Search Results (875)

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification. Full article

Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15–19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R², mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R² = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R² = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R² = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions. Full article

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy. Full article

Accurate gas consumption forecasting is critical for modern energy systems due to complex consumer behavior and regulatory requirements. Deep neural networks (DNNs), such as Seq2Seq with attention, TiDE, and Temporal Fusion Transformers, are promising for modeling complex temporal relationships and non-linear dependencies. This study compares state-of-the-art architectures using real-world data from over 100,000 consumers to determine their practical viability for forecasting gas consumption under operational and regulatory conditions. Particular attention is paid to the impact of data quality, feature attribution, and model reliability on performance. The main use cases for natural gas consumption forecasting are tariff setting by regulators and system balancing for suppliers and operators. The study used monthly natural gas consumption data from 105,527 households in the Volyn region of Ukraine from January 2019 to April 2023 and meteorological data on average monthly air temperature. Missing values were replaced with zeros or imputed using seasonal imputation and the K-nearest neighbors. The results showed that previous consumption is the dominant feature for all models, confirming their autoregressive origin and the high importance of historical data. Temperature and category were identified as supporting features. Improvised data consistently improved the performance of all models. Seq2SeqPlus showed high accuracy, TiDE was the most stable, and TFT offered flexibility and interpretability. Implementing these models requires careful integration with data management, regulatory frameworks, and operational workflows. Full article

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability. Full article

Current data-driven methods for leak localization (LL) in water distribution networks (WDNs) rely on two unrealistic assumptions: they frame LL as a node-classification task, requiring leak examples for every node—which rarely exists in practice—and they validate models using random data splits, ignoring the temporal structure inherent in hydraulic time-series data. To address these limitations, we propose a temporal, regression-based alternative that directly predicts the leak coordinates, training exclusively on past observations and evaluating performance strictly on future data. By comparing five machine-learning techniques—k-nearest neighbors, linear regression, decision trees, support vector machines, and multilayer perceptrons—in both classification and regression modes, and using both random and temporal splits, we show that conventional evaluation methods can misleadingly inflate model accuracy by up to four-fold. Our results highlight the importance and suitability of a temporally consistent, regression-based approach for realistic and reliable leak localization in WDNs. Full article

Accurate prediction of the dynamic compressive strength of brittle engineering materials is of significant theoretical and engineering importance for underground engineering design, safety assessment, and dynamic hazard prevention. To enhance prediction accuracy and model interpretability, this study proposes a novel framework integrating stacking ensemble learning with SHapley Additive exPlanations (SHAP) for dynamic strength prediction. Leveraging multidimensional input variables, including static strength, strain rate, P-wave velocity, bulk density, and specimen geometry parameters, we constructed six machine learning regression models: K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), LightGBM, XGBoost, and Multilayer Perceptron Neural Network (MLPNN). Through comparative performance evaluation, optimal base models were selected for stacking ensemble training. Results demonstrate that the proposed stacking model outperforms individual models in prediction accuracy, stability, and generalization capability. Further SHAP-based interpretability analysis reveals that strain rate dominates the prediction outcomes, with its SHAP values exhibiting a characteristic nonlinear response trend. Additionally, structural and mechanical variables such as static strength, P-wave velocity, and bulk density demonstrate significant positive contributions to model outputs. This framework provides a robust tool for intelligent prediction and mechanistic interpretation of the dynamic strength of brittle materials. Full article

Soil heavy metal contamination poses significant risks to ecosystems and human health, necessitating accurate prediction methods for effective monitoring and remediation. We propose a multi-model and variable combination framework to improve the prediction of soil heavy metal content by integrating diverse environmental and spatial features. The methodology incorporates environmental variables (e.g., soil properties, remote sensing indices), spatial autocorrelation measures based on nearest-neighbor distances, and spatial regionalization variables derived from interpolation techniques such as ordinary kriging, inverse distance weighting, and trend surface analysis. These variables are systematically combined into six distinct sets to evaluate their predictive performance. Three advanced models—Partial Least Squares Regression, Random Forest, and a Deep Forest variant (DF21)—are employed to assess the robustness of the approach across different variable combinations. Experimental results demonstrate that the inclusion of spatial autocorrelation and regionalization variables consistently enhances prediction accuracy compared to using environmental variables alone. Furthermore, the proposed framework exhibits strong generalizability, as validated through subset analyses with reduced training data. The study highlights the importance of integrating spatial dependencies and multi-source data for reliable heavy metal prediction, offering practical insights for environmental management and policy-making. Compared to using environmental variables alone, the full framework incorporating spatial features achieved relative improvements of 18–23% in prediction accuracy (R²) across all models, with the Deep Forest variant (DF21) showing the most substantial enhancement. The findings advance the field by providing a flexible and scalable methodology adaptable to diverse geographical contexts and data availability scenarios. Full article

The selection of environmental variables with different spatial resolutions is a critical factor affecting the accuracy of machine learning-based fishery forecasting. In this study, spring-season survey data of Decapterus macarellus in the South China Sea from 2016 to 2024 were used to construct six machine learning models—decision tree (DT), extra trees (ETs), K-Nearest Neighbors (KNN), light gradient boosting machine (LGBM), random forest (RF), and extreme gradient boosting (XGB)—based on seven environmental variables (e.g., sea surface temperature (SST), chlorophyll-a concentration (CHL)) at four spatial resolutions (0.083°, 0.25°, 0.5°, and 1°), filtered using Pearson correlation analysis. Optimal models were selected under each resolution through performance comparison. SHapley Additive exPlanations (SHAP) values were employed to interpret the contribution of environmental predictors, and the maximum entropy (MaxEnt) model was used to perform habitat suitability mapping. Results showed that the XGB model at 0.083° resolution achieved the best performance, with the area under the receiver operating characteristic curve (ROC_AUC) = 0.836, accuracy = 0.793, and negative predictive value = 0.862, outperforming models at coarser resolutions. CHL was identified as the most influential variable, showing high importance in both the SHAP distribution and the cumulative area under the curve contribution. Predicted suitable habitats were mainly located in the northern and central-southern South China Sea, with the latter covering a broader area. This study is the first to systematically evaluate the impact of spatial resolution on environmental variable selection in machine learning models, integrating SHAP-based interpretability with MaxEnt modeling to achieve reliable habitat suitability prediction, offering valuable insights for fishery forecasting in the South China Sea. Full article

Background: Recent research highlights uncertainties surrounding the metabolic effects of nicotine in young adults, particularly among people who use e-cigarettes. While traditional smoking is known to alter body composition, the metabolic impact of using e-cigarettes remains less understood. Methods: In this cross-sectional study, body composition (via bioelectrical impedance analysis) and lifestyle data were collected from 60 university students (mean age: 21.7 ± 1.9 years), who were classified as people who use e-cigarettes exclusively, people who smoke cigarettes exclusively, or people who have never used nicotine products. To address confounding by sex and age, inverse probability of treatment weighting (IPTW) was applied. Results: After adjustment, people who use e-cigarettes had significantly higher body fat percentage compared to people who have never used nicotine (β = 5.45, p = 0.001), while no significant differences were found between people who smoke cigarettes and other groups. Energy drink consumption was also positively associated with body fat percentage and metabolic age. Machine learning models, particularly k-nearest neighbors, achieved moderate classification accuracy (up to 72%) in distinguishing people who use nicotine from people who have never used nicotine based on physiological and lifestyle features. Conclusions: It is important to note that the majority of participants were metabolically healthy, and the observed differences occurred within a clinically normal range. While these findings suggest associations between e-cigarette use and higher adiposity in young adults, no causal inferences can be made due to the observational design. Further longitudinal studies are needed to explore the potential metabolic implications of nicotine use. Full article

Water injection is widely recognized as one of the most important operational approaches for enhanced oil recovery in oilfields. However, this process faces significant challenges due to the formation of sulfate and carbonate mineral scales caused by high salinity in both injected water and formation water. To address this issue, the use of mineral scale inhibitors has emerged as a valuable solution. In this study, we evaluated the performance of seven machine learning algorithms (Gradient Boosting Machine; k-Nearest Neighbors; Decision Tree; Random Forest; Linear Regression; Neural Network; and Gaussian Process Regression) to predict inhibitor efficiency. The models were trained on a comprehensive dataset of 661 samples (432 for training; 229 for testing) with 66 features including temperature; concentrations of various ions (sodium; calcium, magnesium; barium; strontium; chloride; sulfate; bicarbonate; carbonate, etc.), and inhibitor dosage levels (DTPMP, PPCA, PBTC, EDTMP, BTCA, etc.). The results showed that GPR achieved the highest prediction accuracy with R² = 0.9608, followed by Neural Network (R² = 0.9230) and Random Forest (R² = 0.8822). These findings demonstrate the potential of machine learning approaches for optimizing scale inhibitor performance in oilfield operations Full article

Fault diagnosis and identification are important goals in ensuring the safe production of industrial processes. This article proposes a data reconstruction method based on Center Nearest Neighbor (CNN) theory for fault diagnosis and abnormal variable identification. Firstly, the k-nearest neighbor (k-NN) method is used to monitor the process and determine whether there is a fault. Secondly, when there is a fault, a high-precision CNN reconstruction algorithm is used to reconstruct each variable and calculate the reconstructed control index. The variable that reduces the control index the most is replaced with the reconstructed variable in sequence, and the iteration is carried out until the control index is within the control range, and all abnormal variables are finally determined. The accuracy of the CNN reconstruction method was verified through a numerical example. Additionally, it was confirmed that the method is not only suitable for fault diagnosis of a single sensor but also can be used sensor faults that occur simultaneously or propagate due to variable correlation. Finally, the effectiveness and applicability of the proposed method were validated through the penicillin fermentation process. Full article

As robotic systems become more prevalent in daily life and industrial environments, ensuring their reliability through autonomous self-diagnosis is becoming increasingly important. This study investigates whether acoustic sensing can serve as a viable foundation for such self-diagnostic systems by examining its effectiveness in localizing structural faults. This study focuses on developing a fault diagnosis framework for robots using acoustic sensing technology. The objective is to design a simple yet accurate system capable of identifying fault locations and types of robots based solely on sound data, without relying on traditional sensors or cameras. To achieve this, sweep signals were applied to a modular robot, and acoustic responses were collected under various structural configurations over five days. Frequency-domain features were extracted using the Fast Fourier Transform (FFT), and classification was performed using five machine learning models: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost, and Multi-Layer Perceptron (MLP). Among these, MLP achieved the highest accuracy (71.4%), followed by SVM (65.7%), LightGBM (62.9%), KNN (60%), XGBoost (57.1%), and RF (51.4%). These results demonstrate the feasibility of diagnosing structural changes in robots using acoustic sensing alone, even with a compact hardware setup and limited training data. These findings suggest that acoustic sensing can provide a practical and efficient approach for robot fault diagnosis, offering potential applications in environments where conventional diagnostic tools are impractical. The study highlights the advantages of incorporating acoustic sensing into fault diagnosis systems and underscores its potential for developing accessible and effective diagnostic solutions for robotics. Full article

Fibrous biomaterials are essential in biomedical engineering, tissue engineering, and filtration due to their specific transport and mechanical properties. Fluid flow through these materials is critical for their function. However, many biological fluids exhibit non-Newtonian behavior, characterized by micro-rotational effects, which traditional models often overlook. The current study presents a machine learning (ML) framework for the prediction and understanding of hydraulic permeability in fibrous biomaterials with a micropolar fluid. A dataset of 1000 numerical simulations was generated by varying the micropolar fluid properties and the fiber volume fraction in a periodic porous structure with nine parallel cylindrical fibers in a square lattice. Six powerful ML algorithms were deployed: Decision Trees (DT), Random Forests (RF), XGBoost, LightGBM, Support Vector Regression (SVR), and k-Nearest Neighbors (kNN). The balance of predictive capacity to unseen data values (tracking $R^2$ values and error metrics) with computational efficiency for all algorithms was assessed. The best-performing ML algorithm was subsequently used to interpret the decisions made by the model using Shapley Additive exPlanations (SHAP) analysis and understand the role of feature importances. The SHAP findings highlight the potential of ML in capturing complex fluid interactions and guiding the design of advanced fibrous biomaterials with optimized hydraulic permeability. Full article