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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]

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]

With the rapid development of China's economy, the protection of buildings has attracted the attention of many researchers. Although there is no such massive demolition in the past, natural damage still exists. Identify the collected historical building protection data through multifeature deep learning, and provide protection plans through the information in the database. In order to solve the problem of restoration of natural damage more professionally and efficiently, this paper collects the architectural features and restoration methods of each building in different processes through multifeature deep learning based on the current state of building information in China. Based on the collected information, this paper establishes the building information model, and stores and manages the building information. According to the Newton deep learning optimization algorithm, this paper enhances the algorithm to accurately collect building information and uses the collaborative filtering algorithm to provide users with a repair plan. This paper uses the GRU-based recommendation model to pass the threshold cycle unit algorithm for the probability of each building being selected in the list of similar buildings at a time point. Under the two conditions of 10 and 20 recommended numbers, the user coverage rate of the recommended case of deatomized building photos can reach 100%. And this paper recommends high-probability solutions for users to achieve automation, diversification, and intelligence. [ABSTRACT FROM AUTHOR]

The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location. This paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT). The algorithm used demonstrates a binary classification with high accuracy by comparing individual faults with good condition. Efficient and effective fault detection can be observed from the results obtained. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, people have entered a situation of information overload, how do users filter out the information they need from a large amount of information. When users browse the website, they will record their search or click behavior, and the recommendation system will mine the data based on these data, and recommend the information they need for each user. With the birth of the recommender system, it has indeed changed the way people obtain information. Instead of relying solely on search engines to obtain information, it can obtain the information they want without people's "consciousness." This shift has made it easier for people to access information. This paper conducts research on travel recommendation during the Spring Festival holiday. The paper introduces deep learning model and data mining technology, proposes that the recommendation system has three important modules, and obtains the corresponding flowchart. The recommendation system was optimized, and a comparison chart of coverage before and after optimization was obtained. Before optimization, the coverage rate of cities and scenic spots was 45.52% and 21.25%, respectively, and reached 55.65% and 49.81% after optimization. [ABSTRACT FROM AUTHOR]

The emergence of portable devices provides great convenience for image acquisition, but the image resolution is too low, which affects the identification and use of the image. Multiframe super-resolution algorithm makes a great contribution to extracting image features, but there is a problem with too much computation. Based on this, this paper proposes a low-resolution image fusion algorithm based on deep learning. Based on the introduction of extracting image features from low-resolution images, this paper proposes to fuse multiple low-resolution images, reduce the time of calculation error per frame, reduce the amount of calculation, extract feature information, and improve the alexnet network model to realize image extraction. The convolutional self-coding fusion network is used to realize image fusion and complete the reconstruction and fusion of low-resolution images. In the simulation analysis of the fusion algorithm, objective indexes such as network structure and loss function are selected to evaluate the effectiveness of the algorithm. This paper reduces the computational complexity of image acquisition and ensures that the amount of calculation can be reduced in each iteration. Analyze the effectiveness of the fusion algorithm, comprehensively evaluate the objective indicators, and verify the advantages of the algorithm through indicators such as gradient change and information entropy. [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]

With the current exchange and communication between different countries becoming more and more frequent, the language conversion of different countries has become a difficult problem. The analysis of a series of problems in cross-language discourse conversion, the study of the discourse conversion path, and innovation motivation based on the deep learning theory of cross-language transfer, it has theoretical and practical significance. This paper aims at the technical difficulties in speech conversion methods to effectively utilize the local mode information of signal time spectrum and the long-term correlation of speech signal. A discourse conversion method based on convolutional recurrent neural network model is proposed. In the model, the extended convolutional neural network is used to model the long-term correlation of speech signals. In the part of speech fundamental frequency estimation, the prosodic information generated by the decomposition of the fundamental frequency by continuous wavelet transform is used as the training target of the fundamental frequency estimation model. The experimental results show that the speech transformation method based on the convolutional cyclic network model proposed in this paper has better quality and intelligibility than the speech transformed by the contrast method. [ABSTRACT FROM AUTHOR]

There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students' class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students' learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students' inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application. [ABSTRACT FROM AUTHOR]

Physical education class helps students develop whole-body and fine motor skills and improve their strength, balance, and cardiovascular health. Sports also provide students with numerous social, psychological, and emotional benefits, which in turn improve their learning status and academic achievements. Nowadays, sports achievements are becoming more and more important, and many schools pay more and more attention to the development of sports. In order to improve sports achievements and predict sports achievements, we can combine them with relevant deep learning models to analyze sports achievements from multiple angles and factors, so as to improve sports achievements and predict sports achievements according to relevant influencing factors. Combined with the experimental part in this paper, the gradient compression algorithms under the deep learning model are compared. According to the data, the compression ratio of the AdaComp algorithm model is 1%, the training time is 5839 s, the average accuracy rate is about 90.9%, and the average loss value is 0.324. The compression ratio of the ProbComm-LPAC algorithm model is 1%, the training time is 5505 s, the average accuracy rate is about 91.8%, and the average loss value is 0.271. The compression ratio of the LAQ algorithm model is approximately 1%, the training time is 5467 s, the average accuracy rate is about 90.8%, and the average loss value is 0.554. When the number of training rounds increases from 20 to 5000, the accuracy of the three algorithm models is on the rise, but the accuracy of ProbComp-LPAC model is higher among the three models. When the number of training rounds increases, the data set loss rate of the three models is declining, indicating that the more the training times, the higher the correct rate, the smaller the loss value, and the higher the efficiency. Through four dimensions related to the influence of sports achievements-interest in seeking knowledge, ability pursuit, altruistic orientation, and reputation acquisition, this paper studies the influencing factors of sports achievements. According to the research data, most people think that the interest in seeking knowledge accounts for a large proportion of the factors affecting sports performance, with an average of 38.6709, accounting for 32% in the four dimensions. Through the study of students' gender and origin, this paper explores the analysis of the four dimensions of sports performance. It is believed that interest in knowledge is the most important factor. The average values of the four dimensions are 48.98, 52.37, 48.12, and 51.34, respectively. In order to accurately predict sports achievements, the characteristics of sports achievements prediction are sorted, among which the maximum number of action exercises is 0.24, the average score of sports action tests is 0.16, the video viewing time is 0.13, the sign-in rate is 0.09, and the minimum homework completion rate is 0.03. Predicting sports achievements through these characteristics can improve the accuracy of prediction. When the number of features in sports performance prediction gradually increases, the accuracy of sports performance prediction is also increasing. When the number of features is 8 and 9, the prediction accuracy is about 0.64. [ABSTRACT FROM AUTHOR]

China's cultural classics have high artistic and ideological values in the world, which implies China's historical heritage and the inheritance of the cultural situation of the Chinese nation for thousands of years. At present, with the rapid development of China's economy, especially in the development environment of cultural globalization, China's traditional cultural classics have attracted worldwide attention along with historical and cultural treasures. In fact, other countries need to translate cultural classics out of their love for China's cultural classics or academic research. However, there are a large number of cultural classics in China. According to relevant data, there are approximately 35,000 kinds of cultural classics, out of which only 0.2% are translated into foreign languages. In order to make China's excellent cultural classics known to the world and let more people in other countries understand Chinese culture, this paper studies the translation of cultural classics through in-depth learning. Firstly, this paper introduces the basic concept of deep learning and proposes an algorithm that deeply studies the convolution layer and pool layer. Secondly, the algorithm establishes a deep learning model that calculates and counts the text information of ancient books through explicit intertextuality. Thirdly, the model carries out automatic text translation and lists the analysis process of cultural classics translation based on intertextuality, so as to study cultural classics translation. The obtained results can promote the development of cultural classics translation in China. For the translation of complex cultural classics, the effects of the three models are tested experimentally, of which the sequence model (seq) is the best. This model is fast and simple to extract the text of literary classics. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explore the characteristics of the use of verbal collocations in English, to compare the use of verbal collocations in the English translation and the original English text, and then to compare and analyse the characteristics of the choice of verbal collocations in the English text. In this paper, we take bilateral marked causative complex sentences as the object of study and use deep learning methods to automatically explore the implied features of complex sentences while incorporating the significant knowledge of relational words in linguistic research. The experimental results achieved an F1 value of 92.13%, which is better than that of the existing comparison models, demonstrating the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Football is regarded as the world's number one sport and is loved by all countries, and large-scale football matches are held basically every year. The key to football matches is to shoot goals, and how to improve the accuracy of football shooting requires the identification and analysis of football shooting actions. Deep learning enables machines to imitate human activities such as seeing, hearing, and thinking. It solves many complex pattern recognition problems. Especially, the deep learning algorithm is unique in the recognition of pictures with high accuracy, and it provides technical support for the recognition and analysis of football shooting actions. What this paper will discuss is the recognition method of football shooting action based on a deep learning algorithm. Experiments show that the football shooting action recognition method developed in this paper has a great effect on promoting the accuracy of football shooting, which can make the accuracy rate reach about 96%. The research in this paper has great reference value and practical significance for the team's ability to shoot and grasp the opportunity to score. [ABSTRACT FROM AUTHOR]

It is of great significance to accurately predict the operation of the system economy, analyze the gains and losses of macrocontrol policies, evaluate the operation quality of the economic system, and correctly formulate the future development plan and strategy. This paper introduces the deep belief network, which has attracted much attention in the field of deep learning in recent years, into the research of system economic operation and management. This method solves the problems of slow training and learning speed, easy to fall into local minima and insufficient generalization of BP artificial neural network in the research of system economic operation and management. Taking the consumer price index and total import and export volume of F Province as the research object, the experiment proves that DBN has better application in system economic operation and management than BP neural network and vector autoregressive analysis. This paper analyzes and compares the modeling performance of DBN, BP neural network, and VaR method from many aspects, such as prediction accuracy, training convergence speed, and pretraining with or without samples. Relevant empirical results show that DBN has better economic prediction performance than BP neural network and ver. On the other hand, DBN can effectively use nonstandard samples to pretrain network weight parameters. Therefore, DBN is a better operation and management modeling means of economic system, with excellent practicability and application, and is expected to be popularized and applied in the field of economic forecasting. [ABSTRACT FROM AUTHOR]

The use of deep learning methods to solve problems in the field of artwork prices has attracted widespread attention, especially the superiority of long short-term memory network (LSTM) in dealing with time series problems. However, the potential for deep learning in the prediction of artwork price has not been fully explored. This paper proposes a deep prediction network structure that considers the correlation between time series data and the combination of two-way LSTM as well as one-way LSTM networks to predict the price of artworks. This paper proposes a deep-level two-way and one-way LSTM to predict the price of artworks in the art market. Taking into account the potential reverse dependence of the time series, the bidirectional LSTM layer is used to obtain bidirectional time correlation from historical data. This research uses a matrix to represent the artwork price data and fully considers the spatial correlation characteristics of the artwork price. Simultaneously, this paper uses the two-way LSTM network to correlate the potential contextual information of the historical data of the artwork price stream and fully perform feature learning. This study applies the two-way LSTM network layer to the building blocks of the deep architecture to measure the inverse dependence of the price fluctuation data. The comparison with other prediction models shows that the LSTM neural network fused with one-way and two-way proposed in this paper is superior to other neural networks for predicting price of artworks in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

The option pricing estimation of financial market can be transformed into the calculation of high-dimensional integrals. In order to obtain the actual price, option pricing system can only rely on modern numerical methods; on the other hand, the improvement of calculation methods and technologies has also shifted the focus of design and application of option pricing system from a strict closed model to a less advanced but computationally intensive calculation. High-performance computing can establish a reasonable analysis model, realistically simulate the effects of various designed performance and parameters, and provide technical support for a large number of fast numerical calculations and simulations. Deep reinforcement learning can obtain a description of empirical knowledge through the learning of historical information and use the acquired experience to deal with future problems, which can well solve classification problems, regression problems, and optimization problems. Therefore, based on summarizing and analyzing previous research results, this paper expounds the current research status and significance of the design and application of financial market option pricing systems, elaborates the development background, current status, and future challenges of high-performance computing and deep reinforcement learning, introduces the methods and principles of high-performance computing platform and deep reinforcement learning algorithm, conducts no-arbitrage pricing model design, performs risk-neutral pricing model design, discusses the design of finance market option pricing system based on high-performance computing and deep reinforcement learning, analyzes the financial market option pricing considering random interest rates, implements financial market option pricing considering transaction costs, explores the application of financial market option pricing system based on high-performance computing and deep reinforcement learning, and finally carries out empirical experiment and its result analysis. The study results show that the financial market option pricing system based on high-performance computing and deep reinforcement learning records the basic information of high-performance computer system users and option pricing information in the form of relational data in a relational database. The financial market option pricing system describes the state of the controller and the upcoming option pricing situation. The system's initialization uses multiple arrays from a stochastic strategy to initialize option pricing and evaluation buffers, which uniformly selects an option pricing from the initial option pricing set in each cycle. The study results of this paper provide a reference for further researches on the design and application of financial market option pricing system based on high-performance computing and deep reinforcement learning. [ABSTRACT FROM AUTHOR]

Human action recognition is one of the hotspots in computer vision research. Its purpose is to detect and recognize target actions from videos, so that computer systems can understand human actions, and thus it has great research significance. Based on the action features of famous sports, this paper proposes an action recognition scheme based on RGB-D video compression to establish action features and deep learning as a means of recognition. By establishing the connection between the bone data of the three-dimensional data type and the depth image data, the depth sequence is analyzed and expressed as a three-level structure diagram sequence, which is the overall figure sequence, partial figure sequence, and joint point figure sequence, and then passes through the two-way pool. The sorting algorithm extracts the action features in the three picture sequences and compresses and generates three types of structured images of the corresponding picture sequences, and these three types of structured images are used as the feature expression of the video. When constructing a three-level structure diagram sequence, the innovation of this paper is to splice the extracted key unit image blocks to obtain a three-level structured moving image based on the three-key unit splicing, so that the image is not only retained. In addition to time-space information, the structure information of the depth image is also strengthened, and the amount of calculation is reduced at the same time. Finally, the three types of structured images are input into the convolutional neural network, respectively, and the judgment and recognition results obtained are multiplicatively fused to obtain the final recognition rate of the action. [ABSTRACT FROM AUTHOR]

With the popularization of higher education, China's higher education has moved from the stage of elite education to the stage of universal education, and the number of graduates is also increasing. At present, college students are facing tremendous pressure on employment. One is the huge number of jobs, the other is the difference in professional needs, and the third is the spread of job information, which makes it difficult for college students to find a job that suits them. In order to solve this dilemma, this paper analyzes the related technologies and in-depth basic theories of data mining. After introducing several traditional recommendation algorithms, the traditional convolutional network method is improved from three aspects: activation function, pooling strategy, and loss function. Finally, using the hybrid convolutional neural network, a career recommendation model for college students based on deep learning and machine learning is proposed, and simulation experiments are carried out on it. The main research work is as follows: (1) a hybrid convolutional neural network is proposed, which uses convolution operation to learn high-level features to achieve personalized employment recommendation; (2) the training optimization strategy of the hybrid convolutional neural network is studied, aiming at the activation function, pooling processing, and loss function, and the feasibility of the optimization method is verified through simulation experiments; (3) finally, according to the evaluation index of the recommendation algorithm (recall rate and F1-Score), the recall rate of the algorithm in this paper is nearly 15% higher than that of the DNN model. The experiment is compared with the traditional commonly used recommendation algorithm, and the comparative analysis of the experimental results proves the effectiveness of the algorithm for the employment recommendation of college students. [ABSTRACT FROM AUTHOR]

The openness of modern network intelligent education provides a broader learning space for Chinese language learners. People pay more and more attention to network learning, and network intelligent teaching platforms are constantly emerging under this background. Intelligence is one of the most important characteristics of ITS. However, the current network teaching system is far from enough in the intellectualization of teaching content, form, and process. In order to improve the intelligence level of ITS, this paper studies the use of DL network with strong self-learning ability to build ITS. From the point of view of teaching students in accordance with their aptitude and accurately reflecting students' learning state and characteristics, this paper analyzes the influencing factors in students' learning process, puts forward factors such as students' learning style and learning habits into the construction of a student model, and designs the student model. The future trend of ITS is predicted, and it is pointed out that this field with attractive development prospect is worthy of further research and design. [ABSTRACT FROM AUTHOR]

Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) plays a vital role in detecting intruder attacks, and it is also used to monitor the traffic in the network. The paper is aimed to control the attacks using the machine learning (ML) technique integrated with the artificial bee colony (ABC) named Group-ABC (G-ABC). The IDS detector has been implemented and further simulation results have been determined using the G-ABC. The evaluation has been carried out using the measures such as precision, recall, accuracy, and F-measure. Different attacks such as user to root (U2R), probe, root to local (R2L), backdoors, worms, and denial-of-service (DoS) attacks have been detected. The simulation analysis is performed using two datasets, namely, the NSL-KDD dataset and UNSW-NB15 dataset, and comparative analysis is performed against the existing work to prove the effectiveness of the proposed IDS. The objective of the work is to determine the intruder attacker system using the deep learning technique. [ABSTRACT FROM AUTHOR]

Accurate selection of embryos with the maximum implementation condition is a necessary step to increase the effectiveness of fertility treatment in in vitro fertilization (IVF). The deep learning algorithms presented high potential for monitoring and visualizing embryo features such as cell numbers and their morphological development in time series manner. Due to the ability of the computer vision and deep learning algorithms, this paper aimed to present a novel deep learning approach to distinguish simultaneous abnormality of embryos in time-lapse systems for detecting live and non-live births in IVF. The approach is composed of local binary convolutional neural network (LBCNN) and long short-term memory (LSTM). The LBCNN improved accuracy of classification by employing deep and local feature sets with lower number of learnable parameters in comparison with a standard convolutional layer. Moreover, LSTM network is employed to analyze temporal information of time-lapse embryos. The results indicate that the proposed approach achieves significant results in ROC analysis (0.98) in 5 days of monitoring compared to state-of-the-art models. In addition, the approach showed compatible results in early diagnosis of abnormality detection (72 hours) with 82.8% accuracy of classification compared to the pretrained well-known convolutional neural network (CNN) models and baseline CNN. [ABSTRACT FROM AUTHOR]

The oral English test in domestic universities requires teachers to modify a large number of candidates' oral recordings. This is the work of using time repeatedly. Using the CALL system to realize the automation of conversation recording can reduce the burden of teachers' work. Therefore, it is of great practical significance to develop an automatic and accurate scoring system for oral English. With the development of artificial intelligence, deep learning technology has been gradually applied in various fields. Similarly, in the application of oral scoring, deep learning technology makes the implementation of such a system possible. Based on the deep learning technology, this paper proposes an automatic scoring algorithm for spoken language and implements a detailed design and evaluation system. The system consists of two modules. The pronunciation standard of spoken pronunciation and the content of spoken pronunciation are scored, and the sum of these two scores is the final score. Finally, this paper uses 650 oral English recordings from a college English test to train the artificial neural network. Experimental results show that if the training data set is small, the BP network model can obtain better comprehensive evaluation performance. [ABSTRACT FROM AUTHOR]

As a form of artistic expression, dance accompanied by music enriches the cultural life of human beings and stimulates the creative enthusiasm of the public. Choreography is usually done by professional choreographers. It is highly professional and time-consuming. The development of technology is changing the way of artistic creation. The development of motion capture technology and artificial intelligence makes computer-based automatic choreography possible. This paper proposes a method of music choreography based on deep learning. First, we use Kinect to extract and filter actions and get actions with high authenticity and continuity. Then, based on the constant Q transformation, the overall note density and beats per minute (BPM) of the target music are extracted, and preliminary matching is performed with features such as action speed and spatiality, and then, the local features of the music and action segments based on rhythm and intensity are matched. The experimental results show that the method proposed in this paper can effectively synthesize dance movements. The speed and other characteristics of each movement segment in the synthesis result are very uniform, and the overall choreography is more aesthetic. [ABSTRACT FROM AUTHOR]

With the improvement of living standards, more and more people are pursuing personalized routes. This paper uses personalized mining of interest points of ethnic minority tourism demand groups, extracts customer data features in social networks, and constructs data features of interesting topic factors, geographic location factors, and user access frequency factors, using LDA topic models and matrix decomposition models to perform feature vectorization processing on user sign-in records and build deep learning recommendation model (DLM). Using this model to compare with the traditional recommendation model and the recommendation model of a single data feature module, the experimental results show the following: (1) The fitting error of DLM recommendation results is significantly reduced, and its recommendation accuracy rate is 50% higher than that of traditional recommendation algorithms. The experimental results show that the DLM constructed in this paper has good learning and training performance, and the recommendation effect is good. (2) In this method, the performance of the DLM is significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm. Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively. [ABSTRACT FROM AUTHOR]

As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning in recent years, recommendation algorithm combined with deep learning model has become an important direction in the field of recommendation in the future. Personalized learning resource recommendation is the main way to realize students' adaptation to the learning system. Based on the in-depth learning mode, students' online learning action data are obtained, and further learning analysis technology is used to construct students' special learning mode and provide suitable learning resources. The traditional method of introducing learning resources mainly stays at the level of examination questions. What ignores the essence of students' learning is the learning of knowledge points. Students' learning process is affected by "before" and "after" learning behavior, which has the characteristics of time. Among them, bidirectional length cyclic neural network is good at considering the "front" and "back" states of recommended nodes when recommending prediction results. For the above situation, this paper proposes a recommendation method of students' learning resources based on bidirectional long-term and short-term memory cyclic neural network. Firstly, recommend the second examination according to the knowledge points, predict the scores of important steps including the accuracy of the recommended examination of the target students and the knowledge points of the recommended examination, and finally cooperate with the above two prediction results to judge whether the examination questions are finally recommended. Through the comparative experiment with the traditional recommendation algorithm, it is found that the student adaptive learning system based on the deep learning model proposed in this paper has better stability and interpretability in the recommendation results. [ABSTRACT FROM AUTHOR]

Financial text-based risk prediction is an important subset for financial analysis. Through automatic analysis of public financial comments, fundamentals on current financial expectations can be evaluated. A deep learning method for financial risk prediction based on sentiment classification is proposed in this paper. The proposed method consists of two steps. Firstly, the abstract of the financial message is extracted according to the seq2seq model. During the extraction process, the seq2seq model can cope with the situation of different input message lengths. After the abstraction, invalid information in the financial messages can be effectively filtered, thus accelerating the subsequent sentiment classification step. The sentiment classification step is performed through the GRU model according to the abstracted texts. The proposed method has the following advantages: (1) it can handle financial messages of different lengths; (2) it can filter out the invalid information of financial messages; (3) because the extracted abstract is more refined, it can speed up the subsequent sentiment classification step; and (4) it has better sentiment classification accuracy. The proposed method in this paper is then verified through financial message dataset from the financial social network StockTwits. By comparing the classification performances, it can be seen that compared with the classical SVM and LSTM methods, the proposed method in this paper can improve the accuracy of sentiment classification by 5.57% and 2.58%, respectively. [ABSTRACT FROM AUTHOR]

Traditional sports aid systems analyze sports data via sensors and other types of equipment and can support athletes with retrospective analysis, but they require several sensors and have limited data. This paper examines a sports aid system that uses deep learning to recognize, review, and analyze behaviors through video acquisition and intelligent video sequence processing. This paper's primary research is as follows: (1) With an eye on the motion assistance system's application scenarios, the network topology and implementation details of the two-stage Faster R-CNN and the single-stage YOLOv3 target detection algorithms are investigated. Additionally, training procedures are used to enhance the algorithm's detection performance and training speed. (2) To address the issue of target detection techniques' low detection performance in complicated backgrounds, an improved scheme from Faster R-CNN is proposed. To begin, a new approach replaces the VGG-16 network in the previous algorithm with a ResNet-101 network. Second, an expansion plan for the dataset is provided. (3) To address the short duration of action video and the high correlation of image sequence data, we present an action recognition method based on LSTM. To begin, we will present a motion decomposition scheme and evaluation index based on the key transaction frame in order to simplify the motion analysis procedure. Second, the spatial features of the frame images are extracted using a convolutional neural network. Besides, the spatial and temporal aspects of the image sequence are fused using a two-layer bidirectional LSTM network. The algorithm suggested in this research has been validated using a golf experiment, and the results are favorable. [ABSTRACT FROM AUTHOR]

This paper adopts a deep learning approach to analyze and study the mechanism of quantitative enhancement of college students' employment and entrepreneurial abilities in the context of the digital era. The deep learning connotation is predetermined as five abilities, which are metacognitive ability, active communication and cooperation ability, deep processing ability, creative practice ability, and learning empathy experience, and, based on this, the deep learning questionnaire is designed, and it is reclassified by exploratory factor analysis to reduce the dimensionality, and the specific indicators and scientific connotation dimensions of the deep learning questionnaire are determined; and, through the deep learning of each dimension, the problems of deep learning of college students are examined and in-depth analysis is conducted, and the inner relationship and correlation among the dimensions of deep learning of college students are derived through correlation analysis. The success of innovation and entrepreneurship depends on the innovation and entrepreneurial ability of college students, and the formation of the ability influenced various factors. Therefore, not only is studying the influencing factors of college students' innovation and entrepreneurship ability in line with the requirements of the times and social development, but also it can solve real problems. This thesis adopts a combination of two methods, qualitative research and quantitative research, to study the influencing factors of college students' innovation and entrepreneurship ability and tries to ensure the scientificity, accuracy, and comprehensiveness of the conclusion. In this paper, we analyzed the requirements of the employment prediction system for graduating secondary school students, carried out the software framework and database design of the employment analysis and prediction system for secondary school students, and designed the system modularly based on the analysis results. By applying the proposed deep feedforward neural network prediction model to the prediction system, a software system applicable to the employment prediction and guidance of secondary school students is implemented. [ABSTRACT FROM AUTHOR]

The competition among enterprises is becoming increasingly fierce. The research on the financial management evaluation model is helpful for enterprises to find possible risks as soon as possible. This paper constructs the financial management evaluation model based on the deep belief network and applies the analytic hierarchy process to determine the weight of financial management evaluation indicators, which is compared with other classical deep learning evaluation methods, such as KNN, SVM-RBF, and SVM linear. It has achieved an accuracy of more than 81%, showing a satisfactory prediction effect, which is of great significance to formulate corresponding countermeasures, strengthen financial management, improve the capital market system, and promote high-quality economic development. In addition, aiming at the problem of abnormal financial data, this paper uses the new sample dataset obtained by principal component analysis for convolution neural network model learning, which enhances the prediction accuracy of the model and fully shows that deep learning is feasible in the research of financial management prediction, and there is still a lot of space to explore. [ABSTRACT FROM AUTHOR]

The decisions and actions of operators in the operation of transport infrastructure play a crucial role in the sustainability of the project but are influenced by factors such as the strength of government regulation and the frequency of use by users. The influence of social recognition and acceptance by the authorities on the decisions of the various parties involved in a project is becoming increasingly significant. To address this issue, this paper attempts to apply prospect theory to characterize the impact of changes in recognition on the decisions of project managers and the government from the perspective of recognition and to construct a tripartite evolutionary game model for the government, the operator, and the users, taking into account the combined effect of multiple factors, to explore the evolutionary law of the operator's strategy choice. Evolutionary game theory, in which each person is considered irrational and behavior is changeable, is more realistic. The addition of prospect theory allows the model to more realistically reflect the decisions of each participant in the game process when faced with risk. The results of the study show that there is an optimal level of external regulation to maximize the benefits for all three parties in the game, strong government regulation does not necessarily improve service quality, operators tend to provide low-quality services in the game process and incentives should be increased, and that attempts should be made to provide users with a variety of transport infrastructure options to ensure that users' interests are maximized. The paper further analyzes the indicators established by sensitive factors using BP neural networks on the basis of the analysis of transportation infrastructure operation and impact sensitive factors using evolutionary games and finds that the conclusions obtained by deep learning algorithms are more consistent with those obtained using evolutionary games, achieving cross-validation of the results. The reliability of the result is improved, and it is demonstrated that deep learning algorithms can be introduced as a supplement in the process of future analysis of transportation infrastructure operations. Finally, management suggestions are made in light of the actual situation. [ABSTRACT FROM AUTHOR]

In the present context, the deep learning approach is highly applicable for identifying cyber-attacks on intrusion detection systems (IDS) in cyber-physical security systems. As a key part of network security defense, cyber-attacks can change and penetrate the security of the network system, then, the role of an IDS is to detect suspicious behaviors and act appropriately to protect the network from the onset of attacks. Machine learning and deep learning techniques are important for current intrusion detection systems. However, traditional intrusion detection systems are far from being able to quickly and accurately identify complex and diverse network attacks and obtained low accuracy and detection rates, thus, these methods frequently fail to manage big amounts of data in a vast network infrastructure and utilize a lot of features leads to poor performance. For addressing these issues and improving the accuracy and scalability, in this paper, we have implemented the deep learning method based on a new approach multilayer long short-term memory (LSTM) model for detecting attacks on a network. The novelty of the proposed scheme is that the optimum multilayer architecture is built to achieve maximum accuracy in the network architecture in order to boost performance using stacking multiple layers of LSTM cells in a more effective manner, and better stability to perform consistently in both binary classification and multiclass classification on NSL-KDD datasets. Experimental tests with KDDTest + datasets show that the proposed multilayer LSTM model provides outstanding results with 95% and 96% accuracy, respectively, in binary and multiclass classification. In order to deal with actual datasets and obtain good performance in the network design, our optimum multilayer architecture must be put into practice in order to execute real-time applications. Therefore, the results are better and more robust than the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Computer vision and image processing techniques are considered efficient tools for classifying various types of fruits and vegetables. In this paper, automated fruit classification and detection systems have been developed using deep learning algorithms. In this work, we used two datasets of colored fruit images. The first FIDS-30 dataset of 971 images with 30 distinct classes of fruits is publicly available. A major contribution of this work is to present a private dataset containing 761 images with eight categories of fruits, which have been collected and annotated by ourselves. In our work, the YOLOv3 deep learning object detection algorithm have been used for individual fruit detection across multiple classes, and ResNet50 and VGG16 techniques have been utilized for the final classification for the recognition of a single category of fruit in images. Next, we implemented the automatic fruit classification models with flask for the web framework. We got 86% and 85% accuracies from the public dataset with ResNet50 and VGG16, respectively. We achieved 99% accuracy with ResNet50 and 98% accuracy with the VGG16 model on the custom dataset. The domain adaptation approach is used in this work so that the proposed deep learning-based prediction model can cope with real-world problems of diverse domains. Finally, an Android smartphone application has been developed to classify and detect fruits with the camera in real-time. All the images uploaded from the Android device are automatically sent and consequently analyzed on the web, and finally, the processed data and results are returned to the smartphone. The custom dataset and implementation codes will be available after the manuscript has been accepted. The custom dataset can be found at: https://github.com/SumonAhmed334/dataset_fruit. [ABSTRACT FROM AUTHOR]

To improve classroom teaching behavior recognition and evaluation accuracy, this paper proposes a new model based on deep learning. First, we obtain the classroom teaching behavior characteristic data through the SVM's linear separable initial and determine the relationship of the characteristic sample data in the hyperplane. Then, we obtain the heterogeneous support vector of the online learning behavior characteristic sample data in the SVM's hyperplane and complete the extraction of data with the help of convolutional neural networks. We then use a decision matrix to analyze the hierarchical process, determine the weight of classroom teaching behavior indicators, verify their consistency, and complete the evaluation by calculating the membership of evaluation factors. The experimental results show that the identification and evaluation method of classroom teaching behavior in this paper can effectively improve the identification accuracy of the classroom teaching behavior. [ABSTRACT FROM AUTHOR]

This paper investigates the extraction of volleyball players' skeleton information and provides a deep learning-based solution for recognizing the players' actions. For this purpose, the convolutional neural network-based approach for recognizing volleyball players' actions is used. The Lie group skeleton has a large data dimension since it is used to represent the features retrieved from the model. The convolutional neural network is used for feature learning and classification in order to process high-dimensional data, minimize the complexity of the recognition process, and speed up the calculation. This paper uses the Lie group skeleton representation model to extract the geometric feature of the skeleton information in the feature extraction stage and the geometric transformation (rotation and translation) between different limbs to represent the volleyball players' movements in the feature representation stage. The approach is evaluated using the datasets Florence3D actions, MSR action pairs, and UTKinect action. The average recognition rate of our method is 93.00%, which is higher than that of the existing literature with high attention and reflects better accuracy and robustness. [ABSTRACT FROM AUTHOR]

Cultural and creative design is a new design mode based on the Internet platform, which gathers the design wisdom of the public and provides online solutions for design tasks. Evaluation indicators are used to score cultural and creative design schemes that require manual construction of an evaluation matrix, which makes the evaluation efficiency very low and increases the product design cycle. Therefore, based on the deep belief network of cultural creation, the method of constructing the evaluation model of the scheme is designed in this paper. According to the characteristics of the deep belief network, the k-medoids clustering method is adopted, and the clustering key value k is set. Meanwhile, the clustering result is calculated, and k central point elements are obtained. Moreover, project the central point elements back to the cultural and creative design plan to obtain the characteristic plan, and use manual evaluation to calculate the comprehensive score. Besides, the feature scheme data and the data simulation formula are used to construct the training set of the deep belief network, which are used to train the evaluation model of the deep belief network to solve the problem of the length of time that requires a large amount of manual evaluation to construct the output of the training set. In addition, according to the number of evaluation indicators, the number of nodes in the input layer of the deep belief network is determined to define the initialization method of network weights and biases, formulating experimental procedures to determine the optimal network structure. In the simulation experiment, through the test analysis of the comparison model, the function synchronization rate in this paper is 4.2% better than that of the comparison method. After multiple iterations, the test effect is 7 times higher than that of the comparison method, realizing the optimal evaluation of the design schemes that have not been manually evaluated in the same design scheme set. [ABSTRACT FROM AUTHOR]

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models. [ABSTRACT FROM AUTHOR]

Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of artificial intelligence, information technology, intelligent digital video surveillance systems, real-time sports competition playback, and other technologies have emerged one after another, making the advantages of deep learning-based football posture detection tasks become more obvious. Related models and methods have been applied to the research field of sports posture estimation and have achieved great improvement, surpassing the traditional football posture estimation method based on manual design features in one fell swoop. In addition, the application of video foreground detection has developed rapidly and has great application value in sports analysis. Therefore, this paper proposes a novel football motion detection approach combining foreground detection and deep learning for real-time detection of football player posture. The main task of foreground target detection is to extract the interesting foreground target in the real monitoring scene and use it as the target of interest for subsequent analysis. Then, we propose a triple DetectNet detection framework based on deep learning technology, which can quickly and robustly realize the three-dimensional pose estimation of multiperson motion. For input, the triple DetectNet framework uses three neural networks and is executed in three stages; the first stage is to use the DetectNet (DN) network to detect the bounding box of each person separately, the second stage uses the 2DPoseNet (2DPN) network to estimate each of the corresponding two-dimensional poses of the individual, and the third stage uses the 3DPoseNet (3DPN) network to obtain the 3D pose of the person. This paper also conducted experiments on four datasets, and the results proved the superiority and success of this algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method of extracting roads and bridges from high-resolution remote sensing images based on deep learning. Edge detection is performed on the images in the road area along with the road skeleton line, and the result of the detected binary edge is vectorized. The interference of protective belts on both sides of the road, road vehicles, road green belts, traffic signs, etc. and the shadow interference of the bridge itself are eliminated to determine the parallel sides of the road. The bridge features on the road are used to locate the detected bridge and obtain information such as the location, length, width, and direction of the bridge, verifying the experimental results of the Shaoguan Le point images. In addition, in order to learn higher-level road feature information, the algorithm in this paper introduces the hollow convolution and multicore pooling modules. Secondly, the residual refinement network further refines the output of the prediction network to improve the ambiguity of the prediction network results. In addition, in view of the small proportion of road pixels in remote sensing images, the network also integrates binary cross entropy, structural similarity, and intersection ratio loss function to reduce road information loss. The applicability of the proposed study was tested, and the results show that the algorithm is very effective for the extraction of road and bridge targets. [ABSTRACT FROM AUTHOR]

Big data is a massive and diverse form of unstructured data, which needs proper analysis and management. It is another great technological revolution after the Internet, the Internet of Things, and cloud computing. This paper firstly studies the related concepts and basic theories as the origin of research. Secondly, it analyzes in depth the problems and challenges faced by Chinese government management under the impact of big data. Again, we explore the opportunities that big data brings to government management in terms of management efficiency, administrative capacity, and public services and believe that governments should seize opportunities to make changes. Brainlike computing attempts to simulate the structure and information processing process of biological neural network. This paper firstly analyzes the development status of e-government at home and abroad, studies the service-oriented architecture (SOA) and web services technology, deeply studies the e-government and SOA theory, and discusses this based on the development status of e-government in a certain region. Then, the deep learning algorithm is used to construct the monitoring platform to monitor the government behavior in real time, and the deep learning algorithm is used to conduct in-depth mining to analyze the government's intention behavior. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology and communication, digital music has grown and exploded. Regarding how to quickly and accurately retrieve the music that users want from huge bulk of music repository, music feature extraction and classification are considered as an important part of music information retrieval and have become a research hotspot in recent years. Traditional music classification approaches use a large number of artificially designed acoustic features. The design of features requires knowledge and in-depth understanding in the domain of music. The features of different classification tasks are often not universal and comprehensive. The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not performing well on multiclassification problems and not having the ability to be trained on large-scale data. Therefore, this paper converts the audio signal of music into a sound spectrum as a unified representation, avoiding the problem of manual feature selection. According to the characteristics of the sound spectrum, the research has combined 1D convolution, gating mechanism, residual connection, and attention mechanism and proposed a music feature extraction and classification model based on convolutional neural network, which can extract more relevant sound spectrum characteristics of the music category. Finally, this paper designs comparison and ablation experiments. The experimental results show that this approach is better than traditional manual models and machine learning-based approaches. [ABSTRACT FROM AUTHOR]

The method based on deep learning shows excellent performance in the recognition and classification of surface defects of some industrial products. The method based on deep learning has high efficiency in the identification and classification of surface defects of industrial products, and the false detection rate and missed detection rate are relatively low. However, the recognition accuracy of defect detection and classification of most industrial products needs to be improved, especially for those with similar contours and relatively large structural different casting. This paper takes casting defect detection as the goal and proposes a convolutional neural network casting defect detection and classification (RCNN-DC) algorithm based on the recursive attention model. Through this model, the casting can be better identified and detected, and casting defects can be avoided as much as possible, which is of great significance to the technological development of the industry. First, use a large amount of readily available defect-free sample data to detect anomalous defects. Next, we compare the accuracy and performance of the detection model and the general recognition model. The research results show that the test effect of the RCNN-DC casting defect detection network model is significantly better than the traditional detection model, with a classification accuracy of 96.67%. Then, we compare the RCNN-DC network with three classic popular networks, GooGleNet, ResNet-50, and AlexNet. Among them, AlexNet and ResNet-50 achieved 95.00% and 95.56% classification accuracy, respectively, while GooGleNet achieved slightly better results of 96.38%. In contrast, the accuracy of RCNN-DC is 1.67% higher than that of AlexNet, while the number of FLOPs is reduced by 17.2 times, and the accuracy is 1.09% higher than that of ResNet-50, while the number of FLOPs is reduced by 99.7 times, and the accuracy is higher than GooGleNet 0.29% while FLOPs whose number has been reduced by 36.5 times. [ABSTRACT FROM AUTHOR]

The diminishing natural sand has facilitated the booming of the sand manufacturing industry, and intelligent management of sand factories, in a time- and cost-efficient way, has become a growing tendency for the future. A role has been played in achieving intelligent management by constructing a smart supply chain. However, the smart sand factories are hardly involved in previously reported studies, which is inconsistent with related studies on smart factories and the Industrial Internet of Things (IIoT). In this paper, a smart supply chain management system (SSCMS) is constructed to realize the intelligence and automatization of the management of sand factories, using edge-computing and deep learning techniques. Along the supply chain, the deep learning model is used to realize the automatic identification of sand, avoiding the disadvantages of human identification, while improving the quality of sand factory operations. In order to relieve the pressure of network bandwidth, reduce system delay, and improve system operation efficiency, we use edge-computing technology to process data at the edge. To verify the performance of the constructed system, a sand factory simulation platform is established. Experiments show that the most critical indicator in the system, the accuracy rate of sand type identification, is above 98%, and the sand type identification time is only 0.022 s. In general, compared with traditional supply chain management, the constructed smart supply chain improves the quality and efficiency of sand factory operations, and all indicators of the designed system have achieved satisfactory results. [ABSTRACT FROM AUTHOR]

The front-page news in authoritative newspapers usually represents extremely significant national policies. Accurately classifying the front-page news from an amount of news helps us to quickly acquire and deeply understand the changing political and economic situations. In this paper, we propose a front-page news classification model StackText based on stacking the textual context and attribute information of news. In the proposed model, we first balance the classification size in the training set through the weighted random sampling algorithm, then construct the context and attribute feature vectors through the textual embedding and statistical analysis of news, and finally pretrain a classifier StackNet based on a neural network to realize the front-page news classification in the testing set. Taking People's Daily as the experimental example, we compare the proposed model with benchmark methods based on statistical learning and deep learning. Based on the news sets in four stages of People's Daily, the experimental results of the front-page news classification show that the proposed model achieves the highest average accuracy and a better balance between precision and recall, which is verified by the corresponding F 1 -score. [ABSTRACT FROM AUTHOR]

The purpose is to convert the contents of the grotto murals into a digital vision based on deep learning (DL) and virtual reality (VR) technology to protect the precious grotto murals. The grotto murals are analyzed and modeled by introducing the related concepts of the digital library, using VR technology, and taking the cultural relics data acquisition technology as the basic framework. Then, the cultural relics information collection technology under DL is combined with VR technology, the color and materialization concepts of grotto murals are integrated, and a digital recognition model of grotto mural restoration and simulation is constructed. The color analysis scheme of grotto murals is established. Through the analysis of color and material, the comprehensive recognition rate of this color model can reach 70%, and the material recognition of color painting can also ensure one-to-one restoration. Besides, the model can be applied to the simulation and restoration of grotto murals and provide some help for the protection of grotto murals. This paper has practical application value for the protection and restoration of mural art. [ABSTRACT FROM AUTHOR]

A scoring approach of business English oral training based on a deep neural network is put forward. Based on speech recognition technology, automatic correction technology, deep learning technology, machine learning technology, and the scoring data of business English oral training generated by the general computerized examination platform system, this paper uses the audio data of students' oral expression questions collected by the existing general computerized examination platform, and studies artificial intelligence technology, deep learning technology, and machine learning technology to train the intelligent scoring system, it realizes the intelligent scoring of oral expression questions, which plays a positive role in language training guidance and correction. Finally, the accuracy of the intelligent scoring system is verified by the correlation between the score of the intelligent scoring system and the manual score of teachers. The higher the correlation with manual scoring, the higher the scoring accuracy of the intelligent scoring system. [ABSTRACT FROM AUTHOR]

Audio processing has become an inseparable part of modern applications in domains ranging from health care to speech-controlled devices. In automated audio segmentation, deep learning plays a vital role. In this article, we are discussing audio segmentation based on deep learning. Audio segmentation divides the digital audio signal into a sequence of segments or frames and then classifies these into various classes such as speech recognition, music, or noise. Segmentation plays an important role in audio signal processing. The most important aspect is to secure a large amount of high-quality data when training a deep learning network. In this study, various application areas, citation records, documents published year-wise, and source-wise analysis are computed using Scopus and Web of Science (WoS) databases. The analysis presented in this paper supports and establishes the significance of the deep learning techniques in audio segmentation. [ABSTRACT FROM AUTHOR]

The rapid development of information technology has promoted the growth of deep learning, artificial intelligence, and big data technology. Nowadays, the artistic form of traditional opera creation is accepted and respected by people—more and more people like the art form. However, the artistic creation of traditional opera needs inspiration. The essence of inspiration creation is to reconstruct its objective structure. The deep learning algorithm's essence is to extract all of the attributes of sample self-learning input data and use them as inspiration for artistic production. First, this paper briefly introduces the deep learning and evolution strategy and uses these algorithms in opera art creation to construct 1 + λ. With the help of this evolutionary algorithm, an optimal solution is obtained through random evolution. The evolution strategy establishes the evolution function matrix. Starting from the situation of students learning opera art, this article examines the process of creating opera art using an in-depth learning and evolution technique. The results show that 96 percent of the students have contact with opera while watching an opera tour. During this, they were not interested in the performance of literary drama in traditional opera. However, it was noticed that they were deeply interested in martial arts, clown performances, and drama stage performances. Finally, the audience group of opera artistic creation is analyzed in the form of opera animation of "A Journey to the West: The Return of the Great Sage." It signifies that the opera's leading audience group is aged 25 to 29. However, they account for only 30 percent. [ABSTRACT FROM AUTHOR]

Point cloud local feature extraction places an important part of point cloud deep learning neural networks. Accurate extraction of point cloud features is still a challenge for deep learning networks. Oversampling and feature loss of point cloud model are important problems in the accuracy of image point cloud deep learning network. In this paper, we propose an adaptive clustering method for point cloud feature extraction—adaptive optimal means clustering (AOMC)—and apply it to point cloud deep learning network tasks. This method solves the problem of determining the number of clustering centers in the process of point cloud feature extraction so that the feature points contain the whole point cloud model and avoid the problem of losing detail features. Specifically, according to the loss characteristics of point cloud clustering, AOMC selects a different number of clustering centers for various models. Moreover, in light of the density distribution of the point cloud, the radius of the clustering subset is determined. This method effectively improves the accuracy of the point cloud deep learning network on object classification and parts segmentation. Our method reaches demand on Modelnet10 and shapenetcore_partanno_segmentation_benchmark datasets. In terms of deep learning network optimization, it has good performance. Additionally, our method has high accuracy and low algorithm complexity. [ABSTRACT FROM AUTHOR]