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Two predominant methodologies in forecasting temporal processes include traditional time series models and machine learning methods. This paper investigates the impact of time series cross‐validation (TSCV) on both approaches in the context of a case study predicting the incidence of COVID‐19 based on wastewater data. The TSCV framework outlined in the paper begins by engineering interpretable features hypothesized as potential predictors of COVID‐19 incidence. Feature selection and hyperparameter tuning are then utilized with TSCV to identify the best features and hyperparameters for optimal model performance given a specific forecast horizon. While evidence supporting the utility of TSCV for auto‐regressive integrated moving average model with exogenous variables (TS‐ARIMAX) forecasts is lacking in this study, such an approach proves advantageous for gradient boosting machine forecasts (TS‐GBM). In Wyoming, for instance, TS‐GBM had a 34.9% improvement compared to naïve predictions, whereas GBM without TSCV only had a 15.6% improvement. However, TSCV also enhances interpretability for both TS‐ARIMAX and TS‐GBM models as this approach selects specific features, such as lagged values of COVID‐19 cases, based on forecast performance and forecast length. Future research should work to explore the influence of stationarity and model averaging on the performance of TSCV in forecasting applications. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and Objectives: Fumonisin (FUM), a secondary metabolite from Fusarium spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois‐centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa‐centric model. Corn samples (n = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (n = 89). Findings: Applying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois‐ and Iowa‐centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois‐ and Iowa‐centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%. Conclusions: FUM‐GBM analyses determined the top influential predictor for the Illinois‐centric model was satellite‐acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa‐centric model was precipitation (PRCP) in October. Significance and Novelty: Results indicate that meteorological and agronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn. [ABSTRACT FROM AUTHOR]

When utilizing the Crop Water Stress Index (CWSI), the most critical factor is accurately measuring canopy temperature, which is typically done using infrared sensors and imaging cameras. In this study, however, we aimed to develop a machine learning model capable of predicting leaf temperature based on environmental data, without relying on sensors, for calculating CWSI. The data underwent preprocessing to remove outliers and missing values. The number of training data points for each factor was 307,924. After data preprocessing, a Pearson correlation analysis (bivariate correlation coefficient) was conducted to select the training data for model operation. The relationship between leaf temperature and air temperature showed a strong positive correlation of 0.928 (p < 0.01). Solar radiation and relative humidity were also found to have high correlations. However, wind speed and soil moisture tension showed very low correlations with leaf temperature and were excluded from the model operation. The Decision Tree, Random Forest, and Gradient Boosting models were selected, and each model was evaluated using RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Squared Error), and R2 (coefficient of determination). The evaluation results showed that the Gradient Boosting model had a high R2 (0.97) and low RMSE (0.88) and MAE (0.54), making it the most suitable model for predicting leaf temperature. Through the leaf temperature prediction model developed in this study, labor and costs associated with sensors can be reduced, and by applying it to real agricultural settings, it can improve crop quality and enhance the sustainability of agriculture. [ABSTRACT FROM AUTHOR]

The increase in population is a huge threat to mankind and especially in cities, it is difficult to manage energy consumption, resource allocation, and maintaining security. This leads to large-scale urbanization and the formation of new cities with the integration of new technologies. The creation of smart cities could be a preferred choice to manage a huge population in a small area. This could be done with the usage of the Internet of Things (IoT) which employs different sensors to perform various tasks like data collection, traffic control, and weather detection. The data collected with these sensors are stored in the cloud and used for various applications. The large amount of data present in the IoT and its transparent nature attract various attackers. This leads to an increasing number of cyber-attacks in IoT, generating security issues to the data confidentiality for the individuals in the smart cities. Hence there is a need for the detection of these attacks commonly referred to as intrusions. The mechanism built to detect the intrusions is known as the Intrusion Detection System (IDS). Machine Learning (ML) is the commonly used technique in the creation of IDS as it shows superior performance in detection and classification works. This paper proposes an Ensemble Gradient Random forest-based Leopard Seal Search Optimization (EGR-LSS) algorithm. The hyperparameters of the proposed system are optimized using the Leopard Seal Search optimization algorithm. The comprehensive experiments are conducted to assess the proposed EGR-LSS model's detection efficacy utilizing the CICIDS2017 dataset. The proposed model outperformed the state-of-the-art techniques including CNN, AI-BC, and NB, and gained accuracy of 98.75%, recall of 97.4%, precision of 98.7%, and F1-score of 96.9% respectively. Overall, the proposed model provided reliable strong cyber threat detection performance and it increased the prediction speed by significantly reducing the risk prediction time. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructures, exploiting vulnerabilities within existing security protocols and disrupting the seamless availability of online services. The intricate interconnections of nodes within computer networks contribute to the dynamic structure of this environment, complicating efforts to establish a secure and productive user experience. Effectively mitigating DDoS attacks in this complex networked setting remains a challenge. While current strategies primarily rely on anomaly detection and signature-based techniques, utilizing statistical analysis and predefined patterns to identify and thwart attacks, none have consistently demonstrated efficacy or reliability. Consequently, there is a compelling need for advancements in security mechanisms to address DDoS threats more effectively. This research introduces an innovative and highly efficient approach that incorporates various classification algorithms, including Random Forest, Decision Tree, Gradient Boosting, Linear SVM, Logistics, K-nearest neighbors (KNN), and AdaBoost, for DDoS attack detection. The performance of these machine learning classifiers is evaluated using key metrics such as accuracy, recall, F1-score, and precision. Remarkably, experimental results reveal outstanding accuracy rates, with Random Forest achieving the highest accuracy in detecting attacks. Additionally, a genetic algorithm is employed to select optimal features from the dataset, further enhancing the performance of the classifiers. This results in a notable 25% increase in accuracy, surpassing AdaBoost and Logistics, with K-nearest neighbors emerging as the top performer in terms of accuracy. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOxs) are some of the most important hazardous air pollutants from industry. In China, the annual NOx emission in the waste gas of industrial sources is about 8.957 million tons, while power plants remain the largest anthropogenic source of NOx emissions, and the precise control of NOx in power plants is crucial. However, due to inherent issues with measurement and pipelines in coal-fired power plants, there is typically a delay of about three minutes in NOx measurements, bringing mismatch between its control and measurement. Measuring delays in NOx from power plants can lead to excessive ammonia injection or failure to meet environmental standards for NOx emissions. To address the issue of NOx measurement delays, this study introduced a hybrid boosting model suitable for on-site implementation. The model could serve as a feedforward signal in SCR control, compensating for NOx measurement delays and enabling precise ammonia injection for accurate denitrification in power plants. The model combines generation mechanism and data-driven approaches, enhancing its prediction accuracy through the categorization of time-series data into linear, nonlinear, and exogenous regression components. In this study, a time-based method was proposed for analyzing the correlations between variables in denitration systems and NOx concentrations. This study also introduced a new evaluation indicator, part of R2 (PR2), which focused on the prediction effect at turning points. Finally, the proposed model was applied to actual data from a 330 MW power plant, showing excellent predictive accuracy, particularly for one-minute forecasts. For 3 min prediction, compared to predictions made by ARIMA, the R-squared (R2) and PR2 were increased by 3.6% and 30.6%, respectively, and the mean absolute error (MAE) and mean absolute percentage error (MAPE) were decreased by 9.4% and 9.1%, respectively. These results confirmed the accuracy and applicability of the integrated model for on-site implementation as a 3 min advanced prediction soft sensor in power plants. [ABSTRACT FROM AUTHOR]

The research focuses on incorporating microgrids into engineering curricula for achieving voltage stability in today's power systems. This helps to meet the increasing demand for engineers to integrate distributed power generation and renewable energy sources. Some limitations of the current literature include the absence of models outlining approaches to microgrid education and limited insight into teaching strategies for electrical power systems. The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study's objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. The major findings identified exam score as the most significant determiner of student performance (weight ≈ 0.40). Based on the clustering analysis, it was found that microgrid systems can be grouped into four operational states. It was also seen that linear regression models were highly accurate and better than other highly complex models, like Decision Tree, with a model accuracy of R2 ≈ 0.4. One of the study's major strengths is the potential impact of the proposed framework for integrating microgrids into engineering education on the professional training of engineers. This framework, based on theoretical knowledge and practical experience as well as on developing advanced analytical skills, can significantly enhance the professional training of engineers to deal with the complexities of contemporary power systems, including microgrids and sustainable energy progress. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's. The significant number of individuals afflicted with this illness makes it essential to develop a method to diagnose the conditions in their early phases. PD is typically identified from motor symptoms or via other Neuroimaging techniques. Expensive, time-consuming, and unavailable to the general public, these methods are not very accurate. Another issue to be addressed is the black-box nature of machine learning methods that needs interpretation. These issues encourage us to develop a novel technique using Shapley additive explanations (SHAP) and Hard Voting Ensemble Method based on voice signals to diagnose PD more accurately. Another purpose of this study is to interpret the output of the model and determine the most important features in diagnosing PD. The present article uses Pearson Correlation Coefficients to understand the relationship between input features and the output. Input features with high correlation are selected and then classified by the Extreme Gradient Boosting, Light Gradient Boosting Machine, Gradient Boosting, and Bagging. Moreover, the weights in Hard Voting Ensemble Method are determined based on the performance of the mentioned classifiers. At the final stage, it uses SHAP to determine the most important features in PD diagnosis. The effectiveness of the proposed method is validated using 'Parkinson Dataset with Replicated Acoustic Features' from the UCI machine learning repository. It has achieved an accuracy of 85.42%. The findings demonstrate that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases. [ABSTRACT FROM AUTHOR]

This study implements a set of machine learning algorithms to building material science, which predict the compressive strength of zeolite and diatomite substituting concrete mixes in sodium chloride solution. Particularly, Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting algorithms are exploited and their optimal parameters are tuned. In the training and testing of these models, 28 day, 56 day, and 90 day compressive strength observations of 63 samples of 7 different concrete mixtures substituting Portland cement, zeolite, diatomite, zeolite + diatomite were used. Consequently, compressive strength experimentation results and machine learning predictions were compared through statistical methods such as RMSE, MAPE, and R2. Results denote that the prediction performance of machine learning is improving with tuned models. Particularly, RMSE, MAPE, R2 scores of Categorical Boosting are, respectively, 1.15, 1.45%, and 98.03% after parameter tuning design. The results denote that presented machine learning model can provide an advantage in the cost and duration of the compressive strength experiments. [ABSTRACT FROM AUTHOR]

It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC). A case-control study utilizing data on PHC visits for sex-, age, and PHC-matched controls. Stochastic gradient boosting was used to construct a model for predicting cases of diabetes based on diagnostic codes from PHC consultations during the year before index (diagnosis) date and number of consultations. Variable importance was estimated using the normalized relative influence (NRI) score. Risks of having diabetes were calculated using odds ratios of marginal effects (OR ME). Four groups by age and sex were studied, age-groups 35–64 years and ≥ 65 years in men and women, respectively. The most important predictive factors were hypertension with NRI 21.4–29.7 %, and obesity 4.8–15.2 %. The NRI for other top ten diagnoses and administrative codes generally ranged 1.0–4.2 %. Our data confirm the known risk patterns for predicting a new diagnosis of diabetes, and the need to test blood glucose frequently. To assess the full potential of ML for risk prediction purposes in clinical practice, future studies could include clinical data on life-style patterns, laboratory tests and prescribed medication. • We present a model that can classify patients with newly diagnosed diabetes based on data that are present in patient records. • Previously known diagnoses can predict the risk of a future diabetes diagnosis. • Many diagnoses that are informative for the AI are discovered and may be used to identify individuals with an increased risk of diabetes. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a chronic malady that is measured to be life-threatening. Diabetes Mellitus type 2 is accounts for the majority of increased risk of Neuropathy and Nephropathy. Utilizing machine learning (ML) predictive model algorithms can aid in the management of diabetes mellitus type 2 (T2DM) by lowering the risk of neuropathy and nephropathy problems in early stages. The increased screening can be avoided the loss of life our study creates predictive model to forecast the diabetes through Gradient Boosting, SVM and Random forest algorithms. The development and work by comparing the performance accomplished using various parameters and algorithms, it originates that by using a selected number of features, we can still build sufficient results. [ABSTRACT FROM AUTHOR]

Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (Cc), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (w), initial void ratio (e0), and specific gravity (Gs), plays a vital role in understanding soil behavior. This study employs machine learning algorithms—the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)—to predict the Cc values based on a dataset comprising 915 samples. The dataset includes LL, PL, W, PI, Gs, and e0 as the inputs, with Cc as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R2 of 0.925 and a testing R2 of 0.930 with the input combination [w, PL, LL, PI, e0, Gs]. The RFR model follows closely, with a training R2 of 0.970 and a testing R2 of 0.926 using the same input combination. The ABR model records a training R2 of 0.847 and a testing R2 of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the Cc value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing. [ABSTRACT FROM AUTHOR]

Complexity and resource constraints accompany urban growth. According to UN figures, cities currently use 75% of the global energy, with 70% of the greenhouse gas emissions being mostly generated from transportation and residence buildings. Furthermore, city residents are susceptible to the consequences of climate change. Therefore, a feasibility study for the possibility of implementing a smart city was conducted in this paper. The results show that the current situation of the cities is/renders them far from being smart, while the environmental aspect needs to be controlled by the use of IoT sensors. The utilized Hyperd algorithm gave highly accurate prediction results. [ABSTRACT FROM AUTHOR]

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and tensile strength of rebars, concrete compressive strength and cover, embedment length, and test method. The dataset contains two test methods: pullout tests and beam tests. Four types of rebar, including carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), basalt, and steel rebars, were considered. The boosting ML models applied in this study include AdaBoost, CatBoost, Gradient Boosting, XGBoost, and Hist Gradient Boosting. After hyperparameter tuning, these models demonstrated significant improvements in predictive accuracy, with XGBoost achieving the highest R2 score of 0.95 and the lowest Root Mean Square Error (RMSE) of 2.21. Shapley values analysis revealed that tensile strength, elastic modulus, and embedment length are the most critical factors influencing bond strength. The findings offer valuable insights for applying ML models in predicting bond strength in FRP-reinforced UHPC, providing a practical tool for structural engineering. [ABSTRACT FROM AUTHOR]

This study introduces machine learning (ML) algorithms to predict hydrogen (H 2) thermodynamic properties for geological storage, focusing on its mixtures with natural gas, nitrogen (N 2), and carbon dioxide (CO 2). Employing 1167 data samples, this research utilizes multiple linear regression (MLR), random forest (RF), gradient boosting (GB), and decision tree (DT) models, enhanced by sensitivity analysis for feature engineering. GB model's superior accuracy and efficiency over conventional statistical linear regression methods. The finding reveals that the GB model achieves superior performance, with an R2 value of 0.9998 for thermal capacity and the lowest mean absolute error (MAE)/root mean square error (RMSE) across all H 2 properties—density (1.51%, 2.06 kg/m3), viscosity (0.000078%, 0.000117 cp), thermal conductivity (0.000427%, 0.000849 W/m.K), and thermal capacity (8.79%, 26.06 J/g.K). Moreover, this ML-based approach not only demonstrates remarkable accuracy and efficiency but also suggests the potential of smart paradigms to further enhance the safety and transportability of underground hydrogen storage projects. Ultimately, this work will help the reservoir simulation at field scale to be more robust and reliable. [Display omitted] • Machine learning employed in the prediction of hydrogen's thermodynamic properties. • Four advanced ML-based models constructed by variables such as pressure, temperature, and hydrogen fraction. • Gradient boosting algorithm comparatively showed the highest accuracies. • ML achieved the lowest MAE/RMSE values for viscosity 0.000078/0.000117. [ABSTRACT FROM AUTHOR]

The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R2 and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis. [ABSTRACT FROM AUTHOR]

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models' average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%. [ABSTRACT FROM AUTHOR]

Forecasting monthly and seasonal rainfall is very important from a hydrological point of view, including the water resources management, as well as from climatic and atmospheric hazards perspective. Monthly and seasonal forecasts are widely used in sectors such as energy and agriculture. On the other hand, it has been proven that large-scale atmospheric teleconnections and indices have an effect on the atmospheric conditions of different parts of the earth. Machine learning methods are widely used to predict different atmospheric variables. The reason for using these methods is the high modeling ability of these algorithms when faced with a large amount of data. At the same time, these algorithms have many other capabilities such as the ability to model uncertainty. In this article, three machine learning methods, namely artificial neural network, gradient boosting and random forest are used for prediction. The performance of these algorithms will be compared with each other in monthly and seasonal forecasting scales. The studied area is a part of northwestern Iran, including the area of Lake Urmia basin. Three evaluation criteria, namely Nash-Sutcliffe, correlation coefficient and root mean square error were used to evaluate the results. Moreover, by using the Nino 3.4, WP, and NAO indices, the regional average monthly rainfall in the region is predicted with the help of three mentioned machine learning methods. The reason for choosing these three indicators is their effectiveness on the rainfall of the studied area. Two monthly and seasonal modeling methods are compared. Since teleconnections may be effective on precipitation with a delay, different delays of zero to three months were considered for all three studied teleconnections and the best results were obtained. Three methods of machine learning, i.e., random forest, neural network and gradient boosting were used. In the monthly mode, the performance of the gradient boosting method (with a three-month delay for the WP index and the other two indices without delay) was better than the random forest method, and the neural network ranked last in terms of performance. In terms of estimating the extreme precipitation, the gradient boosting method has an acceptable performance. In seasonal modeling, for the three seasons of spring (March-April-May), autumn (September-October-November) and winter (December-January-February), the performance of random forest and for the summer season (June-July-August), the performance of the gradient boosting method were superior to other methods. [ABSTRACT FROM AUTHOR]

Soil nutrient content varies spatially across agricultural fields in hard-to-predict ways, particularly in floodplains with complex fluvial depositional history. Satellite reflectance data from the Sentinel-2 (S2) mission provides spatially continuous land reflectance data that can aid model development when used with point observations of nutrients. Reflectance from vegetation is assumed to obstruct land reflectance of bare soil, such that researchers have masked vegetation in models. We developed a routine for masking vegetation within Google Earth Engine (GEE) using Random Forest classification for iterative application to libraries of S2-images. Using gradient boosting, we then developed soil nutrient models for surface soils at a 250-ha agricultural site using S2 images. Soils were sampled at 2145 point locations to a 23-cm depth and analyzed for Ca, K, Mg, P, pH, S, and Zn. Results showed that masking vegetation improved model performance for models from subsets of the data (80% of samples used for model development, 20% validation), but full data sets did not require masking to achieve accuracy. Models of Ca, K, Mg, and S were successful (validation R2 > 0.60 to 0.96), but models for pH, P, and Zn failed. Bare soil composite images from S2 data are helpful in predicting soil fertility in low-relief floodplains. [ABSTRACT FROM AUTHOR]

Background: Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a quantitative structure–activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. Methods: The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew's correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models. Results: A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power. Conclusions: The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nitrogen is one of the most important macronutrients for crops, and, in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation of ≥0.7 were selected for subsequent use in random forest, gradient boosting, and artificial neural networks to determine their relationship with nitrogen levels measured in the laboratory. Random forest showed no relationship, yielding an R2 of zero; and gradient boosting and the classical method were similar with 0.7; whereas artificial neural networks yielded the best results with an R2 of 0.93. Thus, estimating nitrogen levels using this algorithm is reliable, by feeding it with data from the Modified Chlorophyll Absorption Ratio Index, Transformed Chlorophyll Absorption Reflectance Index, Modified Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index, and Transformed Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index [ABSTRACT FROM AUTHOR]

In the field of geomatics, artificial intelligence (AI) and especially machine learning (ML) are rapidly transforming the field of geomatics with respect to collecting, managing, and analyzing spatial data. Feature selection as a building block in ML is crucial because it directly impacts the performance and predictive power of a model by selecting the most critical variables and eliminating the redundant and irrelevant ones. Random forests have now been used for decades and allow for building models with high accuracy. However, finding the most expressive features from the dataset by selecting the most important features within random forests is still a challenging question. The often-used internal Gini importances of random forests are based on the amount of training examples that are divided by a feature but fail to acknowledge the magnitude of change in the target variable, leading to suboptimal selections. Shapley values are an established and unified framework for feature attribution, i.e., specifying how much each feature in a trained ML model contributes to the predictions for a given instance. Previous studies highlight the effectiveness of Shapley values for feature selection in real-world applications, while other research emphasizes certain theoretical limitations. This study provides an application-driven discussion of Shapley values for feature selection by first proposing four necessary conditions for a successful feature selection with Shapley values that are extracted from a multitude of critical research in the field. Given these valuable conditions, Shapley value feature selection is nevertheless a model averaging procedure by definition, where unimportant features can alter the final selection. Therefore, we additionally present Conditional Feature Selection (CFS) as a novel algorithm for performing feature selection that mitigates this problem and use it to evaluate the impact of model averaging in several real-world examples, covering the use of ML in geomatics. The results of this study show Shapley values as a good measure for feature selection when compared with Gini feature importances on four real-world examples, improving the RMSE by 5% when averaged over selections of all possible subset sizes. An even better selection can be achieved by CFS, improving on the Gini selection by approximately 7.5% in terms of RMSE. For random forests, Shapley value calculation can be performed in polynomial time, offering an advantage over the exponential runtime of CFS, building a trade-off to the lost accuracy in feature selection due to model averaging. [ABSTRACT FROM AUTHOR]

Featured Application: This study presents a novel approach to use ML models to estimate the hygroscopic properties of a natural fiber: Borassus fruit fibres (BNF). This knowledge is helpful to engineers designing and choosing materials for various applications in the building sector, as they can better understand how the material absorbs moisture. The ML models could potentially replace more expensive and time-consuming methods for testing these properties. This research focuses on the environmental-friendly production of Borassus fruit fibers (BNF), its characterization, and hygroscopic properties determination via Dynamic Vapor Sorption (DVS). The experimental results obtained from the hygroscopic behavior analysis were used to create a primary dataset to train and test Decision Tree Regression (DTR) and Gradient Boosting Regressor (GBR) models. The created primary dataset comprised 294 observations, from which 80% were used to train the models, and the remaining 20% were used for the testing of the two models. The models exhibited high accuracy, easy interpretability on the small-size dataset, and flexibility with regards to the nature of the relationship between the input and output variable. Both models successfully predicted the hygroscopic behavior with the Gradient Boosting Regressor outperforming Decision Tree Regression by indicating values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF, and values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF. This suggests that the Gradient Boosting Regressor illustrated the maximum accuracy. The outcomes can be utilized to provide an alternative for traditional methods, which can often be costly and time-consuming by improving the engineering properties of BNF. The models can be used in the construction sector to lower costs as they are able to pinpoint elements influencing the characteristics for specific applications to grasp its various properties through the prediction of its hygroscopic properties. [ABSTRACT FROM AUTHOR]

Physical exercise spaces emerged as popular facilities due to recognizing the significance of physical well-being. This study investigates the relationship among physiological responses, human body energy transfer modes, and indoor environmental conditions in influencing thermal comfort perception within indoor physical exercise space. Seven male participants engaged in a 30 min constant-work-rate cycling exercise and a 20 min resting period in a climatic chamber. The physiological and environmental responses were recorded during the experiments, and the body's energy transfer modes were calculated using the collected data. The dataset was prepared using the 2 min averages of the collected data and calculated parameters across the experiment phases, including the features of skin temperature, core temperature, skin relative humidity, heart rate, oxygen consumption, body's heat transfer rates through convection, radiation, evaporation, and respiration, net metabolic heat production rate (metabolic rate minus external work rate), indoor air temperature, indoor relative humidity, air velocity, and radiant temperature. Gradient boosting regressor (GBR) was selected as the analyzing method to estimate predicted mean vote (PMV) and thermal sensation vote (TSV) indices during exercise and resting periods using features determined in the study. Thus, the four GBR models were defined as PMV-Exercise, PMV-Resting, TSV-Exercise, and TSV-Resting. In order to optimize the models' performances, the hyperparameter tuning process was executed using the GridSearchCV method. A permutation feature importance analysis was performed, emphasizing the significance of net metabolic heat production rate (24.2%), radiant temperature (17.0%), and evaporative heat transfer rate (13.1%). According to the results, PMV-Exercise, PMV-Resting, and TSV-Resting GBR models performed better, while TSV-Exercise faced challenges in predicting exercise thermal sensations. Critically, this study addresses the need to understanding the interrelationship among physiological responses, environmental conditions, and human body energy transfer modes during both exercise and resting periods to optimize thermal comfort within indoor exercise spaces. The results of this study contribute to the operation of indoor gym environments to refine their indoor environmental parameters to optimize users' thermal comfort and well-being. The study is limited to a small sample size consisting solely of male participants, which may restrict the generalizability of the findings. Future research could explore personalized thermal comfort control systems and synergies between comfort optimization and energy efficiency in indoor exercise spaces. [ABSTRACT FROM AUTHOR]

This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model's predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model's forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model's prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen. [ABSTRACT FROM AUTHOR]

The food and beverage industry significantly impacts the global economy, subject to various influential factors. This study aims to develop an AI-powered model to enhance the understanding of regional food and beverage sales dynamics with a primary goal of globalizing food items based on ingredient consumption metrics. Methodologically, this research employs Long-Short Term Memory (LSTM) architecture RNN to create a framework to predict food item performance using historical time series data. The model's hyperparameters are optimized using genetic algorithm (GA), resulting in higher accuracy and a more flexible model suitable for growing and real-time data. Data preprocessing involves comprehensive analysis, cleansing, and feature engineering, including the use of gradient boosting models with K-fold cross-validation for revenue prediction. Historical sales data from 1995 to 2014, sourced from Kaggle open-source database, are prepared to capture temporal dependencies using sliding window techniques, making it suitable for LSTM model input. Evaluation metrics reveal the hybrid LSTM-GA model's efficacy, outperforming baseline LSTM with an MSE reduction from 0.045 to 0.029. Ultimately, this research underscores the development of a model that harnesses historical sales data and sophisticated machine learning techniques to forecast food item sales growth, empowering informed investment decisions and strategic expansions in the global food market. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive approach to forecast future hedge fund performances and reporting stops to a commercial database within a subsequent year. We found that gradient boosting of decision trees is well suited to make a prognosis about the future development and reporting stops of hedge funds. The derived models are trained and evaluated using a panel of 5,592 individual hedge funds. We rank the impact of 22 variables that are computed out of hedge fund reporting (micro variables) and three different market environments (macro variables) on the predictability of hedge fund performance. In this way, we show the economic reasonability of the computed models and demonstrate the superiority of statistical learning algorithms. [ABSTRACT FROM AUTHOR]

Hypertension is a significant public health concern, particularly among workers, due to its association with increased risk of cardiovascular and cerebrovascular diseases. This study aimed to identify key factors influencing blood pressure control in hypertensive male workers aged 40 and above using the Histogram-based Gradient Boosting (HGB) algorithm. Data were drawn from the 2017–2020 Korean National Health and Nutrition Examination Survey (KNHANES), including 1114 male participants who reported being diagnosed with hypertension by a physician. The HGB model was compared with five other machine learning models: Random Forest, XGBoost, LightGBM, CatBoost and AdaBoost. The HGB model demonstrated superior performance with an accuracy of 82.3%, precision of 80.5%, recall of 78.9% and F1-score of 79.7%. Feature importance analysis revealed that age, Body Mass Index (BMI) and physical activity were the most significant factors influencing blood pressure control. Other notable factors included sodium intake, stress levels and medication adherence. The study’s findings underscore the importance of targeted interventions focusing on these key factors to improve hypertension management strategies. By employing advanced machine learning techniques, this research provides valuable insights into the determinants of blood pressure control, offering a foundation for developing effective strategies to reduce hypertension-related complications and mortality among Korean male workers.

Introduction. The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health. Materials and methods. This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting. The calculations used the AAindex matrices in parallel to the Hamming distance. The calculations were carried out with different types and values of antigenic escape thresholds, on four data sets. The results were compared using common binary classification metrics. Results. Significant differentiation is shown depending on the data sets used. The best results were demonstrated by all three models for the forecast autumn season of 2022, which were preliminary trained on the February season of the same year (Auroc 0.934; 0.958; 0.956, respectively). The lowest results were obtained for the entire forecast year 2023, they were set up on data from two seasons of 2022 (Aucroc 0.614; 0.658; 0.775). The dependence of the results on the types of thresholds used and their values turned out to be insignificant. The additional use of AAindex matrices did not significantly improve the results of the models without introducing significant deterioration. Conclusion. More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.

Abstract This study investigates the use of machine learning techniques and the proper selection of input data to estimate permeability in geosciences, using six types of input logs: gamma ray (GR), resistivity (RT), effective porosity (PHIE), density (RHO), sonic (DT), and compensated neutron porosity (NPHI). A total of 57 models were constructed using combinations of these logs and tested using five machine learning methods: Extreme Learning Machine (ELM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP). This approach produced 285 unique permeability predictions. RF had the highest correlation coefficient (0.925) and average error (0.196), indicating a precision-correlation trade-off. The ELM approach had the lowest average error, 0.083, and a correlation value of 0.871. Testing on a blind well revealed that the GB and RF approaches were highly effective in predicting permeability, with R² values of 0.92 and 0.90, respectively, even in untested settings. The findings emphasize the need of using appropriate machine learning algorithms and input data to improve model accuracy and reliability.

This study investigates the frequency and injury severity of pedestrian crashes across Texas using tree-based machine learning models. Ten years of police records are used along with roadway inventory and other sources to map 78,000 + pedestrian crashes over 700,000 road segments. Methods like random forests (RF), gradient boosting (LightGBM and XGBoost), and Bayesian additive regression trees (XBART) are applied and compared. The crash frequency models indicate that highway design variables have significant impacts on crash frequencies. Severity models show how higher speed limits significantly increase the likelihood of pedestrian fatalities and severe injuries, and how intoxication (of drivers or pedestrians) lead to more severe injuries. The 4 specifications perform similarly in predicting crash counts, with LightGBM having much faster computing times. Across the crash-severity models, XBART achieved greater precision values but with significantly higher computating times. [ABSTRACT FROM AUTHOR]

There has been a rapid evolution of tree-based ensemble algorithms which have outperformed deep learning in several studies, thus emerging as a competitive solution for many applications. In this study, ten tree-based ensemble algorithms (random forest, bagging meta-estimator, adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), histogram-based GBM, categorical boosting (CatBoost), natural gradient boosting (NGBoost), and the regularised greedy forest (RGF)) were comparatively evaluated for the enhancement of Copernicus digital elevation model (DEM) in an agricultural landscape. The enhancement methodology combines elevation and terrain parameters alignment, with feature-level fusion into a DEM enhancement workflow. The training dataset is comprised of eight DEM-derived predictor variables, and the target variable (elevation error). In terms of root mean square error (RMSE) reduction, the best enhancements were achieved by GBM, random forest and the regularised greedy forest at the first, second and third implementation sites respectively. The computational time for training LightGBM was nearly five-hundred times faster than NGBoost, and the speed of LightGBM was closely matched by the histogram-based GBM. Our results provide a knowledge base for other researchers to focus their optimisation strategies on the most promising algorithms. [ABSTRACT FROM AUTHOR]

Confronted with the ongoing demographic shift characterized by an aging population, society grapples with emerging challenges that extend beyond the provision of targeted health services for the elderly. The focus has broadened to encompass the promotion of well-being and vitality throughout the aging process. Addressing these multifaceted issues demands a comprehensive approach that integrates biomedical components with physical, psychological, and social interventions. In the context of my project, a unique strategy is employed, placing significant emphasis on leveraging big data analytics and machine learning. The primary objective is to systematically observe and characterize the physiological conditions of the elderly, facilitating healthcare professionals in monitoring behaviors and promoting active aging. This undertaking involves meticulous data collection and analysis, employing machine learning algorithms (support vector machine (SVM), gradient boosting) within a framework that harnesses extensive data analytics. Ultimately, this approach enables the identification and characterization of daily routines and physiological states of individuals, contributing to a holistic understanding of aging. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has led many colleges and students to use online learning. In educational databases with so much data, evaluating student development is difficult. E-learning is essential for egalitarian education since it uses technology and contemporary learning techniques. This review research found three ways for predicting online course performance: i) To choose the best features to raise student performance; ii) The most effective algorithms for transforming unbalanced data into balanced data; and iii) The best machine learning algorithms to predict online course performance. This study also offered insights into using hybrid techniques and optimization algorithms to educational data sets to improve student performance prediction. The utilization of data from independent e-learning products to enhance education today requires data processing to ensure quality. In addition to these techniques, our abstract highlights the effectiveness of hybrid feature selection methods like L2 regularization (Ridge) and recursive feature elimination (RFE) and ensemble learning models like random forest, gradient boosting, and AdaBoost. These approaches considerably improve prediction accuracy and tackle huge and sophisticated educational dataset challenges. Our work uses advanced machine-learning approaches to optimize e-learning settings and boost academic achievements in the shifting online education landscape caused by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

A chest X-ray can convey a lot about a patient's condition. However, it requires a specialized and skilled doctor to determine the type of lung disease with high accuracy. Here comes the role of deep learning techniques (DL) and artificial intelligence (AI) in accelerating the process of detecting lung diseases and classifying them with high precision, which saves time and effort for the patient and the doctor alike. This work presents a proposed model for a machine learning (ML) and AI system to analyze chest X-ray images and categorize them into four cases normal, viral pneumonia, bacterial pneumonia, and coronavirus disease 2019 (COVID-19). The system relies on extracting Mel frequency cepstral coefficient (MFCC) features from a dataset consisting of 4,800 chest X-ray images, and then these features are used to train four basic classifiers based on the data mining tool Orange3, which are adaptive boosting (AdaBoost), decision trees (DTs), gradient boosting (GB), and random forest (RF). The model was tested and evaluated, where the AdaBoost classifier excelled with an accuracy of 100%, followed by RF with an accuracy of 99.5%. Finally, GB and DTs came with a classification accuracy of 98.5%, and 97.2%, respectively. [ABSTRACT FROM AUTHOR]

The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R2 and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis.

Abstract Cervical cancer is one of the most dangerous malignancies in women. Prolonged survival times are made possible by breakthroughs in early recognition and efficient treatment of a disease.The existing methods are lagging on finding the important attributes to predict the survival outcome. The main objective of this study is to find individuals with cervical cancer who are at greater risk of death from recurrence by predicting the survival.A novel approach in a proposed technique is Triangulating feature importance to find the important risk factors through which the treatment may vary to improve the survival outcome.Five algorithms Support vector machine, Naive Bayes, supervised logistic regression, decision tree algorithm, Gradient boosting, and random forest are used to build the concept. Conventional attribute selection methods like information gain (IG), FCBF, and ReliefFare employed. The recommended classifier is evaluated for Precision, Recall, F1, Mathews Correlation Coefficient (MCC), Classification Accuracy (CA), and Area under curve (AUC) using various methods. Gradient boosting algorithm (CAT BOOST) attains the highest accuracy value of 0.99 to predict survival outcome of recurrence cervical cancer patients. The proposed outcome of the research is to identify the important risk factors through which the survival outcome of the patients improved.

Abstract This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

We conducted a study to evaluate the potential and robustness of gradient boosting algorithms in rock burst assessment, established a variational autoencoder (VAE) to address the imbalance rock burst dataset, and proposed a multilevel explainable artificial intelligence (XAI) tailored for tree-based ensemble learning. We collected 537 data from real-world rock burst records and selected four critical features contributing to rock burst occurrences. Initially, we employed data visualization to gain insight into the data's structure and performed correlation analysis to explore the data distribution and feature relationships. Then, we set up a VAE model to generate samples for the minority class due to the imbalanced class distribution. In conjunction with the VAE, we compared and evaluated six state-of-the-art ensemble models, including gradient boosting algorithms and the classical logistic regression model, for rock burst prediction. The results indicated that gradient boosting algorithms outperformed the classical single models, and the VAE-classifier outperformed the original classifier, with the VAE-NGBoost model yielding the most favorable results. Compared to other resampling methods combined with NGBoost for imbalanced datasets, such as synthetic minority oversampling technique (SMOTE), SMOTE-edited nearest neighbours (SMOTE-ENN), and SMOTE-tomek links (SMOTE-Tomek), the VAE-NGBoost model yielded the best performance. Finally, we developed a multilevel XAI model using feature sensitivity analysis, Tree Shapley Additive exPlanations (Tree SHAP), and Anchor to provide an in-depth exploration of the decision-making mechanics of VAE-NGBoost, further enhancing the accountability of tree-based ensemble models in predicting rock burst occurrences.

This research endeavored to harness the capabilities of machine learning algorithms, specifically gradient boosting (GB) and random forest (RF), to ascertain the optimal algorithm for precisely analyzing the determinants influencing Egypt's unemployment rate. Among the algorithms employed, the study identified the gradient boosting (GB) algorithm as the most accurate in this context. The investigation discerned vital variables that substantially influence Egypt's unemployment rate. The analysis revealed that the industry sector value notably impacts the unemployment rate, accounting for 37.3%, and the GDP growth contributes 23.7%. Additionally, variables such as the value of imports (11.7%) and labor force participation (9.5%) emerged as significant determinants. Moreover, the research demonstrated that the unemployment rate exhibits negative correlations with factors including the value added in the agriculture sector, foreign direct investment (FDI), GDP growth, and gross fixed capital formation. Conversely, the unemployment rate positively correlates with variables such as inflation, human development index, imports, labor force participation, industry sector value added, and services sector value.

Nitrogen is one of the most important macronutrients for crops, and, in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation of ≥0.7 were selected for subsequent use in random forest, gradient boosting, and artificial neural networks to determine their relationship with nitrogen levels measured in the laboratory. Random forest showed no relationship, yielding an R2 of zero; and gradient boosting and the classical method were similar with 0.7; whereas artificial neural networks yielded the best results with an R2 of 0.93. Thus, estimating nitrogen levels using this algorithm is reliable, by feeding it with data from the Modified Chlorophyll Absorption Ratio Index, Transformed Chlorophyll Absorption Reflectance Index, Modified Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index, and Transformed Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index

Abstract Objective To develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models. Methods This study involved a comprehensive analysis of clinical, demographic, and laboratory data from 8,495 subjects in Taiwan Biobank (TWB) after propensity score matching to address potential confounding factors. Key variables included age, gender, lipid profiles (T-CHO, HDL_C, LDL_C, TG), smoking and alcohol consumption habits, and renal and liver function markers. The performance of multiple machine learning models was evaluated. Results The cohort comprised 1,699 individuals with CAD identified through self-reported questionnaires. Significant differences were observed between CAD and non-CAD individuals regarding demographics and clinical features. Notably, the Gradient Boosting model emerged as the most accurate, achieving an AUC of 0.846 (95% confidence interval [CI] 0.819–0.873), sensitivity of 0.776 (95% CI, 0.732–0.820), and specificity of 0.759 (95% CI, 0.736–0.782), respectively. The accuracy was 0.762 (95% CI, 0.742–0.782). Age was identified as the most influential predictor of CAD risk within the studied dataset. Conclusion The Gradient Boosting machine learning model demonstrated superior performance in predicting CAD within the Taiwanese cohort, with age being a critical predictor. These findings underscore the potential of machine learning models in enhancing the prediction accuracy of CAD, thereby supporting early detection and targeted intervention strategies. Trial registration Not applicable.

Abstract Background Takotsubo syndrome (TTS) is an acute heart failure syndrome with symptoms similar to acute myocardial infarction. TTS is often triggered by acute emotional or physical stress and is a significant cause of morbidity and mortality. Predictors of mortality in patients with TS are not well understood, and there is a need to identify high-risk patients and tailor treatment accordingly. This study aimed to assess the importance of various clinical factors in predicting 30-day mortality in TTS patients using a machine learning algorithm. Methods We analyzed data from the nationwide Swedish Coronary Angiography and Angioplasty Registry (SCAAR) for all patients with TTS in Sweden between 2015 and 2022. Gradient boosting was used to assess the relative importance of variables in predicting 30-day mortality in TTS patients. Results Of 3,180 patients hospitalized with TTS, 76.0% were women. The median age was 71.0 years (interquartile range 62–77). The crude all-cause mortality rate was 3.2% at 30 days. Machine learning algorithms by gradient boosting identified treating hospitals as the most important predictor of 30-day mortality. This factor was followed in significance by the clinical indication for angiography, creatinine level, Killip class, and age. Other less important factors included weight, height, and certain medical conditions such as hyperlipidemia and smoking status. Conclusions Using machine learning with gradient boosting, we analyzed all Swedish patients diagnosed with TTS over seven years and found that the treating hospital was the most significant predictor of 30-day mortality.

Significantly frolicking under the management of large developers, the housing and utilities sector, just like some other business, requires clear financial and resource planning. A qualitative forecast that takes into account relevant data not only retrospective data on sales volumes of services, but also the size of utility payments, prices for housing and communal services, etc., allows effective planning of business management activities. This paper is devoted to the problem of forecasting cash receipts for a management company. Based on the analysis of publications devoted to the issues of forecasting and planning income for management companies, it is revealed that the list is limited, indicating the narrow focus of the problem and its certain complexity. The study proposes a variant algorithm for forecasting the income of a management company engaged in integrated services in the domain of housing and communal services and providing living conditions for residents of apartment buildings, which is based on the method of machine learning.