scholarly journals A Study on Machine Vision Techniques for the Inspection of Health Personnels’ Protective Suits for the Treatment of Patients in Extreme Isolation

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 743 ◽  
Author(s):  
Alice Stazio ◽  
Juan G. Victores ◽  
David Estevez ◽  
Carlos Balaguer
Keyword(s):  
Logistic Regression ◽  
Machine Vision ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Classification Algorithms ◽  
Adaptive Boosting ◽  
Blood Stains ◽  
Extreme Gradient Boosting ◽  
Vector Machines

The examination of Personal Protective Equipment (PPE) to assure the complete integrity of health personnel in contact with infected patients is one of the most necessary tasks when treating patients affected by infectious diseases, such as Ebola. This work focuses on the study of machine vision techniques for the detection of possible defects on the PPE that could arise after contact with the aforementioned pathological patients. A preliminary study on the use of image classification algorithms to identify blood stains on PPE subsequent to the treatment of the infected patient is presented. To produce training data for these algorithms, a synthetic dataset was generated from a simulated model of a PPE suit with blood stains. Furthermore, the study proceeded with the utilization of images of the PPE with a physical emulation of blood stains, taken by a real prototype. The dataset reveals a great imbalance between positive and negative samples; therefore, all the selected classification algorithms are able to manage this kind of data. Classifiers range from Logistic Regression and Support Vector Machines, to bagging and boosting techniques such as Random Forest, Adaptive Boosting, Gradient Boosting and eXtreme Gradient Boosting. All these algorithms were evaluated on accuracy, precision, recall and F 1 score; and additionally, execution times were considered. The obtained results report promising outcomes of all the classifiers, and, in particular Logistic Regression resulted to be the most suitable classification algorithm in terms of F 1 score and execution time, considering both datasets.

Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports

2020 ◽  
pp. 865-874
Author(s):  
Enrico Santus ◽  
Tal Schuster ◽  
Amir M. Tahmasebi ◽  
Clara Li ◽  
Adam Yala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Systems ◽  
High Performance ◽  
Feature Model ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Extreme Gradient Boosting ◽  
Pathology Reports

PURPOSE Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the case. For this reason, in the clinical setting, rules are still common. We suggest 2 methods to leverage the knowledge encoded in pre-existing rules to inform ML decisions and obtain high performance, even with scarce annotations. METHODS We collected 501 prostate pathology reports from 6 American hospitals. Reports were split into 2,711 core segments, annotated with 20 attributes describing the histology, grade, extension, and location of tumors. The data set was split by institutions to generate a cross-institutional evaluation setting. We assessed 4 systems, namely a rule-based approach, an ML model, and 2 hybrid systems integrating the previous methods: a Rule as Feature model and a Classifier Confidence model. Several ML algorithms were tested, including logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGB). RESULTS When training on data from a single institution, LR lags behind the rules by 3.5% (F1 score: 92.2% v 95.7%). Hybrid models, instead, obtain competitive results, with Classifier Confidence outperforming the rules by +0.5% (96.2%). When a larger amount of data from multiple institutions is used, LR improves by +1.5% over the rules (97.2%), whereas hybrid systems obtain +2.2% for Rule as Feature (97.7%) and +2.6% for Classifier Confidence (98.3%). Replacing LR with SVM or XGB yielded similar performance gains. CONCLUSION We developed methods to use pre-existing handcrafted rules to inform ML algorithms. These hybrid systems obtain better performance than either rules or ML models alone, even when training data are limited.

scholarly journals Exploiting Visual Features in Financial Time Series Prediction

2020 ◽  
Vol 14 (2) ◽  
pp. 61-76
Author(s):  
Adil Gürsel Karaçor ◽  
Turan Erman Erkan
Keyword(s):  
Financial Time Series ◽  
Time Series Prediction ◽  
Visual Features ◽  
Gradient Boosting ◽  
Support Vector ◽  
Foreign Exchange Rates ◽  
Extreme Gradient Boosting ◽  
Vector Machines ◽  
Direction Of Movement ◽  
Visual Properties

The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name ‘Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.

scholarly journals Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 522
Author(s):  
Congcong Sun ◽  
Hui Tian ◽  
Chin-Chen Chang ◽  
Yewang Chen ◽  
Yiqiao Cai ◽  
...  
Keyword(s):  
Statistical Characteristics ◽  
Gradient Boosting ◽  
Support Vector ◽  
Final State ◽  
Markov Transition Matrix ◽  
Markov Transition ◽  
Extreme Gradient Boosting ◽  
Vector Machines ◽  
Feature Based ◽  
Series Of Experiments

Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams.

scholarly journals Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad-Reza Mohammadi ◽  
Fahime Hadavimoghaddam ◽  
Maryam Pourmahdi ◽  
Saeid Atashrouz ◽  
Muhammad Tajammal Munir ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Equations Of State ◽  
Optimal Operation ◽  
Operating Conditions ◽  
Hydrogen Solubility ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting

AbstractDue to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

scholarly journals Application of machine learning algorithms for flood susceptibility assessment and risk management

2021 ◽  
Author(s):  
R. Madhuri ◽  
S. Sistla ◽  
K. Srinivasa Raju
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Influencing Factors ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Climate Change Scenarios ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Normalised Difference Vegetation Index

Abstract Assessing floods and their likely impact in climate change scenarios will enable the facilitation of sustainable management strategies. In this study, five machine learning (ML) algorithms, namely (i) Logistic Regression, (ii) Support Vector Machine, (iii) K-nearest neighbor, (iv) Adaptive Boosting (AdaBoost) and (v) Extreme Gradient Boosting (XGBoost), were tested for Greater Hyderabad Municipal Corporation (GHMC), India, to evaluate their clustering abilities to classify locations (flooded or non-flooded) for climate change scenarios. A geo-spatial database, with eight flood influencing factors, namely, rainfall, elevation, slope, distance from nearest stream, evapotranspiration, land surface temperature, normalised difference vegetation index and curve number, was developed for 2000, 2006 and 2016. XGBoost performed the best, with the highest mean area under curve score of 0.83. Hence, XGBoost was adopted to simulate the future flood locations corresponding to probable highest rainfall events under four Representative Concentration Pathways (RCPs), namely, 2.6, 4.5, 6.0 and 8.5 along with other flood influencing factors for 2040, 2056, 2050 and 2064, respectively. The resulting ranges of flood risk probabilities are predicted as 39–77%, 16–39%, 42–63% and 39–77% for the respective years.

scholarly journals Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Cheng Qu ◽  
Lin Gao ◽  
Xian-qiang Yu ◽  
Mei Wei ◽  
Guo-quan Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Acute Pancreatitis ◽  
Logistic Regression ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.

scholarly journals Establishment of prediction models for lung cancer NOG/PDX models: A guideline for machine learning in small biomedical datasets

2020 ◽  
Author(s):  
Haoyue Guo ◽  
Li Diao ◽  
Hui Qi ◽  
Chunlei Dai ◽  
Yu Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Prediction Models ◽  
Gene Mutations ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stepwise Logistic Regression ◽  
Extreme Gradient Boosting ◽  
Nog Mice ◽  
Pdx Models

Abstract Background: Targeted therapy and immune checkpoint inhibitors are the most promising treatments for lung cancers but still facing multiple challenges, including resistance and individual difference. Therefore, patient-derived tumor xenografts (PDX) models are developed for drug discovery and screening. NOG mice is under the destruction of the interleukin-2 (IL-2) receptor common gamma chain, which is appropriate for building PDX models to test immunotherapies. However, current studies have little understanding of the causes of genotype mismatches in PDX or NOG/PDX models, which leads to a massive economic and time loss.Methods: Lung cancer tissues from 53 patients were obtained and engrafted into NOG mice. All of the patients' tumors and NOG/PDX models were detected for common gene mutations. Seventeen clinicopathological features were organized and input to stepwise logistic regression based on the lowest Akaike information criterion (AIC), least absolute shrinkage and selection operator (LASSO)-logistic regression, support vector machine recursive feature elimination (SVM-RFE), eXtreme Gradient Boosting (XGBoost), Gradient Boosting & Categorical Features (CatBoost), and synthetic minority over-sampling technique (SMOTE). Finally, the performance of all models was evaluated by the accuracy, area under the receiver operating characteristic curve (AUC), and F1 score in 100 testing groups.Results: Fifty-three lung cancer NOG/PDX models were successfully established, with a genotype matching rate of 79.2% (42/53). Two multivariable logistic regressions revealed that age, the number of driver mutations, epidermal growth factor receptor (EGFR) gene mutations, the type of prior chemotherapy, prior tyrosine kinase inhibitors (TKIs) therapy, and the source were potent predictors. Moreover, CatBoost (mean accuracy=0.960; mean AUC=0.939; mean F1 score=0.908) and 8-feature SVM (mean accuracy=0.950; mean AUC=0.934; mean F1 score=0.903) showed the best performance compared with the other algorithms. Moreover, the combination of SMOTE with SVM significantly improved the predictive capability (mean accuracy: 0.961 vs. 0.958, P=0.025; mean AUC: 0.940 vs. 0.935, P=0.045; mean F1 score: 0.909 vs. 0.903, P=0.047).Conclusions: We established an optimal predictive model to screen lung cancer patients for NOG/PDX models, and also offered a general approach for building prediction models in small unbalanced biomedical samples.

scholarly journals Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles

2020 ◽  
Vol 12 (12) ◽  
pp. 1973
Author(s):  
Alim Samat ◽  
Erzhu Li ◽  
Wei Wang ◽  
Sicong Liu ◽  
Cong Lin ◽  
...  
Keyword(s):  
Random Forest ◽  
Image Classification ◽  
Classification Accuracy ◽  
Regression Tree ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting

To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.

Sign in / Sign up

Export Citation Format

Share Document