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A novel hybrid approach using SVM and spectral indices for enhanced land use land cover mapping of coastal urban plains

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GEOCARTO INTERNATIONAL 2022年第16期37卷 4714-4736页

作者： Nandam, Vineela Patel, P. L. Sardar Vallabhbhai Natl Inst Technol Surat Dept Civil Engn Surat India

In present study, Landsat images of years 2001, 2009 and 2018 are used for LUC mapping of a coastal-urban-floodplain wherein built-up and coastal-barren classes have been identified to be the most confusing classes for interpretation. Otsu's thresholding techniques have been used for mapping of waterbodies, built-up, and coastal-barren lands. The performance of most commonly used built-up indices have been assessed, among which BCI performed best for the study area. A new index, called Coastal-Barren-Index (CBI), has been developed using the spectral characteristics of SWIR1 and green spectral reflectance bands. A critical comparison of SVM and RFC classifiers are reported, and, finally, a hybrid approach is proposed as a combination MNDWI-CBI-SVM for mapping of the study area with Overall Accuracy 90.5% and Kappa value 0.87. The proposed approach is validated for an independent site, and, can be considered as generic in nature for LUC mapping of coastal urban plains.

关键词： Spectral index land use Otsu’ s thresholding support vector machine classifier Landsat

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Automated detection of myocardial infarction from ECG signal using variational mode decomposition based analysis

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HEALTHCARE TECHNOLOGY LETTERS 2020年第6期7卷 155-160页

作者： Kapfo, Ato Dandapat, Samarendra Bora, Prabin Kumar Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 Assam India

In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode, and the PCs provide the representation of the ECG signal with less redundancy. K-nearest neighbour and support vector machine classifier are utilised to assess the performance of the extracted features for the detection and classification of MI and normal (healthy control). The proposed method achieved a specificity of 99.88%, sensitivity of 99.90%, and accuracy of 99.88%. Experimental results demonstrate that the proposed method with the multiscale mode energy and PC features achieved better output compared to the previously published work.

关键词： feature extraction signal classification principal component analysis medical signal processing covariance matrices medical signal detection support vector machines electrocardiography support vector machine classifier classification MI multiscale mode energy PC automated detection myocardial infarction ECG signal variational mode decomposition method diagnostic information electrocardiogram signal principal component multiscale covariance matrices mode energies neighbour

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Computer assisted optical screening of human ovarian cancer using Raman spectroscopy

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PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY 2016年第0期15卷 94-99页

作者： Ullah, Irfan Ahmad, Iftikhar Nisar, Hasan Khan, Saranjam Ullah, Rahat Rashid, Rashad Mahmood, Hassan Pakistan Inst Engn & Appl Sci Islamabad 45650 Pakistan Natl Inst Lasers & Optron NILOP Islamabad 45650 Pakistan Citi Lab Islamabad Pakistan

Conventional screening tools for ovarian cancer such as cancer antigen (CA-125) and trans-pelvic ultrasound have poor sensitivity and specificity, indicating the need for better and more reliable screening methodologies. Here, we investigate the capability of Raman spectroscopy as a screening technique for ovarian cancer. Raman spectra from the blood serum of healthy control and ovarian cancer subjects were measured. Highly significant Raman shifts (p < 0.0001) and intensity variations were observed in the cancer group as compared to the healthy group. These spectral differences were exploited by support vector machine classifier towards computer assisted classification. Calculated evaluation metrics such as sensitivity (=90), specificity (-100), positive predictive value (=100) and negative predictive value (=87.5) for such classification indicated that these results are promising, with potential future application of Raman spectroscopy for ovarian cancer screening. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Raman spectroscopy Ovarian cancer support vector machine classifier Computer assisted classification

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Novel breast cancer classification framework based on deep learning

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IET IMAGE PROCESSING 2020年第13期14卷 3254-3259页

作者： Salama, Wessam M. Elbagoury, Azza M. Aly, Moustafa H. Pharos Univ Fac Engn Dept Basic Sci Alexandria Egypt Arab Acad Sci Technol & Maritime Transport Coll Engn & Technol Dept Elect & Commun Engn Alexandria Egypt

Breast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG-16, are utilised and re-trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the support vector machine (SVM) classifier is utilised instead of the last fully connected layer. Our models performance are verified utilising k-fold cross-validation. Our proposed techniques are trained and evaluated on three mammographic datasets: mammographic image analysis society, digital database for screening mammography (DDSM) and the curated breast imaging subset of DDSM. This paper explains end-to-end fully convolutional neural networks without any prepossessing or post-processing. The proposed technique of employing ResNet50 hybridised with SVM achieves the best performance, specifically with the DDSM dataset, producing 97.98% accuracy, 98.46% area under the curve, 97.63% sensitivity, 96.51% precision, 95.97% F1 score and computational time 1.8934 s.

关键词： support vector machines pattern classification mammography medical image processing feature extraction image classification cancer learning (artificial intelligence) convolutional neural nets screening mammography curated breast imaging subset end-to-end fully convolutional neural networks ResNet50 SVM DDSM dataset computational time breast cancer classification framework deep learning low computational requirements digital database mammographic image analysis society mammographic datasets cross-validation models performance fully connected layer support vector machine classifier tagged data data augmentation transfer learning

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Spatiotemporal feature extraction for facial expression recognition

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IET IMAGE PROCESSING 2016年第7期10卷 534-541页

作者： Kamarol, Siti Khairuni Amalina Jaward, Mohamed Hisham Parkkinen, Jussi Parthiban, Rajendran Monash Univ Malaysia Sch Engn Elect & Comp Syst Engn Petaling Jaya 46150 Selangor Malaysia Univ Eastern Finland Sch Comp Joensuu 80100 Finland

A key issue regarding feature extraction is the capability of a technique to extract distinctive features to represent facial expressions while requiring a low computational complexity. In this study, the authors propose a novel approach for appearance-based facial feature extraction to perform the task of facial expression recognition on video sequences. The proposed spatiotemporal texture map (STTM) is capable of capturing subtle spatial and temporal variations of facial expressions with low computational complexity. First, face is detected using Viola-Jones face detector and frames are cropped to remove unnecessary background. Facial features are then modelled with the proposed STTM, which uses the spatiotemporal information extracted from three-dimensional Harris corner function. A block-based method is adopted to extract the dynamic features and represent the features in the form of histograms. The features are then classified into classes of emotion by the support vector machine classifier. The experimental results demonstrate that the proposed approach shows superior performance compared with the state-of-the-art approaches with an average recognition rate of 95.37, 98.56, and 84.52% on datasets containing posed expressions, spontaneous micro-expressions, and close-to-real-world expressions, respectively. They also show that the proposed algorithm requires low computational cost.

关键词： face recognition emotion recognition image sequences image classification image representation video signal processing support vector machines image texture spatiotemporal feature extraction facial expression recognition appearance-based facial feature extraction video sequences spatiotemporal texture map STTM spatial facial expression variation temporal facial expression variation low computational complexity Viola-Jones face detector facial feature modelling 3D Harris corner function block-based method feature representation support vector machine classifier posed expressions spontaneous microexpressions close-to-real-world expressions

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ON THE ANALYTICAL STUDY OF THE SERVICE QUALITY OF INDIAN RAILWAYS UNDER SOFT-COMPUTING PARADIGM

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TRANSPORT 2024年第1期39卷 54-63页

作者： Majumder, Saibal Singh, Aarti Singh, Anupama Karpenko, Mykola Sharma, Haresh Kumar Mukhopadhyay, Somnath Dr. BC Roy Engn Coll Dept Comp Sci & Engn Data Sci Durgapur India FORE Sch Management New Delhi India Birla Inst Management Technol Dept Strateg Environm Management Greater Noida India Vilnius Gediminas Tech Univ Dept Mobile Machinery & Railway Transport Vilnius Lithuania Birla Inst Management Technol Dept Operat Management & Decis Sci Greater Noida India Assam Univ Dept Comp Sci & Engn Silchar India

Indian Railway Catering and Tourism Corporation (IRCTC) is among the busiest railways reservation systems since the Indian Railways (IR) is the vital and economical mode of transportation in India. Hence, rating of the trains seems to be critical aspect for selecting an appropriate train for travelling. In this study, we have considered 7 vital attributes of 500 popular trains and rate their performance based on 7 important related attributes. For this purpose, we have employed 2 different approaches to analyse of the train attributes, which eventually contribute to the overall performance of the trains. Here, we have developed a rule based rough set decision support system to analyse the criticality of the train attributes while rating the train performance. Furthermore, we have also used 3 machine Learning (ML) model estimators: Extra Trees classifier (ETC), support vector machine classifier (SVMC) and Multinomial Naive Bayes classifier (MNBC) and perform their comparative analysis with respect to 7 performance metrics while predicting the overall train rating based.

关键词： rough set theory extra trees classifier support vector machine classifier multinomial naive Bayes classifier performance metrics

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Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

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BMC BIOINFORMATICS 2008年第1期9卷 1-14页

作者： Zheng, Ce Kurgan, Lukasz Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Background: beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. Results: We show that (1) all four predicted secondary structures are useful;(2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment;(3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q(total), Q(predicted) and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q(total) barrier and achieves Q(total) = 80.9%, MCC = 0.47, and Q(predicted) higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Conclusion: Experiments show that the proposed method constitutes an improvement o

关键词： support vector machine Secondary Structure support vector machine classifier Matthews Correlation Coefficient Position Specific Score Matrix

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Comparison of ICA approaches for facial expression recognition

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SIGNAL IMAGE AND VIDEO PROCESSING 2009年第4期3卷 345-361页

作者： Buciu, I. Kotropoulos, C. Pitas, I. Univ Oradea Fac Elect Engn & Informat Technol Dept Elect Oradea 410087 Romania Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

Independent component analysis (ICA) and Gabor wavelets extract the most discriminating features for facial action unit classification by employing either a cosine similarity measure (CSM) classifier or support vector machines (SVMs). So far, only the ICA approach, which is based on the InfoMax principle, has been tested for facial expression recognition. In this paper, in addition to the InfoMax approach, another five ICA approaches extract features from two facial expression databases. In particular, the Extended InfoMax ICA, the undercomplete ICA, and the nonlinear kernel-ICA approaches are exploited for facial expression representation for the first time. When applied to images, ICA treats the images as being mixtures of independent sources and decomposes them into an independent basis and the corresponding mixture coefficients. Two architectures for representing the images can be employed yielding either independent and sparse basis images or independent and sparse distributions of image representation coefficients. After feature extraction, facial expression classification is performed with the help of either a CSM classifier or an SVM classifier. A detailed comparative study is made with respect to the accuracy offered by each classifier. The correlation between the accuracy and the mutual information of independent components or the kurtosis is evaluated. Statistically significant correlations between the aforementioned quantities are identified. Several issues are addressed in the paper: (i) whether features having super-and sub-Gaussian distribution facilitate facial expression classification;(ii) whether a nonlinear mixture of independent sources improves the classification accuracy;and (iii) whether an increased "amount" of sparseness yields more accurate facial expression recognition. In addition, performance enhancements by employing leave-one-set of expressions-out and subspace selection are studied. Statistically significant differences in accuracy b

关键词： Independent component analysis Super-Gaussian distribution Sub-Gaussian distribution Nonlinear mixtures of independent sources Cosine similarity measure classifier support vector machine classifier Facial expression recognition Mutual information Kurtosis Correlation Statistical significance

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Regional landscape mapping through a method of chain standardization of Landsat images

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LANDSCAPE AND URBAN PLANNING 2015年 134卷 1-9页

作者： Meng, Qingmin Mississippi State Univ Dept Geosci Starkville MS 39762 USA

Large area landscape analysis and modeling are often based on the data of land cover categories, which represent the interface between natural conditions and human impacts over time and across space. This study proposes a method of imagery chain standardization, which is straightforward and simple for landscape scientists to do large area land cover analysis with commonly available Landsat data. Unlike image normalization methods using complicated atmospheric correction and radiometric conservation, chain standardization proposes that for a large area, one Landsat image locating around the center of this area is used as the reference image. The reference image is first used to standardize its nearest neighboring images by applying a simple linear regression equation without intercept, which is developed based on the overlap imagery areas. Likewise, the firstly standardized images are used as new reference images to standardize their neighboring images further away from the study area center, and till all images covering a study area are transformed to the standard of the reference image. We test imagery chain standardization by using support vector machine classifier to classify the land cover of the states of Louisiana and Arkansas, USA, into agriculture, barren lands, forest, urban, water, and wetlands. 66-scene commonly available Landsat TM images are standardized, which results in promising mosaics and classification performance with overall accuracy values about 90% and Kappa coefficients close to 1. The imagery chain standardization is a promising method for landscape scientists to do regional landscape mapping and modeling. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Regional landscape analysis Land cover mapping Imagery chain standardization Linear regression support vector machine classifier

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A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

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HEALTHCARE TECHNOLOGY LETTERS 2014年第4期1卷 98-103页

作者： Tripathy, R. K. Sharma, L. N. Dandapat, S. Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 Assam India

A new measure for quantifying diagnostic information from a multilead electrocardiogram (MECG) is proposed. This diagnostic measure is based on principal component (PC) multivariate multiscale sample entropy (PMMSE). The PC analysis is used to reduce the dimension of the MECG data matrix. The multivariate multiscale sample entropy is evaluated over the PC matrix. The PMMSE values along each scale are used as a diagnostic feature vector. The performance of the proposed measure is evaluated using a least square support vector machine classifier for detection and classification of normal (healthy control) and different cardiovascular diseases such as cardiomyopathy, cardiac dysrhythmia, hypertrophy and myocardial infarction. The results show that the cardiac diseases are successfully detected and classified with an average accuracy of 90.34%. Comparison with some of the recently published methods shows improved performance of the proposed measure of cardiac disease classification.

关键词： MECG data matrix PMMSE cardiac disease classification cardiac dysrhythmia cardiovascular disease diagnostic feature vector diagnostic information diseases electrocardiography hypertrophy least square classifier medical diagnostic computing medical signal processing multilead electrocardiogram multivariate multiscale sample entropy myocardial infarction principal component principal component analysis support vector machine classifier support vector machines

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