Candidate Features Research Articles

A residuals-distribution-guided local optimization approach to B-spline fitting in capturing image outlines

The B-spline technique is attractive to fit complex image outlines because of its freeform character. However, knot placement remains a difficult task. In this paper, the knots optimization problem is converted to an issue of feature points optimization, and a residuals-distribution-guided local optimization (RDG-LO) method is proposed to promote the quality of fitted curves. The RDG-LO algorithm states the moving direction and distance of candidate feature points according to the distribution of residuals. For a fixed number of feature points, the accuracy of the fitted curve is improved by adjusting the positions of feature points and the length of the fitted curve. Experimental results show that the fitted curve after optimization possesses advantages in both accuracy and simplicity. This algorithm is of great significance in the three-dimensional digital reconstruction of biomedical images.

Automatic detection of microaneurysms in fundus images based on multiple preprocessing fusion to extract features

Diabetic retinopathy (DR) is the main cause of new cases of blindness, and retinal microaneurysm (MA) is one of the early clinical manifestations of DR. However, it is difficult to directly observe MA with naked eyes because of their small size and poor contrast with the surrounding background. This paper presents a new method for automatic detection of MAs from fundus images. It mainly includes four steps: image preprocessing, candidate region extraction, feature extraction and classification. At the stage of feature extraction, different from traditional methods, we propose a feature extraction method based on multi-preprocessing fusion. In this paper, features are extracted from the original green channel image and the original gray image respectively, and then different processing methods are used to extract the same features from multiple pre-processed images. Finally, the extracted features are classified by multi-layer perceptron (MLP) classifier to determine whether the candidate object is microaneurysm (MA). The method is evaluated on e-ophtha-MA database and achieved high sensitivity. Using the FROC (free-response ROC) indicator, the detection achieves the F-score, accuracy, sensitivity, specificity and AUC (area under the curve) of 0.507, 0.940, 0.870, 0.938 and 0.9821, respectively, compared to the most advanced methods available. The proposed method of multi-preprocessing and fusion feature extraction is beneficial to classification and improves the detection performance.

Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

BackgroundFalls are the most common adverse outcome of depression in older adults, yet a accurate risk prediction model for falls stratified by distinct long-term trajectories of depressive symptoms is still lacking. MethodsWe collected the data of 1617 participants from the China Health and Retirement Longitudinal Study register, spanning between 2011 and 2018. The 36 input variables included in the baseline survey were regarded as candidate features. The trajectories of depressive symptoms were classified by the latent class growth model and growth mixture model. Three data balancing technologies and four machine learning algorithms were utilized to develop predictive models for fall classification of depressive prognosis. ResultsDepressive symptom trajectories were divided into four categories, i.e., non-symptoms, new-onset increasing symptoms, slowly decreasing symptoms, and persistent high symptoms. The random forest-TomekLinks model achieved the best performance among the case and incident models with an AUC-ROC of 0.844 and 0.731, respectively. In the chronic model, the gradient boosting decision tree-synthetic minority oversampling technique obtained an AUC-ROC of 0.783. In the three models, the depressive symptom score was the most crucial component. The lung function was a common and significant feature in both the case and the chronic models. ConclusionsThis study suggests that the ideal model has a good chance of identifying older persons with a high risk of falling stratified by long-term trajectories of depressive symptoms. Baseline depressive symptom score, lung function, income, and injury experience are influential factors associated with falls of depression evolution.

Development of multi-epitope vaccines against the monkeypox virus based on envelope proteins using immunoinformatics approaches.

Since May 2022, cases of monkeypox, a zoonotic disease caused by the monkeypox virus (MPXV), have been increasingly reported worldwide. There are, however, no proven therapies or vaccines available for monkeypox. In this study, several multi-epitope vaccines were designed against the MPXV using immunoinformatics approaches. Three target proteins, A35R and B6R, enveloped virion (EV) form-derived antigens, and H3L, expressed on the mature virion (MV) form, were selected for epitope identification. The shortlisted epitopes were fused with appropriate adjuvants and linkers to vaccine candidates. The biophysical andbiochemical features of vaccine candidates were evaluated. The Molecular docking and molecular dynamics(MD) simulation were run to understand the binding mode and binding stability between the vaccines and Toll-like receptors (TLRs) and major histocompatibility complexes (MHCs). The immunogenicity of the designed vaccines was evaluated via immune simulation. Five vaccine constructs (MPXV-1-5) were formed. After the evaluation of various immunological and physicochemical parameters, MPXV-2 and MPXV-5 were selected for further analysis. The results of molecular docking showed that the MPXV-2 and MPXV-5 had a stronger affinity to TLRs (TLR2 and TLR4) and MHC (HLA-A*02:01 and HLA-DRB1*02:01) molecules, and the analyses of molecular dynamics (MD) simulation have further confirmed the strong binding stability of MPXV-2 and MPXV-5 with TLRs and MHC molecules. The results of the immune simulation indicated that both MPXV-2 and MPXV-5 could effectively induce robust protective immune responses in the human body. The MPXV-2 and MPXV-5 have good efficacy against the MPXV in theory, but further studies are required to validate their safety and efficacy.

Water Stream Extraction via Feature-Fused Encoder-Decoder Network Based on SAR Images

The extraction of water stream based on synthetic aperture radar (SAR) is of great significance in surface water monitoring, flood monitoring, and the management of water resources. However, in recent years, the research mainly uses the backscattering feature (BF) to extract water bodies. In this paper, a feature-fused encoder–decoder network was proposed for delineating the water stream more completely and precisely using both the BF and polarimetric feature (PF) from SAR images. Firstly, the standard BFs were extracted and PFs were obtained using model-based decomposition. Specifically, the newly model-based decomposition, more suitable for dual-pol SAR images, was selected to acquire three different PFs of surface water stream for the first time. Five groups of candidate feature combinations were formed with two BFs and three PFs. Then, a new feature-fused encoder–decoder network (FFEDN) was developed for mining and fusing both BFs and PFs. Finally, several typical areas were selected to evaluate the performance of different combinations for water stream extraction. To further verify the effectiveness of the proposed method, two machine learning methods and four state-of-the-art deep learning algorithms were utilized for comparison. The experimental results showed that the proposed method using the optimal feature combination achieved the highest accuracy, with a precision of 95.21%, recall of 91.79%, intersection over union (IoU) score of 87.73%, overall accuracy (OA) of 93.35%, and average accuracy (AA) of 93.41%. The results showed that the performance was higher when BF and PF were combined. In short, in this study, the effectiveness of PFs for water stream extraction was verified and the proposed FFEDN can further improve the accuracy of water stream extraction.

Development of a prediction model for the Acquisition of Extended Spectrum Beta-Lactam Resistant Organisms in U.S. international travellers.

Extended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies. We used data collected from a cohort of 528 international travellers enrolled in a multicenter US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition. A CPR using machine learning and logistic regression on ten features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69-0.71). We also demonstrate that a four feature model performs similarly to the ten feature model, with a cvAUC of 0.68 (95% confidence interval 0.67-0.69). This model uses traveller's diarrhoea, and antibiotics as treatment, destination country waste management rankings, and destination regional probabilities as predictors. We demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.

A Synthetic Feature Processing Method for Remaining Useful Life Prediction of Rolling Bearings

In the context of industrial big data, the data-driven remaining useful life prediction for rolling bearings has been greatly developed. Aimed at the shortcomings of feature selection, feature fusion, and health state segment, this article proposes a synthetic feature processing method for remaining useful life prediction of rolling bearings. First, a double-layer feature selection method is proposed to screen the feature subset from the candidate features in order to lift the degradation trend sensibility and eliminate redundancy. Second, the health indicator is constructed by a comprehensive method based on the adaptive feature fusion method and auto association kernel regression model. Third, the bottom-up algorithm is utilized to divide the health states of health indicator and obtain the first prediction time point. In this way, the remaining useful life prediction error brought by random-state dividing can be avoided. Finally, in the case of dynamic working conditions and multiple failure modes, the remaining useful life prediction model is built by long short-term memory neural network and piecewise linear fitting health indicator to map the relationship between fitting health indicator and remaining useful life. The proposed piecewise linear fitting health indicator is compared with the original health indicator and original vibration signal. The experimental results demonstrate the more robust and accurate prediction effectiveness of the method.

Quantitative ultrasound for preterm birth risk prediction—Part 1: Statistical evaluation

Hypothesis: Predicting the spontaneous preterm birth (sPTB) risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared to using only HC data. HC data defined herein includebirth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment, and physical examination data. Study population included 248 full-term births (FTBs) and 26 sPTBs. Advanced statistical analyses were performed for supervised classification containing 53 scaled candidate features (48 QUS, 5 HC) using nested fivefold cross-validation of L1-penalized linear logistic regression with 1000 repetitions to identify potential predictors. Statistical models for HC data alone and HC + QUS data were compared with likelihood-ratio test, cross-validated receiver operating characteristic (ROC) area under the curve (AUC), sensitivity, and specificity. To assess performance, the ROC-AUC was estimated with 10-fold cross-validation logistic regression and 1000 repetitions. Averaged ROC curves plus AUCs were computed using threshold averaging. AUC confidence intervals and test statistics to test the two ROC curves’ differences were constructed via DeLong method. Combined HC and QUS data identified women at sPTB risk with better AUC (0.68; 95% CI, 0.57–0.78) than those of HC data alone (0.53; 95% CI, 0.40–0.66). [Work supported by NIHR01HD089935.]

False discovery rate-controlled multiple testing for union null hypotheses: a knockoff-based approach.

False discovery rate (FDR) controlling procedures provide important statistical guarantees for replicability in signal identification based on multiple hypotheses testing. In many fields of study, FDR controling procedures are used in high-dimensional (HD) analyses to discover features that are truly associated with the outcome. In some recent applications, data on the same set of candidate features are independently collected in multiple different studies. For example, gene expression data are collected at different facilities and with different cohorts, to identify the genetic biomarkers of multiple types of cancers. These studies provide us with opportunities to identify signals by considering information from different sources (with potential heterogeneity) jointly. This paper is about how to provide FDR control guarantees for the tests of union null hypotheses of conditional independence. We present a knockoff-based variable selection method (Simultaneous knockoffs) to identify mutual signals from multiple independent datasets, providing exact FDR control guarantees under finite sample settings. This method can work with very general model settings and test statistics. We demonstrate the performance of this method with extensive numerical studies and two real-dataexamples.

On Semiparametrically Dynamic Functional-Coefficient Autoregressive Spatio-Temporal Models with Irregular Location Wide Nonstationarity

Nonlinear dynamic modeling of spatio-temporal data is often a challenge, especially due to irregularly observed locations and location-wide nonstationarity. In this article we propose a semiparametric family of Dynamic Functional-coefficient Autoregressive Spatio-Temporal (DyFAST) models to address the difficulties. We specify the autoregressive smoothing coefficients depending dynamically on both a concerned regime and location so that the models can characterize not only the dynamic regime-switching nature but also the location-wide nonstationarity in real data. Different smoothing schemes are then proposed to model the dynamic neighboring-time interaction effects with irregular locations incorporated by (spatial) weight matrices. The first scheme popular in econometrics supposes that the weight matrix is pre-specified. We show that locally optimal bandwidths by a greedy idea popular in machine learning should be cautiously applied. Moreover, many weight matrices can be generated differently by data location features. Model selection is popular, but may suffer from loss of different candidate features. Our second scheme is thus to suggest a weight matrix fusion to let data combine or select the candidates with estimation done simultaneously. Both theoretical properties and Monte Carlo simulations are investigated. The empirical application to an EU energy market dataset further demonstrates the usefulness of our DyFAST models. Supplementary materials for this article are available online.

시계열 데이터의 자동기계학습을 위한 메타 모델 기반의 특징 선택 방법

Feature engineering is a key step to construct machine learning model as it determines the upper limit of model’s performance. However, designing feature engineering is generally iterative, complex and time-consuming step. Also, the large scale of time series data is rapidly generated from the industry, but there is a shortage of data scientists to handle them. So, it has become necessary to automate this process. In this paper, we aim to develop a meta model-based feature selection method that can learn about which features work best given the dataset. The proposed meta-model is a kind of warm-start that can search from the candidate features that is expected to be good without starting a new search for each data. Proposed method is compared by real time-series datasets obtained from UEA & UCR Time Series Classification Repository. Then, we show the proposed method outperforms random search in terms of F1-measure.

EvaGoNet: An integrated network of variational autoencoder and Wasserstein generative adversarial network with gradient penalty for binary classification tasks

Feature engineering is an effective method for solving classification problems. Many existing feature engineering studies have focused on image or video data and not on structured data. This study proposes EvaGoNet, which refines the decoder module of the Gaussian mixture variational autoencoder using the Wasserstein generative adversarial network with gradient penalty (WGANgp) and embeds the top-ranked original features to update the latent features based on their discriminative powers. Comprehensive experiments show that EvaGoNet-encoded features outperform existing classifiers on 12 benchmark datasets, particularly on the small, imbalanced datasets col (accuracy = 0.8581), spe (accuracy = 1.0000), and leu (accuracy = 0.8021). EvaGoNet-engineered features improve binary classification task outcomes on six high-dimensional, imbalanced bioOMIC datasets. EvaGoNet achieves a medium-ranked training speed among the compared algorithms and considerably fast prediction speeds in the predictions of the testing samples. Therefore, EvaGoNet can be a candidate feature engineering framework for many practical applications that require one training procedure and many prediction tasks of the testing samples. EvaGoNet is implemented in Python TensorFlow and is available at https://healthinformaticslab.org/supp/resources.php.

Sea-Surface Small Target Detection Based on Four Features Extracted by FAST Algorithm

On account of current algorithm and parameter design difficulties and low detection accuracy in feature extractions of small target detections in sea clutter environment, this paper proposes a correspondingly improved four feature extraction method by FAST. After the short-time Fourier transform is applied, a time–frequency distribution spectrogram of original data is generated. Candidate feature points (CFP) are first extracted by FAST algorithm, and then a four-feature extraction is implemented with FAST and DBSCAN combined. The feature distinction is enhanced through a feature optimization. Upon the construction of the four-dimensional feature vectors, XGBoost classifier algorithm classifies and detects these feature vectors. The genetic algorithm optimizes the hyperparameters in XGBoost and updates the decision threshold in real time to control the detection method’s false alarm rate. The IPIX dataset is employed for experimental verification. Verification results confirm that this proposed detection method has better performance than several other currently used detection methods. The detection performance is improved by 7% and 13.8% when observation time is set at 0.512 s and 1.024 s, respectively.

A reliable matching algorithm for heterogeneous remote sensing images considering the spatial distribution of matched features

ABSTRACT Owing to the differences in sensor types,resolutions, and imaging conditions of heterologous remote sensing images, thematching results of remote sensing images, such as low accuracy, few matched pairs,and low distribution quality, are not ideal, which makes precise registrationbetween heterogeneous images difficult. To mitigate this, we propose a reliablematching algorithm for heterogeneous remote sensing images that considers thespatial distribution of the matched features. First, feature-based matchingalgorithms such as the scale-invariant feature transform (SIFT) algorithm orthe speeded-up robust features algorithm are used to match images to obtain aninitial set of matched pairs and a set of candidate features. Then, accordingto the stability of the spatial distribution of locally correctly matchedfeatures, the distance and angular proximity between matched features and theirneighbours are calculated to obtain the accuracy of the matched pairs and removeincorrectly matched pairs. Finally, the random sample consensus (RANSAC)algorithm was used to fit the transformation model between images, and thefinal matched feature selection algorithm and automatic transformation erroralgorithm were used to detect candidate features to increase the number ofmatched pairs. Experimental analysis of heterogeneous multiscale andmultitemporal optical remote sensing images demonstrates the superiorcapability of the proposed algorithm over commonly used algorithms, includingSIFT, RANSAC, locality preserving matching, learning a two-class classifier formismatch removal, and linear adaptive filtering algorithms. In particular, whenthe precision of the initially matched pair is low, the proposed algorithm canachieve excellent results.

Differentially Private Feature Selection Based on Dynamic Relevance for Correlated Data

<p>Traditional feature selection methods are only concerned with high relevance between selected features and classes and low redundancy among features, ignoring their interrelations which partly weak classification performance. This paper developed a dynamic relevance strategy to measure the dependency among them, where the relevance of each candidate feature is updated dynamically when a new feature is selected. Protecting sensitive information has become an important issue when executing feature selection. However, existing differentially private machine learning algorithms have seldom considered the impact of data correlation, which may cause more privacy leakage than expected. Therefore, the paper proposed a differentially private feature selection based on dynamic relevance measure, namely DPFSDR. Firstly, as a correlation analysis technique, the weighted undirected graph model is constructed via the correlated degree, which can reduce the dataset’s dimension and correlated sensitivity. Secondly, as a feature selection criterion, F-score with differential privacy is adopted to measure the feature importance of each feature. Finally, to evaluate the effectiveness of feature selection, differentially private SVM combined with dynamic relevance measure is utilized to choose features. Experimental results show that the proposed DPFSDR algorithm can effectively obtain the optimal feature subset, and improve data utility while preserving data privacy.</p> <p> </p>

Neighbor Reweighted Local Centroid for Geometric Feature Identification.

Identifying geometric features from sampled surfaces is a significant and fundamental task. The existing curvature-based methods that can identify ridge and valley features are generally sensitive to noise. Without requiring high-order differential operators, most statistics-based methods sacrifice certain extents of the feature descriptive powers in exchange for robustness. However, neither of these types of methods can treat the surface boundary features simultaneously. In this paper, we propose a novel neighbor reweighted local centroid (NRLC) computational algorithm to identify geometric features for point cloud models. It constructs a feature descriptor for the considered point via decomposing each of its neighboring vectors into two orthogonal directions. A neighboring vector starts from the considered point and ends with the corresponding neighbor. The decomposed neighboring vectors are then accumulated with different weights to generate the NRLC. With the defined NRLC, we design a probability set for each candidate feature point so that the convex, concave and surface boundary points can be recognized concurrently. In addition, we introduce a pair of feature operators, including assimilation and dissimilation, to further strengthen the identified geometric features. Finally, we test NRLC on a large body of point cloud models derived from different data sources. Several groups of the comparison experiments are conducted, and the results verify the validity and efficiency of our NRLC method.

Predicting total knee replacement at 2 and 5 years in osteoarthritis patients using machine learning

ObjectivesKnee osteoarthritis is a major cause of physical disability and reduced quality of life, with end-stage disease often treated by total knee replacement (TKR). We set out to develop and...

Comparison of phenomics and cfDNA in a large breast screening population: the Breast Screening and Monitoring Study (BSMS)

To assess their roles in breast cancer diagnostics, we aimed to compare plasma cell-free DNA (cfDNA) levels with the circulating metabolome in a large breast screening cohort of women recalled for mammography, including healthy women and women with mammographically detected breast diseases, ductal carcinoma in situ and invasive breast cancer: the Breast Screening and Monitoring Study (BSMS). In 999 women, plasma was analyzed by nuclear magnetic resonance (NMR) and Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) and then processed to isolate and quantify total cfDNA. NMR and UPLC-MS results were compared with data for 186 healthy women derived from the AIRWAVE cohort. Results showed no significant differences between groups for all metabolites, whereas invasive cancers had significantly higher plasma cfDNA levels than all other groups. When stratified the supervised OPLS-DA analysis and total cfDNA concentration showed high discrimination accuracy between invasive cancers and the disease/medication-free subjects. Furthermore, comparison of OPLS-DA data for invasive breast cancers with the AIRWAVE cohort showed similar discrimination between breast cancers and healthy controls. This is the first report of agreement between metabolomics and plasma cfDNA levels for discriminating breast cancer from healthy subjects in a true screening population. It also emphasizes the importance of sample standardization. Follow on studies will involve analysis of candidate features in a larger validation series as well as comparing results with serial plasma samples taken at the next routine screening mammography appointment. The findings here help establish the role of plasma analysis in the diagnosis of breast cancer in a large real-world cohort.

The Potential of Surface-Immobilized Antimicrobial Peptides for the Enhancement of Orthopaedic Medical Devices: A Review

Due to the well-known phenomenon of antibiotic resistance, there is a constant need for antibiotics with novel mechanisms and different targets respect to those currently in use. In this regard, the antimicrobial peptides (AMPs) seem very promising by virtue of their bactericidal action, based on membrane permeabilization of susceptible microbes. Thanks to this feature, AMPs have a broad activity spectrum, including antibiotic-resistant strains, and microbial biofilms. Additionally, several AMPs display properties that can help tissue regeneration. A possible interesting field of application for AMPs is the development of antimicrobial coatings for implantable medical devices (e.g., orthopaedic prostheses) to prevent device-related infection. In this review, we will take note of the state of the art of AMP-based coatings for orthopaedic prostheses. We will review the most recent studies by focusing on covalently linked AMPs to titanium, their antimicrobial efficacy and plausible mode of action, and cytocompatibility. We will try to extrapolate some general rules for structure-activity (orientation, density) relationships, in order to identify the most suitable physical and chemical features of peptide candidates, and to optimize the coupling strategies to obtain antimicrobial surfaces with improved biological performance.

A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification

Feature selection (FS) in data mining and machine learning has attracted extensive attention. The purpose of FS in a classification task is to find the optimal subset of features from given candidate features. Recently, more and more meta-heuristic algorithms have been used to deal with the FS problems. However, meta-heuristic algorithms suffer from certain issues, such as large search space for solutions and huge time consumption. Moreover, most of existing meta-heuristic algorithms focus only on the selection of an optimal feature subset, and pay little attention to the optimal design of the classifier. In this article, we propose a joint multiobjective optimization method for both feature selection and classifier design, called JMO-FSCD. The proposed approach uses neural network as a classifier and introduces a non-iterative algorithm for training the classifier so as to ensure good performance and fast learning. A new coding scheme is also designed for optimizing FS and classifier simultaneously. For demonstrating the superiority of the proposed approach, its performance is compared with those of six state-of-the-art FS algorithms. Experimental results on thirty-five benchmark data sets reflect the superior performance of the proposed JMO-FSCD.