Intelligent Recognition Algorithm Research Articles

An Intelligent Parameter Identification Algorithm of Linear Fuzzy Fractional Differential Equation

Abstract The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy.

Context-aware semantic segmentation network for tunnel face feature identification

The automated interpretation of tunnel face geological information is significant to the construction decision-making of rock mass engineering. An intelligent recognition algorithm, named the Transformer and Convolution neural networks Semantic segmentation Network (TCSeNet), is introduced to overcome the low interpretation accuracy caused by certain limitations of existing automated interpretation methods. Firstly, a hybrid encoder is constructed to extract global and local context information using Transformer and Convolution Neural Networks, respectively. Furthermore, a context feature adaptive selection module is designed to cross-fusion the information. Secondly, a feature pyramid-like decoder is constructed for multi-scale object problems. Furthermore, a multi-scale feature refinement module is designed to improve the ability of multi-scale feature expression. A tunnel face dataset is constructed for training and testing. The research shows that the mIoU of TCSeNet reaches 92.57%. This indicates that the TCSeNet is suitable for automatic interpretation of tunnel face geological information.

Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning

The current methods for detecting joints on tunnel face rely primarily on manual sketches, which are associated with issues of low detection efficiency and subjectivity. To address these concerns, this paper presents an intelligent recognition and segmentation algorithm based on Mask R-CNN (mask region-based convolutional neural network) for detecting joint targets on tunnel face images and automatically segmenting them, thereby improving detection efficiency and objectivity of the results. Additionally, to tackle the challenge of low detection accuracy in existing image processing methods, particularly for complex tunnel joint surfaces in dark environments, the paper introduces a path aggregation network (PANet) to enhance the fusion capability of feature information in Mask R-CNN, thereby improving the accuracy of the intelligent detection method. The algorithm was trained on a dataset of 800 tunnel face images, and the research findings demonstrate that it can quickly detect the position of joints on tunnel face images and assign masks to the joint pixel regions to achieve joint segmentation. The mean average precision (mAP) of the detection boxes and segmentation in the 80 test set images were 58.0% and 49.2%, respectively, which outperforms the original Mask R-CNN algorithm and other intelligent recognition and segmentation algorithms.

Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved Single Shot Multibox Detector

Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved Single Shot Multibox Detector

Intelligent Recognition Algorithm for Calligraphy Fonts Based on Texture Mapping

Intelligent Recognition Algorithm for Calligraphy Fonts Based on Texture Mapping

CBAM-YOLOv5: A Promising Network Model for Wear Particle Recognition

The intelligent recognition technology for ferrography images is one of the important methods for diagnosis fault and state detection of machines. In allusion to these questions for the influences of wear particle images’ blurring, background intricacy, wear particle overlapping and lack of light, and others which lead to be the reason for the difficulty of achieving accurate identification, missed detection, and false detection, an intelligent recognition algorithm for ferrography wear particle based on convolutional block attention module (CBAM) and YOLOv5 is proposed. Firstly, it needs enhancement to improve contrast for ferrography wear particle images and lower background interference by adaptive histogram homogenization algorithm. Then, under the framework of YOLOv5 algorithm, the depthwise separable convolution is added to improve the detection speed of the network, and the detection accuracy of the entire network is improved by optimizing the loss function. Moreover, increase weight ratio on wear particle in images by adding a convolution block CBAM model and increase feature representative capability in detection network with YOLOv5 algorithm detection network, which can improve detection accuracy for wear particle. Finally, compare the algorithm with the three classical homologous series object detection algorithm. The experimental results show that the detection accuracy of the model can reach 96.7%, and the detection speed is 32 FPS for the images with a resolution of 1280 × 720 . It can be developed and applied to the fault diagnosis and condition monitoring of mechanical equipment.

Intelligent Recognition of Key Earthquake Emergency Chinese Information Based on the Optimized BERT-BiLSTM-CRF Algorithm

To address the problems of the text of earthquake emergency information keeps changing incrementally with the time of an earthquake’s occurrence and there being more and more information categories, thus making it difficult to identify earthquake emergency key information, this paper proposes an intelligent recognition algorithm of earthquake emergency information based on the optimized BERT-BiLSTM-CRF algorithm. Based on the historical seismic emergency information dataset of the past 10 years, first, the BIO sequence labeling method is used to classify the seismic entities, and the BERT pretraining model is constructed to represent the seismic emergency text with sentence-level feature vectors. The BiLSTM algorithm is used to obtain the contextual information of the bidirectional seismic emergency text, and we introduce the attention mechanism to enhance the recognition effect of the seismic emergency key information in the statements. Finally, we use conditional randomization to enhance the recognition of earthquake emergency key information in the utterance. The conditional randomization algorithm is applied to extract the dependency relationship between adjacent vectors and improve the accuracy identification to realize the intelligent recognition of earthquake emergency information. The experimental results show that our model can extract earthquake emergency information from online media efficiently and accurately, with better performance than other baseline models.

Intelligent Recognition Algorithm of Multiple Myocardial Infarction Based on Morphological Feature Extraction

Myocardial infarction is a type of heart disease marked by rapid progression and high mortality. In this paper, a novel intelligent recognition algorithm of multiple myocardial infarctions using a bidirectional long short-term memory (BiLSTM) neural network classification was proposed. This algorithm was based on morphological feature extraction, which can greatly improve the diagnostic efficiency of doctors for different kinds of myocardial infarction diseases. The algorithm includes noise reduction and beat segmentation of electrocardiogram (ECG) signals from the Physikalisch-Technische Bundesanstalt (PTB) database. According to the medical diagnosis guide, the distance feature of the whole waveform and the amplitude feature of the branch lead waveform are extracted. According to the extracted features, the long short-term memory network (LSTM) and the BiLSTM neural networks are built to classify and recognize heartbeats. The experimental results show that the accuracy of the morphological feature + BiLSTM algorithm in MI detection is 99.4%. At the same time, among the six common myocardial infarction diseases, the location and recognition rate of the culprit vessel is high. The sensitivity, specificity, PPV, NPV, and F1 score parameters all reach more than 98.4%, and the kappa coefficient also reaches 0.983, while the overall accuracy reaches 98.6%. The accuracy of this algorithm is improved by at least 1% compared with that of other existing algorithms. Thus, this study exhibits a very important clinical application value.

Design of the Intelligent Recognition Model for English Translation Based on the BP Neural Algorithm

With the development of modern intelligent recognition, many intelligent recognition translation tools have emerged. These translation tools mainly include machine learning, neural network, KNN, and other artificial intelligence technologies. These technologies have been applied to many fields. Among them, machine translation is the most important and widely used one. A large number of English translation technologies have appeared in this development era. However, the translation accuracy of intelligent recognition technology cannot be guaranteed. Under the background of this English translation environment, we design an intelligent recognition algorithm of English translation based on the BP neural algorithm to improve the rationality of English translation and analyze the intelligent recognition model of English translation. The following conclusions are drawn from the experimental comparison Four Methods of Translation. (1) Compared with some similar algorithms, machine translation based on the BP neural algorithm has many characteristics, such as convenience, which are very suitable for English translation. (2) The intelligent recognition model of English translation using the BP neural network makes the sentence flow higher, which can solve some problems in translation and achieve coherent translation in context. (3) The intelligent recognition model with the BP neural network as the core realizes a variety of permutations and combinations of different characteristics of complex English sentences, solves many poor English sentences, and significantly improves the accuracy of English translation.

Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System

People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage.

Rapid screening of illegally added drugs in functional food using a miniature ion trap mass spectrometer

With the expansion of the functional food market, the qualification assessment of these products has become a major challenge, and efficient analytical tools are urgently needed. Here, a miniature mass spectrometer (MS) with self-aspiration capillary electrospray ionization (SACESI) source and ion trap analyzer was developed for rapid screening of various illegally added drugs in functional foods. No chromatographic separation was required, but a simplified two-step pretreatment method was developed to reduce the operational procedures and time consumption of the entire analysis. SACESI source uses capillary action to drive solution injection, which utilizes a simple structure and convenient operation to constitute a kind of disposable MS detection solution. To achieve accurate and automatic identification, an intelligent recognition algorithm with steps of spectrum preprocessing, characteristic peak matching, and support vector machine learning was constructed. The relative accuracy of rapid screening of 31 suspicious drugs in various samples is up to 99.78%. It achieves 100% correct identification for the 55 batches of actual samples captured by on-site inspection, which demonstrates the feasibility of the proposed analytical system and strategy in food safety applications.

The Improving Effect of Intelligent Speech Recognition System on English Learning

To improve the effect of English learning in the context of smart education, this study combines speech coding to improve the intelligent speech recognition algorithm, builds an intelligent English learning system, combines the characteristics of human ears, and studies a coding strategy of a psychoacoustic masking model based on the characteristics of human ears. Moreover, this study analyzes in detail the basic principles and implementation process of the psychoacoustic model coding strategy based on the characteristics of the human ear and completes the channel selection by calculating the masking threshold. In addition, this study verifies the effectiveness of the algorithm in this study through simulation experiments. Finally, this study builds a smart speech recognition system based on this model and uses simulation experiments to verify the effect of smart speech recognition on English learning. To improve the voice recognition effect of smart speech, band-pass filtering and envelope detection adopt the gammatone filter bank and Meddis inner hair cell model in the mathematical model of the cochlear system; at the same time, the masking effect model of psychoacoustics is introduced in the channel selection stage to prevent noise. Sex has been improved, and the recognition effect of smart voice has been improved. The analysis shows that the intelligent speech recognition system proposed in this study can effectively improve the effect of English learning. In particular, it has a great effect on improving the effect of oral learning.

Fiber Identification in Cross Section of Blended Yarn on Back Propagation Neural Network

An intelligent recognition algorithm was developed to identify fibers in the cross sections of blended yarn containing meta-aramid 1313 (Nomex), poly(phenylene-1,3,4-oxadiazole) (POD), flame resistant viscose, and flame-resistant vinylon. The yarn cross section image was obtained at x400 magnification. Drawing software was used to manually isolate single fiber images for training the back propagation (BP) neural network model in Matlab language image processing software. The GrabCut algorithm was used to de-noise the image and separate the target from the background. Finally, single fiber images and fiber distributions were obtained through the program. The result showed that the BP neural network model with the GrabCut algorithm can identify fiber type in a complex background more easily and more accurately than traditional algorithms.

Intelligent Recognition Algorithm-Based Color Doppler Ultrasound in the Treatment of Dangerous Placenta Previa.

The study focused on the clinical diagnostic value of color Doppler ultrasound of dangerous placenta previa patients under the guidance of intelligent recognition algorithms. 58 patients with placenta previa and placenta accreta admitted to the hospital for treatment were selected as research subjects. The color Doppler ultrasound under the guidance of intelligent recognition algorithm was compared with the two-dimensional ultrasound for specificity, sensitivity, and accuracy. The color Doppler ultrasound results showed that, of the 58 patients, there were 32 cases of complete placenta previa and 26 cases of incomplete placenta previa, which were consistent with the surgical pathology results. It was found that patients with malignant placenta previa and placenta accreta had thickened placenta, disappeared posterior placental space, myometrium <2 mm, and increased incidence of cervical enlargement (P < 0.05). In conclusion, the recognition accuracy of color Doppler ultrasound under the guidance of the intelligent recognition algorithm is more than 90%, and it can effectively identify dangerous placenta previa, assisting doctors in diagnosis and treatment of dangerous placenta previa.

Research on Music Emotion Intelligent Recognition and Classification Algorithm in Music Performance System

The music performance system works by identifying the emotional elements of music to control the lighting changes. However, if there is a recognition error, a good stage effect will not be able to create. Therefore, this paper proposes an intelligent music emotion recognition and classification algorithm in the music performance system. The first part of the algorithm is to analyze the emotional features of music, including acoustic features, melody features, and audio features. Then, the three kinds of features are combined together to form a feature vector set. In the latter part of the algorithm, it divides the feature vector set into training samples and test samples. The training samples are trained by using recognition and classification model based on the neural network. And then, the testing samples are input into the trained model, which is aiming to realize the intelligent recognition and classification of music emotion. The result shows that the kappa coefficient k values calculated by the proposed algorithm are greater than 0.75, which indicates that the recognition and classification results are consistent with the actual results, and the accuracy of recognition and classification is high. So, the research purpose is achieved.

Research on CNN-based intelligent recognition method for negative images of weld defects

The existing technology of automatic classification and recognition of welding negative images by computer is difficult to achieve a multiple classification defect recognition while maintaining a high recognition accuracy, and the developed automatic recognition model of negative image defect cannot meet the actual needs of the field. Therefore, the convolutional neural network (CNN)-based intelligent recognition algorithm for negative image of weld defects is proposed, and a B/S (Browser/Server) architecture of weld defect feature image database combined with CNN is established subsequently, which converted from the existing CNN by the migration learning method. It makes full use of the negative big data and simplifies the algorithm development process, so that the recognition algorithm has a better generalization ability and the training algorithm accuracy of 97.18% achieved after training. The results of the comparison experiments with traditional recognition algorithms show that the CNN-based intelligent recognition algorithm for defective weld negatives has an accuracy of 92.31% for dichotomous defects, which is significantly better than the traditional recognition algorithm, the established recognition algorithm effectively improving the recognition accuracy and achieving multi-category defect recognition. At the same time, the CNN-based defect recognition method was established by combining the image segmentation algorithm and the defect intelligent recognition algorithm, which was applied to the actual negative images in the field with good results, further verifying the feasibility of CNN-based intelligent recognition algorithm in the field of defect recognition of welding negative images.

Adaptive Recognition of Motion Posture in Sports Video Based on Evolution Equation

In the field of sports, the formulation of existing training plans mainly relies on the manual observation and personal experience of coaches. This method is inevitably subjective. The application of artificial intelligence technology to the training of athletes to recognize athletes’ posture can help coaches assist in decision-making and greatly enhance athletes’ competitive ability. The human body movements embodied in sports are more complicated, and the accurate recognition of sports postures plays an active and important role in sports competitions and training. In this paper, inertial sensor technology is applied to attitude recognition in motion. First, in order to improve the accuracy of attitude calculation and reduce the noise interference in the preparation process, this article uses differential evolution algorithm to apply attitude calculation to realize multisensor data fusion. Secondly, a two-level neural network intelligent motion gesture recognition algorithm is proposed. The two-level neural network intelligent recognition algorithm effectively recognizes similar actions by splitting the traditional single-level neural network into two-level neural networks. Experiments show that the experimental method designed in this article for the posture in motion can obtain the motion information of the examinee in real time, realize the accurate extraction of individual motion data, and complete the recognition of the motion posture. The average accuracy rate can reach 98.85%. There is a certain practical value in gesture recognition.

Research on Intelligent Recognition Algorithm of Pneumonia Based on Deep Convolution and Attention Neural Network

Pneumonia is a common infection that inflames the air sacs in the lungs, causing symptoms such as difficulty breathing and fever. Although pneumonia is not difficult to treat, prompt diagnosis is crucial. Without proper treatment, pneumonia can be fatal, especially in children and the elderly. Chest x-rays are an affordable way to diagnose pneumonia. Investigating an algorithmic model that can reliably and intelligently classify pneumonia based on chest X-ray images could greatly reduce the burden on physicians. The advantages and disadvantages of each of the four convolutional neural networks VGG16, ResNet50, DenseNet201, and DWA algorithm models are analyzed and given by comparing and investigating each model. The VGG16, ResNet50, and DenseNet201 network models are compared with the DWA model. When training the depthwise separable convolution with attention neural network (DWA), the training accuracy reaches 97.5%. The validation accuracy was 79% due to the model’s tendency to overfit, and the test dataset had 1175 X-ray images with a test accuracy of 96.1%. The experimental results illustrate the effectiveness of the attention mechanism and the reliability of the deeply separable convolutional neural network algorithm. The successful application of the deep learning algorithm proposed in this paper on pneumonia recognition will provide an objective, accurate, and fast solution for medical practitioners and can provide a fast and accurate pneumonia diagnosis system for doctors.

Recognition and Volume Measurement of Intracranial Hematoma through CT Images under Intelligent Recognition Algorithm

In order to realize the automatic recognition of intracranial hematoma and accurate measurement of hematoma volume in patients with ICH (intracerebral hemorrhage), the FCM algorithm (fuzzy c-means algorithm) was improved in this study, and a new level set segmentation algorithm based on FCM was obtained, FCRLS (fuzzy c-means regularized level set). Then, 120 ICH patients were used as the research objects, and the FCRLS algorithm was evaluated by the recall, precise, and F1-score values to evaluate the effect of intracranial hematoma recognition. The CT images of 48 patients with intracranial hematoma were used as the data set of the FCRLS algorithm. The hematoma was segmented, and the DSC (Dice similarity coefficient) value and running time were used to evaluate the segmentation results of the algorithm. At the same time, the LS (level set) algorithm and the FCM algorithm were introduced for comparison. The results show that the recall value of the FCRLS algorithm is 0.89, the precise value is 0.94, the F1-score value is 0.91, the Dice coefficient is 94.81%, and the running time is 14.48 s. Compared with the LS algorithm and the FCM algorithm, the above five indicators have significant differences ( P &lt; 0.05 ). Hematoma volume measurement found that the average error of FCRLS algorithm from expert measurement results was 5.62%, which was statistically significant compared with LS algorithm and FCM algorithm ( P &lt; 0.05 ). In summary, the FCRLS algorithm can accurately identify the cerebral hematoma area of ICH patients, has an ideal segmentation effect on the hematoma, and can accurately measure the true volume of the hematoma, which is worthy of clinical application.