Network For Recognition Research Articles

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Emotion is a human sense that can influence an individual’s life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

A deep neural network for hand gesture recognition from RGB image in complex background

A deep neural network for hand gesture recognition from RGB image in complex background

Reservoir Computing Based on Oxygen-Vacancy-Mediated X-ray Optical Synaptic Device for Medical CT Bone Diagnosis.

Recognition and judgment of X-ray computed tomography (CT) images play a crucial role in medical diagnosis and disease prevention. However, the storage and calculation of the X-ray imaging system applied in the traditional CT diagnosis is separate, and the pathological judgment is based on doctors' experience, which will affect the timeliness and accuracy of decision-making. In this paper, a simple-structured reservoir computing network (RC) is proposed based on Ga2O3 X-ray optical synaptic devices to recognize medical skeletal CT images with high accuracy. Through oxygen vacancy engineering, Ga2O3 X-ray optical synaptic devices with adjustable photocurrent gain and a persistent photoconductivity effect were obtained. By using the Ga2O3 X-ray optical synaptic device as a reservoir, we constructed an RC network for medical skeletal CT diagnosis and verified its image recognition capability using the MNIST data set with an accuracy of 78.08%. In the elbow skeletal CT image recognition task, the recognition rate is as high as 100%. This work constructs a simple-structured RC network for X-ray image recognition, which is of great significance in applications in medical fields.

ActiWise: Insight on Human Activity Recognition Using Deep Learning Approaches

In this study, we investigate the fusion of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for human activity recognition (HAR). By integrating hierarchical spatial features extracted by CNNs with LSTM networks' temporal modelling capabilities, our approach excels in discerning nuanced patterns from raw sensor data collected via wearable devices. Through rigorous experimentation and validation, our CNN+LSTM model demonstrates robust performance in accurately classifying a spectrum of human activities. This research advances HAR methodologies, shedding light on the synergistic interplay between spatial and temporal modelling in activity recognition, with implications across healthcare, sports analytics, and human-computer interaction domains. Index Terms- Human activity recognition, deep learning, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM).

Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

EEG-ConvNet: Convolutional networks for EEG-based subject-dependent emotion recognition

Biomedical researchers face a significant challenge in identifying emotions from electroencephalogram (EEG) signals due to their intricate and dynamic nature. The deep learning (DL) models, particularly the convolutional neural networks (CNNs), have shown significant potential in identifying the emotions for the EEG signals. However, most current DL models require complex feature engineering implicated in increased computational complexities. This research introduces a new CNN, i.e., the EEG-ConvNet, to overcome these limitations and challenges. The proposed EEG-ConvNet comprises five convolutional layers with batch normalization and max pooling. In addition, fine-tuning techniques improve the validation of pre-trained models. The study also employs the Short-time Fourier transform (STFT) and Mel spectrograms involving EEG signals from the SEED dataset. The suggested approaches effectively extract and organize emotion-related information from simple 2D spectrograms derived from 1D EEG data. The pre-trained GoogLeNet and ResNet-34 models are fine-tuned on these simple spectrograms to discover relevant features. For interpretability, the study employs explainable artificial intelligence (XAI) methods, specifically Gradient class activation mapping (Grad-CAM) and integrated gradients (IG). The STFT-based GoogLeNet and ResNet-34 models achieve accuracies of 99.97% and 99.95%, respectively. The Mel spectrogram-based GoogLeNet and ResNet-34 models achieve accuracies of 99.49% and 99.31%, respectively. The suggested EEG-ConvNet achieves an accuracy of 99.03% on STFT spectrograms. The EEG-ConvNet has a prediction time of only 6.5 ms, paving the way for real-time emotion recognition. While comparing with the previously published DL models, the proposed classification models exhibit better classification performances on the common SEED dataset.

A channel-fused gated temporal convolutional network for EMG-based gesture recognition

Background:EMG gesture recognition can be widely applied in many fields, such as prosthetic control and human–computer interaction, to enhance the standard of human life. Purpose:This study aims to develop a deep learning-based model to identify multiple complex gestures from raw EMG signals. Methods:We propose a new channel-fused gated temporal convolutional network. First, a channel fusion and gating mechanism is designed to improve temporal convolutional networks, allowing the model to obtain higher-level features. Second, we improve the channel fusion module by the short-term average energy to fuse the EMG signals of multi-channels more accurately. Results:The model is evaluated on a public dataset of EMG gestures, NinaPro DB5. Results demonstrated that the unbalanced accuracy rate of 53 gesture actions reaches 92.71%, and the balanced accuracy rate 74.79%. Further, ablation experiments validates the effectiveness of each model module. Conclusions:This study demonstrates our proposed approach can improve gesture recognition accuracy for complex gestures and has great potential for practical applications.

Implementation on FingerScripter: Converting Fingerspelling into Text and Speech

Abstract: Communication might seem a simple act for normal people, but for the ones who are suffering from physical or psychological disability find it very difficult for communicating with others. This communication barrier needs to be removed for which there are various techniques that are being used, out of which some are verbal, non-verbal, visual and written. For mute and deaf people these communication barrier is a major problem which can adversely affect their lives adding to their already challenging lives. For this sign languages are the best possible option, as they bridge the gap between communication of these deaf and mute people with normal people. We have created a machine learning model in order to solve this issue which will identify these signs and interpret them to detect which alphabet is signified. The model will be detecting the American Sign Language (ASL) which is one of the most widely used sign language and a type of fingerspelling. The model will form words out of the identified alphabets which will also be then converted into speech. The key algorithm which will be used is a special type of Convolutional Neural Network (CNN) for image recognition. All the documentation regarding the model along with its working is provided in this paper. With high precision and accuracy the model aims to ease the communication of deaf and mute people.

Capsule Broad Learning System Network for Robust Synthetic Aperture Radar Automatic Target Recognition with Small Samples

The utilization of deep learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has witnessed a recent surge owing to its remarkable feature extraction capabilities. Nonetheless, deep learning methodologies are often encumbered by inadequacies in labeled data and the protracted nature of training processes. To address these challenges and offer an alternative avenue for accurately extracting image features, this paper puts forth a novel and distinctive network dubbed the Capsule Broad Learning System Network for robust SAR ATR (CBLS-SARNET). This novel strategy is specifically tailored to cater to small-sample SAR ATR scenarios. On the one hand, we introduce a United Division Co-training (UDC) Framework as a feature filter, adeptly amalgamating CapsNet and the Broad Learning System (BLS) to enhance network efficiency and efficacy. On the other hand, we devise a Parameters Sharing (PS) network to facilitate secondary learning by sharing the weight and bias of BLS node layers, thereby augmenting the recognition capability of CBLS-SARNET. Experimental results unequivocally demonstrate that our proposed CBLS-SARNET outperforms other deep learning methods in terms of recognition accuracy and training time. Furthermore, experiments validate the generalization and robustness of our novel method under various conditions, including the addition of blur, Gaussian noise, noisy labels, and different depression angles. These findings underscore the superior generalization capabilities of CBLS-SARNET across diverse SAR ATR scenarios.

Crossmixed convolutional neural network for digital speech recognition.

Digital speech recognition is a challenging problem that requires the ability to learn complex signal characteristics such as frequency, pitch, intensity, timbre, and melody, which traditional methods often face issues in recognizing. This article introduces three solutions based on convolutional neural networks (CNN) to solve the problem: 1D-CNN is designed to learn directly from digital data; 2DS-CNN and 2DM-CNN have a more complex architecture, transferring raw waveform into transformed images using Fourier transform to learn essential features. Experimental results on four large data sets, containing 30,000 samples for each, show that the three proposed models achieve superior performance compared to well-known models such as GoogLeNet and AlexNet, with the best accuracy of 95.87%, 99.65%, and 99.76%, respectively. With 5-10% higher performance than other models, the proposed solution has demonstrated the ability to effectively learn features, improve recognition accuracy and speed, and open up the potential for broad applications in virtual assistants, medical recording, and voice commands.

Cross Stage Partial Dilated Convolution Network for License Plate Recognition

Cross Stage Partial Dilated Convolution Network for License Plate Recognition

Research on the innovative application of Shen Embroidery cultural heritage based on convolutional neural network

In order to protect intangible cultural heritage and promote outstanding cultural works, this article introduces innovative research on Shen Embroidery using convolutional neural networks. The dataset of Shen Embroidery was preprocessed to augment the data required for experimentation. Moreover, the approach of transfer learning was introduced to fine-tune the recognition network. Specifically, Spatial Pyramid Pooling (SPP) is employed by replacing the avg pool in the MobileNet V1 network, achieving the fusion of local and global features. The experimental results showed that the improved MobileNet V1 achieved a recognition accuracy of 98.45%, which was 2.3% higher than the baseline MobileNet V1 network. The experiments demonstrated that the improved convolutional neural network can efficiently recognize Shen Embroidery and provide technical support for the intelligent development of intangible cultural heritage.

Utilizing Deep Neural Networks for Chrysanthemum Leaf and Flower Feature Recognition

Chrysanthemums, a significant genus within the Asteraceae, hold a paramount position in the global floricultural industry, second only to roses in market demand. The proliferation of diverse chrysanthemum cultivars presents a formidable challenge for accurate identification, exacerbated by the abundance of varieties, intricate floral structures, diverse floret types, and complex genetic profiles. Precise recognition of chrysanthemum phenotypes is indispensable to navigating these complexities. Traditional methods, including morphology studies, statistical analyses, and molecular markers, have fallen short due to their manual nature and time-intensive processes. This study presents an innovative solution employing deep learning techniques for image-based chrysanthemum phenotype recognition. Leveraging machine learning, our system autonomously extracts key features from chrysanthemum images, converting morphological data into accessible two-dimensional representations. We utilized Support Vector Machine (SVM) and Multilayer Perceptron (MLP) algorithms to construct frameworks for processing image data and classifying chrysanthemum cultivars based on color, shape, and texture. Experimental results, encompassing 10 cultivars, 10 flower colors, and five flower shapes, consistently demonstrated recognition accuracy ranging from 79.29% up to 97.86%. This tool promises streamlined identification of flower traits, and we anticipate its potential for real-time identification enhancements in future iterations, promising advances in chrysanthemum cultivation and exportation processes. Our approach offers a novel and efficient means to address the challenges posed by the vast diversity within chrysanthemum species, facilitating improved management, breeding, and marketing strategies in the floricultural industry.

Multiscale temporal network for continuous sign language recognition

Multiscale temporal network for continuous sign language recognition

R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption

Point clouds obtained with 3D scanners in realistic scenes inevitably contain corruption, including noise and outliers. Traditional algorithms for cleaning point cloud corruption require the selection of appropriate parameters based on the characteristics of the scene, data, and algorithm, which means that their performance is highly dependent on the experience and adaptation of the algorithm itself to the application. Three-dimensional object recognition networks for real-world recognition tasks can take the raw point cloud as input and output the recognition results directly. Current 3D object recognition networks generally acquire uniform sampling points by farthest point sampling (FPS) to extract features. However, sampled defective points from FPS lower the recognition accuracy by affecting the aggregated global feature. To deal with this issue, we design a compensation module, named offset-adjustment (OA). It can adaptively adjust the coordinates of sampled defective points based on neighbors and improve local feature extraction to enhance network robustness. Furthermore, we employ the OA module to build an end-to-end network based on PointNet++ framework for robust point cloud recognition, named R-PointNet. Experiments show that R-PointNet reaches state-of-the-art performance by 92.5% of recognition accuracy on ModelNet40, and significantly outperforms previous networks by 3–7.7% on the corruption dataset ModelNet40-C for robustness benchmark.

Water transport in PEMFC with metal foam flow fields: Visualization based on AI image recognition

Water transport is an essential process in proton exchange membrane fuel cells (PEMFC). For the novel metal foam flow fields, water transport is difficult to be visualized and still remains inadequately understood. In this work, for the first time, the two-phase flows in the metal foam flow fields of a transparent PEMFC was visualized by using an artificial intelligence (AI) image recognition method. In order to distinguish water from the metal foam ligaments, a scoring system was developed, which effectively solved the problem of inconsistent reflectance of liquid water between the ligament and the gas diffusion layer. According to the state of liquid water, the scoring system categorized the local images into three labels of waterless state, wet state, and slug state for AI image recognition network training. The trained AI image recognition model was then used to visualize the liquid water state in the metal foam flow fields under different operating conditions of relative humidity (RH), temperature, and inlet flow rate. Results show that, since the porous structure of the metal foam provides flexible transport paths, the PEMFC can operate effectively in a wide range of wet states. Even a 25% difference in the wet-state percentage between different operating conditions yields an almost consistent voltage. The increased slugs in the flow fields are the primary reason for the performance degradation of the PEMFC. At a high current density of 2 A/cm2, the voltage is as low as 0.03 V when the slug state occupies 35% of the flow fields (100% RH, 60 °C, 1.0 SLPM cathode inlet flow rate), and as high as 0.56 V when the slug state is absent in the flow fields (100% RH, 60 °C, 2.0 SLPM cathode inlet flow rate). The present work provides a novel approach for the studies of water transport in PEMFC.

Lightweight Encoder with Attention Mechanism for Pipe Recognition Network

Lightweight Encoder with Attention Mechanism for Pipe Recognition Network

Quantum-Inspired Vortex Search Algorithm with Deep Neural Networks for Multi-Pose Facial Expression Recognition

Quantum-Inspired Vortex Search Algorithm with Deep Neural Networks for Multi-Pose Facial Expression Recognition

The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks.

We aimed to develop and validate a deep convolutional neural network (DCNN) model capable of accurately identifying spondylolysis or spondylolisthesis on lateral or dynamic X-ray images. A total of 2449 lumbar lateral and dynamic X-ray images were collected from two tertiary hospitals. These images were categorized into lumbar spondylolysis (LS), degenerative lumbar spondylolisthesis (DLS), and normal lumbar in a proportional manner. Subsequently, the images were randomly divided into training, validation, and test sets to establish a classification recognition network. The model training and validation process utilized the EfficientNetV2-M network. The model's ability to generalize was assessed by conducting a rigorous evaluation on an entirely independent test set and comparing its performance with the diagnoses made by three orthopedists and three radiologists. The evaluation metrics employed to assess the model's performance included accuracy, sensitivity, specificity, and F1 score. Additionally, the weight distribution of the network was visualized using gradient-weighted class activation mapping (Grad-CAM). For the doctor group, accuracy ranged from 87.9 to 90.0% (mean, 89.0%), precision ranged from 87.2 to 90.5% (mean, 89.0%), sensitivity ranged from 87.1 to 91.0% (mean, 89.2%), specificity ranged from 93.7 to 94.7% (mean, 94.3%), and F1 score ranged from 88.2 to 89.9% (mean, 89.1%). The DCNN model had accuracy of 92.0%, precision of 91.9%, sensitivity of 92.2%, specificity of 95.7%, and F1 score of 92.0%. Grad-CAM exhibited concentrations of highlighted areas in the intervertebral foraminal region. We developed a DCNN model that intelligently distinguished spondylolysis or spondylolisthesis on lumbar lateral or lumbar dynamic radiographs.

ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition.

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher's gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher's skeleton and then inputs the recognized sequence of the teacher's skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed.