scholarly journals A New Method for Feature Extraction and Classification of Single-Stranded DNA Based on Collaborative Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bingyong Yan ◽  
Haixu Cui ◽  
Haitao Fu ◽  
Jiale Zhou ◽  
Huifeng Wang
Keyword(s):  
Feature Extraction ◽  
Molecular Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Dna Molecules ◽  
Single Stranded Dna ◽  
Collaborative Filter ◽  
Comparison Research ◽  
Extraction Algorithm

The traditional support vector machine algorithm is not enough to classify single-stranded DNA molecules, so this paper proposes an improved threshold extraction algorithm based on collaborative filter for the classification of single-stranded DNA. Firstly, according to the different characteristic curves of the blocking current signals formed by the four bases (A, T, C, and T) that make up DNA molecules crossing the nanopore, the collaborative filter feature extraction algorithm with improved threshold is proposed. Then, the feature information is reconstructed and sent to the SVM classifier for training. Finally, the unfiltered, collaborative filter, improved threshold collaborative filter, and Bessel filter data are, respectively, extracted and sent to the SVM classifier for classification and comparison research. The experimental results show that the improved collaborative filter algorithm has higher accuracy in single-stranded DNA molecular classification.

Kernel Optimized-Support Vector Machine and MapReduce Framework for Sentiment Classification of Train Reviews

2019 ◽  
Vol 27 (06) ◽  
pp. 1025-1050
Author(s):  
Rashmi K. Thakur ◽  
Manojkumar V. Deshpande
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Process ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Mapreduce Framework ◽  
Mapreduce Model

Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, Self-adaptive Lion Algorithm (SLA). In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, NB, NN, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249% specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611% respectively, for train review and movie review databases.

scholarly journals A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 145
Author(s):  
Hongquan Qu ◽  
Zhanli Fan ◽  
Shuqin Cao ◽  
Liping Pang ◽  
Hao Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Power Spectral ◽  
Eeg Data ◽  
Vector Machines ◽  
Full Band ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

scholarly journals HOG based object detection and classification

2018 ◽  
Vol 7 (3.3) ◽  
pp. 151
Author(s):  
N Venkateswara Rao ◽  
G Anil Kumar ◽  
B Harish
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Object Detection ◽  
Real World ◽  
Support Vector ◽  
Svm Classifier ◽  
Robust Method ◽  
Histogram Of Oriented Gradient ◽  
Detection Algorithms

The intension of the project is to classify objects in real world and to tracks them throughout their life spans. Object detection algorithms use feature extraction and learning algorithms to classification of an object category. Our algorithm uses a combination of “histogram of oriented gradient” (HOG) and “support vector machine” (SVM) classifier to classify of objects. Results have shown this to be a robust method in both classifying the objects along with tracking them in real time world.  

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

scholarly journals Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

2018 ◽  
Vol 10 (7) ◽  
pp. 1123 ◽  
Author(s):  
Yuhang Zhang ◽  
Hao Sun ◽  
Jiawei Zuo ◽  
Hongqi Wang ◽  
Guangluan Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Loss Function ◽  
Extraction Method ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Adversarial Networks ◽  
Aircraft Type

Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning

2018 ◽  
Vol 28 (02) ◽  
pp. 1750036 ◽  
Author(s):  
Shuqiang Wang ◽  
Yong Hu ◽  
Yanyan Shen ◽  
Hanxiong Li
Keyword(s):  
Machine Learning ◽  
Surgical Planning ◽  
Diffusion Tensor ◽  
Mean Value ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Control Groups ◽  
Diffusion Tensor Imaging Dti

In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.

scholarly journals Classification of agricultural fields using time series of dual polarimetry TerraSAR-X images

2014 ◽  
Vol XL-2/W3 ◽  
pp. 191-196 ◽  
Author(s):  
S. Mirzaee ◽  
M. Motagh ◽  
H. Arefi ◽  
M. Nooryazdan
Keyword(s):  
Environmental Changes ◽  
Support Vector ◽  
Svm Classifier ◽  
Sensing Applications ◽  
Imaging Characteristics ◽  
Training Samples ◽  
Study Results ◽  
Phenological Changes ◽  
Multi Temporal

Due to its special imaging characteristics, Synthetic Aperture Radar (SAR) has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM) classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient.

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%.

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