Similarity Calculation Method Research Articles

Automatic Translation of Medical Information Based on Text Feature Recognition

In order to improve the effect of the medical information automatic translation system, based on the text feature recognition technology of medical image, this paper constructs an automatic translation system that can recognize medical images. On the basis of the medical information image retrieval based on the combination of text information and visual information, this paper uses automatic image annotation technology and semantic similarity calculation method to extract the text and semantic features of medical information images. Then, this paper uses the inherent multi-information fusion capability of the Bayesian inference network to fuse the text features of medical information images and the semantic features of image content together to realize medical information image retrieval. Finally, this paper designs experiments to test the performance of the medical information automatic translation system. The research shows that the system constructed in this paper has certain effects.

Research on Semantic Similarity of Short Text Based on Bert and Time Warping Distance

The research on semantic similarity of short text plays an important role in machine translation, emotion analysis, information retrieval and other AI business applications. However, according to existing short text similarity research, the characteristics of ambiguous vocabularies are difficult to be effectively analyzed, the solution of the problem caused by words order needs to be further optimized as well. This paper proposes a short text semantic similarity calculation method that combines BERT and time warping distance algorithm, in order to solve the problem of vocabulary ambiguity. The model first uses the pre trained Bert model to extract the semantic features of the short text from the whole level, and obtains a 768 dimensional short text feature vector. Then, it transforms the extracted feature vector into a point sequence in space, uses the CTW algorithm to calculate the time warping distance between the curves connected by the point sequence, and finally uses the weight function designed by the analysis, according to the smaller the time warpage distance is, the higher the degree of small similarity is, to calculate the similarity between short texts. The experimental results show that this model can mine the feature information of ambiguous words, and calculate the similarity of short texts with lexical ambiguity effectively. Compared with other models, it can distinguish the semantic features of ambiguous words more accurately.

Scene Understanding Technology of Intelligent Customer Service Robot Based on Deep Learning

As a value-added service that improves the efficiency of online customer service, customer service robots have been well received by sellers in recent years. Because the robot strives to free the customer service staff from the heavy consulting services in the past, thereby reducing the seller’s operating costs and improving the quality of online services. The purpose of this article is to study the intelligent customer service robot scene understanding technology based on deep learning. It mainly introduces some commonly used models and training methods of deep learning and the application fields of deep learning. Analyzed the problems of the traditional Encoder-Decoder framework, and introduced the chat model designed in this paper based on these problems, that is, the intelligent chat robot model (T-DLLModel) obtained by combining the neural network topic model and the deep learning language model. Conduct an independent question understanding experiment based on question retelling and a question understanding experiment combined with contextual information on the dialogue between online shopping customer service and customers. The experimental results show that when the similarity threshold is 0.4, the method achieves better results, and an F value of 0.5 is achieved. The semantic similarity calculation method proposed in this paper is better than the traditional method based on keywords and semantic information, especially when the similarity threshold increases, the recall rate of this paper is significantly better than the traditional method. The method in this article has a slightly better answer sorting effect on the real customer service dialogue data than the method based on LDA.

Siamese network text similarity calculation with multi-head self-attention mechanism

Calculating text similarity is the key to natural language processing. Existing text similarity calculation methods can not fully extract deep semantic information. And the ability to calculate the similarity of long texts is limited. Aiming at the shortcoming of existing text similarity calculation methods, a Siamese network based on multi-head self-attention mechanism is proposed. This method uses the Siamese model based on bidirectional GRU to accurately extract the semantic information of the context. Then learning deep semantic information of long text by adding multi head self-attention mechanism. Experimental results show that Siamese network with multi-head self-attention mechanism can learn the deep semantic information of long text in the SICK dataset. Compared with other text similarity calculation methods, the correlation coefficient of the proposed method is significantly improved. The effect of processing long text is improved.

Novel similarity calculation method of multisource ontology based on graph convolution network

Novel similarity calculation method of multisource ontology based on graph convolution network

Implementation of English “Online and Offline” Hybrid Teaching Recommendation Platform Based on Reinforcement Learning

At present, there is a serious disconnect between online teaching and offline teaching in English MOOC large-scale hybrid teaching recommendation platform, which is mainly due to the problems of cold start and matrix sparsity in the recommendation algorithm, and it is difficult to fully tap the user's interest characteristics because it only considers the user's rating but neglects the user's personalized evaluation. In order to solve the above problems, this paper proposes to use reinforcement learning thought and user evaluation factors to realize the online and offline hybrid English teaching recommendation platform. First, the idea of value function estimation in reinforcement learning is introduced, and the difference between user state value functions is used to replace the previous similarity calculation method, thus alleviating the matrix sparsity problem. The learning rate is used to control the convergence speed of the weight vector in the user state value function to alleviate the cold start problem. Second, by adding the learning of the user evaluation vector to the value function estimation of the state value function, the state value function of the user can be estimated approximately and the discrimination degree of the target user can be reflected. Experimental results show that the proposed recommendation algorithm can effectively alleviate the cold start and matrix sparsity problems existing in the current collaborative filtering recommendation algorithm and can dig deep into the characteristics of users' interests and further improve the accuracy of scoring prediction.

Accurate Estimation of English Word Similarity Based on Semantic Network

The application of artificial intelligence in the field of English needs to process a large amount of English text data, but the deviation of English word similarity reduces its overall English translation accuracy and data processing efficiency. Therefore, this paper proposes an accurate estimation of English word similarity based on semantic network, which combines a variety of computing methods to form a compound computing structure based on semantic network. The experimental results show that the error between the Semantic Web-based English word similarity calculation method and manual evaluation is small, and the accuracy of English word similarity calculation is improved to a certain extent. In addition, compared with other English word similarity calculation methods, the English word similarity calculation method based on semantic network is more in line with people’s cognition and understanding of knowledge, has higher reliability, and has certain practical value in the field of English.

Research on Building Chinese Blog Semantic Lexicon Based on Semantic Comprehension

Nowadays, investigators of phrases and text sentimental orientation identification mainly divided into two ways: semantic comprehension and machine learning. Machine learning needs a lot of training corpus and also cannot handle general field words effectively, meanwhile, semantic comprehension cannot get high scores at precision and recall. Therefore, in this paper, we propose a new fusion method based on our semantic comprehension for judging phrases’ polarity. In this paper, firstly, we modify the knowledge of Hownet, on the basis of its four primitives, the fifth primitive—sentimental primitive was proposed by our research, and annotated it into Hownet manually; secondly, we propose a new phrase sentimental similarity calculation method to compute word’s sentimental value; at last, we integrate transductive learning into this method to identify phrases’ sentimental orientation. The performance of experiment show that, compared with SVM or traditional semantic comprehension, it can get better results.

Cascade Deep Forest With Heterogeneous Similarity Measures for Drug-Target Interaction Prediction.

Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi-omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug–target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10-fold cross-validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.

Research on grid load model based on SPACK architecture of power big data

Big data service is the key to the construction of smart grid, and the method of analyzing electricity consumption behavior based on improved AP clustering and the method of forecasting electricity load based on random forest is proposed. To address the problem of high complexity of AP clustering analysis, the entropy weight method is used to establish the index weights and improve the similarity calculation method to realize the fast and accurate analysis of customers’ electricity consumption behavior. For the problem of power load prediction, the fuzzy C-means method is used to construct the index weights. The fuzzy C-means is used to construct the historical similarity daily sample set, and the random forest is used to predict the electric load. The SPACK multi-level data processing framework is proposed to meet the needs of parallel processing speed for large amounts of data. The simulation results verify the effectiveness of the method proposed in this article and the important guiding price for the Power grid.

Improved Chinese Sentence Semantic Similarity Calculation Method Based on Multi-Feature Fusion

In this paper, an improved long short-term memory (LSTM)-based deep neural network structure is proposed for learning variable-length Chinese sentence semantic similarities. Siamese LSTM, a sequence-insensitive deep neural network model, has a limited ability to capture the semantics of natural language because it has difficulty explaining semantic differences based on the differences in syntactic structures or word order in a sentence. Therefore, the proposed model integrates the syntactic component features of the words in the sentence into a word vector representation layer to express the syntactic structure information of the sentence and the interdependence between words. Moreover, a relative position embedding layer is introduced into the model, and the relative position of the words in the sentence is mapped to a high-dimensional space to capture the local position information of the words. With this model, a parallel structure is used to map two sentences into the same high-dimensional space to obtain a fixed-length sentence vector representation. After aggregation, the sentence similarity is computed in the output layer. Experiments with Chinese sentences show that the model can achieve good results in the calculation of the semantic similarity.

Spatial Distribution and Geographical Mechanism of Natural Resources in China under the Orientation of the New Economic Demands

The demand structure of resources for new economy is different from the traditional one in that its development may significantly change China’s economic location map and spatial pattern. Based on 343 administrative units of prefecture-level cities in China, this research constructs the measurement index system of terrestrial surface natural resources under the orientation of the new economic demands; this research mainly analyses the spatial distribution characteristics and geographical mechanism of natural resources by means of the spatial autocorrelation and spatial similarity calculation methods. The results show that: (1) The structure and endowment of natural resources under the orientation of the new economic demands need to be reexamined. The significance of a good environment and ecological resources has been highlighted. The coupling of resource elements better reveals the availability of natural resources. (2) The natural resources decrease from southeast to northwest, showing a pattern of “abundant in the south and east and scarce in the north and west”. Natural resources have a significant positive correlation in spatial distribution with two types of agglomeration: high-high agglomeration and low-low agglomeration, showing the local agglomeration feature of “high in the south and low in the north”. (3) Natural factors such as temperature, precipitation and altitude affect the spatial distribution of natural resources, with the temperature being the most significant. This indicates that the original natural environment and its role are the geographical mechanism for the formation and distribution of natural resources. The results could provide a reference for the development and the optimization of China’s new economy.

Research on Recommendation of Online Materials Course Resources Based on Text Similarity

In order to solve the problem that it is difficult for college students to find learning resources related to thecourses they are learning quickly and accurately in blended learning. This paper proposes an online materials course resources recommendation method based on text similarity. Firstly, collecting the data of course resources on the online learning platform through web crawler technology. Secondly, preprocessing the data which contend deleting noise data, the Chinese word segmentation and calculating the course similarity based on cosine similarity then getting the course recommendation results according to the similarity ranking. Thirdly, evaluating the recommendation results and optimizing the similarity calculation method according to the evaluation results. Finally, the learners are recommended curriculum resources according to the similarity ranking results. According to the courses learned on the Superstar platform, the experiment recommends similar course resources on the XueYin Online platform. The results show that the online materials course resources recommendation method based on text similarity can recommend relevant online materials course resources for learners quickly and accurately, which has certain reference significance and application value for online course resources recommendation.

Centrifugirani betonski stupovi armirani nemetalnom armaturom

The article describes the testing of poles for electric power transmission lines, which are made of centrifugally-cast concrete and ultra-high strength concrete, and which are prestressed with Carbon Fibre Reinforced Polymer tendons (CFRP). The presented results of pole ultimate load testing suggest the potential for usage of this construction product, particularly in aggressive environments with high probability of steel corrosion. Part of the tested poles had shear reinforcement in the form of confined Glass Fibre Reinforced Polymer (GFRP) grid, while one share of samples had no shear reinforcement, but the concrete matrix was strengthened with synthetic macro fibres. A review is given on similar product testing, calculation methods, and guidance is provided for further research.

EPRT: An Efficient Privacy-Preserving Medical Service Recommendation and Trust Discovery Scheme for eHealth System

As one of the essential applications of health information technology, the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or an evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters’ privacy and make evaluation trust reliable. To tackle this challenge, this article proposes an efficient service recommendation and evaluation scheme, called EPRT , which is based on a similarity calculation and trust discovery method. This scheme uses homomorphic encryption technology to encrypt the sensitive data and combines the threshold mechanism and double-trap mechanism to realize the secure computing on the encrypted data, so as to ensure that the plaintexts of the final calculation results (e.g., recommendation value and evaluation truth) are only obtained by the authorized subject. In addition, a detailed security analysis shows that the proposed EPRT scheme can achieve the expected security. In addition, performance comparison results are carried out, demonstrating its effectiveness and accuracy.

Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Most methods that model 3D porous media from 2D images are based on binary images. In this paper, we propose a method for reconstructing 3D greyscale isotropic porous media images from a single image. Our proposed method incorporates a fast-sampling procedure to control the continuity and variability between adjoining reconstruction layers, a new similarity calculation method to obtain the most similar patterns from a pattern dictionary, and a central area simulation procedure to solve the block effect problem. The reconstruction results from application of our proposed method to a real reservoir 3D model obtained via computed tomography (CT) and a comparison with the original CT structure demonstrate that our proposed method can reproduce properties such as autocorrelation function, linear function, shape distribution, average shape factor, average pore radius size, average throat radius size, average pore volume, permeability and grey histogram. Further, the comparison results indicate that the statistical characteristics of the reconstructions match the training image and the CT model perfectly.

Solving distribution problems in content-based recommendation system with gaussian mixture model

Recommendation systems play an important role in boosting purchasing consumption for many manufacturers by helping consumers find the most appropriate items. Furthermore, there are quite range of recommendation algorithms so far that can be efficient; however, a content-based algorithm is always the most popular, powerful, and productive method taken at the begin time of any project. In the negative aspect, somehow the accuracy of content-based algorithm results is still a concern that correlates to probabilistic similarity. In addition, the similarity calculation method is another crucial that affect the accuracy of content-based recommendation in probabilistic problems. In order to solve these problems, we propose a new content-based recommendation based on the Gaussian Mixture Model to improve the accuracy with more sensitive results for probabilistic recommendation problems. Our proposed method is experimented on a liquor dataset including six main flavour tastes, liquor main taste tags, and some other criteria. The method clusters n liquor records relied on n vectors of six dimensions into k group (k < n) before applying a formula to sort the results. Compared our proposed algorithm with three other popular models on the above dataset, the accuracy of the experimental results not only outweighs the comparison to those of three other models but also attain a very speedy response time in real-life applications.

Software similarity measurements using UML diagrams: A systematic literature review

Every piece of software uses a model to derive its operational, auxiliary, and functional procedures. Unified Modeling Language (UML) is a standard displaying language for determining, recording, and building a software product. Several algorithms have been used by researchers to measure similarities between UML artifacts. However, there no literature studies have considered measurements of UML diagram similarities. This paper presents the results of a systematic literature review concerning similarity measurements between the UML diagrams of different software products. The study reviews and identifies similarity measurements of UML artifacts, with class diagram, sequence diagram, statechart diagram, and use case diagram being UML diagrams that are widely used as research objects for measuring similarity. Measuring similarity enables resolution of the problem domains of software reuse, similarity measurement, and clone detection. The instruments used to measure similarity are semantic and structural similarity. The findings indicate opportunities for future research regarding calculating other UML diagrams, compiling calculation information for each diagram, adapting semantic and structural similarity calculation methods, determining the best weight for each item in the diagram, testing novel proposed methods, and building or finding good datasets for use as testing material.

Semantic-based Compound Keyword Search over Encrypted Cloud Data

Keyword search over encrypted data is essential for accessing outsourced sensitive data in cloud computing. In some circumstances, the keywords that the user searches on are only semantically related to the data rather than via an exact or fuzzy match. Hence, semantic-based keyword search over encrypted cloud data becomes of paramount importance. However, existing schemes usually depend upon a global dictionary, which not only affects the accuracy of search results but also causes inefficiency in data updating. Additionally, although compound keyword search is common in practice, the existing approaches only process them as single words, which split the original semantics and achieve low accuracy. To address these limitations, we initially propose a compound concept semantic similarity (CCSS) calculation method to measure the semantic similarity between compound concepts. Next, by integrating CCSS with Locality-Sensitive Hashing function and the secure kk<; inline-graphic xlink:href="wang-ieq1-2847318.gif"/>-Nearest Neighbor scheme, a semantic-based compound keyword search (SCKS) scheme is proposed. SCKS achieves not only semantic-based search but also multi-keyword search and ranked keyword search. Additionally, SCKS also eliminates the predefined global library and can efficiently support data update. The experimental results on real-world dataset indicate that SCKS introduces low overhead on computation and the search accuracy outperforms the existing schemes.

Research on non-intrusive unknown load identification technology based on deep learning

With the development of smart grid technology, optimization of energy structure, improvement of power efficiency and reduction of power consumption have become the major development trends. As a key process of power structure analysis, load identification is gaining increasing attention in smart grids. Although lots of load identification methods have been proposed, the introduction of unknown states or new load types is still a challenge for precise identification. In this paper, a similarity calculation method of the space convex hull overlap rate is proposed to deal with the identification of unknown loads based on the low-dimensional feature space model of Siamese neural networks. Moreover, transfer learning is introduced to realize the category-added learning of load pre-identification model for unknown load. The ultimate aim was to establish the identification and rapid modeling of unknown loads. The public datasets Plaid is used to verify the performance of the proposed method. As a result, the high accuracy of load identification for unknown load is proved.