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The rapid development of Internet technology has promoted the vigorous development of the multimedia. As one of the most classic instruments, the violin has been fully developed in its creation, education, and performance. In the face of more and more violin performances, the effective organization and retrieval of these musical works is an urgent problem to be solved, while it is common to classify and organize music based on the emotional properties of the performance. Deep learning is a model based on feature hierarchy and unsupervised feature learning, which has strong learning ability and adaptability. Based on the recurrent neural network (RNN) method, long short-term memory (LSTM) is one of the classic models of deep learning that can effectively learn the characteristics of time series data and achieve effective predictions. Therefore, based on the classical Hevner emotion classification model, this paper proposes an emotion recognition method for dynamic violin performances based on LSTM, which selects acoustic features and classifies the audio acoustic signals contained in the violin performances. In order to verify the effectiveness of this method, this paper carries out data labeled, feature selection, and model test on the actual violin music data by turn. The results show that the proposed method can greatly reduce the training time and improve the prediction accuracy, which reached 83%, higher than the existing methods. Meanwhile, the accuracy and iteration times of violin playing music of different emotional categories are also counted. Moreover, the method is robust to the genre, timbre, and noise changes, and the emotion recognition effect is superior. [ABSTRACT FROM AUTHOR]

Under the current situation, there are many drawbacks in the integrated development of culture and tourism. Therefore, this paper aims to use big data analysis to study the path and development mechanism of cultural and tourism integration, aiming to explore a road of cultural and tourism integration that is most suitable for domestic development. This paper proposes a big data platform based on deep learning to adapt to domestic cultural data and tourism data. The system can intuitively analyze the domestic cultural tourism industry and display the development of cultural tourism with brief pictures and figures. This article analyzes the performance of the designed system and tests the fusion of cultural tourism data. The experimental results of this paper prove that the stability design of the big data cultural tourism industry analysis platform in this paper meets the requirements, and the platform can be stable at 5 Mb/s for 40–180 G big data transmission. The results of the modeling analysis of the integrated development of cultural tourism can be considered; that is, the cultural tourism industry has made great progress in the process of integration, and the integrated development of these two industries is imperative. [ABSTRACT FROM AUTHOR]

In recent years, with the development of enterprises to the Internet, the demand for cloud database is also growing, especially how to capture data quickly and efficiently through the database. In order to improve the data structure at all levels in the process of database analysis engine, this paper realizes the accurate construction and rapid analysis of cloud database based on big data analysis engine technology and deep learning wolf pack greedy algorithm. Through the deep learning strategy, a big data analysis engine system based on the deep learning model is constructed. The functions of deep learning technology, wolf greedy algorithm, and data analysis strategy in the cloud database analysis engine system are analyzed, as well as the functions of the whole analysis engine system. Finally, the accuracy and response speed of the cloud database analysis engine system are tested according to the known clustering data. The results show that compared with the traditional data analysis engine system with character search as the core, the database oriented big data analysis engine system based on a deep learning model and wolf swarm greedy algorithm has faster response speed and intelligence. The research application is that the proposed engine system can significantly improve the effect of the analysis engine and greatly improve the retrieval accuracy and analysis efficiency of fixed-point data in the database. [ABSTRACT FROM AUTHOR]

The development of a new type of international relations is the advancement and improvement of diplomatic thinking among contemporary nations. It also serves as a crucial yardstick for assessing the future global pattern and the direction of order changes. Proper interaction between major powers can foster the growth of new international relations and has a significant impact on advancing global cooperation and the promotion of human peace. The goal of this essay is to examine how friendly interactions between major powers have affected the development of new international relations. A deep learning network model is presented for this purpose. The deep learning model was used to identify the emotions of the survey results, analyze each person's emotional tendencies, and summarize and compare the data. Relevant questionnaire surveys were conducted using the online questionnaire survey method on individuals in various countries. The survey results in this paper demonstrate that 96.5 percent of Chinese, 89.3 percent of Russians, and 81.6 percent of Americans support friendly relations between major nations. Only a very small percentage of the investigators supported hostile relations, with their support being 1.06 percent, 3.11 percent, and 2.94 percent, respectively. Therefore, creating a win-win partnership between major powers is exactly what the people of all nations are calling for. In contrast to the past, it is no longer hostile and violent. People anticipate that more great powers will coexist peacefully. [ABSTRACT FROM AUTHOR]

The oddity in large information is rising step by step so that the current programming instruments faces trouble in supervision of huge information. Moreover, the pace of the irregularity information in the immense datasets is a key imperative to the exploration business. Along these lines, this paper proposes a novel method for taking care of the large information utilizing Spark structure. The proposed method experiences two stages for arranging the enormous information, which includes highlight choice and arrangement, which is acted in the underlying hubs of Spark engineering. The proposed improvement calculation is named Rider Chaotic Biography streamlining (RCBO) calculation, which is the incorporation of the Rider Optimization Algorithm (ROA) and the standard confused biogeography-based-advancement (CBBO). The proposed RCBO-profound stacked auto-encoder utilizing Spark structure successfully handles the large information for achieving powerful huge information arrangement. Here, the proposed RCBO is utilized for choosing reasonable highlights from the monstrous dataset. Besides, the profound stacked auto-encoder utilizes RCBO for preparing so as to characterize colossal datasets. In this research we focused on problem of supervision related to big information of The Cover type Data in UCI machine learning repository. The dataset describes the forest cover set data to predict the forest cover type from cartographic variables. The dataset is multivariate in nature with number of web hits 263,361. The number of instances is 581012 with 54 numbers of attributes and the task associated for the dataset is classification. The examination of the proposed RCBO-profound stacked auto-encoder-based Spark structure utilizing the UCI AI datasets uncovered that the proposed technique beat different strategies, by procuring maximal exactness of 86.71%, dice coefficient of 92.7%, affectability of 75.2% and explicitness of 95.4% separately. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has made good progress and has been applied to face recognition, video monitoring, image processing, and other fields. In this big data background, deep convolution neural network has also received more and more attention. In order to extract the ancient Chinese characters effectively, the paper will discuss the structure model, pool process, and network training of deep convolution neural network and compare the algorithm with the traditional machine learning algorithm. The results show that the accuracy and recall rate of the Chinese characters in the plaque of Ming Dynasty can reach the peak, 81.38% and 81.31%, respectively. When the number of training samples increases to 50, the recognition rate of MFA is 99.72%, which is much higher than other algorithms. This shows that the algorithm based on deep convolution neural network and big data analysis has excellent performance and can effectively identify the Chinese characters under different dynasties, different sample sizes, and different interference factors, which can provide a powerful reference for the extraction of ancient Chinese characters. [ABSTRACT FROM AUTHOR]