Radial Support Vector Machine Research Articles

Software Reliability Prediction using Correlation Constrained Multi-Objective Evolutionary Optimization Algorithm

Software reliability frameworks are extremely effective for estimating the probability of software failure over time. Numerous approaches for predicting software dependability were presented, but neither of those has shown to be effective. Predicting the number of software faults throughout the research and testing phases is a serious problem. As there are several software metrics such as object-oriented design metrics, public and private attributes, methods, previous bug metrics, and software change metrics. Many researchers have identified and performed predictions of software reliability on these metrics. But none of them contributed to identifying relations among these metrics and exploring the most optimal metrics. Therefore, this paper proposed a correlation- constrained multi-objective evolutionary optimization algorithm (CCMOEO) for software reliability prediction. CCMOEO is an effective optimization approach for estimating the variables of popular growth models which consists of reliability. To obtain the highest classification effectiveness, the suggested CCMOEO approach overcomes modeling uncertainties by integrating various metrics with multiple objective functions. The hypothesized models were formulated using evaluation results on five distinct datasets in this research. The prediction was evaluated on seven different machine learning algorithms i.e., linear support vector machine (LSVM), radial support vector machine (RSVM), decision tree, random forest, gradient boosting, k-nearest neighbor, and linear regression. The result analysis shows that random forest achieved better performance.

Identification of high-risk patients for referral through machine learning assisting the decision making to manage minor ailments in community pharmacies.

Background: Data analysis techniques such as machine learning have been used for assisting in triage and the diagnosis of health problems. Nevertheless, it has not been used yet to assist community pharmacists with services such as the Minor Ailment Services These services have been implemented to reduce the burden of primary care consultations in general medical practitioners (GPs) and to allow a better utilization of community pharmacists' skills. However, there is a need to refer high-risk patients to GPs. Aim: To develop a predictive model for high-risk patients that need referral assisting community pharmacists' triage through a minor ailment service. Method: An ongoing pragmatic type 3 effectiveness-implementation hybrid study was undertaken at a national level in Spanish community pharmacies since October 2020. Pharmacists recruited patients presenting with minor ailments and followed them 10days after the consultation. The main outcome measured was appropriate medical referral (in accordance with previously co-designed protocols). Nine machine learning models were tested (three statistical, three black box and three tree models) to assist pharmacists in the detection of high-risk individuals in need of referral. Results: Over 14'000 patients were included in the study. Most patients were female (68.1%). With no previous treatment for the specific minor ailment (68.0%) presented. A percentage of patients had referral criteria (13.8%) however, not all of these patients were referred by the pharmacist to the GP (8.5%). The pharmacists were using their clinical expertise not to refer these patients. The primary prediction model was the radial support vector machine (RSVM) with an accuracy of 0.934 (CI95 = [0.926,0.942]), Cohen's kappa of 0.630, recall equal to 0.975 and an area under the curve of 0.897. Twenty variables (out of 61 evaluated) were included in the model. radial support vector machine could predict 95.2% of the true negatives and 74.8% of the true positives. When evaluating the performance for the 25 patient's profiles most frequent in the study, the model was considered appropriate for 56% of them. Conclusion: A RSVM model was obtained to assist in the differentiation of patients that can be managed in community pharmacy from those who are at risk and should be evaluated by GPs. This tool potentially increases patients' safety by increasing pharmacists' ability to differentiate minor ailments from other medical conditions.

UAV-derived photogrammetric point clouds and multispectral indices for fuel estimation in Mediterranean forests

Sensors attached to unmanned aerial vehicles (UAVs) allow estimating a large number of forest attributes related to forest fuels. This study assesses photogrammetric point clouds and multispectral indices obtained from a fixed-wing UAV for the classification of Prometheus fuel types in 82 forest plots in Aragón (NE Spain). Images captured by an RGB camera and a multispectral sensor allowed generating high density photogrammetric point clouds (RGB: 3000 points/m2; multispectral: 85 points/m2), which were normalized using alternatively a Digital Elevation Model (DEM) of 0.5, 1, and 2 m resolution. A set of structural and textural variables were derived from the normalized point cloud heights, and for the latter, the gray-level co-occurrence matrix (GLCM) approach was used. Multispectral images were also used to create seven spectral vegetation indices. The most relevant structural, textural, and spectral variables to introduce into the fuel types classification models were selected using Dunn's test, which included: the vegetation height at the 50th percentile, the coefficient of variation of the heights, the percentage of returns above 4 m, the mean textural dissimilarity, and the mean of the Green Chlorophyll Index. Three different data samples were introduced in the models: i) the relevant structural and textural variables from the RGB camera (RGB data sample); ii) the relevant structural, textural, and spectral variables from the multispectral sensor (MS data sample); and iii) the relevant structural and textural variables from the RGB camera plus the relevant spectral variable from the multispectral sensor (integrated data sample). After comparing three machine learning classification techniques (Random Forest, and Linear and Radial Support Vector Machine), the best results were obtained with Random Forest with k-fold cross-validation (k-10) and the integrated data sample with normalized point clouds at 0.5 m DEM resolution (overall accuracy = 71%). The variables successfully identified the Prometheus main fire carriers (i.e., shrubs or trees) and confusions were mainly located within the fuel types of the same dominant stratum, especially in fuel types 3 and 6. These results demonstrate the ability of UAV imagery to classify forest fuels in Mediterranean environments when RGB and multispectral data are combined.

Recognition of sugarcane orange and brown rust through leaf image processing

In experimental fields, scientists assess the resistance to orange and brown rust of sugarcane exclusively by identifying and grading infection by visual estimation on the leaves. This is time-consuming and may deliver subjective evaluations, limiting phenotyping experiments. Thus, to facilitate the leaf disease identification process, the goal of this study was to test an image analysis approach to differentiate the two types of rust on sugarcane leaves. Radial Support Vector Machine (SVM) models showed high accuracy (>0.88) in identifying the two types of rust, classifying segments of RGB images of infected leaves generated with object-based image analysis (OBIA) segmentation. This provides a basis for the development of applications that identify the two types of rust automatically through RGB images of sugarcane leaves.

(SDGFI) Student’s Demographic and Geographic Feature Identification Using Machine Learning Techniques for Real-Time Automated Web Applications

Nowadays, Google Forms is becoming a cutting-edge tool for gathering research data in the educational domain. Several researchers are using real-time web applications to collect the responses of respondents. Demographic and geographic features are the most important in the researcher’s study. Identifying students’ demographics (gender, age-group, course, institution, or university) and geographic features (locality and country) is a challenging problem in machine learning. We proposed a novel predictive algorithm, Student Demographic Identification (SDI), to identify a student’s demographic features (age-group, course) with the highest accuracy. SDI has been tested on primary reliable samples. SDI has also been compared with the traditional machine algorithms Random Forest (RF), and Logistic Regression (LR), and Radial Support Vector Machine (R–SVM). The proposed algorithm significantly improved the performance metrics such as accuracy, F1-score, precision, recall, and Matthews Correlation Coefficient (MCC) of these classifiers. We also proposed significant features to identify students’ age-group, course, and gender. SDI has identified the student’s age group with an accuracy of 96% and the course with an accuracy of 97%. Gradient Boosting (GB) has improved the accuracy of LR, R-SVM, and RF to predict the student’s gender. Also, the RF algorithm with the support of GB attained the highest accuracy of 98% to identify the gender of the students. All three classifiers have also identified the student’s locality and institution with an identical accuracy of 99%. Our proposed SDI algorithm may be useful for real-time survey applications to predict students’ demographic features.

Predicting Readmission Following Hospital Treatment for Patients with Alcohol Related Diagnoses in an Australian Regional Health District.

This study aims to investigate the prediction of hospital readmission of alcohol use disorder patients within 28 days of discharge and compare the performance of six machine learning methods i.e., random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM.

Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission.

Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics. We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the number of SBI-negative children subsequently provided antibiotics in the PICU identified as low-risk by each model. A total of 15 074 children met inclusion criteria; 4788 (32%) received antibiotics before PICU admission. Of these antibiotic-exposed patients, 2325 of 4788 (49%) had an SBI. Of the 10 286 antibiotic-unexposed patients, 2356 of 10 286 (23%) had an SBI. In antibiotic-exposed children, a radial support vector machine model had the highest AUROC (0.80) for evaluating SBI, identifying 48 of 442 (11%) SBI-negative children provided antibiotics in the PICU who could have been spared a median 3.7 (interquartile range 0.9-9.0) antibiotic-days per patient. In antibiotic-unexposed children, a random forest model performed best, but was less accurate overall (AUROC 0.76), identifying 33 of 469 (7%) SBI-negative children provided antibiotics in the PICU who could have been spared 1.1 (interquartile range 0.9-3.7) antibiotic-days per patient. Among children who received antibiotics before PICU admission, machine learning models can identify children at low risk of SBI and potentially reduce antibiotic exposure.

SUPPORT VECTOR MACHINE FOR LANDSLIDE ACTIVITY IDENTIFICATION BASED ON VEGETATION ANOMALIES INDICATOR

Landslide activity identification is critical for landslide inventory mapping. A detailed landslide inventory map is highly required for various purposes such as landslide susceptibility, hazard, and risk assessments. This paper proposes a novel approach based on vegetation anomalies indicator (VAI) and applying machine learning method namely support vector machine (SVM) to identify status of natural-terrain landslides. First, high resolution airborne LiDAR data and satellite imagery were used to derive landslide-related VAIs, including tree height irregularities, canopy gap, density of different layer of vegetation, vegetation type, vegetation indices, root strength index (RSI), and distribution of water-loving trees. Then, SVM is utilized with different setting of parameter using grid search optimization. SVM Radial Basis Function (RBF) recorded the best optimal pair value with 0.062 and 0.092 misclassification rate for deep seated and shallow translational landslide, respectively. For landslide activity classification, SVM RBF recorded the best accuracy value for both deep seated and shallow translational landslides with 86.0 and 71.3, respectively. Overall, VAIs have great potential in tackling the landslide activity identification problem especially in tropical vegetated area.

Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson’s disease using two-layer hybrid feature selection

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.

Detection of Diabetic Retinopathy Using Discrete Wavelet Transform with Discrete Meyer in Retinal Images

One of the major complicated issues for extensive term diabetic aspirant is diabetic retinopathy (DR) which is an eye retinal syndrome, leads to blindness. The presence of exudates detects the disease, which can be prevented in the early stages by regular screening. Exudates can be automatically detected through inspecting digital retinal image. To detect the exudates for diagnosis the author proposed an algorithm called K-means Kernel support vector machine Radial basis function (KKR) approach, by the following main stages: extracting vessel and removal of optic disc followed by pre-processing, exudates detection and post processing. Wavelet dependent edge enhancement is used for dark portion separation of exudates in the retinal image by optically designed Wideband bandpass filter. Wavelet toolbox of MATLAB 2018a is used in this KKR algorithm. Statistical and structural texture features can be obtained using K-means segmentation process by integrating Local Binary Pattern (LBP) with Region Of Interest (ROI). Some features are selected and used Neural Network along with Radial Basis Function (RBF) to classify further. The KKR algorithm uses 80 fundus images from DIARETDB1 database and parameters are analyzed such as specificity, sensitivity and accuracy. The results obtained from proposed KKR algorithm have specificity of 81.57%, sensitivity of 87.56% and accuracy of 97.94% respectively.

A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services

A cancer pathology report is a valuable medical document that provides information for clinical management of the patient and evaluation of health care. However, there are variations in the quality of reporting in free-text style formats, ranging from comprehensive to incomplete reporting. Moreover, the increasing incidence of cancer has generated a high throughput of pathology reports. Hence, manual extraction and classification of information from these reports can be intrinsically complex and resource-intensive. This study aimed to (i) evaluate the quality of over 80,000 breast, colorectal, and prostate cancer free-text pathology reports and (ii) assess the effectiveness of random forest (RF) and variants of support vector machine (SVM) in the classification of reports into benign and malignant classes. The study approach comprises data preprocessing, visualisation, feature selections, text classification, and evaluation of performance metrics. The performance of the classifiers was evaluated across various feature sizes, which were jointly selected by four filter feature selection methods. The feature selection methods identified established clinical terms, which are synonymous with each of the three cancers. Uni-gram tokenisation using the classifiers showed that the predictive power of RF model was consistent across various feature sizes, with overall F-scores of 95.2%, 94.0%, and 95.3% for breast, colorectal, and prostate cancer classification, respectively. The radial SVM achieved better classification performance compared with its linear variant for most of the feature sizes. The classifiers also achieved high precision, recall, and accuracy. This study supports a nationally agreed standard in pathology reporting and the use of text mining for encoding, classifying, and production of high-quality information abstractions for cancer prognosis and research.

Machine learning approach to predict leaf colour change in Fagus sylvatica L. (Spain)

The European beech (Fagus sylvatica L.) is one of the most important deciduous tree species in Europe. In this study, we analyse the autumn senescence dynamics of Spanish beech forests using time series satellite data between 2001 and 2017. In addition, we used nine Machine Learning (ML) algorithms to predict the day of the year (DOY) in which their leaf colour change is at 75% (75CF), 10 days ahead, using precipitation and temperature data. The used algorithms were generalized linear model (glm), ridge regression (ridge), least absolute shrinkage and selection operator (lasso), bayesian generalized linear models (bayesglm), partial least squares (pls),weighted k-nearest neighbours (kknn), extreme gradient boosting (xgbTree), support vector machine radial (svmRadial) and random forest (rf). The time series analysis of the actual 75CF did not show any acceleration or deceleration over time. Nevertheless, we noticed a decreasing negative trend between the mean elevation above the mean sea level of the forests and their actual 75CF in terms of Pearson correlation coefficient (r). The best performing ML model was kknn with RMSE = 3 days and R2 = 0.94. To further explore the predictive capacity of the models in a realistic scenario, we held out the last year of the time series (2017) and trained the models with data from 2001 to 2016. The test results proved that rf was the best method in this hypothetical scenario with RMSE = 8 days and R2 = 0.67. This study provides a cost-effective method to predict leaf colour change in beech forests, reducing the shortcomings of previous approaches with a similar goal. It can be used with management purposes for local or regional authorities, as well as being of interest to further investigate climate change impacts on tree species.

Machine learning models applied to TSS estimation in a reservoir using multispectral sensor onboard to RPA

Water quality monitoring is fundamental for the maintenance and conservation of water resources. However, the conventional monitoring method, which uses point sampling, does not adequately represent the spatial variability of its parameters, besides being costly. As a result, remote sensing techniques with the use of orbital sensors have been applied to map some water quality parameters, mainly the Optically Active Components (OACs), such as the Total Suspended Solids (TSS), Chlorophyll-a (Chl-a), and Colored Dissolved Organic Matter (CDOM). However, the monitoring of small reservoirs with orbital sensors always presents limitations, such as spectral and spatial problems or time resolution issues, combined with the fact that the sensors are subject to atmospheric disturbances and clouds. Seeking to overcome these issues, the present work aimed to develop a system for monitoring the concentration of Total Suspended Solids (TSS) in reservoirs, based on multispectral sensors onboard a Remotely Piloted Aircraft (RPA). A MicaSense RedEdge multispectral sensor was used, which has five bands in the blue, green, red-edge, and infrared (NIR) spectrum bands. Four campaigns were carried out in two water reservoirs within the Atlantic Forest biome at different seasons of the year. The data were submitted to five machine learning algorithms: the Random Forest (RF), Support Vector Machine Radial Sigma (SVM-RS), Enhanced Adaptive Regression Through Hinges (EARTH), Multiple Linear Regression (MLR), and Cubist algorithms. The results demonstrated that the SVM-RS model was the best adapted to the data patterns (r2 = 0.87), while the RF, EARTH, MLR, and Cubist models presented limitations that hinder their use in the estimation of TSS in reservoirs. This study demonstrated that the use of a multispectral sensor onboard an RPA has high accuracy in estimating TSS in small reservoirs and that machine learning algorithms can generalize the TSS data well.

Considering Performance in the Automated and Manual Coding of Sociolinguistic Variables: Lessons From Variable (ING).

Impressionistic coding of sociolinguistic variables like English (ING), the alternation between pronunciations like talkin' and talking, has been a central part of the analytic workflow in studies of language variation and change for over a half-century. Techniques for automating the measurement and coding for a wide range of sociolinguistic data have been on the rise over recent decades but procedures for coding some features, especially those without clearly defined acoustic correlates like (ING), have lagged behind others, such as vowels and sibilants. This paper explores computational methods for automatically coding variable (ING) in speech recordings, examining the use of automatic speech recognition procedures related to forced alignment (using the Montreal Forced Aligner) as well as supervised machine learning algorithms (linear and radial support vector machines, and random forests). Considering the automated coding of pronunciation variables like (ING) raises broader questions for sociolinguistic methods, such as how much different human analysts agree in their impressionistic codes for such variables and what data might act as the “gold standard” for training and testing of automated procedures. This paper explores several of these considerations in automated, and manual, coding of sociolinguistic variables and provides baseline performance data for automated and manual coding methods. We consider multiple ways of assessing algorithms' performance, including agreement with human coders, as well as the impact on the outcome of an analysis of (ING) that includes linguistic and social factors. Our results show promise for automated coding methods but also highlight that variability in results should be expected even with careful human coded data. All data for our study come from the public Corpus of Regional African American Language and code and derivative datasets (including our hand-coded data) are available with the paper.

Identifying the Risk Factors Associated with Nursing Home Residents' Pressure Ulcers Using Machine Learning Methods.

Background: Machine learning (ML) can keep improving predictions and generating automated knowledge via data-driven predictors or decisions. Objective: The purpose of this study was to compare different ML methods including random forest, logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM in terms of their accuracy, sensitivity, specificity, negative predictor values, and positive predictive values by validating real datasets to predict factors for pressure ulcers (PUs). Methods: We applied representative ML algorithms (random forest, logistic regression, linear SVM, polynomial SVM, radial SVM, and sigmoid SVM) to develop a prediction model (N = 60). Results: The random forest model showed the greatest accuracy (0.814), followed by logistic regression (0.782), polynomial SVM (0.779), radial SVM (0.770), linear SVM (0.767), and sigmoid SVM (0.674). Conclusions: The random forest model showed the greatest accuracy for predicting PUs in nursing homes (NHs). Diverse factors that predict PUs in NHs including NH characteristics and residents’ characteristics were identified according to diverse ML methods. These factors should be considered to decrease PUs in NH residents.

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification.

The accurate classification of microbes is critical in today’s context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).

Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico

Wheat is one of the most important cereal crops in the world, and its demand is expected to increase about 60% by 2050. Thus, appropriate and reliable yield forecasts are fundamental to ensure price stability and food security around the globe. In this study, we developed a Machine Learning (ML) approach to combine satellite and climate data with antecedent wheat yield information (YieldBaseLine) from 2004 – 2018, at municipal level, in Mexico. We compared the performance of four linear (generalized linear model –glm-, ridge regression –ridge-, lasso, partial least squares –pls-) and four non-linear algorithms (k-nearest neighbours –kknn-, support vector machine radial –svmR-, extreme gradient boosting -xgbTree- and random forest –rf) before harvest time. Additionally, we evaluated their performance using five different feature selection scenarios (No FS, FS = 0.9, FS = 0.75, FS = 0.9 and YieldBaseLine). The models were independently tested using two different approaches: random sampling and selective sampling. In the random sampling, the non-linear models performed generally better under the FS = 0.5 scenario, whereas the non-linear models were less sensitive to feature reduction. The results also evidenced the capacity of the YieldBaseLine predictor, combined with satellite and climate data, to address the inter-annual and spatial variability in the study area. The highest prediction accuracy was obtained by the rf method (No FS) with R2 = 0.84. To further prove the model's operability in a simulated real-case scenario, we held out the last year records (2018) to test the models. The best performing model was again the rf (R2 = 0.81). This study proposes a robust methodology to model crop yield (at large scale) and it may be used with operative purposes. Therefore, it can be of interest to decision and law makers, producers, authorities or the wheat industry. In addition, it can help to establish appropriate food security and trading policies. A similar approach can be applied to other regions or crops.

Regional estimation of garlic yield using crop, satellite and climate data in Mexico

Garlic (Allivium sativum L.) has been consumed by humans since the beginning of recorded history. Despite its culinary and economical importance, little efforts are reported in the literature to forecast garlic yield. In this study, we developed the first attempt to predict garlic yield at large scale using crop, satellite (TERRA-MODIS) and climate data (ERA-5). The study area was located in Mexico (2004 – 2018). We compared the predictive capacity of three Machine Learning (ML) methods: generelised linear model (glm), support vector machine radial (svmR) and random forest (rf); as well as different combinations of predictors. The best performance was obtained by the svmR model (R2 = 0.68 and %RMSE = 20.08%) which combined YieldBaseLine, NDVI and climate data under Feature Selection scenario (FS) = 0.90. The rf method performed similarly well (R2 = 0.66), while glm achieved lower performance (R2 = 0.61). To test the predictive capacity of the models in practice, we additionally used a leave-one-year-out method in a time-wise manner. Models gradually improved over the years as more data was incorporated for training. The svmR and rf methods presented an overall performance of R2 ~0.60 (%RMSE ~20.5%), reaching the highest accuracy (R2 = 0.78, %RMSE ~16.6%) in 2014. Both approaches reveal the potentiality of the proposed models to account for the inter-annual and spatial garlic yield variability in Mexico. This procedure can be adjusted and used for other crop types or locations.

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders.

The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.