Reviewerly/exHarmony huggingface dataset:
Research on Denoising Algorithm of Orthopedic CT Image Based on Wavelet Soft Threshold Filtering

Influenced by relevant conditions, digital images are easily disturbed by external noise in the process of generation, transmission and compression, which makes the image edges and texture details blurred, which is not conducive to the recognition and reading of image information. As far as orthopedic CT images are concerned, when the attenuation coefficients of diseased tissues are very close to those of normal tissues, the existence of noise will reduce the signal-to-noise ratio of the images, which will directly lead to some feature details of the images being submerged in the image noise and unrecognizable. The research on denoising algorithm of orthopedic CT image based on wavelet soft threshold filtering is carried out. In this paper, an improved method based on extreme median filtering and wavelet soft threshold denoising algorithm is proposed to remove the mixed impulse noise and Gaussian white noise in the image. Firstly, the image with mixed noise is filtered by the improved median filtering algorithm based on extreme value, and the impulse noise is removed, so that the image with only Gaussian noise is obtained. The wavelet coefficients obtained are quantized by the improved threshold to obtain the estimated wavelet coefficients. The estimated wavelet coefficients are inversely transformed to reconstruct the image, and the denoised image is obtained. The research shows that wavelet soft threshold filtering has the best denoising effect, MSE, EPI and expert evaluation values are 1.2358, 0.9276 and 88 respectively. This method can protect the edge information and improve the visual effect while effectively denoising, which shows that this method also has high denoising ability.
Research on Optimization of Discontinuous Data Path Mining Based on Fuzzy Clustering Algorithm

Discontinuous data is a kind of large-scale hierarchical data. Generally speaking, this kind of data has time attribute, and it hopes to show some time-varying characteristics on the basis of keeping the hierarchical structure clear. Using path mining to analyze discontinuous data paths in cloud computing environment, we can find the location and data state of discontinuous data paths. Classifying and counting discontinuous data is beneficial to the efficient use of data. In this paper, the optimization of discontinuous data path mining based on fuzzy clustering algorithm is studied. In order to optimize the path mining of discontinuous data based on fuzzy clustering algorithm, this paper extracts the semantic directional features of discontinuous data and quantifies them. On the basis of quantization coding, FCM (fuzzy C means) algorithm is adopted to realize the directional beam clustering of semantic ontology features and improve the path mining algorithm. The research results show that the proposed algorithm is used to optimize the path mining of discontinuous data based on fuzzy clustering algorithm, and the root mean square error of the path mining output is low, which shows that the path mining accuracy is higher than the traditional method and the anti-interference performance is strong.
Design and Optimization of Intelligent Financial Audit System Based on Big Data Algorithm

It is difficult for traditional audit methods to find all kinds of financial problems hidden in massive data. With the continuous development of information technology and network communication technology, computer-aided audit system has been widely used. Based on this, this article develops an intelligent financial audit system which can deal with various data types and automatically discover audit clues, and optimizes it by interdisciplinary thinking and comprehensive application of big data algorithm, data mining technology and financial audit theory. In this article, business intelligence theory and financial audit theory are organically combined to form a new and revolutionary audit theory and means. At the same time, a general process of data auditing by data mining is given, and the design of data mining model from audit data collection to audit data preparation is discussed in detail. The experimental results show that the stability of this system is high, and the accuracy rate can reach 94.87%. It shows that the system has certain reliability, and the constructed audit model and method are feasible, which can effectively improve the work efficiency of auditors.
Design of Digital Media Recommendation System Based on User Behavior Analysis and Collaborative Filtering Algorithm

Using digital interactive platform to design digital media recommendation system to improve the service quality of digital media. The optimal design method of digital media remote sensing is studied. Intelligent recommendation of digital media by combining big data mining and resource scheduling. In this paper, the simulation results show that the precision of collaborative filtering algorithm is higher among the three algorithms, followed by GA (Genetic Algorithm) and finally AI (Artificial Intelligence). When the number of iteration steps reaches 30, the recommended accuracy of this algorithm is 93.3%, 90.7% for GA and 83.8% for AI. Then, the user behavior feature detection model of digital media recommendation is established, and digital media recommendation is carried out in collaborative filtering algorithm. Finally, the simulation experiment shows that this method has superior performance in improving the recommendation ability of digital media.
Defogging Algorithm for Road Environment Landscape Visual Image Based on Wavelet Transform

For the problem of serious image degradation and low contrast in foggy weather, an improved algorithm is proposed. In order to obtain important information of foggy images, an image dehazing method based on wavelet transform is proposed. Perform a layer of wavelet decomposition on the foggy image, perform single-scale enhancement and homomorphic filtering on the low-frequency image, and then linearly combine the two processed low-frequency images to obtain a new low-frequency image, and finally combine the new low-frequency image with the unprocessed image. The high-frequency image is reconstructed by inverse wavelet transform. First, the three color channels of the image are processed by histogram equalization in the color space; at the same time, the components are extracted in the color space, and two-dimensional discrete wavelet transform is performed on them to obtain a low-frequency component and three high-frequency components. The three high-frequency components are subjected to limited contrast histogram equalization processing, and then the two-dimensional discrete wavelet inverse transform is performed to reconstruct the high and low frequency parts, and finally converted back to the color space, and the two images are linearly combined to obtain the final dehaze image. From the perspective of time-frequency analysis, the Fourier transform in the homomorphic filtering algorithm is replaced by the fast wavelet transform, and then the wavelet coefficients are processed with the improved filter in the transform city. Keep the original information of the image not lost, enhance the contrast of the image, and make the processed image more visible. Compared with other dehazing methods, the image processed by this method has higher definition, faster time, higher contrast, better visual sense, higher brightness, more realistic color recovery, and better dehazing effect.
Application of neural network in computer network security evaluation

With the rapid development of computer network technology, it is gradually applied in people’s daily production and life, but in actual use, it can not avoid the reality, that is, there will be hacker intrusion, security holes and virus propagation, and the application of neural network can just make up for this defect, so as to achieve information calculation, control and recognition, and thus improve the security performance and work efficiency of computer network use. This paper mainly starts from the overview of neural network and computer network, analyzes the application of neural network in computer network security evaluation, and conducts in-depth research on computer network security evaluation simulation model of neural network for reference and improvement of the industry.
Construction and Optimization of University Digital Archives Cloud Platform Based on Big Data

The data generated in the process of talent cultivation, scientific research and social service in universities increases exponentially. These data are huge and diverse, which brings challenges to the management departments of universities, especially the archives department in charge of historical records and information resources. The data generated and accumulated in the process of campus informatization in universities have already possessed the characteristics of big data, so it is of great significance to extract valuable information from the massive data for archiving, preservation and utilization. This paper expounds the management of digital archives resources in universities from the background of big data driving, and puts forward a data mining technology of college students' archives based on artificial neural network (ANN), thus providing theoretical support for the construction and optimization of digital archives cloud platform in universities. The results show that the algorithm in this paper can achieve a high recommendation accuracy rate of 93.7% after repeated iterations. Facing the challenge of big data, university archives departments should actively respond from the perspective of data resources, change their working methods and ideas, and master the related technologies of big data.
Bitcoin Price Forecasting Based on ARIMA Model and Multifactorial Linear Regression

Contemporarily, Bitcoin has enjoyed great popularity worldwide based on the development of the blockchain technology. Dozens of researches have been done on forecasting the price of bitcoin, however, the most accurate model remains inconclusive. This paper investigates the performances of Bitcoin price prediction based on ARIMA model and ordinary least square multifactorial linear model in terms of the dataset from May 2019--April 2022 in both short-term and long-term. According to the analysis, linear regression model outperforms ARIMA model in short term test which is significantly accurate. Nevertheless, in terms of long-term prediction, although the linear regression model still performs better than ARIMA model. None of them shows great accuracy on account of uncertain changes that some assumptions are no longer applicable. Therefore, these results shed light on guiding further exploration focusing on cryptocurrency pricing.
Research on Semantic Search Algorithm of Smart Grid Knowledge Map Based on Deep Learning

Knowledge map construction technology is a hot topic in recent years. The essence of knowledge map is a semantic network, and its main goal is to describe various entities and attributes in the real world and the relationship between entities and attributes. At present, the application of knowledge map in smart grid is mainly oriented to equipment operation and maintenance, customer service, knowledge management center, etc. There is still a lot of room for development in the application of knowledge map in smart grid. Based on deep learning, the semantic search algorithm of knowledge map of smart grid is further studied. Besides data prediction, better data classification, prediction and fitting methods are urgently needed in the planning and design, operation regulation and maintenance of smart grid. With so many advantages of deep learning technology, semantic annotation method is used to extract knowledge from structured, semi-structured and unstructured data and capture the relationships among knowledge entities. Later, the algorithm will be further optimized to improve the timeliness and accuracy of the search.
Design of Man-machine Interface of Navigation System Based on Shortest Path Algorithm

With the rapid development of urbanization in various countries, the road traffic in cities is changing with each passing day, and the road conditions faced by drivers are becoming increasingly complex. It is necessary to redefine the man-machine interaction of automobiles to meet the needs of drivers in the new situation. In the process of research and design of human-vehicle interaction mode, the shortest path algorithm is one of the most important algorithms to study the driving behavior and characteristics of users in recent years. Based on the shortest path algorithm, the man-machine interface of navigation system is designed, and the functions of vehicle navigation system are studied. On this basis, the system framework of vehicle navigation system is studied. The experimental simulation shows that the optimization of the shortest path algorithm is carried out step by step, so the optimization algorithm is tested step by step in the experiment, which proves the feasibility of the shortest path algorithm. The shortest path algorithm plays an important role in vehicle navigation. Digital traffic radio stations are set up in cities to broadcast urban traffic information in real time. Vehicle-mounted equipment is accurately positioned by GPS (Global Positioning System), and combined with electronic maps and real-time traffic conditions, the optimal path is automatically matched and autonomous navigation of vehicles is implemented.
Simulation of Chinese Speech Synthesis System Based on Wavelet Optimized Neural Network

Language is the most important communication tool for human beings, because it is natural, convenient, accurate and efficient. With the popularization and application of speech synthesis technology, higher requirements are put forward for the quality of speech synthesis. Speech recognition and speech synthesis technology has become a symbol of the development of modern technology, and it is also an important field of research and development of modern computer technology. Wavelet neural network is a "new neural network model" constructed by combining wavelet transform theory with artificial neural network, which combines the good time-frequency localization property of wavelet transform and the self-learning function of neural network. The former mainstream end-to-end speech synthesis technology of deep neural network has been able to generate audio files similar to real human speech for languages based on Latin characters, such as English. However, Chinese is different from this kind of language. Chinese is a language written continuously, and the correlation between Chinese characters and their pronunciations is low. When the current mainstream deep neural network speech synthesis method is applied to Chinese, there are serious prosodic problems in the synthesized audio, such as inappropriate pauses, broken sentences, mispronunciations of polyphonic words and poor naturalness. In this paper, a wavelet neural network structure is constructed, and the gradient method is used for network training, and good speech recognition results are obtained.
The Analysis of Machine Learning Algorithms in Facial Identification

Since seventies in last century, face recognition has been one of the pivotal and popular field in computer science field. And the traditional algorithms like SVM and KNN have almost been replaced by the deep neural networks, which in need of large datasets and calculating forces. However, not all situations in our daily life fit to this elephantine process. And this paper is aimed to remind people re-concentrate on the traditional features attracting algorithms. Thus, this paper determines to compare the performances of several classic methods of machine learning algorithms in classifying and identifying the faces from data set. And somehow optimize the primitive algorithm models to reach higher accuracy. And then this paper compares the performance of prevailing and advanced method, convolutional neural network model, with such original models, making some conclusions about the processes of machine learning models development.
Simulation Research on Artificial Intelligence Matching of Image Database Based on Neural Network

The most important extension of the traditional relational database is the processing of image data, especially the query based on image content. In recent years, with the rapid development of machine learning, especially DL (Deep Learning) has achieved great success in text, voice, image and other aspects. In this paper, the AI (artificial intelligence) matching simulation of image database based on neural network is studied. Establish an AI matching algorithm for image database based on CNN (Convolutional Neural Network). Firstly, input the image to be matched, extract its feature points through preprocessing, get feature description according to trained CNN, get the feature point correlation of two images through correlation algorithm, and get the final matching result through the algorithm of removing mismatching points. The results show that the algorithm in this paper can get the most data of matching point pairs when the light intensity changes, with 35 pairs of matching points actually, of which 31 pairs are correctly matched, with the highest accuracy rate of 88.57%. Through experimental analysis, the validity and reliability of the intelligent target matching method in image database are proved.
Collaborative Path Planning Arithmetic for Multi Robot System of Power Intelligent Inspection Based on Particle Swarm Optimization

As an artificial system, multi robot system is actually a simulation of the group system in nature and human society. Multi robot path planning is to find a collision free approximate optimal path for each robot in the same workspace, and ensure that each robot can reach the target position safely at the minimum cost. In this article, a collaborative path planning arithmetic based on POS (Particle swarm optimization) is proposed for power intelligent inspection multi robot system. The working environment is scanned by laser sensors, and then the multi robot path planning problem is decomposed into several single robot path planning problems by using PSO arithmetic. Each sub population optimizes one robot individually. Select representative individuals from each sub population to coordinate information interaction, and then select elite individuals as the path of multi robot system optimization. Finally, the simulation test and analysis are carried out. The simulation results show that the proposed arithmetic has a certain accuracy, which is 8.96% higher than the traditional arithmetic. Intelligent patrol multi robot system plays an increasingly important role in the operation and maintenance of primary and secondary equipment and patrol inspection of substation due to its diversified detection means, flexible, objective and intelligent detection. With the help of patrol inspection multi robots, the operation and maintenance cost of substation is further reduced and the automation management level of power grid equipment is enhanced.
An Algorithm of Film Image Coloring and Image Restoration Based on DL

In image processing, color is usually the most basic visual element, and it is the element that people can obtain the most effective information in cognition. It plays an important role in various fields. Black-and-white image coloring is an important part of image processing system. As a computer-aided technology, image coloring has a wide range of applications in film and television processing and old photo restoration. Because the algorithm does not have the ability to understand the movie content, it extracts the feature information of the image and maps it to the similar parts, thus completing the coloring work. According to the research, the human eye can only recognize dozens of gray levels of images, but compared with color images, the human eye can recognize thousands of colors. It can be seen that the influence of color images on human eyes is far greater than that of gray images. In addition, the gray-scale image is obviously not conducive to the feature extraction of the image information by the algorithm, so coloring and pseudo-colorizing the gray-scale image will be beneficial to the feature extraction and expression of the image. And the colored image can better express the meaning of the image, so that the human eye can capture the meaning and information of the image itself faster, better and more accurately, and then give full play to and express the value contained in the image.
The Analysis of Distributed Computing Systems with Machine Learning

The distributed computing system is a hot research field. Deep learning, the Internet of Things, and other technologies are rapidly advancing, necessitating better levels of computation, storage, and communication efficiency for many jobs and applications. The advanced distributed computing model, represented by cloud computing and edge computing, has attracted the attention of researchers. Firstly, this paper reviews the development of distributed computing systems. Then the critical technologies of the distributed computing system, including MSG-based system, MapReduce-like system, and graph computing system, are introduced. Then, this paper presents two typical frameworks of distributed computing systems, including cloud computing and edge computing, and compares and analyzes the latest progress of cloud computing and edge computing in machine learning. In addition, this paper discusses the development prospect and challenges of the distributed computing system, including increasing the scale of training, developing more complex structure other than MEA and ways to make the cloud more efficient.
Performance Analysis of Facial Expression Recognition Algorithms Based on Machine Learning and Deep Learning

As a form of non-verbal communication, facial expressions contain rich emotional information and some related cognitive behaviors, personality and psychological emotions, and play an important role in people’s daily communication. With the rapid development of pattern recognition technology, facial expression recognition based on computer vision has gradually become a research hotspot. The key to facial expression recognition is to extract different facial expression feature points. Its main technical framework includes traditional recognition methods based on hand-crafted features (apparent features and geometric features) and recognition based on deep learning. In exploration of application boundaries between different methods, in this paper, machine learning algorithms (support vector machines, random forests) and convolutional neural networks (AlexNet, VGG16) were selected to construct recognition models, and quantitatively analyzed the changes of different methods on different emotional results. In addition, the current research problems of facial expression recognition are summarized, and the future development direction of this field is prospected.
Large Scale Graph Data Processing Technology on Cloud Computing Environments

With the rapid development of science and technology, information technology and computer technology have become popular fields for research and exploration. In the process of computer big data analysis, technicians shall flexibly apply cloud computing technology to improve the level and effect of analysis work. Big data technology is the main direction of future development and can play an active role in analysis work. Adopt big data processing technology to provide strong data support for comprehensive control, management and decision-making. In view of this, this study mainly describes how cloud computing technology can be effectively used in computer big data analysis.