Search

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction. [ABSTRACT FROM AUTHOR]

In order to solve a series of problems similar to the repetitive construction of resources and low degree of resource sharing in cruciform teaching, this paper studies the digital disarming management. Based on the in-depth analysis of the actual needs of the digital teaching resource service system and the key problems in the system, taking the resources and business process as the starting point, based on Java Web related technology, combined with the related processes of resources and business processing, this paper designs the digital teaching resource system of colleges and universities. The system has the functions of resource digitization, process management, and interaction with other platforms. The system database platform and operation platform are built; the development environment is configured; and the user login service function, data conversion service function, process management service function, and system management service function are completed. The function is tested by unit test, and the performance is tested and analyzed according to user requirements and design objectives. The system adopts Java web development technology, uses S2SH framework as the development basis, and takes Oracle database as the data storage platform through Tomcat6.0 web server for program publishing. Through system testing and analysis, the software can operate normally and achieve the expected functions and can be put into use. [ABSTRACT FROM AUTHOR]

Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of Sci-Tech journals is a widely recognized journal evaluation index that comprehensively reflects the quality and influence of the journals under evaluation. However, traditional journal evaluation methods based on statistical formulas, while relatively simple and fast, have certain limitations. They are not comprehensive enough and do not support the comparison between journals from different disciplines. In recent times, researchers have delved into using multiple suitable indicators for comprehensive journal evaluation, attempting to understand the role each indicator plays in the evaluation process, such as the rank sum ratio. Our paper presents a new dataset constructed from data from journals across various fields obtained from the China Wanfang Literature Platform. We endeavor to explore a series of novel journal evaluation methods based on machine learning, including deep learning models. With these 9 methods, we aim to determine the contribution of 17 journal evaluation indicators to the impact factor and identify important factors that can further enhance the quality and influence of Sci-Tech journals, which has great guiding significance for the future development of journals. [ABSTRACT FROM AUTHOR]

The integration of IoT systems and AI technology is very suitable for interconnected things to enhance intelligence, thus the artificial intelligence of things (AIoT), which enables the establishment of innovative IoT systems and applications for the modern smart city and smart home. The Internet of Things (IoT) consists of interconnected things with built-in and function-oriented sensors, essentially constituting a network of physical devices. The paper titled "Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency" by F. A. Almalki et al. proposes a drone system with AI framework to help clean solar panels mounted on the top of buildings and enhance their efficiency. [Extracted from the article]

Nowadays, with the vigorous development of information management technology, talent management has become a hot field that scholars pay attention to. The flow of talent between companies has become increasingly frequent. A large number of cooperative behaviors have produced a large number of cooperative results and subsequently brought a large amount of data on what to do. A huge network of collaborators has also been quietly formed, and how to mine valuable information from it has become a research hotspot, among which talent recommendation is one of the most important topics. Talent recommendation, when an enterprise introduces high-quality talents, provides valuable reference suggestions and selects candidates. When introducing talents, enterprises should not only consider the ability level of talents but also consider the cooperative relationship between them and enterprise personnel. Therefore, it is necessary to analyze the network of partners to find out the rules. There are only author nodes in the isomorphic collaborator network, and the connection between nodes is the cooperative relationship. On this basis, this paper constructs a heterogeneous collaborator network; that is, there are multiple types of nodes and connections in the network. The main research problem of this paper is to find an indicator to measure the strength of association between scholars in the collaborator network and to recommend potential academic talents for enterprises. Based on the data of academic research cooperation network, we carried out sufficient experiments to demonstrate the effectiveness of the heterogeneous cooperation network model proposed in this paper. [ABSTRACT FROM AUTHOR]

As an important part of the bridge structure system, the underwater pile-pier structure usually occurs various defects on its surfaces due to its complex hydrological environment. The existing conventional defect detection approaches exist two aspects of problems: (1) insufficient definition and color distortion of the underwater images, and (2) low efficiency and error-prone. To solve these problems, this paper proposed the target defect detection model by integrating the image-fusion enhancement algorithm and the deep learning algorithm. Firstly, by analyzing the reasons for the degradation of the underwater images, the ACE (automatic color equalization) and CLAHE (contrast limited adaptive histogram equalization) algorithms are selected to enhance the image, respectively. Secondly, the two enhanced images are fused based on the point sharpness weight, and then the fusion results are further sharpened by the USM (unsharp mask) algorithm, thus obtaining the final fused images. Thirdly, 3,200 fused images are taken as the training set, by adopting the YOLOv3 algorithm to train the detection model, and then the training model is validated and tested by the other each 400 fused images, thus building up the target automatic detection model of underwater pile-pier surface defects. Finally, a series of comparison and discussion were conducted to validate the effectiveness of image-fusion and the robustness and effectiveness of the target detection model. The results found that the target detection model has excellent robustness against noise and effectiveness in the surface defect detection. This indicates that the image-fusion approach proposed in this paper can effectively enhance the image features, and the target detection model is feasible, robust, and effective in the automatic detection of surface defects on underwater pile-pier structures. [ABSTRACT FROM AUTHOR]

As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, "return on registered capital," to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper. [ABSTRACT FROM AUTHOR]

Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model. [ABSTRACT FROM AUTHOR]

The traditional real-time data scheduling method ignores the optimization process of job data that leads to delayed delivery, high inventory cost, and low utilization rate of equipment. This paper proposes a novel real-time data scheduling method based on deep learning and an improved fuzzy algorithm for flexible operations in the papermaking workshop. The algorithm is divided into three parts: the first part describes the flexible job shop scheduling problem; the second part constructs the fuzzy scheduling model of flexible job data in papermaking workshop; and finally the third part uses a genetic algorithm to obtain the optimal solution of fuzzy scheduling of flexible job data in papermaking workshop. The results show that the optimal solution is obtained in 48 seconds at the 23rd attempt (iteration) under the application of the proposed method. This result is much better than the three traditional scheduling methods with which we compared our results. Hence, this paper improves the work efficiency and quality of papermaking workshop and reduces the operating cost of the papermaking enterprise. [ABSTRACT FROM AUTHOR]

The traditional home product design system mainly depends on relatively shallow learning network, relatively simple embedded technology and Internet of things technology. The traditional home design system mainly depends on the traditional self-encoder technology. When combined with the deep neural network, this technology has serious defects in the computer vision algorithm, resulting in the serious waste of the corresponding system storage and computing resources, the corresponding system learning efficiency is relatively poor and the learning ability is weak. Based on this, this paper will build a home product design system based on the deep neural network of self-encoder. By improving the sparsity of self-encoder in the process of learning and training, we can further improve the sparsity of the system and further optimize the structure of self-encoder in the design system, The performance of the deep learning model of the design system is further improved through the hierarchical features continuously learned by the self-encoder in the process of home case design. Based on the optimization of the home product design system in this paper, the system effectively improves and improves the accuracy and stability of the internal feature classifier of the system, and improves the overall performance of the furniture design system. In the specific system construction part, based on ZigBee technology and embedded technology as the design carrier, and adhering to the goal of simplicity, intelligence and convenience, this paper designs and constructs the home product design system. The experimental results show that the noise processing level of the proposed home product design system is lower than 4-5db compared with the traditional design system, and the corresponding image classification accuracy is about 4% higher than the traditional design system. Therefore, the experimental results show that the home design system proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

In order to solve the industrial fake film problem produced by repeated imaging in the field of nondestructive testing, this paper proposes an image similarity comparison algorithm of X-ray film based on the Siamese neural network. Firstly, in order to accurately locate and detect weld areas from high-resolution X-ray film, a robust location and detection algorithm based on the Gaussian function is proposed. Next, in order to solve the problem that the deep learning model system extracts features according to squares by default, which results in the disappearance of film features with high horizontal and vertical features, effective weld regions are used and processed in blocks. Then, a small sample data set for weld similarity discrimination is established by using each block region, and an industrial fake film discrimination model based on twin neural networks is constructed. Finally, the corresponding weights are set for different fake feature information areas, and a similarity comparison system for industrial fake film detection is proposed. The experiment shows that the method in this paper can not only accurately locate the weld area but also accurately detect the fake film in the industry, avoiding the influence of the geometric transformation of the image and the insufficient number of data sets in the process of similarity comparison, and has a good detection effect. [ABSTRACT FROM AUTHOR]

In the paper entitled "Spam Detection Approach for Secure Mobile Message Communication Using Machine Learning Algorithms", L. G. Jun et al. proposed the applications of the machine learning-based spam detection method for accurate detection. The data extraction process of the review showed that (1) latent Dirichlet allocation topic modeling is among the widely used machine learning algorithms in the selected studies and (2) precision and recall are amongst the most utilized evaluation methods for measuring the performance of these machine learning algorithms. Machine learning (ML) and cryptography have many things in common, for instance, the amount of data to be handled and large search spaces. [Extracted from the article]

In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process. [ABSTRACT FROM AUTHOR]

Objective. The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods. This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results. In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion. In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent. [ABSTRACT FROM AUTHOR]

The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. This model first used the extreme gradient boosting decision tree (XGboost) algorithm to sort the preprocessed data, and then it used comparison to weed out 55 features with a higher contribution. Then, the extracted features were fed into the improved one-dimensional convolutional neural network (I1DCNN), and this network training was used to complete the final classification task. The feature selection and improved one-dimensional convolutional neural network (FS-I1DCNN) intrusion detection model not only solved the traditional machine learning method of relying on expert experience to extract features but also improved the detection efficiency of the model, reduced the training time while reducing the dimension, and increased the overall accuracy. In comparison to the I1DCNN model without feature extraction and the conventional one-dimensional convolutional neural network (1DCNN) model, the experimental results demonstrate that the FS-I1DCNN model's overall accuracy increases by 0.67% and 2.94%, respectively. Its accuracy, precision, recall, and F 1 -score were significantly better than those of the other intrusion detection models, including SVM and DBN. [ABSTRACT FROM AUTHOR]

The rapid expansion of metro networks, e.g., in many cities of China, continuously introduces the operation of new stations every year. Due to the lack of historical data and complicate variations of short-term passenger flow in the early stage of operation, it is difficult to accurately predict inbound and outbound passenger flows of new metro stations in the short term, which would be the database for train scheduling for new stations before operation, dynamic capacity optimization for new stations under operation, short-term prediction of cycle sharing demands near new stations, and so on. Traditional methods usually failed to exactly reflect the complicate rules or were unusable without the new station's historical data. In order to solve the above problems, this paper proposes a short-term inbound and outbound passenger flow prediction model for new metro stations at the early stage of operation by combining the K-means clustering algorithm, an improved spatiotemporal long short-term memory model (Sp-LSTM), and a real-time feedback error model (mean absolute error, MAE), where passenger flows' spatial-temporal characteristics and land-use relevance are considered. The application in Guangzhou Metro, China, where Line 21 is regarded as a new line, shows that the proposed K-Sp-LSTM model has the best prediction accuracy compared with traditional methods. [ABSTRACT FROM AUTHOR]

With people's higher and higher spiritual requirements, fiber art is more common in people's lives. From ordinary fiber materials to handicrafts and daily necessities, there are shadows of fiber art. This paper aims to study the application of two-dimensional images in the visual and tactile dimensions of fiber art design. This paper proposes a three-dimensional simulation of two-dimensional images of art based on deep learning technology to achieve the purpose of convenient display and also analyzes the feature extraction in fiber art. In order to comprehensively compare the performance of the algorithms, this paper compares the performance of four categories: reconstruction speed, number of reconstructed point clouds, diversity of reconstructable categories, and reconstruction stability. The experimental results show that the number of point clouds obtained by the algorithm proposed in this paper far exceeds the other two reconstruction processes, and the average time is 34.4 min. This also shows that such algorithmic processes are more robust to clothing diversity. [ABSTRACT FROM AUTHOR]

In recent years, China's economy has been developing rapidly, and people's life and quality of life have been improving; more importantly, people's habits and living habits have also developed from the previous "unhygienic" and "not very careful" to the current "Healthier, more hygienic, greener and more sophisticated" direction. In the process of this development, due to the rapid development of the economy and the industrialization of cities, the incidence of heart disease is also increasing year by year. According to relevant studies, China's urbanization process has been unprecedented, the number of urbanized people in China has exploded in recent years, and the ratio of urban to rural population has increased from about 20 percent to about 75 percent today. The effects of the population and the urbanization of urban architecture are affecting people's physical and mental health, both consciously and unconsciously, causing both positive and negative physical and mental effects on the psychological and physical levels. In this paper, the concept of deep learning is fully utilized to train the CNN neural network model and apply it to the ECG abnormality recognition and prediction examination. In order to fully validate the application and significance of deep learning in ECG abnormality identification and prediction, the whole project was completed through subjective and objective experiments. The experimental results show that, from the subjective aspect, the ECG examination has been accepted by most people for different age groups, and the analysis results of the ECG examination with the deep learning model in this paper are more satisfactory; from the objective aspect, the CNN-ECG abnormality recognition prediction network model trained in this paper has high. The accuracy of the model for ECG abnormality recognition prediction can reach 86% when the learning rate is set to 0.0001 and the batch size is set to 120, and the model can reduce the burden of medical personnel to a certain extent. [ABSTRACT FROM AUTHOR]

Recently, deep learning has been employed in automatic feature extraction and has made remarkable achievements in the fields of computer vision, speech recognition, natural language processing, and artificial intelligence. Compared with the traditional shallow model, deep learning can automatically extract more complex features from simple features, which reduces the intervention of artificial feature engineering to a certain extent. With the development of the Internet and e-commerce, picture advertising, as an important form of display advertising, has the characteristics of high visibility, strong readability, and easy-to-obtain user recognition. An increasing number of Internet companies are paying attention to what kind of advertising pictures can attract more clicks. Based on deep learning technology, this paper studies the prediction model of click-through rate (CTR) for advertising and proposes an end-to-end CTR prediction depth model for display advertising, which integrates the feature extraction of display advertising and CTR prediction to directly predict the probability of an advertisement image being clicked by users. This paper studies the deep-seated nonlinear characteristics through the multilayer network structure of the deep network and carries out several groups of experiments on the private display advertising data set of a commercial advertising platform. The results show that the model proposed in this paper can effectively improve the prediction accuracy of CTR compared with other benchmark models and predict whether an advertisement is clicked or not by given advertisement information and user information. By establishing a reasonable advertising click-through rate prediction model, it can help the platform estimate future revenue so as to make cooperative decisions with advertisers. For advertisers, it is necessary to evaluate the price by predicting the click-through rate and estimate the bidding price of their own advertisements. [ABSTRACT FROM AUTHOR]

In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]

Blockchain technology is widely used in the field of digital right protection technology. The traditional digital right protection scheme is not only inefficient and highly centralized but also has the risk of being modified. Due to its own characteristics, blockchain cannot completely store all the original files of digital resources. In this paper, a convolutional neural network algorithm based on visual priority rule is proposed (CNNVP). This algorithm can recognize facial expressions in the original files of digital resources (for short video of face class). The algorithm extracts facial expression features accurately and makes these features form log files that can represent the original files of digital resources. Then, the paper proposes a short video copyright storage algorithm based on blockchain and facial expression recognition and stores the log file into the blockchain. The above methods not only improve the efficiency of short video copyright storage, reduce the degree of storage centralization, and eliminate the risk that copyright is easy to be modified. Moreover, the computing operation of deep learning technology on short video not only ensures the privacy of storage certificate information but also ensures the possibility of blockchain storage of video information. Experiments show that the algorithm proposed in this paper is more efficient than the traditional copyright storage method. Moreover, the algorithm proposed in this paper can provide technical support to the media resource management department. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a crucial role in addressing various accessibility challenges, including voice recognition, sign language detection and interpretation, navigation systems, speech-to-text conversion, and vice versa, among others. They are computationally powerful enough to handle and run numerous machine and deep learning applications. Among various accessibility challenges, speech disorders represent a disability where individuals struggle to communicate verbally. Similarly, hearing loss is a disability that impairs an individual's ability to hear, necessitating reliance on gestures for communication. A significant challenge encountered by people with speech disorders, hearing loss, or both is their inability to effectively convey or receive messages from others. Hence, these individuals heavily depend on the sign language (a gesture-based communication) method, typically involving hand movements and expressions. To the best of our knowledge, there are currently no comprehensive review and/or survey articles available that cover the literature on speech disabilities and sign language detection and interpretation via smartphones utilizing machine learning and/or deep learning approaches. This study fills the gap in the literature by analyzing research publications on speech disabilities, published from 2012 to July 2023. A rigorous search and standard strategy for formulating the literature along with a well-defined theoretical framework for results and findings have been used. The paper has implications for practitioners and researchers working in accessibilities in general and smart/intelligent gadgets and applications for speech-disabled people in specific. [ABSTRACT FROM AUTHOR]

Partial point cloud registration is an important step in generating a full 3D model. Many deep learning-based methods show good performance for the registration of complete point clouds but cannot deal with the registration of partial point clouds effectively. Recent methods that seek correspondences over downsampled superpoints show great potential in partial point cloud registration. Therefore, this paper proposes a partial-to-partial point cloud registration network based on geometric attention (GAP-Net), which mainly includes a backbone network optimized by a spatial attention module and an overlapping attention module guided by geometric information. The former aggregates the feature information of superpoints, and the latter focuses on superpoint matching in overlapping regions. The experimental results show that the method achieves better registration performance on ModelNet and ModelLoNet with lower overlap. The rotation error is reduced by 14.49% and 17.12%, respectively, which is robust to the overlap rate. [ABSTRACT FROM AUTHOR]

With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers' movement correction, dance self-help teaching, and other application scenarios but also for athletes' movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers' movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms. [ABSTRACT FROM AUTHOR]

With the rise of cultural and creative industries, cultural creativity has gradually become an important factor to promote the value of design in the future, and it is also a trend to integrate "cultural elements" into the design of products. At present, the cultural and creative industries in Western countries and Taiwan are the mainstay of their economic development. We should actively absorb their successful experiences and, with the support of national policies, carry out effective and lasting development of them, as well as continuously improve the quality of cultural and creative products. In today's steady economic development, the emotionalization of cultural and creative consumption has gradually formed a new consumption trend. When cultural and creative consumers buy stationery, they will inevitably have three situations: purchasing instinct, purchasing behavior, and reflection. Therefore, this paper adopts the method based on machine learning to conduct in-depth research on the users of cultural and creative products of "Forbidden Day and Night Set Gift Box." Through the research on the cultural and creative consumption intention of "Forbidden Day and Night Set Gift Box," it can effectively promote the development of domestic cultural and creative enterprises, and then promote the customer satisfaction of cultural and creative enterprises. This paper makes a detailed analysis of it from the perspectives of data processing, feature engineering, classification prediction models, and future development directions. By studying the cultural and creative behavior of users, a deep learning model based on neural network is established. The feature extraction, feature preprocessing, feature selection, and asymmetric data collection in the process of data processing are discussed in depth. In order to further improve the prediction accuracy and conduct more in-depth research, this paper establishes a deep learning prediction model based on depth. This model is experimentally validated and it can be observed that the model is 10% more efficient than the traditional model, so the model can learn data better from the user's behavior in several aspects, and the proposed and practice of this model has good practical significance. [ABSTRACT FROM AUTHOR]

Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding various techniques for predicting the accuracy of the diseases. On the basis of the machine learning algorithm, it was not possible to predict the better accuracy when compared to the deep learning technique; this work has proposed enhanced artificial neural network approaches for the accuracy of lung diseases. Here, the discrete Fourier transform and the Burg auto-regression techniques are used for extracting the computed tomography (CT) scan images, and feature reduction takes place by using principle component analysis (PCA). This proposed work has used the 120 subjective datasets from public landmarks with and without lung diseases. The given dataset is trained by using an enhanced artificial neural network (ANN). The preprocessing techniques are handled by using a Gaussian filter; thus, our proposed approach provides enhanced classification accuracy. Finally, our proposed method is compared with the existing machine learning approach based on its accuracy. [ABSTRACT FROM AUTHOR]

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F 1 -measure reaches 0.67. [ABSTRACT FROM AUTHOR]

This paper explores ear biometrics using a mixture of feature extraction techniques and classifies this feature vector using deep learning with convolutional neural network. This exploration of ear biometrics uses images from 2D facial profiles and facial images. The investigated feature techniques are Zernike Moments, local binary pattern, Gabor filter, and Haralick texture moments. The normalised feature vector is used to examine whether deep learning using convolutional neural network is better at identifying the ear than other commonly used machine learning techniques. The widely used machine learning techniques that were used to compare them are decision tree, naïve Bayes, K-nearest neighbors (KNN), and support vector machine (SVM). This paper proved that using a bag of feature techniques and the classification technique of deep learning using convolutional neural network was better than standard machine learning techniques. The result achieved by the deep learning using convolutional neural network was 92.00% average ear identification rate for both left and right ears. [ABSTRACT FROM AUTHOR]

Transformers are an essential part of power production. Insulating paper began to be widely used in transformers in the 1990s. The superior aramid nanofiber as the matrix gives the aramid nano-insulating paper excellent mechanical properties, insulation performance, temperature resistance, and flexibility. At first, the heat resistance and service life of insulating paper should be satisfied for use in electrical equipment. With the continuous development of power equipment, people have put forward higher requirements on the properties of insulating paper, especially heat resistance and electrical properties. Insulation paper made of aramid fibers have better thermal stability and more advantages in electrical and mechanical properties, which can significantly improve the service life and safety of electrical appliances. The purpose of this article is to study the use of aramid nanopaper-based insulating materials in transformers to explore the effect of transformer discharge mechanism on aramid nanopaper-based insulating materials. This paper proposes to design multiple deep learning models to identify the discharge mode of the voltage transformer, find the characteristic signal, and carry out related tests on the discharge signal of different modes, and find the maximum temperature value of the aramid nanopaper-based insulating material for industrial use. The experimental results in this paper show that the aramid nanopaper-based insulating material can be used in transformers discharge detection well, and the safety rate is increased by 20%. [ABSTRACT FROM AUTHOR]

Garbage is an underutilized resource, and garbage classification is one of the effective ways to make full use of these resources. In order to realize the automation of garbage classification, some deep learning models are used for garbage images recognition. A novel garbage image recognition model Garbage Classification Net (GCNet) based on transfer learning and model fusion is proposed in this paper. After extracting garbage image features, EfficientNetv2, Vision Transformer, and DenseNet, respectively, are combined to construct the neural network model of GCNet. Data augmentation is used to expand the dataset and 41,650 garbage images are contained in the new dataset. Compared with other models through experiments, the results show that the proposed model has good convergence, high recall rate and accuracy, and short recognition time. [ABSTRACT FROM AUTHOR]

When an artist creates an oil painting, it is rich in emotion. Color is the main way to express emotion in oil painting. As far as the art field is concerned, color is an objective phenomenon that people can really feel its unique richness and brilliance, and different people will have different emotions when facing the same picture. Color itself is not emotional, but people have different psychological feelings by looking at different colors. This is the power of color. In the same way, color is also a carrier for painters to convey emotions in oil paintings. The study of emotional color expression in oil painting is more helpful for people to understand the emotion conveyed by oil painting. Color has an important role and an irreplaceable position in oil painting creation. In the creation of oil painting, color expression is combined with emotional expression. The creator conveys his experience and feelings in the form of color. In this paper, we look at the relationship between color and emotion, the emotional expression of color, and the expression of emotion in painting. The creators of oil paintings convey their feelings and experiences in the form of color, which plays a unique and important role in portraying the image of their works, expressing emotions, and creating an atmosphere, and we analyze and reveal how to use paint to express emotions in oil painting. [ABSTRACT FROM AUTHOR]

This paper proposes corresponding teaching methods and instructional modes based on predecessors' research on mathematics instructional mode and the current state of mathematics teaching. In addition, this paper constructs a teaching evaluation model based on DL algorithm based on an in-depth study of DL-related theories in order to accurately and scientifically analyze the problems that exist in mathematics teaching. This paper constructs an instructional quality evaluation index system based on rationality and fairness, and uses the BPNN evaluation model to train and study a set of instructional quality data. Finally, the experimental results show that this system has a high level of stability, with a 96.37 percent stability rate and a 95.42 percent evaluation accuracy rate. The results of this paper's evaluation of the mathematical instructional quality model are objective and reasonable. It can accurately assess instructional quality while also assessing problems in the teaching process based on the instructional quality scores and making reasonable recommendations for teaching improvement based on the weak links in the teaching process. It has the potential to provide a workable system for assessing instructional quality. [ABSTRACT FROM AUTHOR]

This paper investigates AI-based interactive music design and proposes a new music learning mode. It aids in the development of students' inquiry skills and allows teachers to take the lead. At the same time, this paper systematically introduces the status of DL theory's application in music teaching evaluation and uses DL theory to develop a mathematical model for an AI music teaching evaluation system. The construction method of an AI music teaching evaluation model based on DL is detailed in this paper. The model can assess the quality of AI music teaching after the network has been trained. The designed instructional quality evaluation NN is trained and measured in this paper to verify the model's performance. The experimental results show that this model has a prediction accuracy of 94.79 percent, which is approximately 8.52 percent higher than the traditional methods. It has some practicality and feasibility, and it can serve as a useful benchmark for the development of various instructional quality evaluation systems. [ABSTRACT FROM AUTHOR]

In order to study the modern 3D object detection algorithm based on deep learning, this paper studies the point-based 3D object detection algorithm, that is, a 3D object detection algorithm that uses multilayer perceptron to extract point features. This paper proposes a method based on point RCNN. A three-stage 3D object detection algorithm improves the accuracy of the algorithm by fusing image information. The algorithm in this paper integrates the information and image information of the three stages well, which improves the information utilization of the whole algorithm. Compared with the traditional 3D target detection algorithm, the structure of the algorithm in this paper is more compact, which effectively improves the utilization of information. [ABSTRACT FROM AUTHOR]

Image enhancement and reconstruction are the basic processing steps of many real vision systems. Their purpose is to improve the visual quality of images and provide reliable information for subsequent visual decision-making. In this paper, convolution neural network, residual neural network, and generative countermeasure network are studied. A rain fog image enhancement generative countermeasure network model structure including a scalable auxiliary generation network is proposed. The objective loss function is defined, and the periodic consistency loss and periodic perceptual consistency loss analysis are introduced. The core problem of image layering is discussed, and a layering solution framework with a deep expansion structure is proposed. This method realizes multitasking through adaptive feature learning, which has a good theoretical guarantee. This paper can not only bring a pleasant visual experience to viewers but also help to improve the performance of computer vision applications. Through image enhancement technology, the quality of low illumination image can be effectively improved, so that the image has better definition, richer texture details, and lower image noise. [ABSTRACT FROM AUTHOR]

In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Google Scholar) for exploration. Six questions describing the overall trend of DOA estimation using deep learning are put forward. Then, we answered these questions by reviewing the literature. This review is helpful for researchers in this field because it provides more specific and comprehensive information needed for future research. Specifically, we first analyzed the background of the selected papers, including the type of publication, the number of citations, and the country of origin. Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and combination network), and various DOA estimation schemes. Finally, various evaluation criteria (root-mean-squared error, accuracy, and mean absolute error) are used to evaluate the DL technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas, and number of signal sources) affecting DOA estimation are analyzed. Based on our findings, we believe that deep learning can perform DOA estimation well, and there is still room for improvement in deep learning technology. In this study, the factors affecting DOA estimation can be used as the direction for researchers to conduct in-depth research. [ABSTRACT FROM AUTHOR]

Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning. [ABSTRACT FROM AUTHOR]

At present, with the popularity of multimedia, people's appreciation and interest in ink painting art have been deepened. In the research of art works, this paper proposes a rendering method of three-dimensional ink painting effect based on deep learning model, which can simulate the painting skills of ink painting in terms of intensity and distance. Specifically, first construct a two-dimensional ink texture, then map the horizontal component of the texture using the contour information of the model, and finally adjust the ink tone by controlling the coordinates of the vertical component of the texture through the attribute function. Linear attribute function, quadratic attribute function, and attribute function with light source factor are used for simulation. The results show that this method can be used for ink and wash simulation of three-dimensional scene, making the image more layered and more in line with the artist's actual technology. [ABSTRACT FROM AUTHOR]

With the wide application of the Internet and the rapid development of network technology, microblogs and online shopping platforms are playing an increasingly important role in people's daily life, learning, and communication. The length of these information texts is usually relatively short, and the grammatical structure is not standardized, but it contains rich emotional tendencies of users. The features used by custumal machinery schooling methods are too sparse on the vector space model and lack the semantic information of short texts, which cannot well identify the semantic features and potential emotional features of short texts. In response to the above problems, this paper proposes a bidirectional long-term and short-term memory network model based on emotional multichannel, combining the attention mechanism and convolutional neural network features in deep learning and learning the short text by combining shallow learning and deep learning. The semantic information and potential emotional information of the short text can be improved to promote the effective expression of short-text emotional features and improve the short-text emotional classification effect. Finally, this paper compares the above models on multidomain classification data sets such as NLPIR and NLPCC2014. The accuracy and F1 value of the model proposed in this paper have achieved good improvement in the field of short-text sentiment analysis. [ABSTRACT FROM AUTHOR]

Whitelisting is a widely used method in the security field. However, due to the rapid development of the Internet, the traditional whitelisting method cannot promote the security of increasing Internet access. In recent years, with the success of machine learning in different areas, many researchers focus on the security of Internet access through machine learning methods. The most common form of machine learning is supervised learning. Supervised learning requires a large number of labeled samples, but it is difficult to obtain labeled samples in practical applications. This paper introduced an unsupervised deep learning algorithm based on seq2seq, which combined with the recurrent neural network and the autoencoder structure to realize an intelligent boundary security control mechanism. The main methods proposed in this paper are divided into two parts: data processing and modeling. In the phase of data processing, the access text table was coded with dicts, and all sequences were padded to the maximum. In the modeling phase, the network was optimized according to the principle of minimizing the reconstruction error. From the comparative experiments, the proposed method's AUC on the public data set reached 0.99, and its performance is better than several classical supervised learning algorithms, proving that the proposed method has an efficient defense against abnormal network access. [ABSTRACT FROM AUTHOR]

Object tracking is a hot issue in vision technology research; it is used in scenarios like intelligent monitoring, autonomous driving, and robot visual perception. With the rapid development of sports, tracking of targets in complex sports scenes represented by basketball and football has gradually attracted attention. The target tracking algorithm based on machine learning (ML) has been gradually proposed. With the powerful feature extraction for convolutional neural network (CNN), it greatly improves accuracy and has better robustness in the face of complex sports scenes. However, the tracking algorithm based on deep learning has many network layers and parameters, which makes training and update speed of model slower. In this regard, taking basketball as an example, this paper designs a low-parameter deep learning-based complex sports target tracking algorithm, which greatly reduces the size of the model while ensuring the tracking accuracy. Aiming at the problem of large number of parameters and large model of deep learning tracking algorithm, this work proposes a network structure model with asymmetric convolution module. The asymmetric convolution module includes two convolutional layers, the compression layer and the asymmetric layer. To improve accuracy, this work designs a new triplet loss. Compared with original logistic loss, triplet loss function can fully utilize the latent relationship between the inputs, so that the network model can obtain higher tracking accuracy. Finally, this paper proposes a low-parameter deep learning-based target tracking algorithm combining asymmetric convolution and triple loss function. Comprehensive and systematic experiments demonstrate the effectiveness of this work in tracking complex sports objects. [ABSTRACT FROM AUTHOR]

Cellular signaling data have become increasingly indispensable in analyzing residents' travel characteristic. Especially with the enhancement of positioning quality in 4G-LTE and 5G wireless communication systems, it is expected that the identification accuracy of fine-grained travel modes will achieve an optimal level. However, due to data privacy issues, the empirical evaluation of the performance of different identification methods is not yet sufficient. This paper builds a travel mode identification model that utilizes the gated recurrent unit (GRU) neural network. With 24 features as input, this method can identify four traffic modes, including walking, bicycle, car, and bus. Moreover, in cooperation with the operator, we organized an experiment collecting cellular signaling data, as well as the corresponding GPS data. Using the collected dataset as ground-truth data, the performance of the method presented in this paper and other popular methods is verified and compared. The results indicate that the GRU-based method has a better performance, with a precision, recall, and F score of 90.5%. Taking F score as an example, the outcome of the GRU-based method is about 6% to 7% higher than methods based on other machine learning algorithms. Considering the identification accuracy and model training time comprehensively, the method suggested in this paper outperforms the other three deep learning-based methods, namely, recurrent neural network (RNN), long short-term memory network (LSTM), and bidirectional long short-term memory network (Bi-LSTM). This study may provide some insights for the application and development of cellular signaling-based travel information collection technology for residents in the future. [ABSTRACT FROM AUTHOR]

Contemporary multiscale construction object detection algorithms rely predominantly on fully-supervised deep learning, requiring arduous and time-consuming labeling process. This paper presents a novel semisupervised multiscale construction objects detection (SS-MCOD) by harnessing nearly infinite unlabeled images along with limited labels, achieving more accurate and robust detection results. SS-MCOD uses a deformable convolutional network (DCN)-based teacher-student joint learning framework. DCN uses deformable advantages to extract and fuse multiscale construction object features. The teacher module generates pseudolabels for construction objects in unlabeled images, while the student module learns the location and classification of construction objects in both labeled images and unlabeled images with pseudolabels. Experimental validation using commonly used construction datasets demonstrates the accuracy and generalization performance of SS-MCOD. This research can provide insights for other detection tasks with limited labels in the construction domain. [ABSTRACT FROM AUTHOR]

Crack detection at the pixel level across complex scenarios (structural interference and adverse working conditions) is a critical consideration in the maintenance of ballastless track slab (BTS). Although existing deep learning models achieve acceptable accuracy on cracks with a monotonous background, the ground truth with high labor cost is inevitable and their performance in complex scenarios may fall far below their theoretical bounds or even cause "all-black images." A hybrid algorithm based on synthetic data from digital twin model and weakly supervised style transfer is proposed in this paper for addressing the above challenges. The algorithm uses a region-attention strategy to enable the uncontrolled generative adversarial network (GAN) focusing its attention on weak labels containing crack regions, directly obtaining segmentation results with the same style as the ground truth of the crack forest dataset. In addition, a digital twin model that can simulate the real inspection working conditions is established to generate a synthetic crack dataset, enabling the hybrid algorithm to extract the most discriminative features. The results show that the performance of the hybrid algorithm on inspection images across complex scenarios is nearly 25% higher than that of the DeepLabv3+ network, while the time cost consumed is only 0.5% of the latter. The deployment of the region attention strategy also enables the hybrid algorithm to achieve a mean intersection of union (MIoU) of 79.38%, which is nearly twice as much as that of GAN. It not only eliminates the oversegmentation caused by structures such as rails and fastener systems but also overcomes "all-black images." In addition, synthetic data can greatly enlarge the range, type, and number of discriminative crack features compared with data augmentation based on limited real data, thus enhancing the performance of the hybrid algorithm for uncertain inspection data. Particularly, the fully trained hybrid algorithm based on the synthetic dataset shows good adaptability and generalization to adverse working conditions such as uneven lighting, noise, and blur. [ABSTRACT FROM AUTHOR]

Various concrete bridges have been built across oceans, valleys, and mountains; however, the settlement displacement of bridge piers caused by environmental changes or self-weight during construction phases often leads to uneven stresses, cracking, and eventual collapse. To address the labor-intensive and high-cost issues of pier displacement monitoring using contact-type sensors, this paper proposes an automatic vision-based method for measuring pier settlement displacement under complex construction environments, such as complex image backgrounds, varying ambient light, and camera movement. In the proposed method, a deep learning network was first employed to eliminate the adverse effect of complex image backgrounds and varying ambient light on the accuracy of target detection; then, an adaptive displacement extraction algorithm without a human-computer interaction process was developed to automatically extract the center coordinates of targets attaching to the bridge piers and reference platform; finally, the pier settlement displacement was calculated by using the relative displacements obtained by a dual camera system to eliminate the measurement error caused by camera translation and rotation movements. Laboratory tests of a cantilever beam and field tests of a continuous multispan concrete girder highway bridge under construction have successfully validated the effectiveness and robustness of the developed methodology. The results obtained in this paper can provide some insights for engineers in applying computer vision technology for the real-time monitoring of bridge displacements. [ABSTRACT FROM AUTHOR]

In the paper entitled "Optimization Strategy of Multiarea Interconnected Integrated Energy System Based on Consistency Theory" by Yu et al., they study a collaborative optimization strategy of an integrated energy system aiming at improving the energy efficiency for the cluster optimization of an integrated energy system (IES). In the paper entitled "Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network" by Shen et al., they propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve the prediction efficiency. Furthermore, to overcome the problems of traditional IP multicast and software-defined multicasting approaches, they propose an implementable multicast mechanism that delivers data along multicast trees but uses unicast sessions. [Extracted from the article]

In recent years, as a new subject in the computer field, artificial intelligence has developed rapidly, especially in reinforcement learning (RL) and deep reinforcement learning. Combined with the characteristics of Software Defined Network (SDN) for centralized control and scheduling, resource scheduling based on artificial intelligence becomes possible. However, the current SDN routing algorithm has the problem of low link utilization and is unable to update and adjust according to the real-time network status. This paper aims to address these problems by proposing a reinforcement learning-based multipath routing for SDN (RLMR) scheme. RLMR uses Markov Decision Process (MDP) and Q-Learning for training. Based on the real-time information of network state and flow characteristics, RLMR performs routing for different flows. When there is no link that meets the bandwidth requirements, the remaining flows are redistributed according to the Quality of Service (QoS) priority to complete the multipath routing. In addition, this paper defines the forward efficiency (FE) to measure the link bandwidth utilization (LBU) under multipath routing. Simulation results show that compared with the current mainstream shortest path algorithm and ECMP algorithm, the routing algorithm in RLMR has advantages in FE, jitter, and packet loss rate. It can effectively improve the efficiency and quality of routing. [ABSTRACT FROM AUTHOR]

Under the background of quality education, music learning is also changing, from the original shallow learning to deep learning gradually. In-depth learning is a new teaching concept, which pays full attention to students' perception and exploration of music so that students can fully experience the charm of music. It can not only help students master more music knowledge and improve their music skills but also cultivate students' music literacy and enhance their music ability (Świechowski, 2015). Therefore, in junior high school music teaching, teachers should actively apply the deep learning model and then improve the teaching level and comprehensively cultivate students' music literacy (Whitenack and Swanson, 2003). In this paper, two convolution-based deep learning models, Breath1d and Breath2d, were designed and constructed, and a multilayer perceptron (MLP) was used as a benchmark method for performance evaluation, and a long short-term memory (LSTM) network is applied for the classification task. This paper discusses the value and application strategies of deep learning in junior high school music teaching and hopes to provide some reference for all educational colleagues (Zhang and Nauman 2020). [ABSTRACT FROM AUTHOR]

The traditional tennis serve training model has been deeply ingrained in the current training. For athletes, in order to master the essentials of technical movements proficiently, they must practice for a long time and repeatedly according to the guidance of coaches. Effectively correcting the image path of tennis serve can improve the level of tennis training and competition. When correcting the image path of the tennis serve, it is necessary to mark the corners of the error points according to the characteristics of rotating multidimensional characteristics of the serving action. The traditional method adopts the critical node control method to realize the extraction of limb features and complete the path correction of the serving image. The error points of the process are marked, which reduces the accuracy of the path correction. Based on the deep learning model, this paper proposes an optimization modeling method for tennis serve image path correction and builds a visual feature acquisition system for tennis serve action based on remote video monitoring. The processing method designs the edge segmentation algorithm for the collected visual images and, on this basis, marks the corner points of the error points of the serving action and realizes the optimal modeling of the path correction of the tennis serving image. In this paper, the DLT algorithm and MSDLT algorithm are compared first, and then, the DLT algorithm is compared with the algorithm in this paper. The results show that the success rate of this method is about 92%, while the success rate of DLT algorithm is only about 82%. This algorithm has obvious advantages. The method in this paper is used to correct the action shape of the tennis serving action, which has better real time and accuracy and superior performance and can accurately track the visual edge information feature points of the player during the serving process. [ABSTRACT FROM AUTHOR]