support vector machine

List of Articles support vector machine

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1 - Automatic Detection of Grand-Mal Epileptic Seizure and Recognizing Normal Activities in Video by a Combination of Machine Vision and Machine Learning Techniques
A. Hakimi Rad N. Moghadam Charkari
20.1001.1.16823745.1394.13.2.8.5

The most relevant method to detect epileptic seizures is the electroencephalogram (EEG) based signal processing method which, due to the need for installing some electrodes on different places of the person's head, causes many movement problems. The aim of this research More

The most relevant method to detect epileptic seizures is the electroencephalogram (EEG) based signal processing method which, due to the need for installing some electrodes on different places of the person's head, causes many movement problems. The aim of this research is to automatically and intelligently detect grand-mal epileptic seizures and also to recognize normal activities of a person suffering from the disease by video surveillance. In this paper we have used the combination of machine vision and machine learning techniques to automatically detect grand-mal epileptic seizure when the person is lying on the ground or on the bed. After subtracting the background from video frame sequences and extracting the image silhouette, appropriate geometrical features have been extracted and fed to the multi-class support vector machine as the input for automatically classifying the videos and assigning proper activity label. All the implementations have been done on MATLAB R2011a. In this intelligent system the accuracy of detecting and recognizing activities is 90.21%. Using this system in addition to reducing the number of human observers is very helpful for the on time and constant detection of the condition. The need for just a conventional video camera and a computer system makes it affordable for people with different incomes. Because it needs not to be in contact with the person's body, there is no movement problem too. High accuracy verifies the optimal performance of the system. Manuscript profile
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2 - Semi-Supervised Self-Training Classification Based on Neighborhood Construction
mona emadi jafar tanha Mohammadebrahim Shiri Mehdi Hosseinzadeh Aghdam
20.1001.1.16823745.1401.20.3.5.3

Using the unlabeled data in the semi-supervised learning can significantly improve the accuracy of supervised classification. But in some cases, it may dramatically reduce the accuracy of the classification. The reason of such degradation is incorrect labeling of unlabe More

Using the unlabeled data in the semi-supervised learning can significantly improve the accuracy of supervised classification. But in some cases, it may dramatically reduce the accuracy of the classification. The reason of such degradation is incorrect labeling of unlabeled data. In this article, we propose the method for high confidence labeling of unlabeled data. The base classifier in the proposed algorithm is the support vector machine. In this method, the labeling is performed only on the set of the unlabeled data that is closer to the decision boundary from the threshold. This data is called informative data. the adding informative data to the training set has a great effect to achieve the optimal decision boundary if the predicted label is correctly. The Epsilon- neighborhood Algorithm (DBSCAN) is used to discover the labeling structure in the data space. The comparative experiments on the UCI dataset show that the proposed method outperforms than some of the previous work to achieve greater accuracy of the self-training semi-supervised classification. Manuscript profile
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3 - Detection and Analysis of Acoustic Signals of Power Transformers On-Load Tap Changers for Assessment of Their Faults
adel younesi Abbas Ghayebloo Hasanreza Mirzaei

On load tap changers are very important equipment of the power transfor More

On load tap changers are very important equipment of the power transformers. Due to the strongly mechanical movements and high-energy arcs, this equipment has a much higher failure rate with respect to other internal transformer equipment. Online and accurate evaluation of well operation of these equipment by indicators with no interfere on the normal operation of the transformer, is very important issue. In this paper, various faults detecting methods in the tap changer have been discussed an investigated by some extracted features of acoustic signals. These signals have been captured experimentally in various tap changing periods by an accelerometer sensor mounted on a power transformer body. In this paper, in addition to common features, two new feathers entitled time and frequency indicators have been introduced. Finally, for selecting the proper features to faults detection and proposing an effective classification method, some available experimental data were randomly defected by results in the references, and classified successfully as healthy and defective data by support vector machine (SVM) method. Manuscript profile

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List of Articles support vector machine