Performance Evaluation of Spectrogram Based Epilepsy Detection Techniques Using Gray Scale Features

2020 ◽  
pp. 1-7
Author(s):  
Sunandan Mandal ◽  
Kavita Thakur ◽  
Bikesh Kumar Singh ◽  
Heera Ram
Keyword(s):  
Performance Evaluation ◽  
Human Error ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Gray Scale ◽  
Detection Techniques ◽  
Detection Model ◽  
Data Division

Electroencephalogram (EEG) is most common instrument for treatment and diagnosis of brain related diseases. Analysis of EEG signals for treatment of patient is time consuming and not easy task for neurologist. There is always a chance of human error. The purpose of this paper is to present an automatic detection model for epileptic seizure from EEG signals. To fulfill this objective, EEG signals are preprocessed and converted into spectrogram images using Short Time Fourier Transform (STFT). From this spectrogram images gray scale features are extracted. Support Vector Machine (SVM) with six different kernel functions and three data division protocols are utilized for performance evaluation of proposed model. Results show that quadratic SVM classifier has achieved highest classification accuracy.

AUTOMATED DIAGNOSIS OF NORMAL AND ALCOHOLIC EEG SIGNALS

2012 ◽  
Vol 22 (03) ◽  
pp. 1250011 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
SUBHAGATA CHATTOPADHYAY ◽  
JASJIT S. SURI
Keyword(s):  
Surrogate Data ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Frequency Domain Methods ◽  
Significant Difference ◽  
Nonlinear Features ◽  
The Brain

Electroencephalogram (EEG) signals, which record the electrical activity in the brain, are useful for assessing the mental state of a person. Since these signals are nonlinear and non-stationary in nature, it is very difficult to decipher the useful information from them using conventional statistical and frequency domain methods. Hence, the application of nonlinear time series analysis to EEG signals could be useful to study the dynamical nature and variability of the brain signals. In this paper, we propose a Computer Aided Diagnostic (CAD) technique for the automated identification of normal and alcoholic EEG signals using nonlinear features. We first extract nonlinear features such as Approximate Entropy (ApEn), Largest Lyapunov Exponent (LLE), Sample Entropy (SampEn), and four other Higher Order Spectra (HOS) features, and then use them to train Support Vector Machine (SVM) classifier of varying kernel functions: 1st, 2nd, and 3rd order polynomials and a Radial basis function (RBF) kernel. Our results indicate that these nonlinear measures are good discriminators of normal and alcoholic EEG signals. The SVM classifier with a polynomial kernel of order 1 could distinguish the two classes with an accuracy of 91.7%, sensitivity of 90% and specificity of 93.3%. As a pre-analysis step, the EEG signals were tested for nonlinearity using surrogate data analysis and we found that there was a significant difference in the LLE measure of the actual data and the surrogate data.

scholarly journals Cybercrime detection techniques based on support vector machines

2012 ◽  
Vol 2 (1) ◽  
pp. 1 ◽  
Author(s):  
Hanif - Mohaddes Deylami ◽  
Yashwant Prasad Singh
Keyword(s):  
Social Network ◽  
Support Vector Machines ◽  
Classification Accuracy ◽  
Classification Algorithm ◽  
Kernel Functions ◽  
Support Vector ◽  
Classification Algorithms ◽  
Detection Techniques ◽  
Detection Model ◽  
Vector Machines

This paper presents the cybercrime detection model by using support vector machines (SVMs) to classify social network (Facebook) dataset. We try to compare between three kinds of classification algorithms such as: SVMs, AdaBoostM1, and NaiveBayes in order to find a high percentage of classification accuracy. Finally, we conclude SVMs as the best classification algorithm, which uses different breeds of kernel functions in order to improve the classification accuracy on Facebook dataset. Besides, we are using the Weka 3.7.4 software to evaluate classifiers on Facebook dataset.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals A Kernel-Based Approach for Biomedical Named Entity Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rakesh Patra ◽  
Sujan Kumar Saha
Keyword(s):  
Kernel Function ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Kernel Functions ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Named Entity ◽  
Tree Kernel

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

scholarly journals Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

scholarly journals A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 281-285 ◽  
Author(s):  
B. Yekkehkhany ◽  
A. Safari ◽  
S. Homayouni ◽  
M. Hasanlou
Keyword(s):  
Kernel Function ◽  
Temporal Integration ◽  
Experimental Tests ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Multi Temporal ◽  
Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

scholarly journals RESEARCH ON CROP INFORMATION EXTRACTION OF AGRICULTURAL UAV IMAGES BASED ON BLIND IMAGE DEBLURRING TECHNOLOGY AND SVM

2021 ◽  
pp. 33-42
Author(s):  
Zehai Xu ◽  
Haiyan Song ◽  
Zhiming Wu ◽  
Zefu Xu ◽  
Shifang Wang
Keyword(s):  
Information Extraction ◽  
Recognition Rate ◽  
Objective Evaluation ◽  
Kernel Functions ◽  
The Other ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Evaluation Indexes ◽  
Crop Classification

The blurring of crop images acquired by agricultural Unmanned Aerial Vehicle (UAV) due to sudden inputs by operators, atmospheric disturbance, and many other factors will eventually affect the subsequent crop identification, information extraction, and yield estimation. Aiming at the above problems, the new proposed combined deblurring algorithm based on the re-weighted graph total variation (RGTV) and L0-regularized prior, and the other two representative deblurring algorithms were applied to restore blurry crop images acquired during UAV flight, respectively. The restoration performance was measured by subjective vision, and objective evaluation indexes. The crop shape-related and texture-related feature parameters were then extracted, the Support Vector Machine (SVM) classifier with four common kernel functions was implemented for crop classification to realize the purpose of crop information extraction. The deblurring results showed that the proposed algorithm performed better in suppressing the ringing effect and preserving the image fine details, and retained higher objective evaluation indexes than the other two deblurring algorithms. The comparative analysis of different classification kernel functions showed that the Polynomial kernel function with an average recognition rate of 94.83% was most suitable for crop classification and recognition. The research will help in further popularization of crop monitoring based on UAV low-altitude remote sensing.

scholarly journals An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

2021 ◽  
Vol 40 (2) ◽  
pp. 279-297
Author(s):  
Faria Nazir ◽  
Muhammad Nadeem Majeed ◽  
Mustansar Ali Ghazanfar ◽  
Muazzam Maqsood
Keyword(s):  
Language Learning ◽  
Second Language Learners ◽  
Positive Impact ◽  
Detection System ◽  
Confusion Matrix ◽  
Classification Problem ◽  
Support Vector ◽  
Svm Classifier ◽  
Computer Assisted ◽  
Detection Model

Over the last few decades, the field of artificial intelligence and machine learning has evolved. Due to the advancement in these fields, much work has been done to assist language learning with the help of computers called Computer-Assisted Language Learning (CALL). Mispronunciation detection is one of the significant tasks of the CALL system. An efficient mispronunciation detection model has a positive impact on the life of second language learners by providing phoneme level feedback. In this paper, we introduce the phone grouping technique for mispronunciation detection that is based on mistakes probability. We consider mispronunciation detection as a classification problem, traditionally for this purpose, a separate classifier is trained for each phoneme mistake that requires a lot of memory and time. Instead of training a separate classifier, we group the phoneme based on their mistakes probability that helps in reducing the number of the classifiers to be trained and also saves memory and time. We use the Support Vector Machine (SVM) classifier and test the results on the Arabic dataset (28 Phonemes). The performance of our proposed method is evaluated by using accuracy. The results of the model are evaluated using the confusion matrix and gives an accuracy of 88%. Our approach outperforms the existing systems developed for Arabic phonemes in terms of accuracy and is also time/memory efficient.

scholarly journals Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

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