scholarly journals The Modified Principal Component Analysis Feature Extraction Method for the Task of Diagnosing Chronic Lymphocytic Leukemia Type B-CLL

2020 ◽  
Vol 26 (6) ◽  
pp. 734-746
Author(s):  
Mariusz Topolski
Keyword(s):  
Feature Extraction ◽  
Chronic Lymphocytic Leukemia ◽  
Principal Component ◽  
Feature Space ◽  
Lymphocytic Leukemia ◽  
Wilcoxon Test ◽  
Type B ◽  
Medical Problems ◽  
Feature Extraction Method ◽  
Paired Samples

The vast majority of medical problems are characterised by the relatively high spatial dimensionality of the task, which becomes problematic for many classic pattern recognition algorithms due to the well-known phenomenon of the curse of dimensionality. This creates the need to develop methods of space reduction, divided into strategies for the selection and extraction of features. The most commonly used tool of the second group is the PCA, which, unlike selection methods, does not select a subset of the original set of features and performs its mathematical transformation into a less dimensional form. However, natural downside of this algorithm is the fact that class context is not present in supervised learning tasks. This work proposes a feature extraction algorithm using the approach of the pca method, trying not only to reduce the feature space, but also trying to separate the class distributions in the available learning set. The problematic issue of the work was the creation of a method of feature extraction describing the prognosis for a chronic lymphocytic leukemia type B-CLL, which will be at least as good, or even better than when compared to other quality extractions. The purpose of the research was accomplished for binary and three-class cases in the event in which for verification of extraction quality, five algorithms of machine learning were applied. The obtained results were compared with the application of paired samples Wilcoxon test.

Learning Target Class Feature Subspace (LTC-FS) Using Eigenspace Analysis and N-ary Search-Based Autonomous Hyperparameter Tuning for OCSVM

2021 ◽  
Author(s):  
Sanjay Kumar Sonbhadra ◽  
Sonali Agarwal ◽  
P. Nagabhushan
Keyword(s):  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Feature Subset ◽  
Target Class ◽  
Significant Information ◽  
Feature Extraction Method ◽  
Specificity And Sensitivity ◽  
Feature Subspace ◽  
Novel Target

Existing dimensionality reduction (DR) techniques such as principal component analysis (PCA) and its variants are not suitable for target class mining due to the negligence of unique statistical properties of class-of-interest (CoI) samples. Conventionally, these approaches utilize higher or lower eigenvalued principal components (PCs) for data transformation; but the higher eigenvalued PCs may split the target class, whereas lower eigenvalued PCs do not contribute significant information and wrong selection of PCs leads to performance degradation. Considering these facts, the present research offers a novel target class-guided feature extraction method. In this approach, initially, the eigendecomposition is performed on variance–covariance matrix of only the target class samples, where the higher- and lower-valued eigenvectors are rejected via statistical analysis, and the selected eigenvectors are utilized to extract the most promising feature subspace. The extracted feature-subset gives a more tighter description of the CoI with enhanced associativity among target class samples and ensures the strong separation from nontarget class samples. One-class support vector machine (OCSVM) is evaluated to validate the performance of learned features. To obtain optimized values of hyperparameters of OCSVM a novel [Formula: see text]-ary search-based autonomous method is also proposed. Exhaustive experiments with a wide variety of datasets are performed in feature-space (original and reduced) and eigenspace (obtained from original and reduced features) to validate the performance of the proposed approach in terms of accuracy, precision, specificity and sensitivity.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

scholarly journals TH2/TH1 Shift Under Ibrutinib Treatment in Chronic Lymphocytic Leukemia

2021 ◽  
Vol 11 ◽  
Author(s):  
Maria Cristina Puzzolo ◽  
Ilaria Del Giudice ◽  
Nadia Peragine ◽  
Paola Mariglia ◽  
Maria Stefania De Propris ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Chronic Lymphocytic Leukemia ◽  
Complete Response ◽  
Relative Increase ◽  
Lymphocytic Leukemia ◽  
Paired Samples ◽  
Cell Cytokine Production ◽  
Cell Profile

Ibrutinib may revert the T-helper (Th)2 polarization observed in chronic lymphocytic leukemia (CLL) by targeting the IL-2-inducible kinase, that shows a significant homology with the Bruton tyrosine kinase. In the front-line GIMEMA LLC1114 trial (ibrutinib+rituximab for 6 months, followed by ibrutinib maintenance), we investigated the modulation of T-cell cytokine production in 208 peripheral blood paired samples from 71 CLL patients: 71 samples prior to treatment (Day 0, D0) and at day +14 (D14; n=50), at month +8 (M8; 30), +12 (M12; 25), +18 (M18; 22) and +24 (M24; 10) of treatment. We documented a progressive decrease of CD3+CD4+IL-4+ T cells (Th2), that was significant at M8 and at M12 (p=0.019, p=0.002), a relative increase in the CD3+CD4+IFNγ+ T cells (Th1) and a decrease of CD3+CD4+IL-17+ (Th17) cells that was maintained up to M18 (M8 vs D0 p=0.003, M12 vs D0 p=0.003, M18 vs D0 p=0.004) of ibrutinib treatment. The Th2/Th1 ratio significantly decreased already after 14 days of treatment and was maintained thereafter (D14 vs D0 p=0.037, M8 vs D0 p=0.001, M12 vs D0 p=0.005, M18 vs D0 p=0.002). The Th2/Th1 modulation over time was significant only among patients with unmutated IGHV. The Th2/Th1 ratio below a cut-off of 0.088 at M8 was associated with the achievement of a complete response (CR) (p=0.016). Ibrutinib may shape the CLL T-cell profile, limiting Th2 activation and inducing a shift in the Th2/Th1 ratio. The association between the Th2/Th1 ratio decrease and the CR achievement suggests the in vivo generation of a potential host anti-tumor immune activation induced by ibrutinib.

scholarly journals Rapid Identification and Quantitative Analysis of Polycarboxylate Superplasticizers Using ATR-FTIR Spectroscopy Combined with Chemometric Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhiwei Li ◽  
Bo Li ◽  
Zhizhong Zhao ◽  
Weizhong Ma ◽  
Wenju Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Calculation Method ◽  
Principal Component ◽  
Rapid Identification ◽  
Quality Inspection ◽  
Inspection Method ◽  
Feature Extraction Method ◽  
Similarity Calculation ◽  
And Cluster Analysis

Although the quality inspection method of polycarboxylate superplasticizers (PCE) based on macroperformance is still widely used, it has the drawbacks of time-consuming and low precision. This study aims to develop a practicable alternative method for quality inspection of PCE. For this, spectra collection, feature extraction, and cluster analysis were performed up on the PCE samples to demonstrate the feasibility of the method. Also, a new similarity calculation method was introduced in this work. Results show that the solid PCE sample for spectrum collection can be prepared using the simple heating method. High-quality spectra can be rapidly collected by infrared spectrometer combined with ATR accessory. Meanwhile, the accuracy of classification and clustering is high, suggesting that the feature extraction method based on principal component analysis (PCA) is effective. In addition, compared with conventional similarity calculation methods of cosine angle and correlation coefficient, the new similarity calculation method achieves better classification results and better generalization ability. This work provides a method of quantitative analysis and rapid identification of PCE for the construction site.

scholarly journals Biometric authenticator algorithm based on multiresolution analysis

2020 ◽  
Vol 20 (3) ◽  
pp. 1332
Author(s):  
Soumia Kerrache ◽  
Beladgham Mohammed ◽  
Hamza Aymen ◽  
Kadri Ibrahim
Keyword(s):  
Feature Extraction ◽  
Multiresolution Analysis ◽  
Nearest Neighbor ◽  
Curvelet Transform ◽  
Principal Component ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Fusion Approach

Features extraction is an essential process in identifying person biometrics because the effectiveness of the system depends on it. Multiresolution Analysis success can be used in the system of a person’s identification and pattern recognition. In this paper, we present a feature extraction method for two-dimensional face and iris authentication.  Our approach is a combination of principal component analysis (PCA) and curvelet transform as an improved fusion approach for feature extraction. The proposed fusion approach involves image denoising using 2D-Curvelet transform to achieve compact representations of curves singularities. This is followed by the application of PCA as a fusion rule to improve upon the spatial resolution. The limitations of the only PCA algorithm are a poor recognition speed and complex mathematical calculating load, to reduce these limitations, we are applying the curvelet transform. <br /> To assess the performance of the presented method, we have employed three classification techniques: Neural networks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM).<br />The results reveal that the extraction of image features is more efficient using Curvelet/PCA.

Performance Evaluation of MadBoost on Face Detection

2019 ◽  
Vol 892 ◽  
pp. 200-209
Author(s):  
Rayner Pailus ◽  
Rayner Alfred
Keyword(s):  
Feature Extraction ◽  
Face Detection ◽  
Recognition Performance ◽  
Principal Component ◽  
Haar Wavelet ◽  
Extraction Methods ◽  
Face Image ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Face Images

Adaboost Viola-Jones method is indeed a profound discovery in detecting face images mainly because it is fast, light and one of the easiest methods of detecting face images among other techniques of face detection. Viola Jones uses Haar wavelet filter to detect face images and it produces almost 80%accuracy of face detection. This paper discusses proposed methodology and algorithms that involved larger library of filters used to create more discrimination features among the images by processing the proposed 15 Haar rectangular features (an extension from 4 Haar wavelet filters of Viola Jones) and used them in multiple adaptive ensemble process of detecting face image. After facial detection, the process continues with normalization processes by applying feature extraction such as PCA combined with LDA or LPP to extract our week learners’ wavelet for more classification features. Upon the process of feature extraction proposed feature selection to index these extracted data. These extracted vectors are used for training and creating MADBoost (Multiple Adaptive Diversified Boost)(an improvement of Adaboost, which uses multiple feature extraction methods combined with multiple classifiers) is able to capture, recognize and distinguish face image (s) faster. MADBoost applies the ensemble approach with better weights for classification to produce better face recognition results. Three experiments have been conducted to investigate the performance of the proposed MADBoost with three other classifiers, Neural Network (NN), Support Vector Machines (SVM) and Adaboost classifiers using Principal Component Analysis (PCA) as the feature extraction method. These experiments were tested against obstacles of POIES (Pose, Obstruction, Illumination, Expression, Sizes). Based on the results obtained, Madboost is found to be able to improve the recognition performance in matching failures, incorrect matching, matching success percentages and acceptable time taken to perform the classification task.

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