Search Results (4,169)

Vehicular Ad Hoc Networks (VANETs) encounter significant hurdles in anomaly detection owing to their dynamic characteristics, scalability demands, and privacy issues. This research presents a new Adaptive Cluster-Based Federated Learning (ACFL) architecture to tackle these challenges. In contrast to conventional machine learning models, the ACFL framework dynamically organizes cars through the Context-Aware Cluster Manager (CACM), which adjusts clusters according to real-time variables like mobility, node density, and communication patterns. Each cluster utilizes Modified Temporal Neural Networks (MTNNs) for localized anomaly detection, employing time-series analysis to improve precision. Federated learning is enabled via the Hierarchical Aggregation Layer (HAL), which effectively consolidates updates across clusters, ensuring scalability and data confidentiality. The proposed framework was assessed in comparison to established machine learning models, including Support Vector Machines (SVM), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbor (KNN), and the K-Nearest Neighbors with Kernelized Feature Selection and Clustering(KNN-KFSC) approach, utilizing the VeReMi dataset. Findings demonstrate that ACFL surpasses existing models in identifying abnormalities, including Global Positioning System(GPS)spoofing and Denial of Service (DoS) assaults, exhibiting enhanced accuracy, adaptability, and scalability. This work emphasizes the capability of ACFL to tackle urgent security issues in VANET, facilitating the development of secure next-generation intelligent transportation systems. Full article

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application has not yet reached full maturity. We focus on applying machine learning models to create flood susceptibility maps (FSMs) for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. This study utilizes 13 explanatory variables derived from topographical, hydrological, hydraulic, environmental and infrastructure data to train the models, using Storm Daniel—one of the most severe recent events in the region—as the primary reference for model training. The most significant of these variables were obtained from satellite data of the affected areas. Four machine learning algorithms were employed in the analysis, i.e., Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Accuracy evaluation revealed that tree-based models (RF, XGBoost) outperformed other classifiers. Specifically, the RF model achieved Area Under the Curve (AUC) values of 96.9%, followed by XGBoost, SVM and LR, with 96.8%, 94.0% and 90.7%, respectively. A flood susceptibility map corresponding to a 1000-year return period rainfall scenario at 24 h scale was developed, aiming to support long-term flood risk assessment and planning. The analysis revealed that approximately 20% of the basin is highly prone to flooding. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision making for disaster preparedness in the region. Full article

Soil nutrient monitoring is essential to achieving UN development goals and meeting the projected 70% increase in agricultural production from 2009 values by 2050. This study presents a novel, low-cost impedimetric device for the direct and simultaneous measurement of soil ion bioavailability (Na⁺, K⁺), temperature, and humidity. Designed for Arduino integration, the device offers scalable, cost-effective deployment. Different AI algorithms were trained to interpret signals (Support Vector Machine, Random Forest, XBoost), enabling real-time monitoring. Best performance was achieved for XBoost. Calibration was first performed using solutions of known NaCl and KCl concentrations to establish impedance patterns, and benchmarking against fitted Cole model outputs demonstrated high predictive accuracy (R² = 0.99 for both Na⁺ and K⁺). The system operated across a 1–100 kHz impedance range with environmental resolution of ±0.5 °C, ±3% RH, and ±1 hPa, acquiring data every 10 min during in vivo trials. This affordable, AI-enhanced platform has the potential to empower smallholder farmers by reducing reliance on costly laboratory analyses, enabling precise fertiliser application, and integrating seamlessly into smart farming platforms for sustainable yield improvement. Full article

Microbial growth and inhibition are complex biological processes influenced by diverse environmental and chemical factors, posing challenges for accurate modelling and prediction. Traditional mechanistic models often struggle to capture the nonlinear and multidimensional interactions inherent in real-world food systems, especially when multiple environmental variables and inhibitors are involved. This study presents the development of a novel, dynamic software platform that integrates classical predictive microbiology models—including both one-step and two-step frameworks—with advanced machine learning (ML) methods such as Support Vector Regression, Random Forest Regression, and Gaussian Process Regression. Uniquely, this platform enables direct comparisons between two-step and one-step modelling approaches across four widely used growth models (modified Gompertz, Logistic, Baranyi, and Huang) and three inhibition models (Log-Linear, Log-Linear + Tail, and Weibull), offering unprecedented flexibility for model evaluation and selection. Furthermore, the platform incorporates ML-based modelling for both microbial growth and inhibition, expanding predictive capabilities beyond traditional parametric frameworks. Validation against experimental and literature datasets demonstrated the platform’s high predictive accuracy and robustness, with machine learning models, particularly Gaussian Process Regression and Random Forest Regression, outperforming classical models. This versatile platform provides a powerful, data-driven decision-support tool for researchers, industry professionals, and regulatory bodies in areas such as food safety management, shelf-life estimation, antimicrobial testing, and environmental monitoring. Future work will focus on further optimization, integration with large public microbial databases, and expanding applications in emerging fields of predictive microbiology. Full article

In this study, we benchmark various machine learning techniques against a synthetic but physically based reference time series (model-simulated (ERA5-Land/FLake) bottom-temperature series) and assess whether decomposition methods (VMD and EMD) improve forecast accuracy using Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), and Long Short-Term Memory (LSTM) with the monthly average data of Mjøsa, the largest lake in Norway, between 1950 and 2024 from the ERA5-Land FLake model. A total of 70% of the dataset was used for training and 30% was reserved for testing. To assess the performance several metrics, correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Performance Index (PI), RMSE-based RSR, and Root Mean Square Error (RMSE) were used. The results revealed that without decomposition, the GPR-M03 combination outperforms other models (with scores r = 0.9662, NSE = 0.9186, KGE = 0.8786, PI = 0.0231, RSR = 0.2848, and RMSE = 0.2000). Considering decomposition cases, when VMD is applied, the SVM-VMD-M03 combination achieved better results compared to other models (with scores r = 0.9859, NSE = 0.9717, KGE = 0.9755, PI = 0.0135, RSR = 0.1679, and RMSE = 0.1179). Conversely, with decomposition cases, when EMD applied, LSTM-EMD-M03 is explored as the more effective combination than others (with scores r = 0.9562, NSE = 0.9008, KGE = 0.9315, PI = 0.0256, RSR = 0.2978, and RMSE = 0.3143). The results demonstrate that GPR and SVM, coupled with VMD, yield high correlation (e.g., r ≈ 0.986) and low RMSE (~0.12), indicating the ability to reproduce FLake dynamics rather than as accurate predictions of measured bottom temperature. Full article

Background: Suicide remains a leading cause of death among youth, yet effective tools to predict suicide attempts (SA) in individuals under 18 are scarce. This study aims to develop machine learning (ML) models to predict SA in paediatric populations using Google Trends data. Methods: Relative Search Volumes (RSVs) from Google Trends were analysed for terms linked to suicide risk factors. Pearson Correlation Coefficients (PCC) identified terms strongly associated with SA rates. Based on these, several ML models were developed and evaluated, including Random Forest Regression, Support Vector Regression (SVR), XGBoost, and Linear Regression. Model performance was assessed using metrics such as PCC, mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results: Terms related to suicide prevention and symptoms, including psychiatrist and anxiety disorder, showed the strongest correlations with SA rates (PCC ≥ 0.90). Random Forest Regression emerged as the top-performing ML model (PCC = 0.953, MAPE = 20.12%, RMSE = 17.21), highlighting burnout, anxiety disorder, antidepressants, and psychiatrist as key predictors of SA. Other models’ scores were XGBoost (PCC = 0.446, MAPE = 22.57%, RMSE = 18.03), SVR (PCC = 0.833, MAPE = 42.23%, RMSE = 47.32) and Linear Regression (PCC = 0.947, MAPE = 23.64%, RMSE = 17.66). Conclusions: Google Trends–based ML models suggest potential utility for short-term prediction of youth SA. These preliminary findings support the utility of search data in identifying real-time suicide risk in paediatric populations. Full article

Physical and chemical processes related to global and regional climate changes are important factors in shaping the Earth’s surface. These processes form various erosion and deposition landforms on the Earth’s surface. These landforms reflect the traces of past and present climate conditions. This study shows that geomorphometric parameters can effectively distinguish between geomorphometrically and climatically distinct geomorphic provinces. In this context, supervised machine learning models were developed using geomorphometric parameters and the Köppen-Geiger climate classes observed in Türkiye. These models, Random Forest, Support Vector Machines, and K-Nearest Neighbor algorithms, were developed using a training data set. Classification analysis was performed using these models and a test dataset that was independent of the training dataset. According to the classification results, the overall accuracy values for the Random Forest, Support Vector Machines, and K-Nearest Neighbor models were calculated as 99.27%, 99.70%, and 99.30%, respectively. The corresponding kappa values were 0.99, 0.99, and 0.99, respectively. This study shows that among the geomorphometric parameters used in the analyses, maximum altitude, elevation, and valley depth were determined as important parameters in distinguishing geomorphic provinces. Full article

Malware remains one of the most critical threats in the digital ecosystem, targeting both mobile and desktop platforms. Traditional signature-based detection techniques face limitations in identifying polymorphic and zero-day variants. This study proposes a static analysis-based approach using machine learning classifiers, focusing on Random Forest, Decision Tree, and Support Vector Machine (SVM). The dataset was collected from MalwareBazaar, and static features such as PE headers, entropy, and API calls were extracted. Experimental results show that SVM achieved the highest accuracy at 53.2%, while Decision Tree obtained the best F1-score at 61.1%, indicating stronger recall capabilities. Random Forest provided balanced results across all metrics with a shorter training time of 0.23 s, highlighting its efficiency for practical use. These findings demonstrate that while no single classifier dominates across all metrics, Random Forest offers a trade-off between performance and efficiency, making it suitable for large-scale static malware detection systems. Full article

This study investigates the feasibility of using wearable technology and machine learning algorithms to predict academic performance based on physiological signals. It also examines the correlation between stress levels, reflected in the collected physiological data, and academic outcomes. To this aim, six key physiological signals, including skin conductance, heart rate, skin temperature, electrodermal activity, blood volume pulse, inter-beat interval, and accelerometer were recorded during three examination sessions using a wearable device. A novel pipeline, comprising data preprocessing and feature engineering, is proposed to prepare the collected data for training machine learning algorithms. We evaluated five machine learning models, including Random Forest, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosted (CatBoost), and Gradient-Boosting Machine (GBM), to predict the exam outcomes. The Synthetic Minority Oversampling Technique (SMOTE), followed by hyperparameter tuning and dimensionality reduction, are implemented to optimise model performance and address issues like class imbalance and overfitting. The results obtained by our study demonstrate that physiological signals can effectively predict stress and its impact on academic performance, offering potential for real-time monitoring systems that support student well-being and academic success. Full article

Addressing safety, environmental, and economic challenges associated with aging urban underground pipeline infrastructure, this study develops an integrated multi-objective optimization framework for sustainable trenchless spiral wound lining (SWL) rehabilitation. The framework integrates machine learning (ML)-driven predictive modeling with structural performance enhancement technologies to advance urban infrastructure management. To enhance the mechanical performance of SWL liners, a multi-objective structural optimization was conducted to systematically examine the impact of strip profile cross-sectional parameters on ring stiffness (S_p), material consumption (V), and total strip profile height (H). ANSYS finite element analysis was employed to conduct numerical simulations of ring stiffness tests for various liner structures, and S_p was calculated based on the resultant loading force (F). Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were evaluated for predicting F and V. The results demonstrated that the SVR model achieved high accuracy in predicting F (R² = 0.9873), while the XGBoost model exhibited excellent performance in predicting V (R² = 0.97). Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), multi-objective optimization of the SWL liner was performed, yielding an optimized liner that showed a 24.46% improvement in S_p with only a 1.82% increase in V. The established predictive formula for SWL liner S_p increments (R² = 0.9874) provides an efficient tool for structural optimization, offering important technical support and a theoretical foundation for sustainable urban pipeline infrastructure management. Full article

Acid mine drainage (AMD) poses significant environmental and health risks due to its high acidity and elevated metal and sulfate contents. Previous studies have primarily focused on short-term AMD monitoring, with limited attention paid to long-term, spatially resolved datasets and predictive modeling. In this 3.5-year study, six wells down-stream of a mine waste rock pile were monitored, and 132 sets of associated water quality (AWQ), geological (GEO), and climate history (CH) parameters were compiled to develop predictive models for Fe, Cu, and Zn concentrations. Random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms were applied using different combinations of input variables. The combined AWQ-GEO-CH dataset achieved the best overall performance, with XGBoost yielding the highest R² values for Fe (0.81) and Cu (0.77), and SVM performing best for Zn (0.94). CH variables, particularly precipitation and evaporation over 60-day periods, strongly influenced metal concentrations by driving hydrological and solute redistribution processes. AWQ parameters, especially F⁻ and S²⁻, were key predictors for Fe and Zn and ranked second for Cu, likely due to shared upstream sources and coupled geochemical processes such as FeF₃ dissolution. The most impactful GEO factor was the installation of a vertical barrier, which reduced metal concentrations by 73–80%. These findings highlight the value of integrating multi-source datasets with ML for long-term AMD prediction and management. Full article

Lavender is a plant widely used in the cosmetic, pharmaceutical, and food industries, and it is also well known for producing nectar and pollen that bees use to make honey. However, due to increasingly adverse atmospheric conditions in recent years, characterized by prolonged dry spells or intense rainfall focused in short periods, the production of monofloral honey, such as lavender honey, has become increasingly challenging. Therefore, accurate mapping of monofloral zones in order to support beekeepers in placing their beehives in the best location is required. In this context, the town of Kuyucak in Isparta Province (Turkey), renowned for its extensive lavender fields, was selected. Using true orthophoto images from 2020 with a ground sampling distance (GSD) of 30 cm, machine learning classification methods and deep learning techniques were applied to identify and map the correspondent lavender fields. Lavender plants within the region were detected using Maximum Likelihood (ML), Support Vector Machine (SVM), and Random Forest (RF) classifiers, as well as the Mask R-CNN deep learning method. The classification achieved an overall accuracy of 95% and a kappa coefficient of 0.94. Subsequently, assuming a bee foraging range of 3 km, a moving squared window (sizing 3 × 3 km) was used to estimate local areas with potential forage resources and the corresponding honey production potential. The resulting honey potential production maps then used to identify optimal location for beekeepers’ hives in order to maximize lavender honey production. Full article

Prolonged sitting and the adoption of unhealthy sitting postures have been a common issue generally seen among many adults and the working population in recent years. This alone has contributed to the alarming rise of various health issues, such as musculoskeletal disorders and a range of long-term health conditions. Hence, this study proposes the development of a novel smart-sensing chair system designed to analyze and provide actionable insights to help encourage better postural habits and promote well-being. The proposed system was equipped with two 32 × 32 pressure sensor mats, which were integrated into an office chair to facilitate the collection of postural data. Unlike traditional approaches that rely on generalized datasets collected from multiple healthy participants to train machine learning models, this study adopts a user-tailored methodology—collecting data from a single individual to account for their unique physiological characteristics and musculoskeletal conditions. The dataset was trained using five different machine learning models—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN)—to classify 19 distinct sitting postures. Overall, CNN achieved the highest accuracy, with 98.29%. To facilitate user engagement and support long-term behavior change, we developed SitWell—an intelligent postural feedback platform comprising both mobile and web applications. The platform’s core features include sitting posture classification, posture duration analytics, and sitting quality assessment. Additionally, the platform integrates OpenAI’s GPT-4o Large Language Model (LLM) to deliver personalized insights and recommendations based on users’ historical posture data. Full article

This study provides a comprehensive and systematic review of hyperspectral remote sensing in urban areas, with a focus on the evolving roles of airborne and spaceborne platforms. The main objective is to assess the state of the art and identify current trends, challenges, and opportunities arising from the scientific literature (the gray literature was intentionally not included). Despite the proven potential of hyperspectral imaging to discriminate between urban materials with high spectral similarity, its application in urban environments remains underexplored compared to natural settings. A systematic review of 1081 peer-reviewed articles published between 1993 and 2024 was conducted using the Scopus database, resulting in 113 selected publications. Articles were categorized by scope (application, method development, review), sensor type, image processing technique, and target application. Key methods include Spectral Unmixing, Machine Learning (ML) approaches such as Support Vector Machines and Random Forests, and Deep Learning (DL) models like Convolutional Neural Networks. The review reveals a historical reliance on airborne data due to their higher spatial resolution and the availability of benchmark datasets, while the use of spaceborne data has increased notably in recent years. Major urban applications identified include land cover classification, impervious surface detection, urban vegetation mapping, and Local Climate Zone analysis. However, limitations such as lack of training data and underutilization of data fusion techniques persist. ML methods currently dominate due to their robustness with small datasets, while DL adoption is growing but remains constrained by data and computational demands. This review highlights the growing maturity of hyperspectral remote sensing in urban studies and its potential for sustainable urban planning, environmental monitoring, and climate adaptation. Continued improvements in satellite missions and data accessibility will be key to transitioning from theoretical research to operational applications. Full article

Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype conditions can yield values significantly greater than the time to reach the design flood peak. Therefore, estimating the temporal variation in scour depth is necessary. This study evaluates multiple machine learning (ML) models to identify the most accurate method for predicting scour depth (Ds) over time using experimental data. The dataset of 3275 records, including flow depth (Y), abutment length (L), channel width (B), velocity (V), time (t), sediment size (d₅₀), and Ds, was used to train and test Linear Regression (LR), Random Forest Regressor (RFR), Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, LightGBM, and KNN models. Results demonstrated the superior performance of AI-based models over conventional regression. The RFR model achieved the highest accuracy (R² = 0.9956, Accuracy = 99.73%), followed by KNN and GBR. In contrast, the conventional LR model performed poorly (R² = 0.4547, Accuracy = 57.39%). This study confirms the significant potential of ML, particularly ensemble methods, to provide highly reliable scour predictions, offering a robust tool for enhancing bridge design and safety. Full article