vector machine was used for acceptably classifying fault types. The algorithm was tested in different fault scenarios that considered several distances along the transmission line and representation of first and second arcs simulated in the alternative transients program ATP software. Therefore, the main contribution of the analysis performed in this paper is to propose a novel algorithm to discriminate permanent and temporary faults based on the behavior of the faulted phase voltage after single-phase opening of the circuit breakers. Several simulations let the authors conclude that the proposed algorithm is effective and reliable.

A Winch Fault Classification Algorithm Based on Cluster Kernel Semi-Supervised Support Vector Machine

2012 ◽  
Vol 220-223 ◽  
pp. 452-458
Author(s):  
Xian Xin Shi ◽  
Zhong Xiang Zhao ◽  
Chang Jian Zhu ◽  
Xiao Xiao Kong ◽  
Jun Fei Chai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spectral Clustering ◽  
Cluster Algorithm ◽  
Original Data ◽  
Fault Classification ◽  
Support Vector ◽  
The Novel ◽  
High Classification Accuracy ◽  
Novel Approach ◽  
Cluster Assumption

A cluster kernel semi-supervised support vector machine (CKS3VM) based on spectral cluster algorithm is proposed and applied in winch fault classification in this paper. The spectral clustering method is used to re-represent original data samples in an eigenvector space so as to make the data samples in the same cluster gather together much better. Then, a cluster kernel function is constructed upon the eigenvector space. Finally, a cluster kernel S3VM is designed which can satisfy the cluster assumption of semi-supervised study. The experiments on winch fault classification show that the novel approach has high classification accuracy.

Using Context to Discern User Tasks on Desktop

2014 ◽  
Vol 519-520 ◽  
pp. 318-321
Author(s):  
Ning Lv ◽  
Jing Li Zhou ◽  
Lei Hua Qin
Keyword(s):  
Support Vector Machine ◽  
Information Management ◽  
User Behavior ◽  
Personal Information ◽  
Contextual Information ◽  
Support Vector ◽  
Novel Approach ◽  
Information User ◽  
User Tasks ◽  
The Impact

The precise context of user tasks helps to ameliorate personal information management on desktop. This paper introduces a novel approach to discern user tasks using contextual information which is divided into two categories, user behavior based context and text based context. With the contextual information, user tasks are discerned by support vector machine (SVM) method. Experimental results showed the impact of distinct attributes of files on the performance of user task identification.

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