Classification Computation Time Research Articles

Deep learning network to distinguish binary black hole signals from short-duration noise transients

Blip glitches, a type of short-duration noise transient in the LIGO--Virgo data, are a nuisance for the binary black hole (BBH) searches. They affect the BBH search sensitivity significantly because their time-domain morphologies are very similar, and that creates difficulty in vetoing them. In this work, we construct a deep-learning neural network to efficiently distinguish BBH signals from blip glitches. We introduce sine-Gaussian projection (SGP) maps, which are projections of GW frequency-domain data snippets on a basis of sine-Gaussians defined by the quality factor and central frequency. We feed the SGP maps to our deep-learning neural network, which classifies the BBH signals and blips. Whereas the BBH signals are simulated, the blips used are taken from real data throughout our analysis. We show that our network significantly improves the identification of the BBH signals in comparison to the results obtained using traditional-$\chi^2$ and sine-Gaussian $\chi^2$. For example, our network improves the sensitivity by 75% at a false-positive rate of $10^{-2}$ for BBHs with total mass in the range $[80,140]~M_{\odot}$ and SNR in the range $[3,8]$. Also, it correctly identifies 95% of the real GW events in GWTC-3. The computation time for classification is a few minutes for thousands of SGP maps on a single core. With further optimisation in the next version of our algorithm, we expect a further reduction in the computational cost. Our proposed method can potentially improve the veto process in the LIGO--Virgo GW data analysis and conceivably support identifying GW signals in low-latency pipelines.

Online P2P Internet Traffic Classification and Mitigation Based on Snort and ML

Peer to peer applications have modified the nature of internet traffic. It will consume high internet bandwidth and affect the performance of traditional traffic internet applications. Therefore, the management and monitoring activity of internet traffic is the important activities involved in the optimization. In order to detect and mitigate the P2P traffic, port, payload, and transport layer based methods were developed in the past. Nevertheless, the performances of these methods were not up to the expectation. Machine Learning (ML) is one of the promising methods to identify and mitigate the traffic of the Internet. However, the classification accuracy is inconsistent. The reason for the inconsistency is the relevant training datasets generation and feature selection. In this research, a technique based on signature-based and ML is proposed to develop a model for online P2P traffic detection and mitigation. The proposed work can be employed to evaluate the robustness of the online P2P machine learning classifier based on real network traffic traces containing flows labelled by SNORT tool and from special shared resources. Analysis and validation were carried out on traffic traces of University Technology Malaysia. The period of traffic was 2011 and 2013. The output of research is revealing that the proposed work has spent less computation time for classification. This method gives 99.7% accuracy which equals the classification performance attained for P2P using deep packet inspector. The findings show that classifying network traffic at the flow level can differentiate P2P over non-P2P (nP2P) with high confidence for online P2P mitigation.

Online P2P Internet Traffic Classification and Mitigation Based on Snort and ML

Peer to peer applications have modified the nature of internet traffic. It will consume high internet bandwidth and affect the performance of traditional traffic internet applications. Therefore, the management and monitoring activity of internet traffic is the important activities involved in the optimization. In order to detect and mitigate the P2P traffic, port, payload, and transport layer based methods were developed in the past. Nevertheless, the performances of these methods were not up to the expectation. Machine Learning (ML) is one of the promising methods to identify and mitigate the traffic of the Internet. However, the classification accuracy is inconsistent. The reason for the inconsistency is the relevant training datasets generation and feature selection. In this research, a technique based on signature-based and ML is proposed to develop a model for online P2P traffic detection and mitigation. The proposed work can be employed to evaluate the robustness of the online P2P machine learning classifier based on real network traffic traces containing flows labelled by SNORT tool and from special shared resources. Analysis and validation were carried out on traffic traces of University Technology Malaysia. The period of traffic was 2011 and 2013. The output of research is revealing that the proposed work has spent less computation time for classification. This method gives 99.7% accuracy which equals the classification performance attained for P2P using deep packet inspector. The findings show that classifying network traffic at the flow level can differentiate P2P over non-P2P (nP2P) with high confidence for online P2P mitigation.

Multi-color space local binary pattern-based feature selection for texture classification

This paper deals with multi-color space texture classification. Two approaches are proposed and compared: a multi-color space histogram selection (MCSHS) and a multi-color space bin selection. These approaches select local binary pattern (LBP) histograms or LBP bins that have been processed from images coded in multiple color spaces. On the one hand, the proposed LBP-based feature selection scheme overcomes the difficulty of choosing a relevant color space, and on the other hand, it takes advantage of the specific properties of several color spaces by combining them. Experiments show that the MCSHS approach is relevant for color texture classification issues that require good performances whether in accuracy or classification computation time.

Two-dimensional empirical wavelet transform based supervised hyperspectral image classification

Hyperspectral image classification is one of the major field of application for hyperspectral imaging systems. Though hyperspectral data gives accurate results than their multispectral counterparts, they are computationally more complex due to their high dimensionality. One of the classical problem while dealing with supervised hyperspectral classification is the class imbalance problem that arises due to the limited availability of samples for training. In order to deal with high dimensionality, many feature mining techniques has been proposed in literature for hyperspectral images. In this paper, we propose a hyperspectral image classification method based on two-dimensional Empirical Wavelet Transform (2D-EWT) feature extraction and compare it with that of Image Empirical Mode Decomposition (IEMD) based extracted features and raw features. Here, the focus is upon the fact that the number of features trained should be less than what is to be tested. Since the computational time for classification is also of prime importance, only some of the fast and best of the classifiers are selected. Sparse-based classifiers are one of the fast and efficient method for supervised classification of hyperspectral images. Subspace Pursuit (SP) and Orthogonal Matching Pursuit (OMP) algorithms are used in our experiments for sparse-based classification. Other classifiers used are Support Vector Machine (SVM) and Hybrid Support Vector Selection and Adaptation (HSVSA). The proposed methodology gives improved performance in terms of classification evaluation measures for hyperspectral image classification task.

Fast Kernel Sparse Representation Classifier using Improved Smoothed-l0 Norm

The computation time for solving classification problem using sparse representation classifier remains a huge drawback as it is to be implemented in real time applications. The time consuming of sparse representation classifier is mainly due to the sparse signal recovery solver which is based on l1 minimization or Basis Pursuit. Since then, a fast version of sparse signal recovery solver is introduced and it is based on smoothing the discontinuous properties of l0 norm. In this work, a smoothed l0 norm solver is implemented in sparse representation classifier algorithm. This smoothed l0 norm solver is also modified and improved in such a way to increase its classification accuracy and to further reduce the computation time. The use of kernel version of sparse representation classifier to this modified solver is also implemented and described in this paper. Experiments based on human speech data are carried out in order to compare the improved version of sparse representation classifier with the state of the art classifiers. Experimental results prove that the computation time for classification using proposed algorithm is greatly reduced compared to the baseline performances.

Efficient levels of spatial pyramid representation for local binary patterns

Local binary patterns (LBPs) are a well‐known operator that shows the ability for rotation and scale invariant texture classification. A recent extension of this operator is the pyramid transform domain approach on LBPs (PLBP). Obtaining more accuracy by using more pyramid representations is an important result of PLBP, which increases not only feature dimensionality, but also classification computational time (CT). This study illustrates that more pyramid image representations will not improve the performance of the PLBP. We evaluate efficient levels of representation for the PLBP descriptor. In addition, the authors propose some feature selection approaches, such as the multi‐level and multi‐resolution (ML + MR) approach and the ML, MR and multi‐band (ML + MR + MB) approach and discuss their efficiency and CT. Experimental results show that the proposed feature selection approaches improve the accuracy of texture classification with fewer pyramid image representations. In addition, replacing the Chi‐2 similarity measurement with Czekannowski improves the accuracy of texture classification.

ARTIFICIAL NEURAL NETWORK BASED ECG ARRHYTHMIA CLASSIFICATION

Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjunction with ANN classifiers to discriminate ECG signals. Instead of giving the ECG data as such, parameters such as fourth order Auto Regressive model coefficients and Spectral Entropy of the signals has been selected. On testing with the Massachusetts Institute of Technology-Beth Israel Hospital (MIT/BIH) arrhythmia database, it has been observed that PNN has better performance than conventionally used MLFFN in ECG arrhythmia classification. MLFFN with Back Propagation Algorithm gives a classification accuracy of 97.54% and PNN gives 98.96%. The classification by PNN also has an advantage that the computation time for classification is lower than that of MLFFN.