Search Results (7,285)

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

Urban pluvial flooding presents growing challenges for disaster risk management, yet most susceptibility studies rely on watershed-based frameworks that inadequately capture the localized dynamics of urban systems. This study proposes a city-scale flood susceptibility modeling framework for Cincinnati, Ohio. Cincinnati was chosen because it is a city with a documented history of severe urban flooding, including a once-in-a-century storm in 2016. Multi-source historical flood data were compiled from NOAA storm event records and crowdsourced reports to enhance spatial coverage. Four machine learning algorithms (Random Forest, Support Vector Machine, XGBoost, and Logistic Regression) were implemented to identify the most effective approach for urban pluvial flood prediction. Random Forest (RF) and Support Vector Machine (SVM) achieved the highest accuracy (0.84) and demonstrated strong discriminatory power. RF was selected as the optimal model because it had a higher AUC (90%) and the lowest RMSE (0.35). To assess generalizability, the RF model was validated on updated land use data and flood records from a 2020 storm event. It demonstrated robust performance (accuracy = 0.89, RMSE = 0.36, precision = 0.75, recall = 1, and AUC = 0.95), despite urban development changes. This study’s novelty lies in combining multi-source flood records with a grid-based machine learning framework and rigorously validating model robustness under evolving urban conditions. The results advance urban pluvial flood susceptibility modeling and offer actionable guidance for evidence-based flood risk management worldwide. Full article

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease. Optical coherence tomography (OCT) is a non-invasive imaging technique of the retina. The thickness of the ganglion cell–inner plexiform layer (GCIPL) obtained from an OCT image is a valuable biomarker for monitoring MS. Since the functional shape (F-shape)-based technique has proven to be an effective platform for detecting glaucoma using OCT images, in this paper, we develop an F-shape-based framework to distinguish MS subjects from healthy ones using the thickness of GCIPL. The thickness of the GCIPL layers in the macula region of OCT images in a selected region of interest (ROI) for a set of healthy and MS subjects is represented as F-shape objects, which are registered to a common template using atlas registration. The residual F-shapes, defined as the difference between the F-shape of this common template and the individual registered F-shapes, are used to train an support vector machine (SVM) classifier and subsequently to detect MS. Accuracy, sensitivity, specificity, and area under the curve (AUC) are used to evaluate and compare the classification performance of the proposed F-shape-based scheme and those of sectoral-based schemes. The proposed F-shape-based scheme is shown to significantly outperform the sectoral-based schemes. The superior performance of the proposed F-shape-based scheme can be attributed to the use of (i) a highly dense mesh formed on the ROI in the macula region, (ii) atlas registration that puts the F-shapes of all the subjects on a common platform, and (iii) residual thicknesses as input features for the classification. Full article

This paper proposes a system-wide fault diagnosis method for wireless power transfer (WPT) systems. This method enables the comprehensive fault diagnosis of key components in WPT systems by using only a single current sensor. It requires no controller upgrades, offering a cost-effective and minimally invasive solution. The fault diagnosis method is based on a support vector machine (SVM) algorithm; the Hierarchy-SVM algorithm is proposed which reduces training time to 54% and recognition time to 16.5% of those required by traditional multi-class SVM algorithms, while maintaining comparable accuracy, which was tested under the same dataset and hardware configuration. Lastly, experimental verification is conducted. The experimental results demonstrate that the proposed method achieves a more than 95% accuracy rate in identifying various faults, with an average single identification time of average 14.19 ms. Full article

Background: Metabolic syndrome (MetS) leads to alterations in cardiac autonomic control that can be detected from electrocardiogram (ECG)-derived markers, particularly when the cardiovascular system is challenged during an oral glucose tolerance test (OGTT). Methods: In this paper, we present an automated framework for MetS identification using RR intervals and heart rate variability (HRV) features extracted from 12-lead ECG recordings acquired during the five OGTT stages in 40 male participants (15 with MetS, 10 controls, and 15 endurance-trained marathon runners). RR intervals were first derived using a multilead Pan-Tompkins approach with fusion-based validation. From these RR series, HRV descriptors were computed from time-domain statistics (RR mean, SDNN, rMSSD, pNN50), spectral indices (VLF, LF, HF, LF/HF), and nonlinear measures (SD1, SD2, SampEn, DFA-${\alpha }_1$). Conventional HRV analysis revealed pronounced physiological differences between groups: MetS subjects exhibited reduced parasympathetic activity, reflected by lower rMSSD and SD1, lower HF power, and higher LF/HF ratios, whereas marathoners showed greater vagal modulation, higher HF power, and increased signal complexity. Healthy controls showed an intermediate autonomic profile. Using RR sequences and HRV descriptors (256 samples per stage), we trained three multimodal classifiers: a CNN-MLP model with a softmax output, a CNN-MLP model with an SVM head, and a CNN + LSTM-MLP + SVM architecture. Results: All models achieved strong discriminative performance, with accuracies ranging from 0.92 to 0.95, F1-macro values from 0.92 to 0.95, and macro-AUC values from 0.96 to 0.97. The CNN-MLP model achieved the best overall performance, whereas the CNN + LSTM-MLP + SVM model showed strong class discrimination, particularly for endurance athletes, while maintaining competitive recall for MetS. Conclusions: These findings support the feasibility of ECG-based autonomic assessment as a complementary non-invasive approach for early metabolic risk detection in clinical and preventive cardiometabolic screening settings. Full article

In this paper, an improved state-space method (SSM) is proposed for gait feature extraction. By introducing zero-phase component analysis Whitening (ZCA Whitening) and an algorithm to search estimated echo as the preprocessing method, pedestrian echoes are divided into three groups according to the frequency probability density: torso, feet, and other segments. Two channels of echoes are selected as inputs to the SSM, which is employed to identify the corresponding micro-Doppler trajectory. On this basis, five gait features of torso amplitude, stride length, walking cycle, torso maximum speed, and feet maximum speed are extracted. Simulation based on the Boulic model, compared with the traditional SSM, demonstrated that there is no need to estimate the model order and that a more accurate torso micro-Doppler trajectory and effective micro-motion features of the feet can be obtained by the proposed method. Finally, 77 GHz FMCW radar was used to collect the echoes of four pedestrians. The classifier was designed based on a support vector machine (SVM), and the classification experiment verified the effectiveness of the extracted gait features. Full article

Every year, floods disrupt the lives of hundreds of millions of people worldwide. Their impacts are further intensified by climate change, rapid urbanization, and land-use changes, making it crucial to identify areas most susceptible to flooding. While machine learning (ML) models have proven effective in identifying flood susceptibility, their validity and the integration of human risk remain underexplored in geomorphologically complex and highly flood-prone regions. This study developed an ensemble ML framework for flood susceptibility mapping in the Kosi Megafan, located in Nepal and India. We compared its performance with established ML models and a one-dimensional convolutional neural network (1D-CNN), validated results using Dartmouth Flood Observatory (DFO) and Sentinel-1 SAR (Synthetic Aperture Radar) data, and assessed the population exposed to high-risk zones. A total of 13 (8 retained) flood conditioning factors (FCFs) were derived from remote sensing datasets, and a flood inventory was created to train multiple ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), 1D-CNN, and a Stacked Ensemble model. Among these, the stacked ensemble model achieved the highest performance (AUC = 0.76, accuracy = 0.70, precision = 0.69, recall = 0.72, F1-score = 0.70). The resulting susceptibility map identified high-risk zones mainly in the southern and southwestern Megafan, showing strong spatial agreement with the Sentinel-1-derived flood inventory and the DFO flood data (1992–2022). This study highlights the effectiveness of combining SAR-derived flood evidence with ensemble ML approaches for accurate and scalable flood susceptibility mapping in data-scarce, hazard-prone basins. Ultimately, the research supports efforts to build resilience and mitigate the long-term impact of flooding in the region. Full article

Air pollution is one of the fundamental environmental problems that directly threaten public health, ecosystems, and sustainable urban life in regions with high industrialization and urbanization density. This study aims to investigate whether the air pollution dynamics in Gaziantep and Kilis, two neighboring cities in Turkey, exhibit distinctive city-specific characteristics in their time series. In this context, Dynamic Time Warping (DTW) distance matrix and hierarchical clustering approaches were applied to compare the temporal behavior of pollutants from daily time series of PM10, SO₂, CO, and O₃ measurements across provinces between 2021 and 2025. Random Forest (RF), XGBoost, and Support Vector Machines (SVM) models were then developed to examine the separability of cities based solely on pollutant concentrations. The results revealed that the RF and XGBoost models successfully classified the two cities with over 93% accuracy. Additionally, SHAP analysis was used to interpret the contribution of each pollutant within the classification models, indicating that PM10 and SO₂ have relatively higher importance in distinguishing between the two cities. It should be noted that SHAP provides model-based interpretability rather than a direct representation of physical or atmospheric mechanisms. The findings suggest that pollutant time series may exhibit statistically distinguishable structures even between neighboring cities. Full article

In ultra-deep drilling environments, downhole measurement tools often fail or cannot be deployed due to extreme high-temperature and high-pressure (HPHT) conditions. Consequently, mud-logging data become one of the few reliable real-time information sources for evaluating drilling performance and identifying abnormal conditions. This study proposes a data-driven framework for automatic identification of drilling operation statuses using machine learning, with a particular focus on ultra-deep and HPHT wells. A support vector machine (SVM)-based classification workflow was established to recognize nine representative drilling operation statuses from mud-logging data. Through systematic model optimization, the proposed method achieved a classification accuracy of 91.33%. By incorporating a sliding window-based time-series optimization strategy, the overall accuracy was further improved to 95.22%, while the recognition accuracy of HPHT-related operations increased from 77.67% to 89.33%. These results demonstrate that the optimized model possesses strong adaptability and stability under extreme HPHT conditions. This study specifically targets HPHT environments with limited downhole data and incorporates time-series optimization to enhance model robustness. The proposed framework provides a reliable approach with potential for generalization for intelligent operation recognition in ultra-deep drilling, supporting real-time decision-making and improving operational safety and efficiency in challenging environments. Full article

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies. Full article

Suspended sediment concentration (SSC) and sediment flux (SF) are critical indicators of sediment delivery in the Lower Mekong and underpin deltaic geomorphic stability and ecosystem services. With recent evidence of declining sediment supply caused by upstream regulation and intensive in-channel extraction, there is a pressing need for data-efficient tools to reproduce non-linear sediment dynamics and assist management in the Vietnamese Mekong Delta (VMD). This study evaluates three machine-learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—for data-driven prediction of SSC (2009–2023) and SF (2009–2021) at Tan Chau (Viet Nam). The predictive models were developed using daily discharge inputs from Kratie (Cambodia) and local hydrological data, including water levels and discharge, from the Tan Chau station. Across the held-out testing dataset, all models captured substantial variability in both targets, with consistently higher performance for SF than for SSC. RF achieved the highest skill (SSC: R² = 0.783; SF: R² = 0.867), followed by XGBoost and then SVM. Variable-importance analysis indicates that upstream discharge at Kratie is the most influential predictor for both SSC and SF, consistent with basin-scale hydrological forcing governing downstream sediment transport capacity. The observed record at Tan Chau further suggests an attenuation of wet-season SSC peaks during 2018–2022 relative to earlier years, signalling potential sediment-starvation dynamics that warrant continued monitoring. Overall, the results demonstrate the utility of ML-based sediment prediction models as a complement to conventional monitoring and as an evidence base to inform sediment-aware river–delta management and risk mitigation in the Lower Mekong. Full article

Federated learning has become a promising paradigm in the training of decentralized machine learning models across clients without sharing raw data, thereby preserving privacy. Current federated support vector machine methods are mainly based on the learning of a single global model, which inadequately addresses the challenges presented by heterogeneous and non-IID client data distributions. To overcome these limitations, we propose FMT-SVM, a novel federated multi-task learning framework that jointly trains both binary and multi-class classification tasks within each client, where the client uses a unified convolutional neural network encoder to extract common features among tasks, which are passed to task-specific linear SVM heads dedicated to each classification task. By leveraging a primal optimization integrating task covariance and global consensus regularization, FMT-SVM explicitly models relationships between heterogeneous tasks and enforces alignment across clients, effectively handling the non-IID nature of data distributions. Unlike traditional FL methods that exchange entire model parameters or large support vector sets, our method communicates only the compact SVM heads during aggregation, greatly reducing communication overhead and enhancing scalability for clients with limited bandwidth. To further enhance privacy, Gaussian differential privacy mechanisms are applied to client updates, balancing privacy preservation with predictive performance. Experiments are performed on two medical image datasets: the Pediatric Pneumonia Dataset and the Breast Ultrasound dataset, demonstrating that the FMT-SVM framework achieves competitive accuracy on both binary and multi-class tasks while maintaining communication efficiency and privacy guarantees. These results highlight the capability of the proposed FMT-SVM framework as a practical, scalable, and privacy-aware solution for the federated true multi-task learning problem in sensitive healthcare applications. Full article

Electrochemical energy storage serves as a foundational technology in contemporary electrical energy storage systems, with its operational safety and stability being crucial to socio-economic development. The estimation of the state of health (SOH) of energy storage batteries is an essential component for ensuring system safety warnings and lifecycle management. To address the challenges of redundant health feature dimensions, insufficient correlation of influencing factors, and limited prediction accuracy in existing SOH estimation methods, in this paper, a novel state of health estimation framework is introduced, leveraging an Improved Gray Wolf Optimization (IGWO) algorithm to optimize the parameters of a Support Vector Machine (SVM). This model achieves precise prediction of battery health states by extracting multidimensional health features, including the differential temperature, incremental capacity, time interval of equal charge voltage difference (DT-IC-TIECVD) and implementing the improved gray wolf optimization algorithm with support vector machine algorithm (IGWO-SVM). Validated on the Oxford battery aging dataset, the proposed model achieves mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) values of 0.43%, 0.55%, and 0.99, respectively. These results confirm the high accuracy and feasibility of the proposed method, while also providing a novel technical pathway for the health management of energy storage batteries. Full article

Background/Objectives: Minor neurocognitive disorder (minor NCD) represents a heterogeneous and potentially modifiable stage along the continuum from normal aging to dementia, offering a critical window for targeted health promotion interventions. Early identification of individuals at increased risk of progression is essential for implementing preventive strategies that may delay functional decline. This study developed a transparent machine learning (ML) framework to predict diagnostic change from minor to major NCD at 12 and 24 months using baseline demographic, clinical, and multidomain neuropsychological data. Methods: A retrospective cohort of 162 memory clinic patients was analyzed using a rigorously controlled pipeline incorporating nested stratified cross-validation, SMOTE-based imbalance correction, and sequential forward feature selection. Logistic regression, support vector machines (SVMs), and XGBoost were evaluated, with SHapley Additive exPlanations (SHAPs) applied to ensure interpretability. Results: SVM achieved the most balanced predictive performance at both 12 months (accuracy = 0.90) and 24 months (accuracy = 0.81). Short-term progression was primarily driven by subtle multidomain cognitive inefficiencies, while longer-term risk reflected continued cognitive vulnerability modulated by metabolic factors such as diabetes. Conclusions: These findings highlight the potential of explainable ML as a health promotion tool and suggest that explainable ML can uncover clinically meaningful cognitive risk signatures at the earliest stages of NCD. By identifying modifiable systemic contributors alongside cognitive risk profiles, this framework supports precision-oriented preventive strategies and proactive longitudinal monitoring in minor NCD. Full article

Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the demands of high-throughput rapid detection. Although hyperspectral imaging technology offers the potential for non-destructive and rapid detection, existing analytical methods are often limited by issues such as high spectral band redundancy, insufficient feature extraction, and inadequate model stability, which constrain prediction accuracy and practical application potential. To address this, this study proposes a multi-scale residual convolutional neural network (MS-RCNN) based on competitive adaptive reweighted sampling (CARS) for feature band selection, combined with near-infrared hyperspectral imaging technology, to construct a rapid and non-destructive prediction model for the polyphenol content of Vigna umbellata. The model employs a parallel multi-scale convolutional module to extract spectral features with different receptive fields, and incorporates residual connections and adaptive pooling mechanisms to enhance feature reuse and robustness. Experiments compared the performance of partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multi-scale convolutional neural network (MS-CNN), and MS-RCNN models. The results indicate that the MS-RCNN model based on CARS screening achieved the best prediction performance, with a coefficient of determination (R²) of 0.9467, a root mean square error of prediction (RMSEP) of 0.0448, and a residual predictive deviation (RPD) of 4.33. Compared with the optimal PLSR and LSSVM models, its R² values were improved by 0.2078 and 0.1119, respectively. In summary, the MS-RCNN model proposed in this study enables rapid, non-destructive, and accurate prediction of polyphenol content in Vigna umbellata, providing an efficient technical approach for quality detection of edible and medicinal crops. Full article