Gradient Boosting Tree Classifier Research Articles

Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum.

We sought to create a machine learning (ML) model to identify variables that would aid in the prediction of surgical morbidity in cases of placenta accreta spectrum (PAS). A multicenter analysis including all cases of PAS identified by pathology specimen confirmation, across five tertiary care perinatal centers in New York City from 2013 to 2022. We developed models to predict operative morbidity using 213 variables including demographics, obstetrical information, and limited prenatal imaging findings detailing placental location. Our primary outcome was prediction of a surgical morbidity composite defined as including any of the following: blood loss (>1,500 mL), transfusion, intensive care unit admission, vasopressor use, mechanical ventilation/intubation, and organ injury. A nested, stratified, cross-validation approach was used to tune model hyperparameters and estimate generalizability. Gradient boosted tree classifier models incorporated preprocessing steps of standard scaling for numerical variables and one-hot encoding for categorical variables. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), positive and negative predictive values (PPV, NPV), and F1 score. Variable importance ranking was also determined. Among 401 PAS cases, 326 (81%) underwent hysterectomy. Of the 401 cases of PAS, 309 (77%) had at least one event defined as surgical morbidity. Our predictive model had an AUC of 0.79 (95% confidence interval: 0.69, 0.89), PPV 0.79, NPV 0.76, and F1 score of 0.88. The variables most predictive of surgical morbidity were completion of a hysterectomy, prepregnancy body mass index (BMI), absence of a second trimester ultrasound, socioeconomic status zip code, BMI at delivery, number of prenatal visits, and delivery time of day. By identifying social and obstetrical characteristics that increase patients' risk, ML models are useful in predicting PAS-related surgical morbidity. Utilizing ML could serve as a foundation for risk and complexity stratification in cases of PAS to optimize surgical planning. · ML models are useful models are useful in predicting PAS-related surgical morbidity.. · Optimal management for PAS remains unclear.. · Utilizing ML can serve as a foundation for risk and complexity stratification in cases of PAS..

Developmental and Validation of Machine Learning Model for Prediction Complication after Cervical Spine Metastases Surgery.

This is a retrospective cohort study utilizing machine learning to predict postoperative complications in cervical spine metastases surgery. The main objective is to develop a machine learning model that accurately predicts complications following cervical spine metastases surgery. Cervical spine metastases surgery can enhance quality of life but carries a risk of complications influenced by various factors. Existing scoring systems may not include all predictive factors. Machine learning offers the potential for a more accurate predictive model by analyzing a broader range of variables. Data from January 2012 to December 2020 were retrospectively collected from medical databases. Predictive models were developed using Gradient Boosting, Logistic Regression, and Decision Tree Classifier algorithms. Variables included patient demographics, disease characteristics, and laboratory investigations. SMOTE was used to balance the dataset, and the models were assessed using AUC, F1-score, precision, recall, and SHAP values. The study included 72 patients, with a 29.17% postoperative complication rate. The Gradient Boosting model had the best performance with an AUC of 0.94, indicating excellent predictive capability. Albumin level, platelet count, and tumor histology were identified as top predictors of complications. The Gradient Boosting machine learning model showed superior performance in predicting postoperative complications in cervical spine metastases surgery. With continuous data updating and model training, machine learning can become a vital tool in clinical decision-making, potentially improving patient outcomes. Level III.

Understanding the role of driver behaviors and performance in safety-critical events: Application of machine learning

Human factors contribute in some way to almost 93% of road crashes. Because the unavailability of good pre-crash data, the contribution of human factors to safety-critical events (SCEs) and the prediction of crashes using real-world data is lightly researched. This study provides predictive accuracy by harnessing unique real-world naturalistic driving study (NDS) data, which includes dynamic pre-crash information about driving behavior and performance. After cleaning and preprocessing, a final subsample (N = 9,237) was used and split into training and test samples. For consistent comparison of variables’ importance in statistical and machine learning (ML) models, the dominance analysis uncovered the most important predictors used by the ordered Probit model. Next, three non-parametric supervised ML methods, because promising prediction performance and cost-effectiveness, including Naïve Bayes, K-Nearest Neighbors, and Gradient Boosting Tree (GBT) were used. The overall out-of-sample prediction accuracy for the ordered Probit model was 85.75% which was lower than all three ML methods. The GBT showed the highest (91.23%) out-of-sample prediction accuracy. The availability of pre-crash naturalistic data helps significantly improve the prediction accuracy of SCEs as cumulative importance for all available human factors in the GBT classifier was 94%. For practical applications, refer to the article.

House Type Specification for Housing Development Project Using Machine Learning Techniques: A Study From Bangkok Metropolitan Region, Thailand

Specifying the house type of a housing development project is extremely necessary. However, the determination of a project type nowadays has become a delicate matter, requiring the expertise and knowledge of seasoned project developers. This study aimed to apply four machine learning techniques: Decision Tree, Random Forest, Gradient Boosted Tree and Ensemble Classifier, to analyze the data from 179 housing estate projects collected from market reports of real estate companies in Thailand, with a focus on selecting projects with average monthly sales rates that are higher than the average of all acquired projects. This process resulted in a reduced dataset of 59 projects, including 31 townhouses, 22 single-family houses, and six semi-detached houses. As a result, the Ensemble Classifier model has the highest accuracy of 90.91%. The factors most influential in identifying the type of project are the distances from a main road, sky train station, bus station, hospital, and department store. Single-detached house projects are suitable for locations with high potential. The ideal location should be in proximity to a main road, bus station, department store, and hospital. In addition, townhouse projects are ideal for medium-potential locations that are not near shopping malls, but still require proximity to a hospital, sky train station, or bus station. Ultimately, semi-detached house projects are ideal for medium-potential locations that require proximity to a main road for convenient access to sky train station or public transportation, depending on the specific context.

Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm

Competent people are a valuable asset for strong businesses. The issue of retaining competent staff with expertise poses a challenge to business owners. Employee attrition may be costly for businesses since it takes a lot to compensate for their experience and efficiency. As a result, this study proposes a novel model for predicting employee attrition utilizing machine learning techniques. The datasets are collected from Kaggle resource. The dataset has been pre-processed using standard scalar with Label Encoding method. The dataset has been trained with ML algorithm. The best features are selected by using modified genetic algorithm (MGA). The classification has been done with KNN, Gradient Boosting and Extra tree classifier. Finally, the attrition prediction using optimized levy fruit fly optimization (OLFFO). The experimental results are compared with ML algorithms with classification metrics (Accuracy, Precision, recall and f-measure).

Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers

Electroencephalography (EEG) is widely used to monitor epileptic seizures, and standard clinical practice consists of monitoring patients in dedicated epilepsy monitoring units via video surveillance and cumbersome EEG caps. Such a setting is not compatible with long-term tracking under typical living conditions, thereby motivating the development of unobtrusive wearable solutions. However, wearable EEG devices present the challenges of fewer channels, restricted computational capabilities, and lower signal-to-noise ratio. Moreover, artifacts presenting morphological similarities to seizures act as major noise sources and can be misinterpreted as seizures. This paper presents a combined seizure and artifacts detection framework targeting wearable EEG devices based on Gradient Boosted Trees. The seizure detector achieves nearly zero false alarms with average sensitivity values of 65.27%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$65.27\\%$$\\end{document} for 182 seizures from the CHB-MIT dataset and 57.26%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$57.26\\%$$\\end{document} for 25 seizures from the private dataset with no preliminary artifact detection or removal. The artifact detector achieves a state-of-the-art accuracy of 93.95%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.95\\%$$\\end{document} (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms—up to 96%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$96\\%$$\\end{document} compared to standalone seizure detection. Optimized for a Parallel Ultra-Low Power platform, these algorithms enable extended monitoring with a battery lifespan reaching 300 h. These findings highlight the benefits of integrating artifact detection in wearable epilepsy monitoring devices to limit the number of false positives.

Predicting Poor Sleep in People Living with Dementia: An Analysis of In‐Home Activities and Physiology Measurements During the Daytime

AbstractBackgroundThe use of Internet of Things (IoT) technologies can help with remote monitoring of People Living With Dementia (PLWD) at their homes, ensuring effective and prompt provision of healthcare services. This study provides an analysis of data collected as part of a long‐term research project initiated by the UK Dementia Research Institute’s Care Research and Technology Centre, which aims to understand and slow down the progression of dementia. The goal of this work is to examine the relationship between daytime living status (i.e. activities and psychiatric measurements) and nighttime sleep quality, as poor sleep has been linked to the development of dementia. Early prediction of poor nighttime sleep through monitoring daytime activities may offer an opportunity for intervention, leading to an improvement in sleep quality. In order to achieve this goal, we have developed a classification model to predict poor sleep based on daytime living status.MethodThe activity data is from sensors placed throughout each participant’s home, tracking their movements between 6:00 AM and 7:00 PM each day. The physiological measurements are also taken daily, primarily in the daytime. Sleep is considered poor if more than 50∖% of the time is spent in the “Awake” state out of the total sleep duration (the sum of four sleep states: “Awake”, “Deep”, “Light”, “REM”). Machine learning classification models are applied to predict whether nighttime sleep is poor or not. Also, feature importance analysis can be conducted to identify activity patterns that contribute to poor sleep.ResultThe dataset includes 91 participants with dementia and the collection period spans from June 29th, 2021 to December 31st, 2022. A Gradient Boosting tree classifier was trained and evaluated using 10‐fold cross‐validation. 7% of the data are positive samples. The average sensitivity was 81.1% with a standard deviation of 6.6%, and the average specificity was 78.3% with a standard deviation of 2.3%.ConclusionThe study suggests that further improvement can be made by analysing time‐dependency within the data and utilising more advanced models. The combination of IoT and machine learning shows the potential to greatly benefit healthcare services for PLWD on a daily basis.

Predicting active compounds for lung cancer based on quantitative structure-activity relationships

<span lang="EN-US">Recently, advancements in computational and artificial intelligence (AI) methods have contributed in improving research results in the field of drug discovery. In fact, machine learning techniques have proven to be especially effective in this regard, aiding in the development of new drug variants and enabling more precise targeting of specific disease mechanisms. In this paper, we propose to use a quantitative structure-activity relationship-based approach for predicting active compounds related to non-small cell lung cancer. Our approach uses a neural network classifier that learns from sequential structures and chemical properties of molecules, as well as a gradient boosting tree classifier to conduct comparative analysis. To evaluate the contribution of each feature, we employ Shapley additive explanations (SHAP) summary plots to perform features selection. Our approach involves a dataset of active and non-active molecules collected from ChEMBL database. Our results show the effectiveness of the proposed approach when it comes to predicting accurately active compounds for lung cancer. Furthermore, our comparative analysis reveals important chemical structures that contribute to the effectiveness of the compounds. Thus, the proposed approach can greatly enhance the drug discovery pipeline and may lead to the development of new and effective treatments for lung cancer.</span>

NTpred: a robust and precise machine learning framework for in silico identification of Tyrosine nitration sites in protein sequences.

Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, autoimmune diseases and carcinogenesis. Identification of NT modification supports development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. This paper presents the NTpred framework that is competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, which is used to train a Logistic Regression classifier. On the BD1 benchmark dataset, the proposed framework outperforms the existing best-performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on the BD2 benchmark dataset, the proposed framework outperforms the existing best-performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC. NTpred is publicly available for further experimentation and predictive use at: https://sds_genetic_analysis.opendfki.de/PredNTS/.

Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study.

Fatal drug overdose surveillance informs prevention but is often delayed because of autopsy report processing and death certificate coding. Autopsy reports contain narrative text describing scene evidence and medical history (similar to preliminary death scene investigation reports) and may serve as early data sources for identifying fatal drug overdoses. To facilitate timely fatal overdose reporting, natural language processing was applied to narrative texts from autopsies. This study aimed to develop a natural language processing-based model that predicts the likelihood that an autopsy report narrative describes an accidental or undetermined fatal drug overdose. Autopsy reports of all manners of death (2019-2021) were obtained from the Tennessee Office of the State Chief Medical Examiner. The text was extracted from autopsy reports (PDFs) using optical character recognition. Three common narrative text sections were identified, concatenated, and preprocessed (bag-of-words) using term frequency-inverse document frequency scoring. Logistic regression, support vector machine (SVM), random forest, and gradient boosted tree classifiers were developed and validated. Models were trained and calibrated using autopsies from 2019 to 2020 and tested using those from 2021. Model discrimination was evaluated using the area under the receiver operating characteristic, precision, recall, F1-score, and F2-score (prioritizes recall over precision). Calibration was performed using logistic regression (Platt scaling) and evaluated using the Spiegelhalter z test. Shapley additive explanations values were generated for models compatible with this method. In a post hoc subgroup analysis of the random forest classifier, model discrimination was evaluated by forensic center, race, age, sex, and education level. A total of 17,342 autopsies (n=5934, 34.22% cases) were used for model development and validation. The training set included 10,215 autopsies (n=3342, 32.72% cases), the calibration set included 538 autopsies (n=183, 34.01% cases), and the test set included 6589 autopsies (n=2409, 36.56% cases). The vocabulary set contained 4002 terms. All models showed excellent performance (area under the receiver operating characteristic ≥0.95, precision ≥0.94, recall ≥0.92, F1-score ≥0.94, and F2-score ≥0.92). The SVM and random forest classifiers achieved the highest F2-scores (0.948 and 0.947, respectively). The logistic regression and random forest were calibrated (P=.95 and P=.85, respectively), whereas the SVM and gradient boosted tree classifiers were miscalibrated (P=.03 and P<.001, respectively). "Fentanyl" and "accident" had the highest Shapley additive explanations values. Post hoc subgroup analyses revealed lower F2-scores for autopsies from forensic centers D and E. Lower F2-score were observed for the American Indian, Asian, ≤14 years, and ≥65 years subgroups, but larger sample sizes are needed to validate these findings. The random forest classifier may be suitable for identifying potential accidental and undetermined fatal overdose autopsies. Further validation studies should be conducted to ensure early detection of accidental and undetermined fatal drug overdoses across all subgroups.

A machine learning-based KNIME workflow to predict VEGFR-2 inhibitors.

Vascular endothelial growth factors (VEGFs) are specific cytokines involved in angiogenesis and do so via binding to vascular endothelial growth factor receptors (VEGFRs), a type of receptor tyrosine kinase. VEGFs are reported to facilitate angiogenesis in physiological (embryogenesis) and pathological (tumor) conditions. The overexpression of VEGFs and consequently VEGFRs is reported in tumorigenic conditions. Several VEGFR inhibitors currently used as anticancer drugs to prevent angiogenesis are sunitinib, sorafenib, etc. To identify new potential candidates as VEGFR inhibitors, a classification study using a large and diverse dataset of VEGFR inhibitors from the BindingDB database has been conducted. The KNIME platform was used to calculate molecular and fingerprint-based descriptors and several classification algorithms viz. linear regression (LR), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosted tree (GBT) were employed to build the classification model. The model performance was evaluated by accuracy, precision, recall, and F1 score of the test set. The best LR, kNN, DT, RF, and GBT classifiers had the F1 score of 0.81, 0.87, 0.82, 0.87, and 0.87, respectively. The assorted 5120 VEGFR inhibitors were clustered into 10 subsets, and the structural features of each subset were assessed along with the identification of significant fragments in active and inactive compounds. The automated classifier model developed using the KNIME platform could serve as an important platform for screening and designing molecules as VEGFR inhibitors.

A For-Profit Model of Microcredit

Microcredit is a proven means of poverty alleviation. However, its implementation in India is flawed. High interest rates, incompetent use of credit, and insufficient loan-sizes are just some of the problems well-documented in literature. This paper seeks to assess if these problems can be alleviated by a for-profit model of microcredit, and hence if this model can sustainably improve financial inclusion in India. The role of profit-motives in Indian microcredit has only been analyzed from a theoretical perspective thus far, so this paper adds to existing research by bringing in an empirical perspective through data from Microfinance Institutions from the MIX Markets database. A baseline binary Logistic Classifier is trained, alongside proposed models of a Decision Tree Classifier and a Gradient-boosted Tree Classifier. Feature analysis is conducted on the models, and the results are used to devise profit-maximizing strategies. These strategies are then analyzed to assess if profit-driven companies can improve financial inclusion in a socially desirable manner. The study finds that for-profit incentives can increase the spread, efficiency, and accessibility of microcredit while potentially fostering competent use of credit and reducing the cost of borrowing. However, the study also notes the requirement for regulation in the growth and means of enforcing repayment in such for-profit models. Further, the study observes that a for-profit model may increase credit risk and will fail to allocate credit to the lowest strata of the poor. As such, for-profit firms must be complemented by not-for-profit MFIs to improve financial inclusion across the board.

Enzyme Substrate Prediction from Three-Dimensional Feature Representations Using Space-Filling Curves.

Compact and interpretable structural feature representations are required for accurately predicting properties and function of proteins. In this work, we construct and evaluate three-dimensional feature representations of protein structures based on space-filling curves (SFCs). We focus on the problem of enzyme substrate prediction, using two ubiquitous enzyme families as case studies: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine-dependent methyltransferases (SAM-MTases). Space-filling curves such as the Hilbert curve and the Morton curve generate a reversible mapping from discretized three-dimensional to one-dimensional representations and thus help to encode three-dimensional molecular structures in a system-independent way and with only a few adjustable parameters. Using three-dimensional structures of SDRs and SAM-MTases generated using AlphaFold2, we assess the performance of the SFC-based feature representations in predictions on a new benchmark database of enzyme classification tasks including their cofactor and substrate selectivity. Gradient-boosted tree classifiers yield binary prediction accuracy of 0.77-0.91 and area under curve (AUC) characteristics of 0.83-0.92 for the classification tasks. We investigate the effects of amino acid encoding, spatial orientation, and (the few) parameters of SFC-based encodings on the accuracy of the predictions. Our results suggest that geometry-based approaches such as SFCs are promising for generating protein structural representations and are complementary to the existing protein feature representations such as evolutionary scale modeling (ESM) sequence embeddings.

Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease.

To diagnose an illness in healthcare, doctors typically conduct physical exams and review the patient's medical history, followed by diagnostic tests and procedures to determine the underlying cause of symptoms. Chronic kidney disease (CKD) is currently the leading cause of death, with a rapidly increasing number of patients, resulting in 1.7 million deaths annually. While various diagnostic methods are available, this study utilizes machine learning due to its high accuracy. In this study, we have used the hybrid technique to build our proposed model. In our proposed model, we have used the Pearson correlation for feature selection. In the first step, the best models were selected on the basis of critical literature analysis. In the second step, the combination of these models is used in our proposed hybrid model. Gaussian Naïve Bayes, gradient boosting, and decision tree classifier are used as a base classifier, and the random forest classifier is used as a meta-classifier in the proposed hybrid model. The objective of this study is to evaluate the best machine learning classification techniques and identify the best-used machine learning classifier in terms of accuracy. This provides a solution for overfitting and achieves the highest accuracy. It also highlights some of the challenges that affect the result of better performance. In this study, we critically review the existing available machine learning classification techniques. We evaluate in terms of accuracy, and a comprehensive analytical evaluation of the related work is presented with a tabular system. In implementation, we have used the top four models and built a hybrid model using UCI chronic kidney disease dataset for prediction. Gradient boosting achieves around 99% accuracy, random forest achieves 98%, decision tree classifier achieves 96% accuracy, and our proposed hybrid model performs best getting 100% accuracy on the same dataset. Some of the main machine learning algorithms used to predict the occurrence of CKD are Naïve Bayes, decision tree, K-nearest neighbor, random forest, support vector machine, LDA, GB, and neural network. In this study, we apply GB (gradient boosting), Gaussian Naïve Bayes, and decision tree along with random forest on the same set of features and compare the accuracy score.

Detection of Common Cold from Speech Signals using Deep Neural Network.

This paper presents a deep learning-based analysis and classification of cold speech observed when a person is diagnosed with the common cold. The common cold is a viral infectious disease that affects the throat and the nose. Since speech is produced by the vocal tract after linear filtering of excitation source information, during a common cold, its attributes are impacted by the throat and the nose. The proposed study attempts to develop a deep learning-based classification model that can accurately predict whether a person has a cold or not based on their speech. The common cold-related information is captured using Mel-frequency cepstral coefficients (MFCC) and linear predictive coding (LPC) from the speech signal. The data imbalance is handled using the sampling strategy, SMOTE–Tomek links. Then, utilizing MFCC and LPC features, a deep learning-based model is trained and then used to categorize cold speech. The performance of a deep learning-based method is compared to logistic regression, random forest, and gradient boosted tree classifiers. The proposed model is less complex and uses a smaller feature set while giving comparable results to other state-of-the-art methods. The proposed method gives an UAR of 67.71%, higher than the benchmark OpenSMILE SVM result of 64%. The study’s success will yield a noninvasive method for cold detection, which can further be extended to detect other speech-affecting pathologies.

Performance Evaluation of Data-driven Intelligent Algorithms for Big data Ecosystem.

Artificial intelligence, specifically machine learning, has been applied in a variety of methods by the research group to transform several data sources into valuable facts and understanding, allowing for superior pattern identification skills. Machine learning algorithms on huge and complicated data sets, computationally expensive on the other hand, processing requires hardware and logical resources, such as space, CPU, and memory. As the amount of data created daily reaches quintillion bytes, A complex big data infrastructure becomes more and more relevant. Apache Spark Machine learning library (ML-lib) is a famous platform used for big data analysis, it includes several useful features for machine learning applications, involving regression, classification, and dimension reduction, as well as clustering and features extraction. In this contribution, we consider Apache Spark ML-lib as a computationally independent machine learning library, which is open-source, distributed, scalable, and platform. We have evaluated and compared several ML algorithms to analyze the platform’s qualities, compared Apache Spark ML-lib against Rapid Miner and Sklearn, which are two additional Big data and machine learning processing platforms. Logistic Classifier (LC), Decision Tree Classifier (DTc), Random Forest Classifier (RFC), and Gradient Boosted Tree Classifier (GBTC) are four machine learning algorithms that are compared across platforms. In addition, we have tested general regression methods such as Linear Regressor (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Gradient Boosted Tree Regressor (GBTR) on SUSY and Higgs datasets. Moreover, We have evaluated the unsupervised learning methods like K-means and Gaussian Mixer Models on the data set SUSY and Hepmass to determine the robustness of PySpark, in comparison with the classification and regression models. We used ”SUSY,” ”HIGGS,” ”BANK,” and ”HEPMASS” dataset from the UCI data repository. We also talk about recent developments in the research into Big Data machines and provide future research directions.

Real-time internet of medical things framework for early detection of Covid-19.

The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000-2019) based on LUCAS, CORINE and GLAD Landsat.

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.

Automated Detection of Caffeinated Coffee-Induced Short-Term Effects on ECG Signals Using EMD, DWT, and WPD.

The effect of coffee (caffeinated) on electro-cardiac activity is not yet sufficiently researched. In the current study, the occurrence of coffee-induced short-term changes in electrocardiogram (ECG) signals was examined. Further, a machine learning model that can efficiently detect coffee-induced alterations in cardiac activity is proposed. The ECG signals were decomposed using three different joint time–frequency decomposition methods: empirical mode decomposition, discrete wavelet transforms, and wavelet packet decomposition with varying decomposition parameters. Various statistical and entropy-based features were computed from the decomposed coefficients. The statistical significance of these features was computed using Wilcoxon’s signed-rank (WSR) test for significance testing. The results of the WSR tests infer a significant change in many of these parameters after the consumption of coffee (caffeinated). Further, the analysis of the frequency bands of the decomposed coefficients reveals that most of the significant change was localized in the lower frequency band (<22.5 Hz). Herein, the performance of nine machine learning models is compared and a gradient-boosted tree classifier is proposed as the best model. The results suggest that the gradient-boosted tree (GBT) model that was developed using a db2 mother wavelet at level 2 decomposition shows the highest mean classification accuracy of 78%. The outcome of the current study will open up new possibilities in detecting the effects of drugs, various food products, and alcohol on cardiac functionality.

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’ performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees’ performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by “KF” with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.