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HRRP multi-target recognition in a beam using prior-independent DBSCAN clustering algorithm

IET RADAR SONAR AND NAVIGATION 2019年第8期13卷 1366-1372页

作者： Guo, Peng-cheng Liu, Zheng Wang, Jing-jing Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China Xian Elect Engn Res Inst Xian Shaanxi Peoples R China

During the operation of monopulse radar, the high-resolution range profiles (HRRPs) of multiple targets may be overlapped, which will reduce the target recognition performance. In this study, a novel multi-target recognition method is proposed based on prior-independent density-based spatial clustering of applications with noise (PI-DBSCAN) algorithm. In the training phase, various features of training samples are extracted, among which the distribution of strong range cells is utilised to obtain the parameters of PI-DBSCAN algorithm while the others are used to train the support vector machine (SVM) classifier. In the test phase, PI-DBSCAN algorithm is exploited at the radial distance-azimuth plane to segment the test multi-target HRRP. Afterward, the features of the segmented HRRPs are extracted and fed to the SVM classifier to be recognised. The proposed method has no constraint on the target motion and does not need to set any parameters manually, which benefits its application. The experiment results of real measured data show that the proposed method is robust against noise and the increasing target HRRP overlap ratio comparing with traditional methods.

关键词： radar resolution support vector machines radar target recognition pattern clustering radar signal processing radar computing HRRP multitarget recognition prior-independent DBSCAN clustering algorithm monopulse radar high-resolution range profiles multiple targets target recognition performance noise algorithm training phase training samples PI-DBSCAN algorithm support vector machine classifier test phase radial distance-azimuth plane test multitarget HRRP SVM classifier target motion

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New approach to transient stability prediction of power systems in wide area measurement systems based on multiple-criteria decision making theory

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第21期13卷 4960-4967页

作者： Hosseini, Hossein Naderi, Soheil Afsharnia, Saeed Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran

To prevent power system blackouts following a large disturbance such as a fault, the speed and precision of the stability status prediction are two important factors for the remedial action schemes. A new approach for the transient stability prediction is presented employing the multi-criteria decision making (MCD) theory to consider both factors simultaneously in this study. It is shown that they depend on the prediction time, therefore solving the MCD problem results in finding the best time for the stability prediction. As the criteria quantifying is necessary in the MCD methods, in this study a new index is proposed to quantify the prediction speed. Furthermore, the support vector machine (SVM) classifier is employed as a transient stability predictor. In addition, the best features are selected and incorporated as the inputs of the SVM classifier according to the features selection methods. Finally, all proposed schemes are implemented on the IEEE 39-Bus test system. The obtained results show the prediction of stability at the optimum time causing the instability prevention with the less required remedial action.

关键词： power engineering computing power system transient stability pattern classification power system measurement decision making support vector machines transient stability prediction power systems wide area measurement systems multiple-criteria decision making theory power system blackouts stability status prediction remedial action schemes multicriteria decision making theory prediction time MCD problem results criteria quantifying MCD methods prediction speed support vector machine classifier transient stability predictor IEEE 39-Bus test system

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Regularized Least Squares cancer classifiers from DNA microarray data

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BMC BIOINFORMATICS 2005年第4-Sup期6卷 1-11页

作者： Ancona, N Maglietta, R D'Addabbo, A Liuni, S Pesole, G CNR Ist Studi Sistemi Intelligenti Automaz I-70126 Bari Italy CNR Ist Tecnol Biomed Sez Bari I-70126 Bari Italy Univ Milan Dipartimento Sci Biomol & Biotecnol I-20133 Milan Italy

Background: The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information provided by DNA microarrays allows an approach to the problem of cancer analysis from a quantitative rather than qualitative point of view. Cancer classification requires well founded mathematical methods which are able to predict the status of new specimens with high significance levels starting from a limited number of data. In this paper we assess the performances of Regularized Least Squares (RLS) classifiers, originally proposed in regularization theory, by comparing them with support vector machines (SVM), the state-of-the-art supervised learning technique for cancer classification by DNA microarray data. The performances of both approaches have been also investigated with respect to the number of selected genes and different gene selection strategies. Results: We show that RLS classifiers have performances comparable to those of SVM classifiers as the Leave-One-Out (LOO) error evaluated on three different data sets shows. The main advantage of RLS machines is that for solving a classification problem they use a linear system of order equal to either the number of features or the number of training examples. Moreover, RLS machines allow to get an exact measure of the LOO error with just one training. Conclusion: RLS classifiers are a valuable alternative to SVM classifiers for the problem of cancer classification by gene expression data, due to their simplicity and low computational complexity. Moreover, RLS classifiers show generalization ability comparable to the ones of SVM classifiers also in the case the classification of new specimens involves very few gene expression levels.

关键词： support vector machine Acute Myeloid Leukemia support vector machine classifier Generalization Error Empirical Risk

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MixNet: A Robust Mixture of Convolutional Neural Networks as Feature Extractors to Detect Stego Images Created by Content-Adaptive Steganography

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NEURAL PROCESSING LETTERS 2022年第2期54卷 853-870页

作者： Amrutha, E. Arivazhagan, S. Sylvia Lilly Jebarani, W. MEPCO Schlenk Engn Coll Autonomous Ctr Image Proc & Pattern Recognit Dept Elect & Commun Engn Sivakasi 626005 India

Digital steganography, the modern version of the ancient art of data hiding is a prevalent tool for covert communication. Steganalysis, at least as old as steganography comes handy to unearth such hidden channels. The illegal act of information hiding through steganography of digital images can be overcome effectively only by using intelligent steganalytic techniques. In this paper, a novel MixNet framework comprising of six Convolutional Neural Networks (CNNs) is proposed as feature extractors for accomplishing generic steganalysis of spatial content-adaptive algorithms with better detection accuracy. Since the spatial content-adaptive algorithms embed secret bits in the hard to model components of the image like edges or textures, inputs to the CNNs are initially filtered using high pass filters to obtain the embedded content in the form of noise residual. Hierarchical features extracted from these networks are then concatenated and used to train support vector machine classifier. Experimentation is performed using the benchmark BOSSbase v1.01 cover images and stego images are created with three state-of-the-art algorithms HUGO-BD, S-UNIWARD and WOW at five relative payloads 0.1-0.5 bits per pixel (bpp). The experimental results show that the proposed MixNet outperforms the compared related works in literature and proves the robustness of MixNet in detecting content-adaptive steganography.

关键词： Content-adaptive steganography Convolutional neural network Noise residual support vector machine classifier

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Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information

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BMC BIOINFORMATICS 2014年第16-Sup期15卷 1-9页

作者： Paliwal, Kuldip K. Sharma, Alok Lyons, James Dehzangi, Abdollah Griffith Univ Sch Engn Brisbane Qld 4111 Australia Univ S Pacific Sch Engn & Phys Suva Fiji IIIS Brisbane Qld Australia NICTA Brisbane Qld Australia

Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature.

关键词： support vector machine support vector machine classifier Feature Extraction Method Fold Recognition Position Specific Score Matrix

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Protein localization prediction using random walks on graphs

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BMC BIOINFORMATICS 2013年第8-sup期14卷 1-9页

作者： Xu, Xiaohua Lu, Lin He, Ping Chen, Ling Yangzhou Univ Dept Comp Sci Yangzhou 225009 Peoples R China

Background: Understanding the localization of proteins in cells is vital to characterizing their functions and possible interactions. As a result, identifying the (sub) cellular compartment within which a protein is located becomes an important problem in protein classification. This classification issue thus involves predicting labels in a dataset with a limited number of labeled data points available. By utilizing a graph representation of protein data, random walk techniques have performed well in sequence classification and functional prediction;however, this method has not yet been applied to protein localization. Accordingly, we propose a novel classifier in the site prediction of proteins based on random walks on a graph. Results: We propose a graph theory model for predicting protein localization using data generated in yeast and gram-negative (Gneg) bacteria. We tested the performance of our classifier on the two datasets, optimizing the model training parameters by varying the laziness values and the number of steps taken during the random walk. Using 10-fold cross-validation, we achieved an accuracy of above 61% for yeast data and about 93% for gram-negative bacteria. Conclusions: This study presents a new classifier derived from the random walk technique and applies this classifier to investigate the cellular localization of proteins. The prediction accuracy and additional validation demonstrate an improvement over previous methods, such as support vector machine (SVM)-based classifiers.

关键词： support vector machine Random Walk support vector machine classifier Kernel Matrix Label Node

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Forensic detection of image manipulation using the Zernike moments and pixel-pair histogram

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IET IMAGE PROCESSING 2013年第9期7卷 817-828页

作者： Shabanifard, Mahmood Shayesteh, Mahrokh G. Akhaee, Mohammad Ali Urmia Univ Dept Elect Engn Orumiyeh Iran Univ Tehran Fac Engn Sch Elect & Comp Engn Tehran Iran Sharif Univ Technol Dept Elect Engn ACRI Wireless Res Lab Tehran Iran

Integrity verification or forgery detection of an image is a difficult procedure, since the forgeries use various transformations to create an altered image. Pixel mapping transforms, such as contrast enhancement, histogram equalisation, gamma correction and so on, are the most popular methods to improve the objective property of an altered image. In addition, fabricators add Gaussian noise to the altered image in order to remove the statistical traces produced because of pixel mapping transforms. A new method is introduced to detect and classify four various categories including original, contrast modified, histogram-equalised and noisy images. In the proposed method, the absolute value of the first 36 Zernike moments of the pixel-pair histogram and its binary form for each image in the polar coordinates are calculated, and then those features that yield the maximum between-class separation, are selected. Some other features obtained from Fourier transform are also utilised for more separation. Finally, support vector machine classifier is used to classify the input image into four categories. The experimental results show that the proposed method achieves high classification rate and considerably outperforms the previously presented methods.

关键词： Fourier transforms Gaussian noise image classification Zernike polynomials digital forensics forensic detection image manipulation Zernike moments pixel-pair histogram integrity verification image forgery detection image alteration pixel mapping transform contrast enhancement histogram equalisation gamma correction Gaussian noise statistical trace removal histogram equalised images noisy images polar coordinates between-class separation Fourier transform support vector machine classifier input image classification

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Forensic video solution using facial feature-based synoptic Video Footage Record

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IET COMPUTER VISION 2016年第4期10卷 315-322页

作者： Balasubramanian, Yogameena Sivasankaran, Kokila Krishraj, Sindhu Priya Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai Tamil Nadu India

Person specific identification is an important problem in computer vision. However, forensic video analysis is the tool in surveillance applications, such as a specific person Video Footage Record can be used to help personalised monitoring. This study proposes a solution to identify the specific person very quickly through offline which will be valuable to analyse the incident/crime earlier. The main idea of this study is to reduce the enormous volume of video data by using an object-based video synopsis. After that, Viola-Jones face detection, deformable part based models are used to detect the face attributes. Subsequently, histogram of oriented gradients and oriented centre symmetric local binary pattern features are extracted. support vector machine classifier is used to classify the weak and strong features. These strong features are used to recognise the person. The algorithm works well even in complicated situations such as expression changes, pose, illumination variations and even if the face is partially as well as fully occluded in few frames. The advantage of synoptic video helps to recognise the person who is not occluded in some other frames. Experimental results on benchmark and real time datasets demonstrate the effectiveness of the proposed algorithm.

关键词： feature extraction video signal processing face recognition emotion recognition support vector machines digital forensics synoptic video footage record forensic video solution facial feature surveillance applications specific person video footage record personalised monitoring incident-crime analysis object-based video synopsis Viola-Jones face detection deformable part face attribute detection oriented gradient histogram oriented centre symmetric local binary pattern feature extraction support vector machine classifier

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Off-line signature verification: upper and lower envelope shape analysis using chord moments

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IET BIOMETRICS 2014年第4期3卷 347-354页

作者： Kumar, Medam Manoj Puhan, Niladri Bihari Indian Inst Technol Sch Elect Sci Bhubaneswar 751013 Orissa India

Signature is an important and useful behavioural biometric which exhibits significant amount of non- linear variability. In this study, the authors concentrate on finding an envelope shape feature known as 'chord moments'. Central moments such as the variance, skewness and kurtosis along with the first moment (mean) are computed from sets of chord lengths and angles for each envelope reference point. The proposed chord moments adequately quantify the spatial inter-relationship among upper and lower envelope points. The moment-based approach significantly reduces the dimension of highly detailed chord sets and is experimentally found to be robust in handling non-linear variability from signature images. The proposed chord moments coupled with the support vector machine classifier lead to a writer dependent off-line signature verification system that achieves state-of-the-art performance on the noisy Center of Excellence for Document Analysis and Recognition database.

关键词： digital signatures pattern classification support vector machines Center of Excellence for Document Analysis and Recognition database chord lengths chord moments envelope reference point envelope shape analysis nonlinear variability handling offline signature verification signature images support vector machine classifier Moment of force digital signatures Envelope Chord Arc length envelopes signature validation Pattern recognition OFF LINE support vector Network

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MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features

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BMC BIOINFORMATICS 2010年第11-Sup期11卷 1-10页

作者： Ding, Jiandong Zhou, Shuigeng Guan, Jihong Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China

Background: MicroRNAs (simply miRNAs) are derived from larger hairpin RNA precursors and play essential regular roles in both animals and plants. A number of computational methods for miRNA genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known miRNAs and unidentified miRNAs, and the pre-miRNAs with multi-loops or higher minimum free energy (MFE). This paper presents a new computational approach, miRenSVM, for finding miRNA genes. Aiming at better prediction performance, an ensemble support vector machine (SVM) classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-miRNAs with multiloops. Results: We collected a representative dataset, which contains 697 real miRNA precursors identified by experimental procedure and other computational methods, and 5428 pseudo ones from several datasets. Experiments showed that our miRenSVM achieved a 96.5% specificity and a 93.05% sensitivity on the dataset. Compared with the state-of-the-art approaches, miRenSVM obtained better prediction results. We also applied our method to predict 14 Homo sapiens pre-miRNAs and 13 Anopheles gambiae pre-miRNAs that first appeared in miRBase13.0, MiRenSVM got a 100% prediction rate. Furthermore, performance evaluation was conducted over 27 additional species in miRBase13.0, and 92.84% (4863/5238) animal pre-miRNAs were correctly identified by miRenSVM. Conclusion: MiRenSVM is an ensemble support vector machine (SVM) classification system for better detecting miRNA genes, especially those with multi-loop secondary structure.

关键词： support vector machine Majority Vote support vector machine classifier Feature Subset miRNA Gene

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