Improve Recognition Rate Research Articles

A Wushu Leg Gesture Recognition Algorithm Based on Random Forest and Bone Feature Extraction (RF-SFE)

Addressing the challenges of complex movement trajectories and rapid action changes in martial arts performances, this study introduces a novel posture recognition algorithm based on Random Forest and Skeletal Feature Extraction (RF-SFE) for martial arts leg movements. Unlike traditional posture recognition methods that struggle with accuracy, RF-SFE aims to provide intelligent analysis of training postures to assist practitioners in efficient training. The algorithm initially employs advanced skeletal feature extraction techniques to identify and articulate the relative positions and movements specific to martial arts. These extracted spatial features enhance the flexibility in modeling the unique dynamics of martial arts. Subsequently, Random Forest classification is utilized to categorize different leg movements, leveraging its strength in handling high-dimensional data and providing robust classification. Comparative experiments on diverse martial arts posture datasets demonstrate a significant improvement in recognition rates over baseline methods. This validates the effectiveness of the RF-SFE method in recognizing martial arts postures, offering scientific guidance for practitioners’ training regimens.

Enhancing target recognition rate in atmospheric turbulence using orbital angular momentum spectra of vortex beams

Abstract Traditional methods for extracting and recognizing targets from laser echo signals typically involve complex processing and require extensive data. Vortex beams carry orbital angular momentum (OAM), and upon reflection from a target, the distribution of the OAM spectrum carries features related to the target, thereby enriching the dimensions of target recognition. Using the OAM spectrum simplifies the recognition process but faces challenges like atmospheric turbulence that affect beam transmission and target recognition accuracy. Our study employs the Gerchberg–Saxton phase retrieval (GS) algorithm to mitigate the effects of atmospheric turbulence on the beams. Using OAM spectrum data, we achieved effective target recognition with various shapes under atmospheric turbulence through a back-propagation neural network (BPNN). Simulations revealed a recognition rate increase from 76.25% to 96% post-compensation by the GS algorithm. We also found that the highest recognition rate occurs at a target ratio of 0.2. After compensation with the GS algorithm at a target ratio of 0.1, the recognition rate for each shape increased to 99%. This demonstrates the effectiveness of utilizing the OAM spectrum for recognizing diverse target shapes, with the GS algorithm further improving recognition rates. These findings can be applied to intelligent transportation and robotic vision.

Voice recognition enhancement by genetic algorithm

This research paper investigates the application of Genetic Algorithms (GA) in optimizing Artificial Neural Networks (ANN) for phoneme recognition. The study examines the formalism of GA, their parameters, and operators, and describes the genetic strategy adopted for phoneme recognition using the TIMIT sound database. The paper presents the outcomes of experiments conducted on the phonemes of the test base and the DR1 dialect learning base of the TIMIT base, and compares the recognition rates obtained by learning and testing with those guided by Self-Organizing Map (SOM) experiments. The findings suggest that the GA algorithm provides improved recognition rates and extends the search space to a global optimum, but can sometimes produce unacceptable results due to rapid and premature convergence and the overfitting problem. The study proposes a hybrid model between SOM and GA to take advantage of each of their properties and improve recognition rates. The results show that the proposed approach achieves an average recognition rate of over 95%.

Mental health literacy of adolescents in Bermuda, according to age, gender and race.

Mental health literacy (MHL) is an important part of the help-seeking process, yet there is a lack of knowledge about the MHL of adolescents in the Caribbean. This region is important to study as it is underrepresented in mental health research globally. The aim of this study is to explore the ability of adolescents in Bermuda to recognize depression and social phobia (social anxiety) and their beliefs about the sources of help for a peer with these mental health problems. This cross-sectional study surveyed middle and high school students aged 10-19 years in Bermuda. Online surveys conducted between November 2022 and June 2023 gathered demographic data including age, gender and race, and assessed the ability to recognize depression and social anxiety from descriptions provided in randomly assigned vignettes, and beliefs about sources of help. Across 15 middle and high schools, 2423 adolescents (out of 3593 eligible participants) completed all demographic and MHL survey questions (1139 males, 1272 females). Recognition rates for depression and social anxiety were 60% and 53%, respectively. Compared to females, males endorsed a greater variety of help sources. Reporting symptoms of depression or anxiety reduced the likelihood of endorsing multiple sources of help. MHL of adolescents in Bermuda is sub-optimal, particularly for social anxiety. Mental health promotion programs may be useful in improving recognition rates.

Research on Mixed Reality Hand Interaction Technology in Flight Simulation Teaching

Abstract The integration of mixed reality simulation training in institutional education and teaching processes can yield significant quality benefits. Non-wearable interactive methods represent a more natural form of hand interaction within flight cockpits, covering various emerging fields such as mixed reality, computer vision, and human-computer interaction. This paper is guided by a “human-centered” approach to natural human-computer interaction and proposes a virtual hand mixed reality interaction scheme based on hand natural feature points. This study introduces a hand detection, segmentation, and recognition algorithm based on skin colour and key hand feature points. Initially, the algorithm uses the Otsu adaptive threshold algorithm in the YCbCr space for skin colour detection to accommodate varying image brightness levels. Subsequently, it employs a hand Keypoint model for rapid hand detection within skin-like areas while excluding interference from other regions. During the process of hand tracking, optimization prediction is performed using the particle filter algorithm combined with the artificial fish swarm algorithm to achieve tracking accuracy within six pixels in both horizontal and vertical directions. Gesture recognition involves extracting Fourier descriptive contour features of the hand bone structure through identification of feature points. Furthermore, this study combines the optimization capabilities of the artificial fish swarm algorithm to enhance support vector machine model parameter optimization for improved recognition rates. Testing involved 800 processed images as part of a test set for gesture recognition; only five commonly used gestures within flight cockpits were recognized and classified with an accuracy rate reaching up to 98%. Finally, this research presents virtual representations of common operational gestures including natural hand states, control stick manipulation, and throttle handling positions, button activations, and rotational movements via head-mounted displays or screens. The proposed mixed reality-based hand gesture interaction system addresses challenges related to complex background interference during hand detection in cockpit environments as well as issues arising from lighting disturbances. Additionally it resolves problems associated with insufficient particles leading to tracking failures while significantly enhancing registration effects during tracking processes. Moreover it mitigates issues related to Fourier descriptive features being influenced by background variations or changes in pose along with limitations pertaining to expressing singular forms of gestures thereby improving overall classification accuracy.

Recognition of car horns based on principal component analysis of MEL frequency Cepstral coefficients and support vector machine

With the rapid increase in the number of automobiles, the noise pollution caused by car horns is attracting more attention. In order to accurately identify car horn sounds in traffic noise and strengthen traffic law enforcement, research on car horn sound recognition is becoming increasingly important. In this paper, using Principal Component Analysis (PCA) to optimize and reduce the dimensionality of extracted Mel Frequency Cepstral Coefficients (MFCC), feature vectors are input into four models: Support Vector Machine (SVM), Back Propagation (BP) Neural Network, Extreme Learning Machine (ELM), and Random Forest (RF). These models are utilized to classify and identify car horn sounds from traffic noise. The impact of feature dimensions and Signal-to-noise ratio(SNR) on recognition performance is analyzed. The results show that under this method, the recognition rates of the four models can be increased by increasing the number of Mel filters and feature dimensions in MFCC, which can improve recognition performance, especially when the SNR is lower. The SVM model exhibits the most significant improvement in recognition rate. They achieve the best recognition rates for measured car horn sounds of 98.66 %, 93.98 %, 89.44 %, and 95.12 %, respectively. Experimental results demonstrate that the proposed method has good recognition performance and can be effectively applied to the supervision of car horn usage on actual roads.

Six-Axis Robotic Gripping Control Algorithm Based on Deep Learning

Due to several complex factors such as the type, size, and shape of target object, vision-assisted robot grasping technology still faces serious challenges. In this research, a deep learning-based robot hand vision grasping algorithm was developed considering semi-structural environmental constraints. The proposed algorithm could build a deep learning network on the basis of the desired object, perform object recognition, category classification and position judgment, and complete robot hand-grasping tasks. The obtained experimental results demonstrated that the proposed algorithm effectively solved the problem of recognizing and classifying multi-category objects in a semi-structured environment, improving recognition rate and grasping rate and reducing collision rate.

A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton.

Locomotion mode recognition in humans is fundamental for flexible control in wearable-powered exoskeleton robots. This article proposes a hybrid model that combines a dense convolutional network (DenseNet) and long short-term memory (LSTM) with a channel attention mechanism (SENet) for locomotion mode recognition. DenseNet can automatically extract deep-level features from data, while LSTM effectively captures long-dependent information in time series. To evaluate the validity of the hybrid model, inertial measurement units (IMUs) and pressure sensors were used to obtain motion data from 15 subjects. Five locomotion modes were tested for the hybrid model, such as level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending. Furthermore, the data features of the ramp were inconspicuous, leading to large recognition errors. To address this challenge, the SENet module was incorporated, which improved recognition rates to some extent. The proposed model automatically extracted the features and achieved an average recognition rate of 97.93%. Compared with known algorithms, the proposed model has substantial recognition results and robustness. This work holds promising potential for applications such as limb support and weight bearing.

Improvement of recognition rate using data augmentation with blurred images

Improvement of recognition rate using data augmentation with blurred images

Facial Expression Recognition Based on Deep Learning: A Review

This review paper provides a comprehensive analysis of recent advancements in Facial Expression Recognition (FER) through various deep learning models. Seven state-of-the-art models are scrutinized, each offering unique contributions to the field. The MBCC-CNN model demonstrates improved recognition rates on diverse datasets, addressing the challenges of facial expression recognition through multiple branches and cross-connected convolutional neural networks. The Deep Graph Fusion model introduces a novel approach for predicting viewer expressions from videos, showcasing superior performance on the EEV database. Multimodal emotion recognition is explored in the EEG and facial expression fusion model, achieving high accuracy on the DEAP dataset. The Spark-based LDSP-TOP descriptor, coupled with a 1-D CNN and LSTM Autoencoder, excels in capturing temporal dynamics for facial expression understanding. Vision transformers for micro-expression recognition exhibit outstanding accuracy on datasets like CASMEI, CASME-II, and SAMM. Additionally, a hierarchical deep learning model is proposed for evaluating teaching states based on facial expressions. Lastly, a visionary transformer model achieves remarkable recognition accuracy of 100% on SAMM dataset, showcasing the potential of combining convolutional and transformer architectures. This review synthesizes key findings, highlights model performances, and outlines directions for future research in FER.

Underwater acoustic target recognition method based on WA-DS decision fusion

Deep learning methods can recognize the various underwater acoustic targets in complex marine environments. In this study, we apply deep learning methods in decision recognition of underwater acoustic targets. However, decision recognition methods rely on a complex evidence theory that combines the probabilities of various scenarios as evidence sources, and in the process of combination produces conflicting evidence. For this reason, we propose an underwater acoustic target recognition method based on weighted average Dempster -Shafer (WA-DS) decision fusion, which replaces anomalous evidence with weighted average evidence, and solves the problem of synthesizing anomalous evidence. Firstly, the underwater acoustic dataset is preprocessed with downsampling, denoising, and audio segmentation. Then, the Mel-frequency cepstral coefficients (MFCC), Gammatone frequency cepstral coefficients (GFCC), and time–frequency (TF) features are extracted, and the category probabilities are output using the ResNet18 network to learn and train the multi-feature and then output the category probabilities. Finally, the category probabilities of multi-feature are computed as evidence using the Basic Probability Assignment (BPA) function, and the various types of evidence are fused using our decision fusion algorithm. Experimental results show that our decision fusion method is more capable of synthesizing anomalous evidence than traditional decision fusion methods. Our work achieves recognition accuracy of up to 98.34% on the ShipsEar dataset, which is a significant improvement in recognition rate compared to single features.

Research on feature selection algorithm for DVS vibration signal recognition rate improvement

Research on feature selection algorithm for DVS vibration signal recognition rate improvement

MSER: Multimodal speech emotion recognition using cross-attention with deep fusion

In human–computer interaction (HCI) and especially speech signal processing, emotion recognition is one of the most important and challenging tasks due to multi-modality and limited data availability. Nowadays, an intelligent system is required for real-world applications to efficiently process and understand the speaker's emotional state and to enhance the analytical abilities to assist communication by a human-machine interface (HMI). Designing a reliable and robust Multimodal Speech Emotion Recognition (MSER) to efficiently recognize emotions through multi-modality such as speech and text is necessary. This paper propose a novel MSER model with a deep feature fusion technique using a multi-headed cross-attention mechanism. The proposed model utilize audio and text cues to predict the emotion label accordingly. Our proposed model process the raw speech signal and text by CNN and feeds to corresponding encoders for discriminative and semantic feature extractions. The cross-attention mechanism is applied to both features to enhance the interaction between text and audio cues by crossway to extract the most relevant information for emotion recognition. Finally, combining the region-wise weights from both encoders enables interaction among different layers and paths by the proposed deep feature fusion scheme. The authors evaluate the proposed system using the IEMOCAP and MELD datasets and conduct extensive experiments that obtain state-of-the-art (SOTA) results and show a 4.5% improved recognition rate, respectively. Our model secured a significant improvement over SOTA methods, which shows the robustness and effectiveness of the proposed MSER model.

Rapid identification of the geographical origin of Baimudan tea using a Multi-AdaBoost model integrated with Raman Spectroscopy

The potential of Multi-AdaBoost in spectral analysis is substantial, particularly when combined with weak classifiers and trained to develop into a robust classifier. Given the variable quality of Baimudan tea sourced from diverse regions, the novel application of Raman spectroscopy in conjunction with the Multi-AdaBoost model to analyze the geographic origin of Baimudan tea was introduced. Initially, Raman spectra of Baimudan tea from four distinct origins in Fujian province were gathered, namely Fuan (FA), Fuding (FD), Zhenghe (ZH), and Songxi (SX). Decision Tree (DT) and Support Vector Machine (SVM) models were employed as fitting classifiers to construct the Multi-AdaBoost-DT and Multi-AdaBoost-SVM models. The results demonstrated that the Multi-AdaBoost-DT model exhibited significantly improved recognition rates for FA, FD, ZH, and SX origin compared to the DT model, with the average recognition rate increasing from 86.46% to 91.67%. In contrast, the recognition rates for FA and SX origin in the Multi-AdaBoost-SVM model remained unchanged, attributed to the model having reached a local optimum. The recognition rates of FD origin increased from 91.67% to 95.83%, a significant improvement, while those of ZH origin escalated from 83.33% to 87.50%. The average recognition rate increased from 92.71% to 94.79%. Additionally, Multi-AdaBoost-SVM and Multi-AdaBoost-DT enhanced the sensitivity and specificity of the discrimination outcomes. These results corroborated the effectiveness of the proposed Multi-AdaBoost-SVM model in identifying the geographical origin of Baimudan tea. Moreover, the Multi-AdaBoost model demonstrates potential in elevating the discrimination accuracy of weak classifiers, which bodes well for its application in food authentication and quality control.

FaceNet recognition algorithm subject to multiple constraints: Assessment of the performance

Literature has it that the performance of most face recognition algorithms still decline in multiple constrained environments (Occlusions and Expressions), despite the achieved successes of deep learning face recognition algorithms. Using expression variant test face images synthetically occluded at 30% and 40% rates, the study evaluated the performance of FaceNet deep learning model for face recognition under the aforementioned constraints and when three (3) statistical multiple imputation methods (Multivariable Imputation using Chain Equations (MICE), MissForest and Regularized Expectation Maximization (RegEM)) are adopted for occlusion recovery. Results of the study showed improved recognition rates of the study algorithm when the imputation-based recovered faces were used for recognition compared with using their multiple constrained counterparts. However, test faces reconstructed with the MissForest imputation method were more accurately recognized using the FaceNet deep learning algorithm. Furthermore, the study demonstrated that some simple augmentation schemes sufficed to further enhance the performance of the FaceNet model. Specifically, the FaceNet algorithms gave the highest average recognition rates (85.19% and 79.5% for 30% and 40% occlusion levels respectively) under augmentation scheme IV (slight rotations, horizontal flipping, shearing, brightness adjustments, and stretching) using MissForest as the de-occlusion mechanism. The study also found that, no disparity existed in its performance with the choice of either Support Vector Machines (SVM) or City Block (CB) for classification under augmentation scheme IV. The study recommends using the MissForest imputation method in dealing with moderately high occluded test faces with varying expressions to enhance the performance of the FaceNet face recognition model.

A review on speech emotion recognition: A survey, recent advances, challenges, and the influence of noise

Affective Computing systems can detect the emotional state and mindset of an individual. Speech Emotion Recognition (SER) is a unimodal affect computing system based on emotional speech data. It is an active area of research in pattern recognition, computer vision, and deep learning. There is a great deal of literature on SER, but only a few of these works consider how SER performs under noisy conditions. A few surveys exist to review SER, but they either need to cover all aspects of SER in noisy environments or discuss the details thoroughly. In recent years, researchers have had a growing interest in using SER in real-world conditions and have seen improvements in recognition rate. This review compiles the methods and approaches used in noisy SER in the literature up to the mid of 2023. It covers topics such as noisy SER methods, datasets used for SER under noisy conditions, noise used, and toolkits used for noisy SER recognition. Additionally, it focuses on classifiers, features used, and limitations of existing research in noisy SER systems. The review also seeks to answer "Does noise affects performance?" to which the answer is a resounding yes, as demonstrated by the results obtained from this survey.

OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video

In today’s era, video analysis is immensely involved in recognizing the sport-related movement that has become a significant part of human’s life. The intent of this approach is to know about the player’s activities with prior information of tracking objects. It also analyzes the player potential or capacity to lead the winning team. When the player frequently changes their location, object tracking and action recognition will become a quite challenging task. Over the game, various athletes or different objects are considered to assist the system to easily recognize the respective actions of the player. Most of the previous models have been implemented, yet, it faces such consequences to provide promising performance. To meet the pre-requisite, a new multi-athlete tracking model for action recognition in soccer sports is designed with deep learning approaches. Initially, the multi-object tracking video is offered as the input to pre-processing phase. Here, occlusion and background clutter removal and contrast enhancement techniques are utilized to perform pre-processing in the videos. Then, the pre-processed video is offered to the multi-object tracking phase, where the jersey number is observed during multi-object tracking to avoid the identity switch problem. Then, effective multi-object tracking is performed by adaptive YOLOv5. The parameters presented in the improved adaptive YOLOv5 are tuned by proposing a new algorithm as the Random-based Cheetah Red Deer Algorithm (RCRDA). Next, in the action recognition phase, the tracked object from the video is taken based on the Region of Interest (ROI) that is subjected to an action recognition model named Optimized Transformer-based Residual Network with Deep Convolutional Network (OTRN-DCN). At first, ROI is offered as the input to TRN for attaining the feature vectors. Then, the optimal weighted vector extraction is performed, where the weight is tuned by the developed RCRDA. Finally, the attained optimally weighted vectors are given to the DCN phase for attaining recognized action as output. Hence, the developed multi-object tracking and action recognition model will secure an improved recognition rate than the traditional framework.

Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People

Gesture recognition is a developing topic in current technologies. The focus is to detect human gestures by utilizing mathematical methods for human–computer interaction. Some modes of human–computer interaction are touch screens, keyboard, mouse, etc. All these gadgets have their merits and demerits while implementing versatile hardware in computers. Gesture detection is one of the vital methods to construct user-friendly interfaces. Generally, gestures are created from any bodily state or motion but typically originate from the hand or face. Therefore, this manuscript designs an Information Feedback Firefly Algorithm with Nested Deep Learning (IFBFFA-NDL) model for intelligent gesture recognition of visually disabled people. The presented IFBFFA-NDL technique exploits the concepts of DL with a metaheuristic hyperparameter tuning strategy for the recognition process. To generate a collection of feature vectors, the IFBFFA-NDL technique uses the NASNet model. For optimal hyperparameter selection of the NASNet model, the IFBFFA algorithm is used. To recognize different types of gestures, a nested long short-term memory classification model was used. For exhibiting the improvised gesture detection efficiency of the IFBFFA-NDL technique, a detailed comparative result analysis was conducted and the outcomes highlighted the improved recognition rate of the IFBFFA-NDL technique as 99.73% compared to recent approaches.

Re-Evaluation Method by Index Finger Position in the Face Area Using Face Part Position Criterion for Sign Language Recognition.

Several researchers have proposed systems with high recognition rates for sign language recognition. Recently, there has also been an increase in research that uses multiple recognition methods and further fuses their results to improve recognition rates. The most recent of these studies, skeleton aware multi-modal SLR (SAM-SLR), achieved a recognition rate of 98.00% on the RGB video of the Turkish Sign Language dataset AUTSL. We investigated the unrecognized parts of this dataset and found that some signs where the fingers touch parts of the face were not correctly recognized. The proposed method is as follows: First, those with slight differences in top-1 and top-2 evaluation values in the SAM-SLR recognition results are extracted and re-evaluated. Then, we created heatmaps of the coordinates of the index finger in one-handed sign language in the face region of the recognition result in the top-1 to top-3 training data of the candidates based on the face part criteria, respectively. In addition, we extracted four index finger positions from the test data where the index finger stayed longer and obtained the product of the heatmap values of these positions. The highest value among them was used as the result of the re-evaluation. Finally, three evaluation methods were used: the absolute and relative evaluation with two heatmaps and an evaluation method integrating the absolute and relative evaluation results. As a result of applying the proposed method to the SAM-SLR and the previously proposed model, respectively, the best method achieved 98.24% for the highest recognition rate, an improvement of 0.30 points.

군-등변 CNN의 성능 분석

In this paper, we analyze the mathematical structure of group-equivariant CNNs using the vector bundle theory in homogeneous space and compare and analyze their performances with a conventional CNN through various experiments. The group-equivariant CNN uses the group-equivariant convolution, which can be implemented with an increase in weight sharing compared to the existing convolution. It is shown that the symmetric group of a CNN operating in image space is Z² , whereas those of a group-equivariant CNNs are subgroups of SE(2) or E(2). According to the experimental results of three models, such as a CNN and group-equivariant CNNs, one of the latter shows a recognition rate improvement by more than 6.5% compared to the former, especially in the rotated MNIST. Therefore, it is proposed that the symmetric group of the CNN model can be used as a useful index to evaluate its performance.