Application Of Machine Learning Models Research Articles

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Application of Machine Learning in the field of Heart Disease Prediction and its Accuracy Study

The application of machine learning in the healthcare sector is a prominent area of research presently. Among these, there is significant potential in employing machine learning for predicting and treating heart disease. Hence, this study aims to explore the accuracy and application of machine learning models in predictive diagnosis and treatment of heart disease. This paper first presents commonly used machine learning algorithms within relevant fields, followed by gathering data and instances showcasing the use of machine learning in predicting and treating heart disease. Through data analysis, this study summarizes current cutting-edge development directions, trends, as well as the integration of machine learning algorithms into real medical processes. Finally, it concludes by summarizing the existing prospects and challenges faced by machine learning in predicting and treating heart disease. This paper can serve as a valuable source of inspiration and reference for researchers involved in related fields concerning heart disease prediction and treatment.

Machine learning models for predicting geomagnetic storms across five solar cycles using Dst index and heliospheric variables

This study aims to improve the understanding of geomagnetic storms by utilizing machine learning models and analyzing several heliophysical variables, such as the interplanetary magnetic field, proton density, solar wind speed, and proton temperature. Rather than relying on traditional correlation-based methods, we employ advanced machine learning techniques to examine the complex relationships between these factors and geomagnetic storms. Our analysis covers a large dataset spanning six solar cycles, including the current 25th cycle, to provide comprehensive insights into the dynamics of these storms.Our study highlights the significance of the interplanetary magnetic field as a key predictor of geomagnetic storms, challenging previous beliefs that primarily focused on sunspot activity. By using high-resolution data, we uncover new patterns and provide a more detailed analysis of the factors influencing geomagnetic storms. We emphasize the importance of considering a range of heliophysical variables, such as proton temperature and flow pressure, which offer new insights into the complex dynamics driving these storm events.The application of machine learning models, particularly Random Forest and Gradient Boosting, demonstrated superior predictive accuracy compared to traditional methods. Our results reveal that the Dst-index MIN, scalar B, and alpha/proton ratio are among the most influential factors, accounting for a significant portion of the prediction model’s accuracy. These findings underscore the utility of machine learning in identifying critical drivers of geomagnetic activity and enhancing forecast precision.Additionally, our research underscores the need for comprehensive models that can accurately predict geomagnetic storms by integrating various data sources. This machine learning approach not only improves predictive accuracy but also enhances our understanding of the underlying mechanisms of space weather. The insights gained from this study have important implications for both scientific research and practical applications, such as improving early warning systems for geomagnetic storms and mitigating their potential impacts on Earth.

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imaging may contain information that can be used to predict the first-pass effect. Recently, applications of machine learning models have shown promising results in predicting recanalization outcomes, albeit requiring manual segmentation. In this study, we sought to construct completely automated methods using deep learning to predict the first-pass effect from pretreatment CT and MR imaging. Our models were developed and evaluated using a cohort of 326 patients who underwent endovascular thrombectomy at UCLA Ronald Reagan Medical Center from 2014 to 2021. We designed a hybrid transformer model with nonlocal and cross-attention modules to predict the first-pass effect on MR imaging and CT series. The proposed method achieved a mean 0.8506 (SD, 0.0712) for cross-validation receiver operating characteristic area under the curve (ROC-AUC) on MR imaging and 0.8719 (SD, 0.0831) for cross-validation ROC-AUC on CT. When evaluated on the prospective test sets, our proposed model achieved a mean ROC-AUC of 0.7967 (SD, 0.0335) with a mean sensitivity of 0.7286 (SD, 0.1849) and specificity of 0.8462 (SD, 0.1216) for MR imaging and a mean ROC-AUC of 0.8051 (SD, 0.0377) with a mean sensitivity of 0.8615 (SD, 0.1131) and specificity 0.7500 (SD, 0.1054) for CT, respectively, representing the first classification of the first-pass effect from MR imaging alone and the first automated first-pass effect classification method in CT. Results illustrate that both nonperfusion MR imaging and CT from admission contain signals that can predict a successful first-pass effect following endovascular thrombectomy using our deep learning methods without requiring time-intensive manual segmentation.

Using machine learning to classify the immunosuppressive activity of per- and polyfluoroalkyl substances

Per- and polyfluoroalkyl substances (PFASs), one of the persistent organic pollutants, have immunosuppressive effects. The evaluation of this effect has been the focus of regulatory toxicology. In this investigation, 146 PFASs (immunosuppressive or nonimmunosuppressive) and corresponding concentration gradients were collected from literature, and their structures were characterized by using Dragon descriptors. Feature importance analysis and stepwise feature elimination are used for feature selection. Three machine learning (ML) methods, namely Random Forest (RF), Extreme Gradient Boosting Machine (XGB), and Categorical Boosting Machine (CB), were utilized for model development. The model interpretability was explored by feature importance analysis and correlation analysis. The findings indicated that the three models developed have exhibited excellent performance. Among them, the best-performing RF model has an average AUC score of 0.9720 for the testing set. The results of the feature importance analysis demonstrated that concentration, SpPosA_X, IVDE, R2s, and SIC2 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. In conclusion, this study is the first application of ML models to investigate the immunosuppressive activity of PFASs. The variables used in the models can help understand the mechanism of the immunosuppressive activity of PFASs, allow researchers to more effectively assess the immunosuppressive potential of a large number of PFASs, and thus better guide environmental and health risk assessment efforts.

Forecasting infection fatality rate of COVID-19: measuring the efficiency of several hybrid models

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis. Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural Time Series). Results. To assess the performance of these models, we employ key evaluation metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other model ARIMA, BATS, RF and SVM. Conclusion. This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.

Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models

With the development of green, low-carbon, and sustainable economic systems, the issues of high pollution and energy consumption in construction materials have become increasingly prominent. This study focuses on adopting one-part geopolymer (OPG) in soil stabilization for underground engineering, which exhibits environmental and low-carbon advantages. The unconfined compressive strength (UCS) serves as a crucial parameter for assessing stabilized soil’s performance. However, it is necessary to conduct a large number of experiments, inducing high costs and time consumption. In this study, one multiple linear regression model, one Decision Tree (DT) model, five ensemble machine learning (ML) models (i.e. Random Forest [RF], Extra Tree [ET], Gradient Boosting [GB], Gradient Boosting Decision Tree [GBDT], and Extreme Gradient Boosting [XGBoost]), and hybrid models of those single ensemble models with Particle Swarm Optimization (PSO) (i.e. PSO-RF, PSO-ET, PSO-GB, PSO-GBDT, and PSO-XGBoost) were adopted and compared to achieve better prediction on the UCS of the OPG-stabilized soil. Furthermore, the interpretable method including SHAP and PDP (1D and 2D), was employed to investigate the precise mechanisms by which input parameters influenced the output label. The results revealed that the multiple linear regression model delivered the lowest accuracy, and PSO-XGBoost and PSO-ET exhibited the best performance on the prediction of the UCS with R2 value of 0.9964 and 0.9928, respectively. In addition, Curing time exerted the most significant impact on the UCS, followed by FA/GGBFS, Molarity, Water/Binder, and NaOH/Precursor. Compared to the SHAP method, the PDP offered a more intuitive approach to reveal the relationship between the inputs and output. The outcome of the study shed new light on the application of ML models in the prediction of the OPG-stabilized soil’s performance in underground engineering.

An Investigation of Machine Learning Applications in the Financial Fraud Detection

Financial fraud presents itself in various forms, and it often involves intricate financial transaction networks, making it challenging to detect the perpetrators and identify the characteristics of the fraud. In recent years, machine learning has gained widespread application within the financial sector. Therefore, various financial fraud detection models have been developed based on diverse machine learning methodologies. In the method part, this paper provides an overview of the machine learning process and then discusses the application of machine learning models in various financial fraud scenarios. Financial fraud in the insurance field has been further refined into automobile and medical insurance fraud. In automobile insurance fraud detection, some studies applied implicit Naive Bayes Model to analyze observable features and estimate hidden variables, and some studies used resampler to solve data imbalance and adopted 7 kinds of machine learning models for analysis. In health insurance fraud detection, many studies train medical data with multiple models, including AdaBoost, Logistic Regression and Support Vector Machine. In credit card fraud detection, studies use five algorithms, including random forest and decision tree et al., and some construct a model combining Decision Tree (DT) and Logistic Regression (LR). In bank fraud detection, some studies introduce Value-at-Risk to combine it with machine learning algorithms, and some studies propose a decentralized model training method based on federated learning. In the discussion section, this paper addresses the current limitations of the research, such as poor interpretability, uneven distribution of data sets, and issues related to customer privacy, and proposes corresponding solutions.

Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea

Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).

Application of machine learning models for property prediction to targeted protein degraders

Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs’ property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs’ design, to potentially accelerate drug discovery.

Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts

Pesticide pollution has been posing a significant risk to human and ecosystems, and photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as a powerful method for modeling complex water treatment processes. For the first time, this study developed novel ML models that improved the estimation of the photocatalytic degradation of various pesticides using ZnO-based photocatalysts. The input parameters encompassed the source of light, mass proportion of dopants to Zn, initial pesticide concentration (C0), pH of the solution, catalyst dosage and irradiation time. Additionally, physicochemical properties such as the molecular weight of the dopants and pesticides, as well as the water solubility of both dopants and pesticides, were considered. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. Four ML models including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), and coupled simulated annealing-least squares support vector machine (CSA-LSSVM) were developed. In comparison, RBF showed the best accuracy of modeling among all models, with the highest determination coefficient (R2) of 0.978 and average absolute relative deviation (AARD) of 4.80%. RBF model was effective in estimating the photocatalytic degradation of pesticides except for 2-chlorophenol, triclopyr and lambda-cyhalothrin, where CSA-LSSVM model demonstrated superior performance. Dichlorvos was completely degraded by ZnO photocatalyst under visible light. The sensitivity analysis by relevancy factor exhibited that light irradiation time and initial pesticide concentration were the most important parameters influencing photocatalytic degradation of pesticides positively and negatively, respectively. The new ML models provide a powerful tool for predicting pesticide degradation in wastewater treatment, which will reduce photochemical experiments and promote sustainable development.

Machine Learning in Cybersecurity: Advanced Detection and Classification Techniques for Network Traffic Environments

In the digital age, the integrity of business operations and the smoothness of their execution heavily depend on cybersecurity and network efficiency. The need for robust solutions to prevent cyber threats and enhance network functionality has never been more critical. This research aims to utilize machine learning (ML) techniques for the meticulous analysis of network traffic, with the dual goals of detecting anomalies and categorizing network activities to bolster security and performance. Employing a detailed methodology, this study begins with data preparation and progresses through to the deployment of advanced ML models, including logistic regression, decision trees, and ensemble learning techniques. This approach ensures the accuracy of the analysis and facilitates a nuanced understanding of network dynamics. Our findings indicate a notable enhancement in identifying network inefficiencies and in the more accurate classification of network traffic. The application of ML models significantly reduces network delays and bottlenecks by providing a strong defence strategy against cyber threats and network shortcomings, thereby improving user satisfaction, and boosting the organizational reputation as a secure and effective service layer. Conclusively, the research highlights the pivotal role of machine learning in network traffic analysis, offering innovative insights and fresh perspectives on anomaly detection and the identification of malicious activities. It lays a foundation for future explorations and acts as an evaluation benchmark in the fields of cybersecurity and network management.

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability.

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash-Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation-based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Assessment of meteorological parameters in predicting seasonal temperature of Dhaka city using ANN

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011–2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.

Anomaly Detection in Blockchain Transactions: A Machine Learning Approach within the Open Metaverse

This study investigates the application of machine learning models for anomaly detection and fraud analysis in blockchain transactions within the Open Metaverse, amid the growing complexity of digital transactions in virtual spaces. Utilizing a dataset of 78,600 transactions that reflect a broad spectrum of user behaviors and transaction types, we evaluated the efficacy of several predictive models, including RandomForest, LinearRegression, SVR, DecisionTree, KNeighbors, GradientBoosting, AdaBoost, Bagging, XGB, and LightGBM, based on their Mean Cross-Validation Mean Squared Error (Mean CV MSE). Our analysis revealed that ensemble methods, particularly RandomForest and Bagging, demonstrated superior performance with Mean CV MSEs of -0.00445 and -0.00415, respectively, thereby highlighting their robustness in the complex transaction dataset. In contrast, LinearRegression and SVR were among the least effective, with Mean CV MSEs of -224.67 and -468.57, indicating a potential misalignment with the dataset's characteristics. This research underlines the importance of selecting appropriate machine learning strategies in the context of blockchain transactions within the Open Metaverse, showcasing the need for advanced, adaptable approaches. The findings contribute significantly to the financial technology field, particularly in enhancing security and integrity within virtual economic systems, and advocate for a nuanced approach to anomaly detection and fraud analysis in blockchain environments.

Study of recycled concrete properties and prediction using machine learning methods

This paper is divided into two parts, with the first part examining the influence of recycled brick aggregate (BA) ratios on physical properties of blended coarse aggregate (BNA). These properties include apparent density (SSD), water absorption (WA), and crushing value (CV). The compressive strength of recycled concrete (RAC) prepared with BNA was tested. The tested results show that when SSD decreases from 2700 kg/m3 to 2000 kg/m3, the compressive strength of the corresponding RAC decreases by 40.5 %. Moreover, when the WA increases from 5 % to 10 %, the compressive strength of the concrete decreases by 15.9 %. Furthermore, as the CV rises from 9 % to 37 %, the compressive strength of the concrete decreases by 38.4 %. And the second part explores the application of machine learning models to predict the concrete compressive strength, incorporating parameters such as BA replacement rate, SSD, WA, CV, water-to-cement ratio (W/C), and blended coarse aggregate to cement ratio (BNA/C). To improve model accuracy, we integrated data from our own experimental tests with an additional 128 datasets sourced from literature, resulting in a combined dataset of 133 entries for training and testing the machine learning models. Three machine learning models were proposed and compared: the BP neural network, GA-optimized BP (GA-BP) neural network, and convolutional neural network (CNN) model. Compared to the other two models, the CNN model exhibits the highest prediction accuracy and generalization ability, as evidence by its training accuracy of 0.969 and test accuracy of 0.927. The CNN model proved capable of facilitating intelligent RAC mix design in engineering applications. A case study was presented to illustrate the utilization of the CNN model in predicting the W/C and BNA/C ratios for 11 different BA ratios, ensuring the targeted RAC strength is achieved with specific type of BNA.

The ECG Signal Monitoring System Using Machine Learning Methods and LoRa Technology

An electrocardiogram (ECG) is the first step in diagnosing heart disease. Heart rhythm abnormalities are among the early signs of heart disease, which can contribute to a patient’s heart attack, stroke, or sudden death. The importance of the ECGs has increased with the development of technologies based on machine learning and remote monitoring of vital signs. In particular, early detection of arrhythmias is of great importance when it comes to diagnosing a patient with heart disease. This is made possible through recognizing and classifying pathological patterns in the ECG signal. This paper presents a system for mobile monitoring of ECG signals enriched with the results of the study of the application of machine learning models from the group of Tree-based ML techniques and Neural Networks in the context of heart disease classification. The research was carried out through the use of the publicly available PTB-XL database of the ECG signals. The results were analyzed in the context of classification accuracy for 2, 5 and 15 classes of heart disease. Moreover, a novelty in the work is the proposal of machine learning techniques and architectures neural networks, which, have been selected to be applicable to IoT devices. It has been proven that the proposed solution can run in real time on IoT devices.

Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study

Ultra-high-performance geopolymer concrete (UHPGC) is a new category of traditional UHPC developed to meet the desire for ultra-high-strength and green building materials. In the current study, random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) are used to forecast the compressive strength (CS) of UHPGC. Firstly, the findings of the 113 CS tests available in the previous studies were extracted. Twelve feature variables, including GGBS, silica fume, fly ash, and rice husk ash contents as precursors, the Na2SiO3, NaOH, KOH, and extra water content, polypropylene fiber, steel fiber, liquid-to-binder (L/B) ratio, and curing temperature, were investigated. After analyzing the extracted data, it was found that there were more mixtures of steel fiber-based UHPGCs and synthetic fibers compared to mixtures without fibers. This may reduce the accuracy and comprehensiveness of the predictive models used. To address this issue, several experiments were designed, performed, and tested. Overall, the dataset of 128 CS results was used to develop the machine learning (ML) models. The findings validate the effectiveness of the RF, SVR, and XGB models in accurately predicting the strength of the UHPGC, as constructed by their excellent predictive accuracy (R2 > 0.84). The XGB model performance is superior to the RF and SVR models. The feature importance analysis determined that the steel fiber content and L/B ratio were the top two elements that might profoundly impact the CS. Additionally, NaOH and silica fume also have a positive correlation with CS. Conversely, the extra water and percentage of GGBS exhibit a low correlation with the CS. Through the application of ML models, this study not only ascertains the significance of algorithms including RF, SVR, and XGB in precisely forecasting the CS of UHPGC, but also reveals essential understandings regarding the importance of steel fiber content, L/B ratio, and various other pivotal variables, consequently facilitating the development of refined formulations and improved functionalities in eco-friendly construction materials.

Application of Machine Learning Models for Improving Discharge Prediction in Ungauged Watershed: A Case Study in East DuPage, Illinois

Application of Machine Learning Models for Improving Discharge Prediction in Ungauged Watershed: A Case Study in East DuPage, Illinois

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.