Search Results (477)

In the multi-class recognition task for unmanned aerial vehicle (UAV) radio frequency (RF) signals, noise interference and complex backgrounds can significantly degrade recognition performance. The stability and accuracy of existing recognition methods often fail to meet the requirements of practical applications. To enhance the stability and accuracy of UAV RF signal recognition, especially to mitigate performance degradation in complex backgrounds, a UAV RF signal classification method, MD-Net, is proposed that integrates Adaptive Time-Frequency Masking and a dual-network architecture. First, an Adaptive Time-Frequency Masking mechanism is constructed. By analyzing the energy distribution of RF signals in the time-frequency domain, the masking region is automatically determined, ensuring that the training data maintains a diverse distribution across different interference scenarios. This significantly improves the model’s anti-interference performance and discriminative stability in complex environments. Subsequently, a dual-branch recognition network architecture is designed, integrating a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The MLP extracts static amplitude features from the signals, while the LSTM learns time-series features. These two feature types are then fused to achieve complementary characteristics, ultimately enabling accurate classification of UAV RF signals. Extensive comparative experiments conducted on the DroneRF dataset demonstrate that the MD-Net model achieves an average recognition accuracy of 85.58%, an improvement of 5.27 percentage points over the baseline model. The experimental results show that Adaptive Time-Frequency Masking can effectively enhance the model’s adaptability to real-world interference environments, while the dual-network fusion mechanism fully integrates static amplitude and time-series features, providing a feasible and highly reliable technical approach for UAV RF signal recognition. Full article

Digital predistortion (DPD) is essential for mitigating nonlinear distortion in radio-frequency (RF) power amplifiers (PAs), particularly in modern multimode transmitters. Among the existing approaches, the neural-network-based DPD reference model adopted in this work is attractive due to its high modeling accuracy and effective predistortion capability. However, its practical implementation is hindered by the computational complexity of the preprocessing stage, which relies on magnitude extraction, phase normalization, and trigonometric operations. Motivated by this limitation, this work proposes a simplified hardware-efficient formulation, derived from an existing real-valued three-layer perceptron (TLP)-based DPD model, for multimode PA linearization. The proposed approach preserves the main characteristics of the reference model while replacing conventional magnitude and phase normalization with a simplified feature representation derived from complex-valued signal products, eliminating square-root, reciprocal, and trigonometric operations. Two configurations are investigated: a single-network formulation and an iterative cascaded structure composed of compact networks trained sequentially. Simulation results demonstrate accuracy comparable to the reference model while reducing computational complexity by up to 34% in multiplications, 25% in additions, and 73.9% in LUT usage, making the proposed approach suitable for FPGA and ASIC implementations. Full article

The non-invasive determination of live weight and body composition of ewes is an important element in ensuring precision livestock management and animal well-being. Traditional practices tend to be subjective, labor-intensive, or rely on expensive medical imaging such as Computed Tomography (CT). This paper proposes a new hybrid deep learning method to predict live weight and carcass traits in Coopworth ewes. The dataset of 1184 images taken from 156 ewes was analyzed and compared using a hybrid model (ResNet18 with Multi-Layer Perceptron through simple concatenation) and two more advanced models: Attention-Guided Feature Fusion Network (AGFF-Net) based on cross-modal attention and a Vision Transformer-based Hybrid Regressor (ViT-HR). Auxiliary tabular variables are the Body Condition Score (BCS) and size category. The Transformer architecture predicts (R² = 0.93) the live weight of ewes by dynamically ranking each visual patch and asking it to query the self-attention sequence. This technique treats the BCS as a distinct token in the self-attention sequence. Data partitioning at the animal level was stringent, thereby giving strong generalization. Findings indicate that the best advanced fusion systems are far better than baseline concatenation, with a high accuracy confirmed with gold standards obtained by CT. Grad-CAM visual explainability makes sure that models are able to localize biologically relevant anatomical locations successfully. The study closes the gap between complex deep learning models and real-world agriculture implementation to provide a correct, interpretable and scalable solution to real-time livestock measurements. Full article

Existing anomalous traffic detection methods based on feature fusion in Software-Defined Networking (SDN) lack adaptability in weight allocation mechanisms. Consequently, their detection accuracy and model generalization capabilities fail to meet practical security requirements. To solve these limitations, this paper proposes a refined detection method based on hybrid feature selection and gated fusion. First, the framework employs XGBoost combined with the Recursive Feature Elimination (RFE) algorithm. This process identifies shallow statistical features with high discriminative power. Simultaneously, the method utilizes a 1D Convolutional Neural Network (1D-CNN) integrated with a Squeeze-and-Excitation (SE) block to extract deep temporal semantic features. Subsequently, a tailored gated fusion mechanism incorporating linear projection layers for feature alignment adaptively integrates these two categories of features. The fused features are then input into a Multilayer Perceptron (MLP) to execute anomalous traffic detection. Experimental results demonstrate that the proposed method achieves superior performance. Specifically, on the InSDN Dataset, the binary and multi-classification accuracy rates reach 99.91% and 99.88%. Similarly, the accuracy rates on the NSL-KDD dataset are 99.78% and 99.76%. Finally, we established a local simulation environment. Experimental results demonstrate that our method attains an average precision exceeding 93% for anomalous traffic detection in simulated real scenarios. Full article

Accurate estimation of river discharge is fundamental for water resources management, flood forecasting, and drought monitoring in the Amazon River Basin. Rating curves, which relate water level (stage) to discharge, are the primary tool for streamflow estimation. This study evaluates traditional curve-fitting methods and machine learning algorithms for modeling rating curves at the two largest gauging stations in the Amazon River: Itacoatiara and Óbidos. The analysis is based on 70 stage–discharge measurements at Itacoatiara (2008–2023) and 176 measurements at Óbidos (1968–2023). Five modeling approaches were compared: Power Law, Linear Regression, Decision Tree, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Model performance was assessed against official baseline rating curves maintained by Brazil’s National Water Agency (ANA) and the Geological Survey of Brazil (SGB/CPRM) using Root Mean Square Error (RMSE), coefficient of determination (r²), Mean Bias Error (MBE), Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE). Results indicate that ensemble-based machine learning methods, particularly XGBoost (RMSE = 7463 m³/s, NSE = 0.973 at Itacoatiara; RMSE = 18,378 m³/s, NSE = 0.872 at Óbidos), outperformed traditional methods. However, the Decision Tree exhibited overfitting that could not be resolved through pruning, depth limitation, or other strategies given the sample size. Traditional methods such as the optimized Power Law remain practical and transparent alternatives for operational use. The findings suggest that machine learning can complement traditional approaches for improving rating curve accuracy in large tropical rivers, with K-fold cross-validation used to assess variability and performance. Full article

The discrete element method (DEM) is widely used to simulate concrete failure, but calibrating its mesoscopic dynamic parameters is computationally expensive due to the high-dimensional parameter space. This study proposes a physics-informed active learning framework to autonomously calibrate these parameters under impact loads. An FDM-DEM coupled split Hopkinson pressure bar model is established to simulate macroscopic dynamic compressive responses. Subsequently, a Plackett–Burman experimental design reduces the parameter optimization space from 16 to 8 core dimensions. A multi-layer perceptron surrogate model is then constructed. By comparing two heuristic active sampling strategies, results indicate that a parameter priority-guided strategy incorporating physical priors significantly outperforms a mid-value exploration strategy. The proposed approach achieves coefficients of determination exceeding 0.9 for predicting multiple macroscopic dynamic indicators on an independent testing set. Building upon this forward mapping, a robust inverse parameter prediction mechanism is established, achieving a closed-loop reconstruction of 0.8662. This framework provides a reliable, data-efficient, and automated pathway for calibrating complex multiphase particulate systems. Full article

Background: Postoperative neurocognitive disorders (PND) are frequent complications in the elderly surgical patients, with aging recognized as a major risk factor. This study aimed to identify electrophysiological markers and establish an exploratory machine learning framework for PND-related vulnerability prediction using anesthetic electroencephalography (EEG) features in aged mice. Methods: Young and aged mice underwent laparotomy under isoflurane anesthesia with EEG recording. Neurocognitive performance was quantified by 16 standardized behavioral fractions. A semi-supervised K-means algorithm, anchored on young-surgery mice, stratified aged-surgery mice into PND and non-PND clusters. EEG dynamics during anesthesia maintenance and emergence were analyzed, and machine learning models were trained to predict PND from EEG features. Results: At baseline, neurocognitive function was comparable across groups. After anesthesia/surgery, aged mice exhibited selective spatial and contextual memory impairments, with two-thirds classified as PND. During emergence, PND mice displayed elevated δ power and reduced α and β ratios. A Multi-layer Perceptron classifier showed discriminatory performance for PND classification in one evaluation setting (AUC = 0.94). Conclusions: This study identifies emergence-related EEG features associated with postoperative neurocognitive vulnerability in aged mice and provides an exploratory machine learning framework for preclinical risk stratification. These findings support further mechanistic investigation and warrant future validation in human perioperative EEG datasets. Full article

The Internet of Things (IoT) comprises diverse devices connected through heterogeneous communication protocols to deliver a wide range of services. However, the complexity and scale of IoT networks make them difficult to secure. Network intrusion detection systems (NIDSs) have therefore become essential for identifying malicious activities and protecting IoT environments across many applications. Although recent deep learning (DL)-based IDS approaches achieve strong detection performance, they often require substantial computation and storage, which limits their practicality on resource-constrained IoT devices. To balance detection accuracy with computational efficiency, we propose a lightweight deep learning model for IoT intrusion detection. Specifically, our method learns compact, intrusion-relevant representations from traffic features using a two-layer multi-layer perceptron (MLP) embedding backbone, followed by a linear SoftMax classification head for multi-class attack detection. We evaluate the proposed approach on three benchmark datasets, CICIDS2017, NSL-KDD, and CICIoT2023, and the results show strong performance, achieving 99.85%, 99.21%, and 98.45% accuracy, respectively, while significantly reducing model size and computational overhead. The experimental results demonstrate that the proposed method achieves excellent classification performance while maintaining a lightweight design, with fewer parameters and lower FLOPs than existing approaches. Full article

This paper proposes a machine-learning-assisted framework for modal sensitivity analysis of systems with viscoelastic damping elements, including both classical and fractional rheological models. Surrogate models are trained to approximate natural frequencies over a prescribed parameter space using two sampling strategies (Grid and Latin Hypercube) and two regression approaches: multi-layer perceptron (MLP) and Gaussian process regression (GPR). Sensitivities are obtained from the surrogates by finite differences and complemented by model-interpretability measures, namely permutation feature importance (PFI) and Shapley Additive Explanations (SHAP). The surrogate-based results are compared with analytically obtained sensitivities. Local first- and second-order sensitivities of natural frequencies are derived analytically using the direct differentiation method (DDM) for a nonlinear eigenvalue problem formulated in the Laplace domain and further transformed into dimensionless sensitivity measures. The methodology is demonstrated for a single-degree-of-freedom oscillator with classical and fractional Kelvin damper models and a two-story frame equipped with a fractional Kelvin damper. The results show very good agreement between analytical and surrogate-based sensitivities. Feature-importance rankings obtained by PFI and SHAP are consistent with the dimensionless sensitivities and capture changes in parameter influence under varying damping levels. Dispersion studies indicate only minor ranking variations. Full article

Accurate irrigated cropland mapping is critical for agricultural water management and food security. Existing image-based irrigation mapping workflows primarily rely on vegetation indices and synthetic aperture radar (SAR) backscatter features, which have limited capacity to characterize the temporal evolution of irrigation processes and crop growth conditions. The AlphaEarth Foundation (AEF) model developed by Google DeepMind provides compact embeddings with temporal semantic information learned via self-supervision, yet their utility for irrigation mapping has not been systematically assessed. In this study, a comprehensive assessment of AEF embeddings for irrigated cropland mapping was performed in terms of feature separability, classification performance, and spatiotemporal transferability. Experiments were conducted in two representative irrigated regions: the Guanzhong Plain in China and Kansas in the USA. Class separability of the 64 embedding dimensions was quantified using the Jeffries–Matusita (JM) distance. Then, the AEF embeddings were compared with the Sentinel feature set (Sentinel-2 bands, normalized difference vegetation index(NDVI), enhanced vegetation index(EVI), normalized difference water index(NDWI) and Sentinel-1 vertical transmit vertical receive(VV), vertical transmit horizontal receive(VH)) using K-means clustering and supervised classifiers, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Finally, transfer experiments across 2022 and 2024 in the Guanzhong Plain and Kansas were conducted to examine cross-year and cross-region performance. The results showed that AEF embeddings consistently provide stronger class separability in both study areas, with a maximum JM distance of 1.58 (A29). Using AEF embeddings, RF achieved overall accuracies (OA) of 0.95 in the Guanzhong Plain and 0.93 in Kansas, outperforming models based on Sentinel-1/2 bands and indices. Notably, unsupervised K-means clustering on AEF embeddings yielded OA > 0.85, indicating high intrinsic separability between irrigated and rainfed croplands. Transfer experiments further demonstrate stable temporal transfer (cross-year OA > 0.87), whereas cross-region transfer is constrained by differences in irrigation regimes, crop phenology and management practices, resulting in limited spatial generalization (OA~0.3). Overall, this study demonstrates the potential of high-information-density representations from geospatial foundation models for irrigated cropland mapping and provides methodological and technical insights to support transfer learning and operational mapping over large areas. Full article

Background: Predicting genomic alterations from routine hematoxylin and eosin (H&E) whole-slide images (WSIs) may help triage molecular testing. Methods: We retrospectively enrolled 437 patients at Osaka Metropolitan University Hospital across 26 cancers, matched with clinical gene-panel data. We curated 1023 binary endpoints across SNV, CNV, and SV categories. We extracted slide embeddings from five pathology foundation models (Prism, GigaPath, Feather, Chief, and Titan) using a unified feature extraction pipeline and benchmarked them using a lightweight downstream Multi-Layer Perceptron (MLP) classifier. Using the best-performing patch feature system, we trained a multi-instance learning model to assess incremental benefit. Results: Titan achieved the highest and most stable transfer performance, with a median endpoint-wise Area Under the Receiver Operating Characteristic curve (AUROC) of 0.77 in the slide benchmarking; at the patch-level, prediction of APC_SNV reached an AUROC of 0.916, and prediction of KRAS_SNV reached an AUROC of 0.811 on the held-out test set. Conclusions: In a heterogeneous clinical gene-panel setting, pathology foundation models can provide strong baseline genomic-prediction signals without additional fine-tuning. We propose a practical, deployment-oriented two-stage workflow: rapid slide-embedding screening to prioritize robust representations and candidate endpoints, followed by patch-level training for high-value tasks where additional performance gains and interpretable regions are clinically worthwhile. Full article

With the proliferation of online examination systems, protecting academic integrity and reliably detecting cheating have become significant research problems. Current AI-based online monitoring systems can achieve high accuracy by analyzing visual behavioral cues; however, their often black-box nature limits their explainability, reliability, and legal compliance (e.g., GDPR). In contrast, while rule-based approaches are interpretable, they are insufficient for generalizing complex and ambiguous human behaviors. This study proposes an explainable neuro-symbolic framework combining data-driven learning with symbolic reasoning for cheating detection in online exams. The proposed framework comprises three main layers: a neural perceptron layer that generates a suspicious behavior score; a symbolic reasoning layer comprising ANFIS and ILP methods to increase explainability and manage ambiguity; and a neuro-symbolic fusion layer that integrates these two layers. The success of the proposed framework for plagiarism detection was evaluated using a dataset containing visual–behavioral features such as gaze behavior, head pose, hand-object interaction, and device usage, along with the XGBoost method at the neural perceptron layer. Experimental results show that the proposed approach achieves high detection success and supports decision-making using logical rules, thereby reducing false positives. In this respect, the study offers an ethical, transparent, and reliable solution for online exam security. Full article

Shallow water bathymetry data underpins maritime shipping and marine resource survey/protection, but its accuracy is constrained by water heterogeneity and spectral interference. To address this, this study proposes a Spatio-Temporal Coupling and Correlation Adaptive Spectral (STCCAS) inversion method, integrating four machine learning models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Experiments were conducted in Tampa Bay’s nearshore waters, using Sentinel-2 imagery and Airborne LiDAR Bathymetry (ALB) data. Core to STCCAS, the Temporal Stability Index (TSI) quantifies spectral temporal consistency, while the Normalized Difference Turbidity Index (NDTI) characterizes water turbidity, and the two indices synergistically form a dual-scale “spectral reliability-turbidity stability” evaluation system for pixel-level feature quality assessment—coupled with spectral fusion features and spatial location, they jointly realize pixel-level feature reliability weighting and dynamic filtering to build a water condition-adaptive input set. Comparative analysis of inversion performance under the original spectral features (OSFs) inversion method vs. STCCAS inversion method confirms STCCAS significantly boosts accuracy. XGBoost outperforms others, achieving a coefficient of determination (R²) of 0.93, root mean square error (RMSE) of 0.16 m, and mean absolute error (MAE) of 0.12 m. STCCAS breaks the limitations of traditional fixed feature combinations, effectively adapting to nearshore water heterogeneity. It provides a novel method for high-frequency, high-precision shallow water bathymetry inversion, with important practical value for marine environmental monitoring and resource management. Full article

The accurate detection and classification of brain tumors from magnetic resonance imaging (MRI) are critical for diagnosis and treatment planning. While deep learning has shown remarkable success in this domain, many state-of-the-art models rely on complex, end-to-end convolutional neural networks (CNNs) that require extensive computational resources and large, annotated datasets for training. This work proposes a novel and efficient methodology that, for the first time, leverages self-supervised DINO vision transformer backbones (DINO v1, DINOv2, and DINOv3) on a large corpus of natural images as powerful feature extractors for brain tumor analysis. We utilize the rich, general-purpose features from DINO-family backbones without fine-tuning the core model. These extracted features are then fed into a simpler, task-specific classifier (such as a support vector machine or a multi-layer perceptron) for the final detection and multi-class classification (e.g., glioma, meningioma, and pituitary tumor). Our methodology is evaluated on two benchmark medical imaging datasets with various classifying granularities. The results demonstrate that the proposed method achieves competitive and, in some cases, superior classification accuracy compared to representative fine-tuned convolutional neural networks and attention-based architectures, while significantly reducing the number of trainable parameters and training time. In particular, the best configuration achieves up to 98.17% accuracy and an F1-score of 98.18% on the 15-class dataset and 99.08% accuracy and an F1-score of 99.02% on the 4-class dataset. This study confirms the exceptional transfer learning capabilities of self-supervised vision transformers like DINO in the medical imaging domain, establishing it as a highly effective and efficient backbone for robust brain tumor detection and classification systems. Full article

This paper proposed a stacking-based ensemble model to replace the traditional single machine learning model prediction approach, significantly improving the evaluation efficiency of SoC and SoH of lithium batteries. Firstly, a dataset was constructed including three input variables (temperature, current, and voltage) and two output variables (SoC and SoH). Pearson correlation coefficients and histograms were used for preliminary analysis of the correlations and distributions of the dataset. The multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and extreme gradient boosting tree (XGB) were used as base prediction models. Bayesian optimization (BO) was used to fine-tune the parameters of these models, then three statistical indicators were compared to assess the prediction accuracy of the four ML models. Furthermore, MLP, SVM, and RF were selected as base models, while XGB was used as the meta-model, enhancing the integrated performance of the prediction models. SHAP was used to quantify the influence of the output variables on SoC. Finally, linked measures for the prediction model were proposed to achieve autonomous monitoring of drones. The results showed that XGB exhibited superior prediction accuracy, with R² of 0.93 and RMSE of 0.14. The ensemble model obtained using stacking reduced the number of outliers by 89.4%. Current was identified as the key variable influencing both SoC and SoH. Furthermore, the intelligent prediction model proposed in this paper can be integrated with controllers, visualization web pages, and other systems to enable the health status assessment of drones. Full article