Recognition Algorithm Research Articles

Developing a Knowledge-Based Online Recommendation System to Guide STD Patients Through Their Journey

Sexually transmitted diseases (STDs) significantly impact public health, affecting one in five U.S. adults and imposing substantial economic burdens. Many at-risk individuals seek information and support online rather than through regular testing, facing challenges due to fragmented information and a lack of personalized recommendations. This study develops an online health recommendation system (OHRS) tailored for STD patients, integrating informational and emotional support based on their disease journey. Using a BERT-based named entity recognition (NER) algorithm, the system identifies patient emotions and stages from online posts, providing relevant support resources. Data collection for designing a recommendation engine included analyzing online posts and consulting healthcare experts. The system's effectiveness was validated through user-based simulation studies. Key contributions include developing text-mining algorithms, creating a knowledge-based recommendation system, and proposing design principles for AI-driven support systems for stigmatized conditions.

Brhamo: metaheuristic optimization algorithm for speech emotion recognition using spectral and hybrid features

Brhamo: metaheuristic optimization algorithm for speech emotion recognition using spectral and hybrid features

A Projected OMP-Hybridized Discriminative K-SVD-Based Dictionary Learning Algorithm for Human Activity Recognition From Accelerometer Signals

A Projected OMP-Hybridized Discriminative K-SVD-Based Dictionary Learning Algorithm for Human Activity Recognition From Accelerometer Signals

LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches

The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require significant computational power and storage, making them unsuitable for low-power devices like smartwatches. Additionally, discrepancies between training data and real-world data often hinder model generalization and performance. To address these challenges, we propose LIMUNet and its smaller variant LIMUNet-Tiny—lightweight neural networks designed for human activity recognition on smartwatches. LIMUNet utilizes depthwise separable convolutions and residual blocks to reduce computational complexity and parameter count. It also incorporates a dual attention mechanism specifically tailored to smartwatch sensor data, improving feature extraction without sacrificing efficiency. Experiments on the PAMAP2 and LIMU datasets show that the LIMUNet improves recognition accuracy by 2.9% over leading lightweight models while reducing parameters by 88.3% and computational load by 58.4%. Compared to other state-of-the-art models, LIMUNet achieves a 9.6% increase in accuracy, with a 60% reduction in parameters and a 57.8% reduction in computational cost. LIMUNet-Tiny further reduces parameters by 75% and computational load by 80%, making it even more suitable for resource-constrained devices.

Air Handwriting by Using CNN Model

Gesture recognition has been a popular research field under the trend of IoT and intelligent devices. Air-writing is the most challenging and crucial topic in the gesture recognition field. In this paper, we propose a wearable airwriting system that makes users can write the English alphabet in the three-dimensional space without any write rules. The proposed system is based on the Inertial Measurement Unit (IMU), and it uses dynamic time warping (DTW) as the main recognition algorithm. In addition, to improve the recognition accuracy and take a better advantage of the DTW algorithm, we present an adjustment system that gives some new optimization methods to the application of IMU and DTW. In the experiment, the accuracy of recognition is 84.6% for the uppercase alphabet (from „A‟ to „Z‟) in user-dependent case. And we also confirmed that the recognition method only based on the DTW algorithm is one kind of user-dependent methods, which means this method is heavily dependent on personalization.

A Stable Gait Recognition Algorithm Under Multiview and Multiwear Using Millimeter-Wave Radar

A Stable Gait Recognition Algorithm Under Multiview and Multiwear Using Millimeter-Wave Radar

A novel linetype recognition algorithm based on track geometry detection data

Abstract The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm's potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses.

Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.

Fast Identification of Critical Nodes in Complex Network Based on Improved Greedy Algorithm

Abstract Over the past decades, many critical and complex systems, such as power grid, transportation network, and information network, have been effectively modeled using complex network. However, these networks are susceptible to cascading failure, triggered by minor failure, leading to partial or total collapse. Preventing cascading failure necessitates the protection of critical nodes within the network, making the identification of these nodes particularly crucial. In this paper, we introduce an Improved Greedy Algorithm (IGA), inspired by the traditional greedy algorithm and the relationship between the propagation mechanism of cascading failure and N-K failure. This algorithm gets rid of the shortcomings of traditional recognition algorithms for dealing with large-scale networks with long time and low accuracy, and evaluates the critical degree of nodes based on network connectivity and overload rate. The simulation is carried out in Barabsi-Albert (BA) network and IEEE 39-, 118-bus systems, and make comparisons with other different algorithms. The results show that IGA not only has low computational complexity, but also has high accuracy in identifying critical nodes in complex networks.

Movement Recognition and Injury Risk Assessment of Wushu Athletes Based on Computer Vision

This paper aims to study a method of movement recognition and injury risk assessment for Wushu athletes based on computer vision. Studying the optimization of Wushu athletes’ movement trajectory can effectively improve the athletes’ movement quality. Based on a hybrid real-time synchronization algorithm, the arm motion trajectory model of martial arts athletes is studied. A dynamic and static arm recognition algorithm is proposed, and the motion feature extraction method in Wushu movement is studied. Dynamic arm recognition typically relies on the collection of video or image sequences. These sequences contain the position, shape, and motion trajectory of the arm at different time points. Static arm recognition mainly relies on the acquisition of a single image or image frame. The dynamic arm recognition algorithm captures the motion trajectory and changes of the arm by processing video or image sequences, while the static arm recognition algorithm only processes individual images or image frames. On this basis, the design of the computer software architecture of the digital site system is completed. The real-time collection, analysis, display and storage of motion information and assembly configuration are realized. The simulation results show that the method has high accuracy and provides a powerful scientific basis for improving athletes’ movement injuries. To predict the risk of injury an athlete may suffer during training or competition.

Evaluation of Cluster Algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving

Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable and robust navigation system. Radar, in particular, operates with electromagnetic waves and remains effective under a variety of weather conditions. It uses point cloud technology to map the objects in front of you, making it easy to group these points to associate them with real-world objects. Numerous clustering algorithms have been developed and can be integrated into radar systems to identify, investigate, and track objects. In this study, we evaluate several clustering algorithms to determine their suitability for application in automotive radar systems. Our analysis covered a variety of current methods, the mathematical process of these methods, and presented a comparison table between these algorithms, including Hierarchical Clustering, Affinity Propagation Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mini-Batch K-Means, K-Means Mean Shift, OPTICS, Spectral Clustering, and Gaussian Mixture. We have found that K-Means, Mean Shift, and DBSCAN are particularly suitable for these applications, based on performance indicators that assess suitability and efficiency. However, DBSCAN shows better performance compared to others. Furthermore, our findings highlight that the choice of radar significantly impacts the effectiveness of these object recognition methods.

Combined LinkNet-MBi-LSTM for brain activity recognition with new Stockwell transform features.

Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain. There are ongoing initiatives to use DL-based brain activity recognition algorithms to overcome these constraints. Therefore, this work presents a novel hybrid framework for brain activity detection using the enhanced Stockwell transform and an EEG signal that is called LinkNet and modified bidirectional-long short-term memory (LN-MBi-LSTM) model. This framework follows a methodical approach that includes stages for feature extraction, brain activity recognition and preprocessing. Firstly, the improved Weiner filtering (IWF) approach is used to preprocess the EEG input signal. The relevant features are then extracted using a feature extraction technique from the preprocessed EEG signal. To identify the brain activity, these recovered feature sets are subsequently processed separately using LinkNet and modified bidirectional-long short-term memory (MBi-LSTM). A thorough analysis that takes into account both simulation and experimental calculations is part of the validation process for the LN-MBi-LSTM model. Finally, this study demonstrates the therapeutic potential of the LN-MBi-LSTM framework by presenting a strong and verified model for brain activity recognition. With the highest precision of 0.997, the LinkNet-MBi-LSTM model distinguishes itself from other models and confirms its exceptional capacity to produce accurate positive predictions.

A Comparative Study on Detection and Recognition of Nonuniform License Plates

This paper presents a comparative study on license plate detection and recognition algorithms in unconstrained environments, which include varying illuminations, nonstandard plate templates, and different English language fonts. A prime objective of this study is to assess how well these models handle such challenges. These problems are common in developing countries like Pakistan where diverse license plates, styles, and abrupt changes in illuminations make license plates detection and recognition a challenging task. To analyze the license plate detection problem Faster-RCNN and end-to-end (E2E) methods are implemented. For the license plate recognition task, deep neural network and the CA-CenterNet-based methods are compared. Detailed simulations were performed on authors’ own collected dataset of Pakistani license plates, which contains substantially different multi-styled license plates. Our study concludes that, for the task of license plate detection, Faster-RCNN yields a detection accuracy of 98.35%, while the E2E method delivers 98.48% accuracy. Both detection algorithms yielded a mean detection accuracy of 98.41%. For license plate recognition task, the DNN-based method yielded a recognition accuracy of 98.90%, while the CA-CenterNet-based method delivered a high accuracy of 98.96%. In addition, a detailed computational complexity comparison on various image resolutions revealed that E2E and the CA-CenterNet are more efficient than their counterparts during detection and recognition tasks, respectively.

Continuous sign language recognition algorithm based on object detection and variable-length coding sequence.

Currently, continuous sign language recognition faces challenges such as difficulty in acquiring skeletal data, long training time for Three-Dimensional convolutional neural networks, and easy occlusion and blurring of hands. To address these problems, this paper proposes a continuous sign language recognition method based on target detection and coding sequence. The algorithm uses Dual-branch Shuffle Attention Mechanis-You Only Look Once version X (DSA-YOLOX) detection network to detect the head and hands, and encodes the sign language video according to the partition to achieve the transformation from Three-Dimensional to One-Dimensional; and then uses the proposed Bi-directional Long Short-Term Memory (BiLSTM) hand coding sequence classification model with jointly weighted Fast Dynamic Time Warping (FastDTW) to extract hand coding similarity and features while reducing the number of parameters to achieve the classification and recognition of unequal-length hand coding sequences. From the results of ablation experiments and comparison experiments, all parts of the improvement perform well. The word error rate (WER) of this paper's method is reduced by 21.26% compared to Dynamic Time Warping-Hidden Markov Model (DTW-HMM) and 11.53% compared to Long Short-Term Memory-A(LSTM-A); the Giga Floating-point Operations Per Second(GFLOPs) of the algorithm are reduced dramatically, which is about 1/13 of the Visual Alignment Constraint(VAC) model and 1/57 of the Spatial-Temporal Multi-Cue(STMC) model; and the algorithm takes better account of the speed and accuracy of sign language recognition.

Research on gesture recognition technology based on machine learning

Abstract. Hand Gesture Recognition (HGR), as a significant technological advancement in the field of Human-Computer Interaction (HCI), aims to develop systems capable of accurately recognizing and interpreting human gestures for a diverse range of applications, including device control, virtual reality, gesture passwords, and gesture interaction. With the continuous advancement of machine learning algorithms, especially deep learning techniques, machine learning-based gesture recognition has garnered widespread attention. This paper presents a review of the development of gesture recognition techniques from traditional approaches to the current mainstream deep learning-based methods, and outlines the challenges and technical difficulties encountered. It analyzes several of the most popular classification techniques, including Naive Bayes, K-Nearest Neighbors (KNN), Random Forests, XGBoost, Support Vector Classifiers (SVCs), and Convolutional Neural Networks (CNNs). Furthermore, this paper examines the application of these algorithms in both dynamic and static gesture recognition and compares their performance and suitability in different scenarios. The results demonstrate that the accuracy and robustness of gesture recognition systems can be markedly enhanced through the prudent selection and optimization of the algorithms, which serves as a valuable reference for future research and applications.

Polish Speech and Text Emotion Recognition in a Multimodal Emotion Analysis System

Emotion recognition by social robots is a serious challenge because sometimes people also do not cope with it. It is important to use information about emotions from all possible sources: facial expression, speech, or reactions occurring in the body. Therefore, a multimodal emotion recognition system was introduced, which includes the indicated sources of information and deep learning algorithms for emotion recognition. An important part of this system includes the speech analysis module, which was decided to be divided into two tracks: speech and text. An additional condition is the target language of communication, Polish, for which the number of datasets and methods is very limited. The work shows that emotion recognition using a single source—text or speech—can lead to low accuracy of the recognized emotion. It was therefore decided to compare English and Polish datasets and the latest deep learning methods in speech emotion recognition using Mel spectrograms. The most accurate LSTM models were evaluated on the English set and the Polish nEMO set, demonstrating high efficiency of emotion recognition in the case of Polish data. The conducted research is a key element in the development of a decision-making algorithm for several emotion recognition modules in a multimodal system.

Autonomous Cargo Grabbing System for Drones Using Cameras and ROS

Abstract. With the rapid development of low-altitude economy, the functional requirements for logistics UAVs are constantly improving. In this paper, an unmanned aerial vehicle system (UAV) is proposed to realize autonomous grasping of goods. First of all, a new type of grasping device is proposed, which can be easily mounted on the UAV. The camera image stream mounted on the UAV was obtained through camera calibration and corrected. Subsequently, the object recognition algorithm was used to process the image frames, successfully identify the goods to be captured, and calculate the position and direction relationship between the UAV and the target cargo. Next, the speed of the drone is adjusted by the PID controller to achieve a stable landing above the target cargo. Then, the drone is equipped with a robotic claw innovatively designed by our team to complete the gripping of the goods. Subsequently, the drone lifted off and began to deliver cargo. Throughout the grabbing process, a laptop equipped with Ubuntu and ROS systems communicates with the drone via Wi-Fi. The feasibility of the whole system was proved by experiments.

Mental Health Education Evaluation Model Based on Emotion Recognition Algorithm

Mental Health Education Evaluation Model Based on Emotion Recognition Algorithm

Increase of Reliability of Anomalies Detection on Images at Formation of Their Feature Vectors in Wavelet Bases

The method of detection of life rafts and lifeboats in the water area of seas and oceans after shipwrecks based on the recognition of anomalies in the processed images, which increases the probability of recognition of monitoring objects, is proposed. The approach to solving such a problem is substantiated. The formulation of the problem of object recognition from the perspective of binary classification in the detection of anomalies is presented. The analytical expression for the decision-making algorithm is obtained. The possibility of formalization of image matrices in the form of histograms of color (brightness) intensity distributions is considered. The contrast of the feature space on their basis is estimated. It is suggested that the contrast of feature spaces be increased due to the secondary processing of histograms of distributions in the basis of multiple-scale wavelet decomposition. The possibility of realization of wavelet transformations on the basis of Haar functions and Gauss wavelets of the 1st and 2nd orders is considered. The mechanism of formation of secondary feature vectors from three-dimensional wavelet transforms by averaging their coefficients along the time shift axis is substantiated. It is shown that at the same dimensionality of histograms of brightness distribution with newly formed feature vectors, the latter provide higher contrast of feature spaces. It is recommended to use a Gaussian wavelet of the 2nd order for the formalization of images in jpeg format, which provides, other things being equal, a greater magnitude of differences for images containing anomalies. An approach to probabilistic evaluation of the algorithm for automatic image recognition is developed. The analytical expression is obtained and its constituent elements are justified. Graphical dependences of the probability of correct detection (recognition) of anomalies, depending on the size in relation to the total area of the frame and the dispersion of the underlying background are given. The results of the experiment on image recognition with a lifeboat in the ocean water area are presented. The directions of further research are defined.

Информационно-аналитическая система детектирования движения объектов на пешеходном переходе

To manage pedestrian and vehicle flows at intersections, systems are implemented that use adaptive traffic light models, adjusting time intervals based on the volume of pedestrians and vehicles. These systems include video cameras that monitor road user movements, enhancing real-time traffic control. This paper introduces an information and analytical system for managing transport and pedestrian flows using the YOLO neural model, which enables object recognition. The system performs several operations: converting the original image to gray scale, applying Gaussian blurring, detecting object boundaries through the Canny filter and fuzzy logic for edge detection, and contour processing, where each identified contour is assigned a unique number. The neural network then compares detected contours to the training sample data, determining whether the object is a person or a vehicle. The paper presents experimental results for these algorithms in object recognition. Modified software and images of intersections with pedestrian crossings captured from street video cameras in Kursk were used in the experiments. The recognition accuracy rate from the experiments was 72.4%.