Search Results (1,496)

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems. Full article

Rapid Internet of Things (IoT) expansion creates security risks due to resource limits and evolving botnets. While Graph Neural Networks (GNNs) offer accuracy, their computational demands hinder real-time edge deployment. This study presents IoTGuard, based on a ‘Hetero-MLP’ architecture. The model replaces costly message passing with 8-dimensional categorical embeddings to capture protocol semantics. To avoid topology overfitting, L3 identifiers were excluded, relying on 13 L4 attributes selected via Pearson correlation. Evaluations on the NF-BoT-IoT-v2 dataset (37.7 M samples) demonstrate a 12.17 KB (INT8) footprint via post-training quantization. This represents a 1.9× size reduction, enabling independent operation on ARM Cortex-M7 platforms (Arm Ltd., Cambridge, UK) at 37,093 requests per second. The framework achieves a DDoS F1-score of 0.9943 with a false-positive rate of 0.0054. Comparative analysis confirms that while Random Forest is accurate, Hetero-MLP reduces parameters by 25.4× versus standard GAT models. The proposed approach balances detection depth with edge constraints, offering scalable critical infrastructure protection. Full article

Exchange rate forecasting is an important issue in financial market analysis. Currency rates form a dynamic network of connections that can be efficiently modeled using graph neural networks (GNNs). The key mechanism of GNNs is the message passing between nodes, allowing for better modeling of currency interactions. Each node updates its representation by aggregating features from its neighbors and combining them with its own. In convolutional graph neural networks (GCNs), all neighboring nodes are treated equally, but in reality, some may have a greater influence than others. To account for this changing importance of neighbors, graph attention networks (GAT) have been introduced. The aim of the study was to evaluate the effectiveness of GAT in forecasting exchange rates. The analysis covered time series of major world currencies from 2020 to 2024. The forecasting results obtained using GAT were compared with those obtained from benchmark models such as ARIMA, GARCH, MLP, GCN, and LSTM-GCN. The study showed that GAT networks outperform numerous methods. The results may have practical applications, supporting investors and analysts in decision-making. Full article

The chlorophyll content of plant leaves measured by the soil plant analysis development (SPAD) is an important indicator for measuring crop growth status and irrigation effect. The rapid, non-destructive and efficient estimation of crop SPAD values is of great significance to the field management of crops. In this study, the canopy hyperspectral reflectance and SPAD values of winter wheat were obtained, and the spectral curve was changed through four spectral processing methods, including first-order differential (FD), second-order differential (SD), multivariate scattering correction (MSC), and Savitzky–Golay smoothing (SG) to improve the correlation between canopy spectral reflectance and SPAD. Furthermore, to investigate and evaluate the performance of various vegetation indices (VIs) in estimating SPAD values for winter wheat, existing published indices were optimized using random band combinations derived from multiple canopy spectral transformations. The optimized vegetation index was used as the input variable of the model, and six machine learning algorithms, including random forest (RF), long short-term memory network (LSTM), multilayer perceptron (MLP), deep recurrent neural network (Deep-RNN), gated recurrent unit (GRU), and convolutional neural network (CNN), were used to construct the winter wheat SPAD values estimation model, and the model was verified. The experimental results demonstrate that, when utilizing an equivalent number of optimized vegetation indices as input, the GRU-based model achieves higher estimation accuracy compared to other models. Specifically, the coefficient of determination (R²) is improved by 0.12 compared to the RF model, by 0.03 compared to the LSTM model, by 0.12 compared to the MLP model, by 0.02 compared to the Deep-RNN model, and by 0.02 compared to the CNN model. At the same time, the GRU model also has a lower root mean square error (RMSE) and relative error (RE) of 7.37 and 24.90%, respectively. This study provides valuable hyperspectral remote sensing technology support for the implementation of winter wheat SPAD values estimation in the field. Full article

Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set integrating demographic, health, geographic and mobility indicators of Türkiye and its neighboring countries was collected. Türkiye’s neighboring countries are Greece, Bulgaria, Georgia, Armenia, Iran, and Iraq. This dataset, created by combining raw data from these neighboring countries, provides a comprehensive regional representation that allows for both quantitative classification and spatial mapping of epidemiological risk. To address the class imbalance problem, Conditional GAN (CGAN), a class-conditional synthetic example generation approach that enhances high-risk category representation was used. In this study, we proposed a multi-view deep learning model named MV-RiskNet, which effectively models the multi-dimensional data structure by processing each view into independent subnetworks and integrating the representations with an attention-based fusion mechanism for regional epidemic risk prediction. Experimental studies were compared using Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Autoencoder classifier, and Graph Convolutional Network (GCN) models. The proposed MV-RiskNet with CGAN model achieved better results compared to other models, with 97.22% accuracy and 97.40% F1-score. The generated risk maps reveal regional clustering patterns in a spatially consistent manner, while attention analyses show that demographic and geographic features are the dominant determinants, while mobility plays a complementary role, especially in high-risk regions. Full article

Partial discharge (PD) signals in gas-insulated switchgear (GIS) exhibit complex characteristics, and single-modal feature recognition methods face limitations in achieving satisfactory diagnostic accuracy due to incomplete fault information representation. This paper proposes a multi-modal fault diagnosis framework that effectively integrates complementary information from different sensing modalities to improve defect identification performance. First, PRPD time-domain statistical features from HFCT measurements and frequency-domain features from UHF signals are extracted to construct a comprehensive hybrid feature set. Z-score normalization is applied to eliminate scale differences between heterogeneous features. Principal component analysis (PCA) is then employed for dimensionality reduction, preserving essential discriminative information while removing redundancy. Finally, a ResNet-MLP classifier with skip connections is designed to enhance nonlinear feature extraction and alleviate gradient vanishing problems in deep network training. Experimental validation on four typical defect types—protrusion defect, floating discharge, metal particle discharge, and surface discharge on insulator—demonstrates that the proposed method achieves 99.38% classification accuracy on the test set, with consistently high precision, recall, and F1-score across all categories. The proposed approach significantly outperforms standard MLP without residual connections, achieving 98.94% ± 0.49% accuracy compared to 95.47% ± 3.72% over 20 independent runs, demonstrating superior diagnostic accuracy and generalization capability for GIS insulation fault diagnosis. Full article

Single image super-resolution (SISR) is a classical computer vision task that aims to reconstruct a high-resolution image from a low-resolution input, thereby improving detail sharpness and visual quality. In recent years, convolutional neural network (CNN)-based methods and transformer-based methods using self-attention mechanisms have achieved significant progress in visible-image super-resolution. However, the direct application of these two types of methods to infrared images still poses considerable challenges. On the one hand, infrared images generally suffer from low signal-to-noise ratio, blurred edges, and missing details, and relying only on local convolutions makes it difficult to adequately model long-range dependencies across regions. On the other hand, although pure transformer models have a strong global modeling ability, they usually have large numbers of parameters and are sensitive to the amount of training data, making it difficult to balance efficiency and detail restoration in infrared imaging scenarios. To address these issues, we propose a hybrid neural network architecture for infrared image super-resolution reconstruction, termed RDSR (Residual Dual-branch Separable Super-Resolution Network), which organically integrates multi-scale depthwise separable convolutions with shifted-window self-attention. Specifically, we design a dual-branch spatial interaction module (BDSI, Dual-Branch Spatial Interaction) and a multi-scale separable spatial aggregation module (MSSA, Multi-Scale Separable Spatial Aggregation). The BDSI module models correlations along rows and columns through grouped convolutions in the horizontal and vertical directions, effectively strengthening the spatial information interaction between the convolution branch and the self-attention branch. The MSSA module replaces the conventional MLP with three parallel depthwise separable convolution branches, improving the feature representation and nonlinear modeling through multi-scale spatial aggregation and a star-shaped gating operation. The experimental results on multiple public infrared image datasets show that for $\times 2$ and $\times 4$ upscaling, the proposed RDSR achieves higher PSNR and SSIM values than CNN-based methods such as EDSR, RCAN, and RDN, as well as transformer-based methods such as SwinIR, DAT, and HAT, demonstrating the effectiveness of the proposed modules and the overall framework. Full article

Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling. Full article

This research established an experimental database characterizing the solar drying behavior of seven distinct agricultural products (including fruits, vegetables, and meat) in a tunnel-type solar dryer. Based on this experimental data, a dual-model architecture using multilayer perceptron (MLP) neural network was developed to predict both the internal microclimate and the drying kinetics. The first network (ANN 1) mapped meteorological variables to the dryer’s internal conditions, while the second (ANN 2) predicted moisture loss. The results demonstrate distinct predictive capabilities for each physical phenomenon: the thermodynamic model (ANN 1) captured stochastic weather fluctuations, with an R² of 0.9878 and an MAPE of 4.64%. The kinetic model (ANN 2) achieved near-perfect linearity with an R² of 0.9997 and an MAPE of 0.49%, significantly outperforming baseline linear regression models (R² approx 0.78). These findings confirm the system’s capacity to generalize across diverse food types and variable weather conditions, providing a robust tool for future Model Predictive Control (MPC) applications. Full article

Node classification is a fundamental task in graph-based learning, with applications in social networks, citation networks, and biological systems. Learning node representations for different graph datasets is necessary to find the correlation between different types of nodes. Graph Neural Networks (GNNs) play a critical role in providing revolutionary solutions for graph data structures. In this paper, we analyze the effect of combined GNN and multilayer perceptron (MLP) architecture to solve the node classification problem for different graph datasets. The feature information and network topology are efficiently captured by the GNN layer, and the MLP helps to make accurate decisions. We have selected popular datasets, namely Amazon-computer, Amazon-photo, Citeseer, Cora, Corafull, PubMed, and Wikics, for evaluating the performance of the proposed approach. In addition, in the GNN part, we have used six models to find the best model fit in the proposed architecture. We have conducted extensive simulations to find the node classification accuracy for the proposed model. The results show the proposed architecture can outperform previous studies in terms of test accuracy. In particular, the GNN algorithms SAGEConv, GENConv, and TAGConv show superior performance across different datasets. Full article

Accurate non-contact individual identification of pigs is crucial for their intelligent and efficient management. However, traditional recognition technologies generally suffer from weak local feature expression, feature redundancy, and insufficient channel importance modeling. To address these challenges, this study proposes a novel network model, DK-EffiPointMLP, for individual identification based on 3D dorsal point clouds. The model integrates a Dual-branch Local Feature enhancement module (DLF) and an Efficient Partial Convolution-Residual Refinement module (EffiConv). Specifically, the DLF module adopts a dual-branch structure of KNN and dilated KNN to expand the receptive field, while the EffiConv module combines 1D convolution with the SE mechanism to strengthen key channel modeling. To evaluate the model, a dataset of 10 individual pigs with 8411 samples was constructed. Experimental results show that DK-EffiPointMLP achieves accuracies of 96.86% on this self-built dataset and 95.2% on ModelNet40. When re-training all baseline models under the same pipeline and preprocessing protocols, our model outperformed existing mainstream models by 2.74 and 1.1 percentage points, respectively. This approach provides an efficient solution for automated management in commercial farming. Full article

Monocular depth estimation (MDE) is a cornerstone task in 2D/3D scene reconstruction and recognition with widespread applications in autonomous driving, robotics, and augmented reality. However, existing state-of-the-art methods face a fundamental trade-off between computational efficiency and estimation accuracy, limiting their deployment in resource-constrained real-world scenarios. It is of high interest to design lightweight but effective models to enable potential deployment on resource-constrained mobile devices. To address this problem, we present RepACNet, a novel lightweight network that addresses this challenge through reparameterized asymmetric convolution designs and CNN-based architecture that integrates MLP-Mixer components. First, we propose Reparameterized Token Mixer with Asymmetric Convolution (RepTMAC), an efficient block that captures long-range dependencies while maintaining linear computational complexity. Unlike Transformer-based methods, our approach achieves global feature interaction with tiny overhead. Second, we introduce Squeeze-and-Excitation Consecutive Dilated Convolutions (SECDCs), which integrates adaptive channel attention with dilated convolutions to capture depth-specific features across multiple scales. We validate the effectiveness of our approach through extensive experiments on two widely recognized benchmarks, NYU Depth v2 and KITTI Eigen. The experimental results demonstrate that our model achieves competitive performance while maintaining significantly fewer parameters compared to state-of-the-art models. Full article

Connected and Autonomous Vehicles (CAVs) are exposed to increasingly sophisticated cyber threats hidden within high-dimensional, heterogeneous network traffic. A critical bottleneck in existing Intrusion Detection Systems (IDS) is the feature heterogeneity gap: discrete protocol signatures (e.g., flags, services) and continuous traffic statistics (e.g., flow duration, packet rates) reside in disjoint latent spaces. Traditional deep learning approaches typically rely on naive feature concatenation, which fails to capture the intricate, non-linear semantic dependencies between these modalities, leading to suboptimal performance on long-tail, minority attack classes. This paper proposes HCA-IDS, a novel framework centered on Semantics-Aware Cross-Modal Alignment. Unlike heavy-weight models, HCA-IDS adopts a streamlined Multi-Layer Perceptron (MLP) backbone optimized for edge deployment. We introduce a dedicated Multi-Head Cross-Attention mechanism that explicitly utilizes static “Pattern” features to dynamically query and re-weight relevant dynamic “State” behaviors. This architecture forces the model to learn a unified semantic manifold where protocol anomalies are automatically aligned with their corresponding statistical footprints. Empirical assessments on the NSL-KDD and CICIDS2018 datasets, validated through rigorous 5-Fold Cross-Validation, substantiate the robustness of this approach. The model achieves a Macro-F1 score of over 94% on 7 consolidated attack categories, exhibiting exceptional sensitivity to minority attacks (e.g., Web Attacks and Infiltration). Crucially, HCA-IDS is ultra-lightweight, with a model size of approximately 1.00 MB and an inference latency of 0.0037 ms per sample. These results confirm that explicit semantic alignment combined with a lightweight architecture is key to robust, real-time intrusion detection in resource-constrained CAVs. Full article

In complex natural scenarios, facial expression recognition often encounters partial occlusions caused by glasses, hand gestures, and hairstyles, making it difficult for models to extract effective features and thereby reducing recognition accuracy. Existing methods often employ attention mechanisms to enhance expression-related features, but they fail to adequately address the issue where high-frequency responses in occluded regions can disperse attention weights (e.g., incorrectly focus on occluded areas), making it challenging to effectively utilize local cues around the occlusions and limiting performance improvement. To address this, this paper proposes a network based on an adaptive attention mechanism (Adaptive Attention Vision Transformer, AA-ViT). First, an Adaptive Attention module (ADA) is designed to dynamically adjust attention scores in occluded regions, enhancing the effective information in features. Next, a Dual-Branch Multi-Layer Perceptron (DB-MLP) replaces the single linear layer to improve feature representation and model classification capability. Additionally, a Random Erasure (RE) strategy is introduced to enhance model robustness. Finally, to address the issue of model training instability caused by class imbalance in the training dataset, a hybrid loss function combining Focal Loss and Cross-Entropy Loss is adopted to ensure training stability. Experimental results show that AA-ViT achieves expression recognition accuracies of 90.66% and 90.01% on the RAF-DB and FERPlus datasets, respectively, representing improvements of 4.58 and 18.9 percentage points over the baseline ViT model, with only a 24.3% increase in parameter count. Compared to existing methods, the proposed approach demonstrates superior performance in occluded facial expression recognition tasks. Full article

Sustainable pavement solutions that lessen the dependency on virgin materials are required due to mounting environmental and economic pressures. Although recycled asphalt concrete (RAC) has structural and environmental advantages, binder heterogeneity and non-linear material interactions make it difficult to predict the ideal bitumen content in RAC mixtures. This study predicts the bitumen content of asphalt mixtures infused with RAC by combining sophisticated machine learning (ML) with traditional laboratory testing. While this study combines AI-driven predictions with experimental insights to create a state-of-the-art framework for sustainable pavement engineering, 780 data points were obtained from the preparation and testing of three mixtures (0%, 30%, and 50% RAC) for volumetric and mechanical characteristics. Controlled Autoregressive Integrated Moving Average (CARIMA), Swapped Autoregressive Integrated Moving Average (SARIMA), radial basis function artificial neural network (RBF), bagging (BAG), multilayer perceptron (MLP) artificial neural network, and boosting (BOT) ensembles were among the models created. BAG-CARIMA-LGM is a new hybrid model that combines logistic probabilistic generalization, ensemble variance reduction, and time-series forecasting. Higher predictive accuracy and resilience across different RAC levels were attained by the hybrid BAG-CARIMA-LGM model, which performed noticeably better than standalone algorithms. The findings demonstrated improved Marshall stability and controlled flow along with a progressive decrease in mean bitumen content as RAC increased. While 50% RAC with rejuvenators maintained durability and structural integrity, the 30% RAC mixture produced the most balanced performance. The model’s capacity to manage non-linear interactions, volumetric variability, and aging effects was validated by statistical analyses. The BAG-CARIMA-LGM hybrid model optimizes RAC incorporation in asphalt mixtures, supports circular economy goals, and improves technical accuracy. The results point to a revolutionary route towards intelligent, environmentally friendly road systems that support international sustainability objectives. Full article