scholarly journals An Accurate Method to Distinguish Between Stationary Human and Dog targets Under Through-Wall Condition Using UWB Radar

2019 ◽  
Vol 11 (21) ◽  
pp. 2571 ◽  
Author(s):  
Ma ◽  
Liang ◽  
Wang ◽  
Lv ◽  
Yu ◽  
...  
Keyword(s):  
Research Work ◽  
Accurate Method ◽  
Ultra Wideband ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Feature Subset ◽  
Uwb Radar ◽  
Comparison Results ◽  
Wall Condition ◽  
Stability And Accuracy

Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an accurate method for distinguishing stationary human targets from dog targets under through-wall condition based on ultra-wideband (UWB) radar. Eight humans and five beagles were used to collect 130 samples of through-wall signals using the UWB radar. Twelve corresponding features belonging to four categories were combined using the support vector machine (SVM) method. A recursive feature elimination (RFE) method determined an optimal feature subset from the twelve features to overcome overfitting and poor generalization. The results after ten-fold cross-validation showed that the area under the receiver operator characteristic (ROC) curve can reach 0.9993, which indicates that the two subjects can be distinguished under through-wall condition. The study also compared the ability of the proposed features of four categories when used independently in a classifier. Comparison results indicated that wavelet entropy-corresponding features among them have the best performance. The method and results are envisioned to be applied in various practical situations, such as post-disaster searching, hostage rescues, and intelligent homecare.

scholarly journals A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yangyang Ma ◽  
Pengfei Wang ◽  
Wenzhe Huang ◽  
Fugui Qi ◽  
Fulai Liang ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Vital Signs ◽  
Ultra Wideband ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Signal Source ◽  
Vital Signs Monitoring ◽  
Uwb Radar ◽  
Svm Model ◽  
Feature Based

AbstractPets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring.

scholarly journals A genetic programming approach to oral cancer prognosis

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2482 ◽  
Author(s):  
Mei Sze Tan ◽  
Jing Wei Tan ◽  
Siow-Wee Chang ◽  
Hwa Jen Yap ◽  
Sameem Abdul Kareem ◽  
...  
Keyword(s):  
Oral Cancer ◽  
Genetic Programming ◽  
Small Sample ◽  
Cancer Prognosis ◽  
Programming Approach ◽  
Support Vector ◽  
Survival Prediction ◽  
Feature Subset ◽  
Comparison Results ◽  
Bank System

BackgroundThe potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis.MethodGP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS). The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM) and logistic regression (LR) are also done in order to verify the predictive capabilities of the GP.ResultThe result shows that GP performed the best (average accuracy of 83.87% and average AUROC of 0.8341) when the features selected are smoking, drinking, chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis.DiscussionSome of the features in the dataset are found to be statistically co-related. This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis.

scholarly journals Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiao-Hui Wang ◽  
Xiaopan Xu ◽  
Zhi Ao ◽  
Jun Duan ◽  
Xiaoli Han ◽  
...  
Keyword(s):  
Model Development ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Recursive Feature Elimination ◽  
Group Differences ◽  
Support Vector ◽  
Nasopharyngeal Swab ◽  
Feature Subset ◽  
Optimal Subset ◽  
Discharged Patients

Objective: A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols.Materials and Methods: Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12–14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's t-test and support vector machine–based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development.Results: After Student's t-test, 62 radiomics features showed significant inter-group differences (p < 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case.Conclusion: The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.

scholarly journals Non-Invasive Driver Drowsiness Detection System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4833
Author(s):  
Hafeez Ur Rehman Siddiqui ◽  
Adil Ali Saleem ◽  
Robert Brown ◽  
Bahattin Bademci ◽  
Ernesto Lee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Ultra Wideband ◽  
Gradient Boosting ◽  
Support Vector ◽  
Drowsiness Detection ◽  
Non Invasive ◽  
Driver Drowsiness ◽  
Uwb Radar ◽  
Tree Classifier

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

scholarly journals Pelvic floor pressure distribution profile in urinary incontinence: a classification study with feature selection

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8207 ◽  
Author(s):  
Adriano Carafini ◽  
Isabel C.N. Sacco ◽  
Marcus Fraga Vieira
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Urinary Incontinence ◽  
Pelvic Floor ◽  
Pressure Distribution ◽  
Branch And Bound ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Feature Subset ◽  
Pressure Profiles

Background Pelvic floor pressure distribution profiles, obtained by a novel instrumented non-deformable probe, were used as the input to a feature extraction, selection, and classification approach to test their potential for an automatic diagnostic system for objective female urinary incontinence assessment. We tested the performance of different feature selection approaches and different classifiers, as well as sought to establish the group of features that provides the greatest discrimination capability between continent and incontinent women. Methods The available data for evaluation consisted of intravaginal spatiotemporal pressure profiles acquired from 24 continent and 24 incontinent women while performing four pelvic floor maneuvers: the maximum contraction maneuver, Valsalva maneuver, endurance maneuver, and wave maneuver. Feature extraction was guided by previous studies on the characterization of pressure profiles in the vaginal canal, where the extracted features were tested concerning their repeatability. Feature selection was achieved through a combination of a ranking method and a complete non-exhaustive subset search algorithm: branch and bound and recursive feature elimination. Three classifiers were tested: k-nearest neighbors (k-NN), support vector machine, and logistic regression. Results Of the classifiers employed, there was not one that outperformed the others; however, k-NN presented statistical inferiority in one of the maneuvers. The best result was obtained through the application of recursive feature elimination on the features extracted from all the maneuvers, resulting in 77.1% test accuracy, 74.1% precision, and 83.3 recall, using SVM. Moreover, the best feature subset, obtained by observing the selection frequency of every single feature during the application of branch and bound, was directly employed on the classification, thus reaching 95.8% accuracy. Although not at the level required by an automatic system, the results show the potential use of pelvic floor pressure distribution profiles data and provide insights into the pelvic floor functioning aspects that contribute to urinary incontinence.

scholarly journals Hybrid-Recursive Feature Elimination for Efficient Feature Selection

2020 ◽  
Vol 10 (9) ◽  
pp. 3211
Author(s):  
Hyelynn Jeon ◽  
Sejong Oh
Keyword(s):  
Feature Selection ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Feature Subset ◽  
Learning Tasks ◽  
Original Dataset ◽  
Representative Subset ◽  
Low Performance ◽  
Optimal Feature Subset ◽  
Efficient Selection

As datasets continue to increase in size, it is important to select the optimal feature subset from the original dataset to obtain the best performance in machine learning tasks. Highly dimensional datasets that have an excessive number of features can cause low performance in such tasks. Overfitting is a typical problem. In addition, datasets that are of high dimensionality can create shortages in space and require high computing power, and models fitted to such datasets can produce low classification accuracies. Thus, it is necessary to select a representative subset of features by utilizing an efficient selection method. Many feature selection methods have been proposed, including recursive feature elimination. In this paper, a hybrid-recursive feature elimination method is presented which combines the feature-importance-based recursive feature elimination methods of the support vector machine, random forest, and generalized boosted regression algorithms. From the experiments, we confirm that the performance of the proposed method is superior to that of the three single recursive feature elimination methods.

A Comparative Analysis of Rainfall Prediction Using Support Vector Machine and Random Forest

2020 ◽  
Vol 17 (8) ◽  
pp. 3539-3542
Author(s):  
S. Dhamodaran ◽  
G. KipsonRoy ◽  
A. Kishor ◽  
J. Refonaa ◽  
S. L. JanyShabu
Keyword(s):  
Random Forest ◽  
Preventive Measures ◽  
Weather Prediction ◽  
Research Work ◽  
Support Vector ◽  
Svm Classifier ◽  
Rainfall Prediction ◽  
Comparison Results ◽  
Interesting Area ◽  
Better Than

Classification and prediction of various data have become one of the most interesting area of research work. Numerous researchers are working on various data samples for predicting and classifying into various categories. Weather prediction is not an exception. Rainfall prediction is one of the most widely predicted data sample as it needs to be predicted in advance in order to take preventive measures. Heavy rainfall can endanger day to day activities and also could lead to various other disasters such as floods. Though numerous research works have been carried out on this particular area, we have made an comparative analyses of predicting rainfall using SVM classifier and Random Forest classifier. A web portal is built which runs SVM and Random Forest algorithms, that are designed for predicting the rainfall. The comparison results show that SVM classifiers are able to predict better than the Random Forests algorithms by classifying the data sample for about 92%.

scholarly journals Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Kaushalya Dissanayake ◽  
Md Gapar Md Johar
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Decision Tree ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Classification Algorithms ◽  
Feature Subset ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Feature Selection Techniques

Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.

ABC-Gly: identifying protein lysine glycation sites with artificial bee colony algorithm

2019 ◽  
Vol 17 ◽  
Author(s):  
Yanqiu Yao ◽  
Xiaosa Zhao ◽  
Qiao Ning ◽  
Junping Zhou
Keyword(s):  
Support Vector Machine ◽  
Amino Acid ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Training Dataset ◽  
Support Vector ◽  
Supplementary File ◽  
Feature Subset ◽  
Lipid Molecule ◽  
Bee Colony

Background: Glycation is a nonenzymatic post-translational modification process by attaching a sugar molecule to a protein or lipid molecule. It may impair the function and change the characteristic of the proteins which may lead to some metabolic diseases. In order to understand the underlying molecular mechanisms of glycation, computational prediction methods have been developed because of their convenience and high speed. However, a more effective computational tool is still a challenging task in computational biology. Methods: In this study, we showed an accurate identification tool named ABC-Gly for predicting lysine glycation sites. At first, we utilized three informative features, including position-specific amino acid propensity, secondary structure and the composition of k-spaced amino acid pairs to encode the peptides. Moreover, to sufficiently exploit discriminative features thus can improve the prediction and generalization ability of the model, we developed a two-step feature selection, which combined the Fisher score and an improved binary artificial bee colony algorithm based on support vector machine. Finally, based on the optimal feature subset, we constructed the effective model by using Support Vector Machine on the training dataset. Results: The performance of the proposed predictor ABC-Gly was measured with the sensitivity of 76.43%, the specificity of 91.10%, the balanced accuracy of 83.76%, the area under the receiver-operating characteristic curve (AUC) of 0.9313, a Matthew’s Correlation Coefficient (MCC) of 0.6861 by 10-fold cross-validation on training dataset, and a balanced accuracy of 59.05% on independent dataset. Compared to the state-of-the-art predictors on the training dataset, the proposed predictor achieved significant improvement in the AUC of 0.156 and MCC of 0.336. Conclusion: The detailed analysis results indicated that our predictor may serve as a powerful complementary tool to other existing methods for predicting protein lysine glycation. The source code and datasets of the ABC-Gly were provided in the Supplementary File 1.

Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Chun Qiu ◽  
Sai Li ◽  
Shenghui Yang ◽  
Lin Wang ◽  
Aihui Zeng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Expression Profiles ◽  
Independent Set ◽  
Classification Model ◽  
Support Vector ◽  
Feature Subset ◽  
Hub Genes ◽  
Effective Prevention ◽  
Key Genes ◽  
Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

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