Search Results (2,239)

The Coordinate Rotation Digital Computer (CORDIC) algorithm is widely employed in digital signal processing and hardware accelerators because it computes a broad range of elementary functions using iterative shift-and-add operations. Conventional CORDIC implementations, however, execute a fixed number of iterations irrespective of the input characteristics or the precision required, resulting in unnecessary computational overhead and increased execution latency. This work presents an explainable machine learning framework for adaptive iteration optimization in a multi-mode CORDIC architecture supporting circular, hyperbolic, and linear operating modes. A unified prediction framework for calculating the optimal number of iterations is made possible by the developing a generic feature representation to describe the numerical behavior of CORDIC computations across various modes. We systematically evaluated eight regression models, including Linear Regression, Decision Tree, Random Forest, Extra Trees, Support Vector Regression, Multi-Layer Perceptron, and Extreme Gradient Boosting (XGBoost) and LightGBM. Among the models evaluated, the Decision Tree achieved the best performance on an independent test set of 2305 samples from 461 previously unseen input groups, with a MAE of 0.9160 iterations, RMSE of 1.9671, and $R^2$ of 0.6076. Predictions were within one and two iterations of the reference value for 80.26% and 90.07% of the test samples, respectively. Since prediction accuracy alone does not guarantee that the required numerical tolerance will be satisfied, the predicted iteration count was further evaluated using the actual CORDIC error, followed by a safety-correction procedure. The safety-corrected approach achieved 100% tolerance satisfaction on the independent test set, reducing the mean number of iterations from 20 to 11.739, corresponding to a 41.31% reduction in iterations. Model behavior was further interpreted using feature importance analysis, permutation importance, and feature ablation studies to examine the contribution of individual features to iteration prediction. Statistical robustness is established using bootstrap confidence intervals, the Friedman test, and Holm-corrected Wilcoxon signed-rank tests. Full article

Routine monitoring of cadmium (Cd) contamination in rice is essential for public health protection and agricultural trade security. Conventional chemical detection methods are environmentally unfriendly, labor-intensive, and slow. This study presents a rapid, accurate classification approach based on near-infrared reflectance spectroscopy (NIRS) for discriminating Cd-contaminated rice from uncontaminated rice. Five spectral preprocessing methods and three variable selection algorithms were systematically evaluated for their influence on model performance. Classification models were developed using partial least squares discriminant analysis (PLS-DA), K-nearest neighbors (KNN), and support vector machines (SVM). Second derivative (2D) preprocessing yielded the greatest performance gains, raising KNN and SVM test-set accuracy from 73% and 88% to 93% and 91%, respectively. Among the variable selection strategies, the successive projections algorithm (SPA) proved most effective. Under optimized conditions, PLS-DA achieved the best overall performance, attaining 92% accuracy, 89% specificity, and 95% sensitivity on the test set. These results demonstrate the strong potential of NIRS coupled with machine learning for rapid, large-scale Cd surveillance in rice, providing robust technical support for grain quality monitoring and low-cadmium variety breeding programs. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

Although asymbiotic orchid seed germination was first achieved in vitro in 1922, the prediction of germination requirements under in vitro conditions still remains complicated. To address this, we developed a machine learning framework to classify the ex situ asymbiotic germination potential of wild orchids into four discrete groups: Low (0–30%), Mid (31–50%), High (51–80%), and Max (81–100%). Models were trained on a dataset of 203 species, utilizing seed morphometrics—specifically, the embryo-to-testa (E:S) length ratio—alongside core ecological traits (subfamily, growth habit, habitat, and climate zone), as well as chemical scarification duration as a proxy of seed permeability. Validation leveraged novel germination and trait data from 26 taxa from Greece (17) and Armenia (9), published here for the first time. To mitigate class imbalance and prevent algorithmic bias toward highly germinating species, we applied inverse frequency weighting during training. Iterative testing of six algorithms revealed that the “Step 4” feature matrix (excluding climate zone and pretreatment duration) yielded the optimal predictive balance. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) emerged as the superior models, achieving overall accuracies of 44.4% and 61.1%, respectively, with both achieving 100% accuracy for low-germinating species. Finally, we synthesized a novel database compiling new seed morphometrics from Armenia (17 taxa), Greece (52 taxa), and the data from the literature (479 taxa). After filtering previously utilized species, we generated a prediction pool of 361 orchid taxa. Applying our Step 5 KNN and SVM models to forecast their germination behavior revealed distinct variations linked to ecological profiles. This high-accuracy framework, particularly for low-germinability groups, offers a powerful screening tool for ex situ conservation planning. The final trained models are compiled in the publicly available R (v. 4.6.0) package OrchidGermClass. Full article

The construction sector faces a critical need to minimize its carbon footprint, which is currently stimulating the development of geopolymer concrete using recycled coarse aggregates as an eco-friendly material compared with Portland cement. Accurate prediction of the compressive strength of this eco-efficient concrete is complex, however, as a result of the complex, non-linear interactions between many of the mix-design and curing parameters. Although modern scientific literature and engineering practices have increasingly adopted machine learning (ML) for concrete strength prediction, a significant scientific gap remains. Most existing studies rely on “black-box” models that lack sufficient interpretability and frequently overlook the severe risk of data leakage during validation, limiting their practical engineering application. To address this gap, this study proposes a robust, data-leakage-aware framework driven by a rigorous nested GroupKFold cross-validation strategy. By grouping concrete samples by their unique Mix_ID, this approach ensures genuine generalization to entirely unseen mixtures. Within this reliable validation scheme, the CatBoost algorithm is utilized for compressive-strength prediction, with the Dung Beetle Optimizer (DBO) serving as an effective tool for hyperparameter tuning. The evaluation results across multiple random seeds show that the DBO–CatBoost model significantly outperforms the default CatBoost, rigorously tuned baseline models (Support Vector Regression and Random Forest), and a comparative metaheuristic benchmark (PSO–CatBoost). It achieves the most stable distribution of errors and excellent predictive accuracy (Test ${\mathrm{R}}^2=0.9995\pm 0.0002$, RMSE = $0.3828\pm 0.0909$). In addition, the model predictions were demystified using the methods of SHapley Additive exPlanations (SHAP) and partial dependence plots (PDPs). The interpretability analysis revealed strong statistical associations, showing that Curing Time and Coarse Aggregate are the most prominent predictive features and the strongest pairwise interaction between each other; the NaOH molar concentration is the most important second-level influence on optimization of strength. Overall, the framework provides a robust data-driven screening tool that can assist in preliminary mix-design evaluation. By reducing the reliance on extensive empirical “trial and error” approaches, this predictive model supports more efficient material usage and facilitates preliminary optimization of low-carbon concrete formulations. Theoretically, this study advances the fundamental science of geopolymer materials by explicitly quantifying the complex, non-linear interactions between alkaline activators, curing conditions, and recycled aggregates. This provides a robust data-driven theoretical foundation for designing and optimizing next-generation eco-friendly concrete products and structures. Full article

To investigate whether spectral information from the backside of soybean leaves can help to improve the accuracy of disease identification models, based on spectral data from the front and back surfaces of leaves, as well as fused spectral data derived from them, a classification model was established using machine learning algorithms in this study. The study first used a spectral acquisition system to obtain spectral information from the front and back surfaces of the leaves, respectively, and calculated the averages of the two types of data to generate fused spectral data from both surfaces. For the three types of spectral data mentioned above, the following four preprocessing methods were applied: Savitaky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), and second-order derivative (2nd Der). At the same time, five modeling methods—support vector machines (SVM), partial least squares discriminant analysis (PLS-DA), convolutional neural network (CNN), random forest (RF), and back propagation neural network (BPNN)—were introduced to establish classification models of soybean leaf diseases, with the aim of selecting the optimal model that achieves the highest identification accuracy in each type of data. The results of the study indicate the following: Among the classification models based on spectral data from the front surface of the leaves, the BPNN model constructed after SG smoothing preprocessing (SG-BPNN) performed the best, achieving recognition accuracy of 88.89% on the testing set. Among the models based on spectral data from the back surface of the leaves, the MSC-PLS-DA model was identified as the optimal model, achieving an accuracy of 98.61% on the testing set. Among the models based on fused spectral data from both front and back surfaces, the MSC-PLS-DA model also demonstrated optimal performance, achieving a classification accuracy of 100% on the testing set. Its accuracy was 11.11% higher than that of the best model using only front-surface data and 1.39% higher than that of the best model using only back-surface data, which verified the effectiveness of fused spectral information from the front and back surfaces of the leaves in improving the accuracy of the soybean disease classification models. Therefore, this study provides a new approach and theoretical basis for the non-invasive, efficient, and precise detection of soybean diseases, and offers valuable reference for promoting the practical application of spectroscopy in the diagnosis of agricultural diseases. Full article

The development of Motor Imagery (MI) Brain–Computer Interfaces (BCIs) is systematically constrained by low signal-to-noise ratios (SNRs), signal non-stationarity, and acute data scarcity. While complex Blind Source Separation (BSS) methods optimize signal clarity, their computational overhead introduces propagation delays that challenge real-time constraints. This study addresses this engineering trade-off by introducing a localized architectural framework to evaluate whether a lightweight pipeline operating without BSS (No-BSS) is sufficiently efficient for real-time control when compared against two BSS-equipped pipelines utilizing Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD). Validated across the BCI Competition IV Dataset 2A and the PhysioNet MI dataset, all three pipelines share an identical processing chain designed to maximize efficiency. To mitigate low SNRs, an Adaptive Laplacian spatial filter isolates neural intent across target sensorimotor electrodes (C3, C4, and Cz). Data scarcity is countered via a Gaussian noise injection data augmentation strategy, while session-to-session variability is addressed during feature extraction using Wavelet Packet Decomposition (WPD) paired with a Fisher Score criterion to dynamically isolate subject-specific time-frequency nodes. Redundant features are subsequently eliminated using a Genetic Algorithm (GA) before classification. Experimental evaluation reveals a distinct performance stratification: while the ICA (92.80%) and EMD (92.69%) pipelines yield the highest average accuracy for the PhysioNet dataset by isolating non-stationary and physiological noise, the No-BSS baseline (90.28%) remains the superior framework for the BCI Dataset 2A. Across all pipelines across both datasets, a stable classification hierarchy emerges wherein the Support Vector Machine (SVM) leads performance due to its maximum-margin decision boundary, followed by k-Nearest Neighbors (kNN), a modified EEGNet, and Decision Trees. The No-BSS baseline achieves classification accuracies highly competitive with its BSS counterparts while entirely bypassing their algorithmic overhead. Given the strict latency constraints of live BCI control loops, these findings establish the optimized No-BSS pipeline as a highly viable alternative for low-latency, real-time implementations. Full article

Accurate identification of the spatial distribution of agricultural greenhouses is essential for regional agricultural management in Japan. However, an efficient method for systematically mapping greenhouse locations onto existing farmland maps has not yet been established, and field surveys over large areas require substantial time and labor. This study aims to develop an automated approach for detecting agricultural greenhouses and integrating their locations into farmland maps by combining PlanetScope satellite imagery with farmland polygon data developed by the Japanese government. To improve the efficiency of the extraction process, farmland polygons were used to restrict the analysis to known agricultural areas, thereby reducing false detections originating from non-agricultural land. Within these predefined regions, three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Isolation Forest (ISF)—were applied to classify and extract greenhouse features from satellite imagery. After optimizing the hyperparameters of all models, RF and SVM achieved an equivalent peak performance, with an F1-score of 0.86, while ISF reached 0.72. RF was, however, markedly more robust to the polygon-level decision threshold, demonstrating a practical advantage in situations where the threshold cannot be optimized in advance. In addition, an ablation experiment confirmed that without pre-masking with farmland polygons, 81.9% of the pixels predicted as greenhouse were distributed outside the agricultural parcels. The proposed method is expected to serve as an effective approach for efficiently identifying the distribution of agricultural facilities and integrating them with existing farmland information in regions characterized by small and fragmented agricultural fields, such as Japan. Full article

Advancements in pulmonary drug delivery have enabled effective treatment approaches for more severe disease conditions, such as chronic obstructive pulmonary diseases, asthma, cystic fibrosis, and other pulmonary disorders, via targeted, sustained, and immediate drug delivery routes with minimal systemic side effects. However, conventional delivery systems have many limitations, such as poor drug targeting, adherence, and deposition, which ultimately cause variations in drug profiles and therapeutic efficacy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of smart inhaler drug–device combination systems for personalized therapy using predictive formulation parameters, design variables, device performance, and inhalation pattern monitoring. Advanced AI techniques, such as artificial neural networks, deep learning, random forests, support vector machines, deep learning algorithms, and computational modeling, predict the mass median aerodynamic diameter (MMAD), fine-particle fraction (FPF), emitted dose, and regional lung deposition. Smart inhalation devices coupled with digital sensors and computing systems enable the real-time monitoring of inhalation profiles and adherence. Moreover, AI- and ML-enabled Quality by Design (QbD) and digital twin framework technologies enhance the optimization of manufacturing process parameters, consistency, robustness, and scale-up performance. Although several developments have been reported, there is still room for improvement in terms of data heterogeneity, algorithm transparency, interpretability, cybersecurity, regulations, and long-term clinical standardization. This review emphasizes the use of AI to improve the performance of pulmonary drug delivery through smart inhaler drug–device combination therapies, focusing on technological advancements, formulation optimizations, smart inhalers, regulatory issues, current limitations, and future perspectives of AI-based pulmonary drug delivery. Full article

In order to improve the accuracy of near-ground temperature forecasting under winter rain, snow, and freezing weather conditions, three machine learning algorithms, namely neural network, random forest, and support vector machine, were used to train various ground and air elements in the rapidly updated assimilation model of Zhejiang Province based on multi-source observation data, reducing the error of temperature in the model field and forming hourly and 3 km horizontal resolution ground and air temperature datasets for two rainy, snowy, and frozen weather processes. After calibration using the backpropagation neural network algorithm, random forest algorithm, and support vector machine algorithm, the MAE of the simulated field temperature forecast decreased from 1.29 °C to 0.937 °C, 1.01 °C, and 0.988 °C, respectively. The backpropagation neural networks and support vector machine algorithms perform well, but support vector machine algorithms have relatively short computation times. Using 10 feature points for training achieves optimal performance; more points may not necessarily lead to better calibration results. Adding actual data at the initial time of the target point significantly improved the correction effect, and the improvement effect was even better when the forecast lead time was less than 10. The correction effect of the prediction field shows that when the forecast lead time is between 15 h and 24 h, it becomes unstable over time. The mean prediction accuracy of whether the temperature exceeds the 0 °C temperature threshold at 24 forecast moments before calibration is 0.928. After correction, it has been increased to 0.956. Full article

Rolling bearings are indispensable elements in mechanical equipment, and their condition is closely related to system reliability and operational safety. To enhance diagnostic performance with limited samples and reduce the dependence on manual parameter selection, this study proposes a fault diagnosis approach that combines Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN), Black-winged Kite Algorithm (BKA), and Least Squares Support Vector Machine (LSSVM). The original 1-D vibration signals are first processed by CWT to obtain 2-D time–frequency representations. CNN is then employed to learn deep fault-sensitive features, which are subsequently fed into LSSVM for state classification. To further improve classification performance, BKA is used to automatically search for the optimal LSSVM parameters, with validation accuracy adopted as the fitness criterion. Experiments conducted on three public bearing datasets, namely CWRU, JNU, and SEU, indicate that the proposed method outperforms CNN, CNN-SVM, and CNN-BiGRU under small-sample conditions. In addition, t-SNE results show more distinct feature clusters, while BKA exhibits faster convergence and better global search capability than Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Full article

Aiming at the difficulty of extracting fault features from nonlinear and non-stationary vibration signals of planetary gearboxes, a planetary gearbox fault diagnosis method based on Refined Composite Multiscale Fuzzy Entropy (RCMFE), Parametric-t-distributed Stochastic Neighbor Embedding (P-t-SNE), and Artificial Jellyfish Search Algorithm Optimized Support Vector Machine (JS-SVM) is proposed. Firstly, RCMFE is used to calculate and combine the feature vectors of the original fault signals of the planetary gearbox to construct the original high-dimensional fault feature set. Secondly, Parametric-t-SNE (P-t-SNE) based on a deep feedforward neural network is employed to reduce the dimensionality of the high-dimensional features, thereby extracting sensitive low-dimensional features and achieving out-of-sample mapping. Finally, the low-dimensional features are inputted into the JS-SVM for the identification of fault types. The experimental results of planetary gearbox fault diagnosis show that the proposed method can accurately identify common faults in planetary gearboxes, demonstrating promising application prospects. Full article

Accurate assessment and forecasting of air traffic conditions in multi-airport terminal areas are essential for improving early-warning capabilities, mitigating flight conflicts, and alleviating air-route congestion. Accordingly, this study develops an air traffic situation prediction approach that combines an air route–flight state interdependent network with an Optimal Training Sample Online Fuzzy Least-Squares Support Vector Machine (OTSOF-LSSVM). An interdependent network model is first established. The Analytic Hierarchy Process (AHP) is then employed to combine three network indicators, namely node degree, weighted clustering coefficient, and node strength, thereby producing a comprehensive air traffic situation value and its corresponding evolutionary time series. Considering the time-varying and long-periodic properties of this series, an OTSOF-LSSVM-based prediction method is developed. Training samples are selected according to their temporal and spatial proximity to the prediction moment. In addition, block-matrix operations are introduced during model updating to streamline the computational procedure and improve algorithmic efficiency. The proposed approach is validated using actual flight data from the multi-airport terminal area of the Guangdong–Hong Kong–Macao Greater Bay Area. The results demonstrate that the proposed assessment method can effectively characterize the prevailing air traffic situation. Moreover, in comparison with several existing prediction techniques, the proposed method achieves the best overall performance, yielding a mean absolute error of only 0.0111. Full article

This study proposes an adaptive Support Vector Machine (SVM) classification method based on an enhanced Starfish Optimization Algorithm (SFOAE-SVM) for hyperspectral image (HSI) classification. HSI classification remains a critical challenge in remote sensing due to the high dimensionality of spectral features, spectral mixing, scarcity of labeled samples, and complex land-cover distributions. The SFOAE algorithm is used for global hyperparameter optimization of SVMs, accounting for the distributional characteristics of the target HSI data. The approach aims to improve search capability and reduce the likelihood of convergence to local optima by combining multi-dimensional topology-oriented expansion with global exploration. Experimental results demonstrate that SFOAE-SVM achieves competitive classification accuracy and stable performance compared with conventional SVM parameter selection strategies and other optimization-based methods across three benchmark hyperspectral remote-sensing datasets. These results indicate that the proposed method offers a promising optimization-assisted SVM framework for hyperspectral remote-sensing image classification. Full article

Accurate estimation of forest tree species diversity at regional scales is essential for biodiversity monitoring, forest resource management, and ecological conservation. Because tree species differ in canopy spectral responses and phenological dynamics, multi-season remote sensing observations can provide critical information for characterizing species composition and diversity patterns. However, the potential contribution of seasonal image features to improving remote-sensing-based tree species diversity estimation has often been insufficiently considered. In this study, the Yichun forest region in Heilongjiang Province, northeastern China, was selected as the study area. Sentinel-1, Sentinel-2, and topographic data were integrated to extract multi-seasonal spectral, vegetation index, texture, radar, and topographic features. The Boruta algorithm was used for feature selection, and random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector regression (SVR), Bayesian regularized neural network (BRNN), and Stacking ensemble learning were developed to estimate and map Richness, Shannon, and Gini–Simpson indices. The results showed that: (1) Sentinel-2 optical features were the primary information source for tree species diversity estimation, topographic factors further improved model performance, and Sentinel-1 radar features mainly provided complementary structural information; (2) seasonal remote sensing features differed in their predictive ability, with Richness performing better in spring, while Shannon and Gini–Simpson achieved higher accuracy in winter. The four-season fusion scenario produced the highest accuracy for all three indices, with optimal R² values of 0.51, 0.63, and 0.57, respectively; (3) the Stacking ensemble generally improved estimation accuracy and model stability, although the optimal model differed among diversity indices, with Stacking, SVR, and RF performing best for Richness, Shannon, and Gini–Simpson, respectively; and (4) summer Sentinel-2 NDVI, GNDVI, and NDWI contributed strongly to all three indices, elevation was particularly important for Richness, and winter vegetation indices and autumn red-edge bands and texture features were also informative for Shannon and Gini–Simpson. These findings indicate that integrating multi-seasonal remote sensing features and multi-source data using machine learning models can effectively improve forest tree species diversity estimation, providing technical support for regional forest biodiversity monitoring and precision forest management. Full article