This paper introduces the basic algorithm and search strategies of particle swarm optimization (PSO), via presenting the concepts of swap operator and swap sequence an algorithm of a kind of special particle swarm optimization is constructed and then proposes its application to traveling salesman problems(TSP). The experiments show the new PSO can achieve good results.

Particle Swarm Optimization for Traveling Salesman Problems

The education of visually impaired children remains a focal topic, and the implementation of Braille–Chinese translation can facilitate improved understanding and learning of Braille for these children. Braille–Chinese translation refers to the conversion of Braille text into Chinese characters. Owing to the scarcity of Braille–Chinese parallel corpora, achieving Braille–Chinese translation with limited data remains a significant research challenge. This study tackles the challenge by utilizing both Braille joint training and pre-trained language models to conduct analogy learning and transfer learning on a substantial volume of readily available data. The method of Braille joint training also establishes a universal language model in the Braille domain, capable of simultaneously performing multiple tasks like Braille–Chinese translation, Chinese–Braille translation, Braille word segmentation, etc. This greatly caters to users' requirements for Braille information processing while also mitigating extensive deployment expenses associated with multiple models. Furthermore, we put forward a Braille phoneme recognition translation approach distinct from conventional Braille–Chinese translation methodologies. This approach aims to attain precise translation by identifying the Chinese characters corresponding to each Braille phoneme. This method achieves a BLEU score of 96.22 on the Braille–Chinese entity annotation dataset and exhibits high translation efficiency. 

Improving Braille-Chinese translation with jointly trained and pre-trained language models

With the continuous development of digital technology, music teaching is gradually shifting from traditional face-to-face teaching to online teaching and intelligent learning methods. Developing music teaching software plays an important role in improving users’ music learning methods and learning experience. However, current music teaching software still has issues such as poor compatibility, low level of data security and privacy protection, and functional requirements that are still in the early stages. This article aimed to research and develop a music teaching software based on neural network algorithm and user analysis, so as to improve user satisfaction with music teaching software. In the article, the overall design module of the system was first analyzed, and then music teaching resource data was collected and normalized using clustering algorithms to improve the effectiveness of music teaching resource data. Afterwards, a BP (Back Propagation) neural network model with parallel structure was used to optimize the design of the neural network model, and the effectiveness of the neural network model in user interface design was analyzed. Finally, user behavior analysis and personalized recommendation were achieved by constructing a user profile feature dataset. In order to verify the performance of the developed music teaching software, the software performance and security were tested. The results showed that the required loading time for the music score data in test case 1 of this article was only 1.21 s, and the response time was only 0.015 s. At this time, the security was 92.6%; the required loading time for audio data was only 1.06 s. The response time was only 0.019 s, and the security was 88.7%; the loading time required for user interaction data was only 0.78 s. The response time was only 0.012 s, and the safety was 91.3%. The research results indicated that the music teaching software developed in this article could better meet users’ music teaching needs and provide users with a more intelligent, personalized, and efficient music learning experience. 

Music Teaching Software Development Based on Neural Network Algorithm and User Analysis

Piano is used for music and comprises a stringed keyboard instrument wherein the strings are tapped by softer-coated wooden hammers. The score providing music for the piano, often a compressed transcription of orchestral music, is referred to as piano score. Presently, the Internet is overflowing with music score resources. Having so many music score resources available, professional learners and amateur music lovers are unable to identify and obtain music score information that matches their needs and wasting valuable time. Due to the rapid development of deep learning algorithms, some individuals utilize these algorithms to detect piano scores and construct composition systems, reducing the need of traditional machine learning algorithms on manual design and music knowledge guidelines. This paper uses the deep learning algorithm to construct piano score recognition framework based on K-Nearest Neighbor (KNN) algorithm and formulates the recognition system into multinote that significantly improves the recognition rate for the system. The self-attention mechanism is then introduced in order to build a composition system based on a deep learning algorithm in which composition training and processes are described. Finally, a comparative experiment is conducted to evaluate the recognition accuracy for the KNN-based piano score recognition system. The results show that highest recognition accuracy of this system is 67.5%. The effect of composition system is evaluated based on prediction accuracy of notes. Three experiments are conducted to train the composition notes. As a result, the prediction accuracy of experiments 1, 2, and 3 is 89.2%, 91.8%, and 92.7%, respectively, indicating that the system has a high prediction accuracy and a perfect composition effect.

A Deep Learning-Enabled Composition System Based on Piano Score Recognition

According to the structure of sound type library, this paper gives the realization method of multi-characteristic music creation system. Firstly, the whole architecture of music creation is realized by using the modules of computer, memory card, display, computer and sound. An effective multidimensional audio timing modeling method is established by constructing a deep learning method based on long-term memory network based on recurrent neural network and combining with supervision mechanism. Through this model, the music style of the famous singer Justin is generated. The algorithm was compared to Justin's music library, which had a maximum similarity of 98.25%, with 84.15% of the works receiving expert reviews.

Research on Music Creation System Based on Computer Artificial Intelligence Technology

With the rapid development of the internet, recent years has witnessed an express development of artificial intelligence. At present, the deep learning method represented by the long short-term memory (LSTM) network is gradually being studied and applied to the conversion model. However, there is no enough research on the current braille music. This paper uses LSTM network for braille music segmentation research. The conversion of braille music needs to use conversion model to describe and realize equivalent conversion. First, construct a braille music corpus: we analyze the composition rules of braille music symbols, and then study the braille music word segmentation corpus, thus a braille music corpus is manually constructed by using the existing braille music materials. Secondly, construct a braille music participle model: in this paper, we use the framework of in-depth learning to construct a braille music word segmentation model. Finally, multiple iterations of training are performed by using the braille music participle model. Multiple iterations of training can enable the machine to automatically learn the combination rules of braille music in the corpus. Through the experiment, it is found that braille music segmentation based on LSTM network has achieved an expected effect, which lays a foundation for further achieving the conversion of braille music.

Research on Braille Music Segmentation Based on Long Short- Term Memory

It is studied by applying the mutual information which is used to assess the contribution of each attribute that has the different important degrees to the classification in the fuzzy clustering algorithm, then the attribute weighted fuzzy clustering algorithm based on mutual information is proposed. By using the mutual information to quantify the contribution of each attribute to the classification, the attributes are weighted and introduced into the fuzzy C mean algorithm. For incomplete data sets, the missing attribute is also introduced as a target object to be optimized and as a part of the iterative to be optimization. Finally, an example verifies the applicability of the algorithm in dealing with incomplete data sets and incomplete data sets, and analyzes the effect of each attribute value loss on clustering results in incomplete data sets.

Attribute weighted fuzzy clustering algorithm based on mutual information

In order to better construct the R - tree index structure and improve the efficiency of R - tree construction and retrieval, the R - tree construction algorithm is optimized from the following three aspects: the selection and determination of the initial center point, the weighted distance between data combined with the actual situation, and the division of redundant data by combining the theories of nearest neighbor, information entropy and probability statistics. The feasibility of the proposed algorithm is verified by comparing the objective function values, iteration times and time consumption under different cluster numbers. Furthermore, experimental results show that the index structure constructed by this method has smaller overlapping area and more efficient query efficiency.

R - Tree Index Construction of Dynamic K-means Algorithm

In this era of advanced information technology, it is increasingly essential to acquire important information in time. But there exists a wider communicational gap between visually impaired people and visually normal people. The reason is that the current recognition technology of braille is not very mature, which makes the visually impaired people unable to integrate into the current information age. In this paper, the convolutional neural network model, popular at present, is used to establish the recognition model of braille music. Compared with the previous recognition model, it can be seen as a new attempt. At the same time, we performed some basic preprocessing operations on braille music images. The results of every layer of the recognition model are also shown in detail, which is more prominent in the feature extraction of braille characters. In addition, we developed the training algorithm and test algorithm of braille music. Finally, the experimental results show that the recognition model based on convolutional neural network has good effectiveness and strong generalization ability.

Research on Braille Music Recognition Based on Convolutional Neural Network

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Research on Braille Music Segmentation Based on Long Short- Term Memory

Attribute weighted fuzzy clustering algorithm based on mutual information

R - Tree Index Construction of Dynamic K-means Algorithm

Research on Braille Music Recognition Based on Convolutional Neural Network