Search Results (474)

In this study, Response Surface Methodology (RSM) and machine learning models were used to predict the mechanical properties of recycled aggregate concrete (RAC) containing ground granulated blast furnace slag (GGBS) and polypropylene fibers (PPFs). The investigation focused on compressive strength (CS) and split tensile strength (STS) tests at curing periods of 7, 28, 56, and 90 days, with variations in the percentages of GGBS (0–50%), recycled aggregate (RA) (0–100%), and PPF (0–1%). The RSM model showed high accuracy in predicting both CS and STS, with statistically significant results (p-value < 0.0001). Among the machine learning models, the Gradient Boosting Machine (GBM) exhibited the highest performance, achieving an R² value of 0.98961 during the training and testing phases for CS prediction. It also demonstrated strong results for STS prediction, with an MSE of 0.02773, MAPE of 2.69775, and R² value of 0.99404 in the training phase, and an MSE of 0.14141, MAPE of 5.71691, and R² value of 0.96947 during testing. The Stacked Ensemble Learning model performed similarly to GBM, with an R² of 0.99251 during training for STS and 0.96619 during testing. However, GBM consistently outperformed the other models in terms of balancing low error rates and high R² values across both datasets. The Distributed Random Forest model also provided strong performance but slightly higher error rates and lower R² values than GBM. Overall, both GGBS and PPF significantly enhanced the mechanical properties and workability of the concrete, highlighting the importance of these additives in optimizing concrete performance. Full article

Rice is a staple food for almost half of the world’s population, and the stability and sustainability of rice production plays a decisive role in food security. Diseases are a major cause of loss in rice crops. The timely discovery and control of diseases are important in reducing the use of pesticides, protecting the agricultural eco-environment, and improving the yield and quality of rice crops. Deep convolutional neural networks (DCNNs) have achieved great success in disease image classification. However, most models have complex network structures that frequently cause problems, such as redundant network parameters, low training efficiency, and high computational costs. To address this issue and improve the accuracy of rice disease classification, a lightweight deep convolutional neural network (DCNN) ensemble method for rice disease classification is proposed. First, a new lightweight DCNN model (called CG-EfficientNet), which is based on an attention mechanism and EfficientNet, was designed as the base learner. Second, CG-EfficientNet models with different optimization algorithms and network parameters were trained on rice disease datasets to generate seven different CG-EfficientNets, and a resampling strategy was used to enhance the diversity of the individual models. Then, the sequential least squares programming algorithm was used to calculate the weight of each base model. Finally, logistic regression was used as the meta-classifier for stacking. To verify the effectiveness, classification experiments were performed on five classes of rice tissue images: rice bacterial blight, rice kernel smut, rice false smut, rice brown spot, and healthy leaves. The accuracy of the proposed method was 96.10%, which is higher than the results of the classic CNN models VGG16, InceptionV3, ResNet101, and DenseNet201 and four integration methods. The experimental results show that the proposed method is not only capable of accurately identifying rice diseases but is also computationally efficient. Full article

One of the most important nutrients needed for fruit tree growth is nitrogen. For orchards to get targeted, well-informed nitrogen fertilizer, accurate, large-scale, real-time monitoring, and assessment of nitrogen nutrition is essential. This study examines the Leaf Nitrogen Content (LNC) of the custard apple tree, a noteworthy fruit tree that is extensively grown in China’s Yunnan Province. This study uses an ensemble learning technique based on multiple machine learning algorithms to effectively and precisely monitor the leaf nitrogen content in the tree canopy using multispectral canopy footage of custard apple trees taken via Unmanned Aerial Vehicle (UAV) across different growth phases. First, canopy shadows and background noise from the soil are removed from the UAV imagery by using spectral shadow indices across growth phases. The noise-filtered imagery is then used to extract a number of vegetation indices (VIs) and textural features (TFs). Correlation analysis is then used to determine which features are most pertinent for LNC estimation. A two-layer ensemble model is built to quantitatively estimate leaf nitrogen using the stacking ensemble learning (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Boosting Decision Trees (GBDT), Linear Regression (LR), and Extremely Randomized Trees (ERT) are among the basis estimators that are integrated in the first layer. By detecting and eliminating redundancy among base estimators, the Least Absolute Shrinkage and Selection Operator regression (Lasso)model used in the second layer improves nitrogen estimation. According to the analysis results, Lasso successfully finds redundant base estimators in the suggested ensemble learning approach, which yields the maximum estimation accuracy for the nitrogen content of custard apple trees’ leaves. With a root mean square error (RMSE) of 0.059 and a mean absolute error (MAE) of 0.193, the coefficient of determination (R²) came to 0. 661. The significant potential of UAV-based ensemble learning techniques for tracking nitrogen nutrition in custard apple leaves is highlighted by this work. Additionally, the approaches investigated might offer insightful information and a point of reference for UAV remote sensing applications in nitrogen nutrition monitoring for other crops. Full article

With the continuous advancement of urbanization, urban renewal has become a vital means of enhancing urban functionality and improving living environments. Traditional urban renewal research primarily focuses on the macro level, analyzing regions or units, with limited studies targeting individual buildings. Consequently, the unique characteristics and specific requirements of individual buildings during urban renewal have often been overlooked. This study first identified individual buildings undergoing urban renewal in the Longgang and Longhua Districts of Shenzhen, China, from 2018 to 2023 using multisource data such as the 2018 Shenzhen Building Census. A regression analysis based on building characteristics and locational factors was conducted using a stacking ensemble machine learning model. In addition, buildings were categorized into residential, industrial, and commercial types based on their usage, enabling both overall- and category-specific predictions of building renewal. The results show the following: (1) Using the prediction results of multilayer perceptron (MLP) and eXtreme Gradient Boosting (XGBoost) base models as inputs and fusing them with an AdaBoost classifier as the final metamodel, the goodness of fit of the overall building renewal regression model increased by 2.19%. (2) The regression model achieved an overall urban renewal prediction accuracy of 89.41%. Categorizing urban renewal projects improved the goodness of fit for residential and industrial building renewal by 0.14% and 6.13%, respectively. (3) Compared with traditional macro-level evaluation methods, the experimental results of this study improved by 8.41%, and compared with single-model approaches based on planning permit data, the accuracy improved by 29.11%. Full article

Unmanned aerial vehicles (UAVs) are vulnerable to global positioning system (GPS) spoofing attacks, which can mislead their navigation systems and result in unpredictable catastrophic consequences. To address this issue, we propose a detection method based on stacked ensemble learning that combines convolutional neural network (CNN) and extreme gradient boosting (XGBoost) to detect spoofing signals in the GPS data received by UAVs. First, we applied the synthetic minority oversampling (SMOTE) technique to the dataset to address the issue of class imbalance. Then, we used a CNN model to extract high-level features, combined with the original features as input for the stacked model. The stacked model employs XGBoost as the base learner, which is optimized through five-fold cross-validation, and utilizes logistic regression for the final prediction. Furthermore, we incorporated magnetic field data to enhance the system’s robustness, thereby further improving the accuracy and reliability of GPS spoofing attack detection. Experimental results indicate that the proposed model achieved a high accuracy of 99.79% in detecting GPS spoofing attacks, demonstrating its potential effectiveness in enhancing UAV security. Full article

With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conducted case studies on specific companies and industries, confirming the significant interpretability and practical applicability of the proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reached 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research study is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors. Full article

This study introduces BagStacking, an innovative ensemble learning framework designed to enhance the detection of freezing of gait (FOG) in Parkinson’s disease (PD) using accelerometer data. By synergistically combining bagging’s variance reduction with stacking’s sophisticated blending mechanisms, BagStacking achieves superior predictive performance. Evaluated on a comprehensive PD dataset provided by the Michael J. Fox Foundation, BagStacking attained a mean average precision (MAP) of 0.306, surpassing standalone LightGBM and traditional stacking methods. Furthermore, BagStacking demonstrated superior area under the curve (AUC) metrics across key FOG event classes. Specifically, it achieved AUCs of 0.88 for start hesitation, 0.90 for turning, and 0.84 for walking events, outperforming multistrategy ensemble, regular stacking, and LightGBM baselines. Additionally, BagStacking exhibited reduced runtime compared to other ensemble approaches, making it suitable for real-time clinical monitoring. These results underscore BagStacking’s effectiveness in addressing the variability inherent in FOG detection, thereby contributing to improved patient care in PD. Full article

Background: Small cell lung cancer (SCLC) is an extremely aggressive form of lung cancer, characterized by rapid progression and poor survival rates. Despite the importance of early diagnosis, the current diagnostic techniques are invasive and restricted. Methods: This study presents a novel stacking-based ensemble machine learning approach for classifying small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using metabolomics data. The analysis included 191 SCLC cases, 173 NSCLC cases, and 97 healthy controls. Feature selection techniques identified significant metabolites, with positive ions proving more relevant. Results: For multi-class classification (control, SCLC, NSCLC), the stacking ensemble achieved 85.03% accuracy and 92.47 AUC using Support Vector Machine (SVM). Binary classification (SCLC vs. NSCLC) further improved performance, with ExtraTreesClassifier reaching 88.19% accuracy and 92.65 AUC. SHapley Additive exPlanations (SHAP) analysis revealed key metabolites like benzoic acid, DL-lactate, and L-arginine as significant predictors. Conclusions: The stacking ensemble approach effectively leverages multiple classifiers to enhance overall predictive performance. The proposed model effectively captures the complementary strengths of different classifiers, enhancing the detection of SCLC and NSCLC. This work accentuates the potential of combining metabolomics with advanced machine learning for non-invasive early lung cancer subtype detection, offering an alternative to conventional biopsy methods. Full article

Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification. Full article

Stroke prediction is a vital research area due to its significant implications for public health. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. Ivanov et al. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% accuracy and a 97% recall rate. They utilized resampling methods to balance the classes and advanced imputation techniques to handle missing data, underscoring the critical role of data preprocessing in enhancing the performance of Support Vector Machines (SVMs). Hassan et al. addressed missing data and class imbalance using multiple imputations and the Synthetic Minority Oversampling Technique (SMOTE). They developed a Dense Stacking Ensemble (DSE) model with over 96% accuracy. Their results underscore the efficiency of ensemble learning techniques and imputation for handling imbalanced datasets in stroke prediction. Bathla et al. employed various classifiers and feature selection techniques, including SMOTE, for class balancing. Their Random Forest (RF) classifier, combined with Feature Importance (FI) selection, achieved an accuracy of 97.17%, illustrating the positive impact of RF and relevant feature selection on model performance. A comparative analysis indicated that Ivanov et al.’s method achieved the highest accuracy rate. However, the studies collectively highlight that the choice of models and techniques for stroke prediction should be tailored to the specific characteristics of the dataset used. This study emphasizes the importance of effective data management and model selection in enhancing predictive performance. Full article

The accuracy of monthly runoff predictions is crucial for decision-making and efficiency in various areas, such as water resources management, flood control and disaster mitigation, hydraulic engineering scheduling, and agricultural irrigation. Therefore, in order to further improve the accuracy of monthly runoff prediction, aiming at the problem that the traditional Stacking ensemble method ignores (the base model correlation between different folds in the prediction process), this paper proposes a novel Stacking multi-scale ensemble learning model (SWA–FWWS) based on random weight averaging and a K-fold cross-validation weighted ensemble. Then, it is evaluated and compared with base models and other multi-model ensemble models in the runoff prediction of two upstream and downstream reservoirs in a certain river. The results show that the proposed model exhibits excellent performance and adaptability in monthly runoff prediction, with an average RMSE reduction of 6.44% compared to traditional Stacking models. This provides a new research direction for the application of ensemble models in reservoir monthly runoff prediction. Full article

Accurate crop yield prediction is crucial for optimizing cultivation practices and informing breeding decisions. Integrating UAV-acquired multispectral datasets with advanced machine learning methodologies has markedly refined the accuracy of crop yield forecasting. This study aimed to construct a robust and versatile yield prediction model for multi-genotyped oat varieties by investigating 14 modeling scenarios that combine multispectral data from four key growth stages. An ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support vector machines (SVM), Cubist, and extreme gradient boosting (XGBoost)—to predict oat yield. The results show that, for single growth stages, base models achieved R² values within the interval of 0.02 to 0.60 and RMSEs ranging from 391.50 to 620.49 kg/ha. By comparison, the StackReg improved performance, with R² values extending from 0.25 to 0.61 and RMSEs narrowing to 385.33 and 542.02 kg/ha. In dual-stage and multi-stage settings, the StackReg consistently surpassed the base models, reaching R² values of up to 0.65 and RMSE values as low as 371.77 kg/ha. These findings underscored the potential of combining UAV-derived multispectral imagery with ensemble learning for high-throughput phenotyping and yield forecasting, advancing precision agriculture in oat cultivation. Full article

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts. Full article

In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed for the smart monitoring and assessment of water quality. Leveraging the strengths of Ensemble Learning models and Explainable Artificial Intelligence (XAI), EWAIS not only enhances the prediction accuracy of water quality but also provides transparent insights into the factors influencing these predictions. EWAIS integrates multiple Ensemble Learning models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, and Voting Ensemble Learning (VEL)—to classify water as drinkable or non-drinkable. The system incorporates advanced techniques for handling missing data and statistical analysis, ensuring robust performance even in complex urban datasets. To address the opacity of traditional Machine Learning models, EWAIS employs XAI methods such as SHAP and LIME, generating intuitive visual explanations like force plots, summary plots, dependency plots, and decision plots. The system achieves high predictive performance, with the VEL model reaching an accuracy of 0.89 and an F1-Score of 0.85, alongside precision and recall scores of 0.85 and 0.86, respectively. These results demonstrate the proposed framework’s capability to deliver both accurate water quality predictions and actionable insights for decision-makers. By providing a transparent and interpretable monitoring system, EWAIS supports informed water management strategies, contributing to the sustainability and well-being of urban populations. This framework has been validated using controlled datasets, with IoT implementation suggested to enhance water quality monitoring in smart city environments. Full article

The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse of dimensionality, which significantly impacts IDS efficacy, and the limited effectiveness of singular learning classifiers in handling complex, imbalanced, and multi-categorical traffic datasets. To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. Furthermore, we introduce multi-stacking ensemble (MSE), a novel ensemble method, to enhance attack prediction capabilities. The evaluation on benchmark datasets such as CICIDS2017 and UNSW-NB15 demonstrates that our IDS surpasses current models across various performance metrics. Full article