Network For Recognition Research Articles

Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition

Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition

Automated detection of stale beef from electronic nose data

AbstractAccurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K‐nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10‐fold cross‐validation technique. All these methods reached an accuracy of 95% or above, with F1 scores and AUC values above 0.96. The support vector machine algorithm outperformed all other models, achieving perfect recognition with 100% accuracy and F1/AUC scores of 1.0. Our study demonstrates that electronic nose data combined with support vector machine can be used to successfully discriminate between stale and fresh beef, paving the way for novel research directions in the detection of stale beef.

Detecting command injection attacks in web applications based on novel deep learning methods

Web command injection attacks pose significant security threats to web applications, leading to potential server information leakage or severe server disruption. Traditional detection methods struggle with the increasing complexity and obfuscation of these attacks, resulting in poor identification of malicious code, complicated feature extraction processes, and low detection efficiency. To address these challenges, a novel detection model, the Convolutional Channel-BiLSTM Attention (CCBA) model, is proposed, leveraging deep learning techniques to enhance the identification of web command injection attacks. The model utilizes dual CNN convolutional channels for comprehensive feature extraction and employs a BiLSTM network for bidirectional recognition of temporal features. An attention mechanism is also incorporated to assign weights to critical features, improving the model’s detection performance. Experimental results demonstrate that the CCBA model achieves 99.3% accuracy and 98.2% recall on a real-world dataset. To validate the robustness and generalization of the model, tests were conducted on two widely recognized public cybersecurity datasets, consistently achieving over 98% accuracy. Compared to existing methods, the proposed model offers a more effective solution for identifying web command injection attacks.

Occluded facial expression recognition based on deep learning

Abstract. When employing Convolutional Neural Networks (CNNs) for facial expression recognition, several challenges are often encountered, such as facial occlusions, the limited size of reliable expression datasets, and inadequate precision in recognition outcomes. This paper preprocesses the dataset to enhance its reliability. By leveraging data synthesis and augmentation techniques, it employs a method of randomly generating occlusion blocks to integrate and expand the dataset. Based on the ResNet-18 network, the model is optimized by incorporating an attention mechanism, thereby improving the network's precision and robustness in recognizing facial expressions.

RJAN: Region‐based joint attention network for 3D shape recognition

AbstractAs an essential field of multimedia and computer vision, 3D shape recognition has attracted much research attention in recent years. Multiview‐based approaches have demonstrated their superiority in generating effective 3D shape representations. Typical methods usually extract the multiview global features and aggregate them together to generate 3D shape descriptors. However, there exist two disadvantages: First, the mainstream methods ignore the comprehensive exploration of local information in each view. Second, many approaches roughly aggregate multiview features by adding or concatenating them together. The information loss for some discriminative characteristics limits the representation effectiveness. To address these problems, a novel architecture named region‐based joint attention network (RJAN) was proposed. Specifically, the authors first design a hierarchical local information exploration module for view descriptor extraction. The region‐to‐region and channel‐to‐channel relationships from different granularities can be comprehensively explored and utilised to provide more discriminative characteristics for view feature learning. Subsequently, a novel relation‐aware view aggregation module is designed to aggregate the multiview features for shape descriptor generation, considering the view‐to‐view relationships. Extensive experiments were conducted on three public databases: ModelNet40, ModelNet10, and ShapeNetCore55. RJAN achieves state‐of‐the‐art performance in the tasks of 3D shape classification and 3D shape retrieval, which demonstrates the effectiveness of RJAN. The code has been released on https://github.com/slurrpp/RJAN.

DMFGAN: a multifeature data augmentation method for grape leaf disease identification.

The use of deep learning techniques to identify grape leaf diseases relies on large, high-quality datasets. However, a large number of images occupy more computing resources and are prone to pattern collapse during training. In this paper, a depth-separable multifeature generative adversarial network (DMFGAN) was proposed to enhance grape leaf disease data. First, a multifeature extraction block (MFEB) based on the four-channel feature fusion strategy is designed to improve the quality of the generated image and avoid the problem of poor feature learning ability of the adversarial generation network caused by the single-channel feature extraction method. Second, a depth-based D-discriminator is designed to improve the discriminator capability and reduce the number of model parameters. Third, SeLU activation function was substituted for DCGAN activation function to overcome the problem that DCGAN activation function was not enough to fit grape leaf disease image data. Finally, an MFLoss function with a gradient penalty term is proposed to reduce the mode collapse during the training of generative adversarial networks. By comparing the visual indicators and evaluation indicators of the images generated by different models, and using the recognition network to verify the enhanced grape disease data, the results show that the method is effective in enhancing grape leaf disease data. Under the same experimental conditions, DMFGAN generates higher quality and more diverse images with fewer parameters than other generative adversarial networks. The mode breakdown times of generative adversarial networks in training process are reduced, which is more effective in practical application.

An Improved Multi-Scale Feature Extraction Network for Rice Disease and Pest Recognition

In the process of rice production, rice pests are one of the main factors that cause rice yield reduction. To implement prevention and control measures, it is necessary to accurately identify the types of rice pests and diseases. However, the application of image recognition technologies focused on the agricultural field, especially in the field of rice disease and pest identification, is relatively limited. Existing research on rice diseases and pests has problems such as single data types, low data volume, and low recognition accuracy. Therefore, we constructed the rice pest and disease dataset (RPDD), which was expanded through data enhancement methods. Then, based on the ResNet structure and the convolutional attention mechanism module, we proposed a Lightweight Multi-scale Feature Extraction Network (LMN) to extract multi-scale features at a finer granularity. The proposed LMN model achieved an average classification accuracy of 95.38% and an F1-Score of 94.5% on the RPDD. The parameter size of the model is 1.4 M, and the FLOPs is 1.65 G. The results suggest that the LMN model performs rice disease and pest classification tasks more effectively than the baseline ResNet model by significantly reducing the model size and improving accuracy.

A GCN and Transformer complementary network for skeleton-based action recognition

Graph Convolution Networks (GCNs) have been widely used in skeleton-based action recognition. Although there are significant progress, the inherent limitation still lies in the restricted receptive field of GCN, hindering its ability to extract global dependencies effectively. And the joints that are structurally separated can also have strong correlation. Previous works rarely explore local and global correlations of joints, leading to insufficiently model the complex dynamics of skeleton sequences. To address this issue, we propose a GCN and Transformer complementary network (GTC-Net) that allows parallel communications between GCN and Transformer domains. Specifically, we introduce a graph convolution and self-attention combined module (GAM), which can effectively leverage the complementarity of GCN and self-attention to perceive local and global dependencies of joints for the human body. Furthermore, in order to address the problems of long-term sequence ordering and position detection, we design a position-aware module (PAM), which can explicitly capture the ordering information and unique identity information for body joints of skeleton sequence. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets are conducted to evaluate our proposed method. The results demonstrate that our method can achieve competitive results on both datasets.

YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO

Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively integrates multi-scale features through local and global attention mechanisms. Additionally, the CIOUNMS algorithm addresses the issue of target loss in cases of high overlap, improving boundary box filtering. Evaluated on the VOC2012 pedestrian dataset, YOLO-ESL achieved an accuracy of 93.7%, surpassing the baseline model by 3.0%. Compared to existing methods, this model not only demonstrates strong performance in handling occluded and small object detection but also remarkable robustness and efficiency.

ASK-HAR: Attention-Based Multi-Core Selective Kernel Convolution Network for human activity recognition

Human Activity Recognition (HAR) is an increasingly popular field of study aimed at automatically identifying and categorizing human movements and activities using various tracking devices and measurements, such as sensors and cameras. Smartphones have emerged as a prevalent sensor modality for HAR, offering abundant data on an individual’s movements through GPS, accelerometers, and gyroscopes. In conventional convolutional neural networks (CNNs), the artificial neurons within each feature layer typically possess identical receptive fields (RFs). This paper introduces a novel model, ASK-HAR (Attention-based Multi-Core Selective Kernel Convolution Network for HAR), which enhances HAR by performing kernel selection between multiple branches with different RFs using attention mechanisms. The selective kernel mechanism is leveraged to optimize HAR performance. Additionally, the CBAM attention module is employed for time series feature extraction and activity recognition within the overall framework. Extensive experiments conducted on benchmark HAR datasets, including UCI-HAR, USC-HAD, WISDM, PAMAP2, and DSADS, demonstrate that the ASK-HAR model consistently achieves high accuracy across all datasets.

3D skeleton aware driver behavior recognition framework for autonomous driving system

The recognition of the driver’s behaviors inside an autonomous vehicle can effectively address emergency handling in autonomous driving and is crucial for ensuring the driver’s safety. Driver behavior recognition is a challenging task due to factors such as variations, diversities, complexities, and strong interferences in behaviors. In this paper, to realize the application in the autonomous driving scenes, a novel 3D skeleton aware behavior recognition framework is proposed to recognize various driver behaviors in autonomous driving systems. First, a 3D human pose estimation network (Pose-GTFNet) with temporal Transformer and spatial graph convolutional network (GCN) is designed to infer 3D human poses from 2D pose sequences. Second, based on the obtained 3D human pose sequences, a behavior recognition network (Beh-MSFNet) with multi-skeleton feature fusion is designed to recognize driver behaviors. In the experiments, the Pose-GTFNet and Beh-MSFNet can get the best performance compared with most state-of-the-art (SOTA) methods on the Human3.6M human pose dataset, JHMDB and SHREC action recognition dataset, respectively. In addition, the proposed driver behavior recognition framework can achieve SOTA performance on the Drive&Act and Driver-Skeleton driver behavior datasets.

Micro-expression recognition method integrating LBP and parallel attention mechanism

Aiming at the problems of weak intensity of facial micro-expression changes, background noise interference and low feature differentiation, a micro-expression recognition network integrating LBP and parallel attention mechanism is proposed. The network inputs RGB images into the densely connected improved Shuffle Stage branch to extract global facial features and enhance the association of contextual semantic information; the LBP image is input into the local texture feature branch composed of multi-scale hierarchical convolutional neural network to extract detail information; after the dual-branch feature extraction, the parallel attention mechanism is introduced at the back end of the network to improve the feature fusion ability, suppress background interference, and focus on the micro-expression feature interest area; the proposed method is tested on three public datasets including CASME, CASME II and SMIC, and the recognition accuracy rates are , and respectively ; the experimental results show that the proposed method effectively improves the accuracy of micro-expression recognition, which is better than many current advanced methods.

Application of neural network for automatic symbol recognition in production of electronic navigation charts from paper charts

Abstract This research boarded on a novel initiative to replace the error-prone and labour-intensive process of converting Paper Nautical Chart (PNC) symbols to Electronic Navigational Chart (ENC) symbols with a more efficient and automated manner using Artificial Intelligence (AI). The proposed method applies the Convolutional Neural Network and YOLOv5 model to recognise and convert symbols from PNC into their corresponding ENC formats. The model's competence was evaluated with performance metrics including Precision, Recall, Average Precision, and mean Average Precision. Among the different variations of the YOLOv5 models tested, the YOLOv5m version revealed the best performance achieving a mean Average Precision of 0 ⋅ 837 for all features. A confusion matrix was used to visualise the model's classification accuracy for various chart symbols, underlining strengths and identifying areas for improvements. While the model has demonstrated high ability in identifying symbols like ‘Obstruction’ and ‘Major/Minor Lights’, it exhibited lesser accuracy with ‘Visible Wreck’ and ‘Background’ categories. Further, the developed graphical user interface (GUI) allowed users to interact with the artificial neural network model without demanding detailed knowledge of the underlying programming or model architecture.

Lightweight Multifeature Fusion Network for Finger Vein Recognition

Lightweight Multifeature Fusion Network for Finger Vein Recognition

RAPNet: Resolution-Adaptive and Predictive Early Exit Network for Efficient Image Recognition

RAPNet: Resolution-Adaptive and Predictive Early Exit Network for Efficient Image Recognition

A Multi-task Neural Network for Image Recognition in Magnetically Controlled Capsule Endoscopy.

Physicians are required to spend a significant amount of reading time of magnetically controlled capsule endoscopy. However, current deep learning models are limited to completing a single recognition task and cannot replicate the diagnostic process of a physician. This study aims to construct a multi-task model that can simultaneously recognize gastric anatomical sites and gastric lesions. A multi-task recognition model named Mul-Recog-Model was established. The capsule endoscopy image data from 886 patients were selected to construct a training set and a test set for training and testing the model. Based on the same test set, the model in this study was compared with the current single-task recognition model with good performance. The sensitivity and specificity of the model for recognizing gastric anatomical sites were 99.8% (95% confidence intervals: 99.7-99.8) and 98.5% (95% confidence intervals: 98.3-98.7), and for gastric lesions were 98.8% (95% confidence intervals: 98.3-99.2) and 99.4% (95% confidence intervals: 99.1-99.7). Moreover, the positive predictive value, negative predictive value, and accuracy of the model were more than 95% in recognizing gastric anatomical sites and gastric lesions. Compared with the current single-task recognition model, our model showed comparable sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (p < 0.01, except for the negative predictive value of ResNet, p > 0.05). The Areas Under Curve of our model were 0.985 and 0.989 in recognizing gastric anatomical sites and gastric lesions. Furthermore, the model had 49.1M parameters and 38.1G Float calculations. The model took 15.5ms to recognize an image, which was less than the superposition of multiple single models (p < 0.01). The Mul-Recog-Model exhibited high sensitivity, specificity, PPV, NPV, and accuracy. The model demonstrated excellent performance in terms of parameters quantity, Float computation, and computing time. The utilization of the model for recognizing gastric images can improve the efficiency of physicians' reports and meet complex diagnostic requirements.

HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.

EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.

Rapid Aircraft Wake Vortex Identification Model Based on Optimized Image Object Recognition Networks

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model’s feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network’s focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights.

Dynamical semantic enhancement network for continuous sign language recognition

Dynamical semantic enhancement network for continuous sign language recognition

Efficient multi‐perspective jamming feature perception network for suppressive jamming recognition with limited training samples

Efficient multi‐perspective jamming feature perception network for suppressive jamming recognition with limited training samples