VSM Model Research Articles

Research on the Value and Language Features of Chinese Language and Literature Texts Based on Text Mining Technology

The linguistic characteristics of a literary work are the way of thinking embodied in the author’s use of language. From the textual value of Chinese language literature, this paper analyzes the spiritual connotation of Chinese language literature from two dimensions: reading and education. Based on the web crawler technology, we obtain the text data of Chinese language literature from three writers, Bajin, Yu Zheng and Qiong Yao, preprocess the data through data cleaning, Chinese word segmentation, de-duplication, etc., and extract the feature values of the text by using the TF-IDF algorithm. Then the text documents are mapped onto vectors using the VSM model, and the parameters of the LDA topic model are estimated by the Gibbs sampling algorithm in order to better obtain the topic changes of the Chinese language literature texts. This paper carries out linguistic feature verification from the lexical and similarity features of Chinese language literary texts. It is found that the difference in lexical density between Ba Jin’s Cold Night and Resting Garden is only 2.1 percentage points, and the frequency of the verb “to say” is 1,213 times and 735 times respectively. The average sentence lengths of Yu Zheng and Qiong Yao fluctuate within the range of [18.49,34.27], and Qiong Yao’s works have a higher thematic concentration than Zheng Zheng’s works. Analyzing the linguistic features of Chinese language literary texts based on text mining techniques helps to understand the authors’ language usage methods and helps to promote innovative expression paths in literary texts.

Integration and analysis of diverse healthcare data sources: A novel solution

Contemporary healthcare analytics requires informed decision-making through seamless integration, correlation, and curation of diverse data from sources like clinical trials, research publications, ubiquitous devices, and standard terminologies. Modern healthcare systems need to monitor temporal changes, manage key features, and deliver robust search capabilities, extending beyond electronic health records. However, existing systems lack readiness for comprehensive healthcare analytics tasks, necessitating a sophisticated solution. Our work introduces a groundbreaking comprehensive framework for managing, integrating, and processing continuously evolving healthcare data, with a focus on establishing an efficient architecture for data processing and ensuring interoperability and consistency. We incorporate a time dimension to capture critical changes for efficient data analysis and decision-making, extending from clinical trials to mapping clinical trial data to clinical research. Moreover, we curate disparate datasets, including trials, academic publications, standard medical terms, concepts, and ubiquitous device data. Employing highly efficient algorithms and methods, we optimize time and space complexity, validating the feasibility of our proposed solution. Our results demonstrate maximum linear change detection and update processing latency, showcasing efficiency compared to state-of-the-art methods. Additionally, our methods for profiling crucial entities in clinical trial data achieve consistent average accuracy, notably with the VSM model. This innovative approach significantly advances meeting dynamic requirements in contemporary healthcare analytics, particularly in clinical trials.

Practical Application of Professional Group Curriculum Resource Library in Art Design Teaching

Abstract This paper studies the relevant functions and contents of the professional group course resource library and constructs the architecture of the professional group course resource library. In the specific construction, the Lian Li VSM model and LDA topic model establish the domain ontology of the resource library, retrieve the content of educational resources from the resource document based on the retrieval request for resource filtering, extraction, and integration, and through the information mining algorithm to browse the degree of interest in the learning resources to calculate, to achieve the personalized recommendation of educational resources. Finally, the satisfaction of teachers and students with the professional group course resource library was analyzed respectively, and the effect of the application of the professional group course resource library in art and design teaching practice was studied using t-test and regression analysis. The results show that the regression coefficient values of the professional group course resource library on teachers’ concept of teaching, teaching content, and teaching effect are 0.148, 0.145, and 0.059, respectively. The t-values are 2.342, 2.644, and 1.212, respectively, with p<0.05, and the professional group course resource library has a significant positive effect on the teaching of art design. This study provides a new direction for the development of art design teaching, and it offers important reference content for universities to build a professional group course resource library.

Intelligent VSM Model: a way to adopt Industry 4.0 technologies in manufacturing industry

Intelligent VSM Model: a way to adopt Industry 4.0 technologies in manufacturing industry

Systems thinking for the transition of existing technologies to blockchain technologies

The 4th Industrial evolution has brought along a lot of technological achievements which can change the form of humanity. Peer-to-peer networks (Distributed networks), network of sensors (Internet of Things), algorithms capable to take decisions (Artificial Intelligence), computers with the ability of self-learning (Machine Learning), more complex queries for analyzing the data, that we are collecting since the birth of internet (Data Science) and new electronic money(cryptocurrencies) are some of the characteristics of those new technologies. But the adoption of those achievements (known as Digital Transformation or Digitization) demands Managers open-minded, well-educated on those technologies and ready to trace the new possible Risks. They must also be capable to use the Systems Thinking, as the Blockchain Technologies have created an Ecosystem (Sociotechnical Systems); the combination of Social Systems (Organizations - Companies), whose behavior is not predictable, and Mechanical Systems (technical equipment) with a predefined way of function. So, this kind of Systems (Sociotechnical) need a more delicate approach using a combination of, not only Systemic methodologies and technics, but also other theories and proper tools. We are going to publish a series of articles in which we are going to specify the proper theories and methodologies in each phase of the digital transformation. Thus, the purpose of this study is to explain to the new generation of Managers how the Systems Thinking, DCSYM Methodology and VSM Model, are applied on those Ecosystems.

An Intelligent Test Paper Generation Method to Solve Semantic Similarity Problem

In order to solve the problem of semantic duplication in the Intelligent Test Paper Generation Method, Genetic Particle Swarm Optimization Algorithm was used to search multi groups of test papers that conform to the constraints of the test. Then the idea of density entropy was used to screen the test papers, so that test papers will cover more questions uniformly. After that, analyze the semantic similarity of the test papers that use the TextRank algorithm to extract the key words of test questions, and use knowledge-keywords weighted VSM model to calculate the semantic similarity of test questions. Eliminate the high repetition rate of the test papers and avoid the repetition of the inspection points. The experimental results show that the algorithm can solve the semantic similarity problem in intelligent test paper generation of mass item bank.

Evolution of Management Cybernetics and Viable System Model

The Viable System Model have pulled in system specialists’ interests lately. This field could help system analyzers and designers to deal with systems unpredictability and help them to handle dynamic evolving situations. This paper investigates Viable System Model recorded and late research patterns. To start with, presenting and creating verifiable pattern of this model has been examined. At that point late distribution patterns have been checked on by reviewing 1000 most essential and cited works Based on Google Scholar rank. Cybernetics developed in the Josiah Macy conferences, beginning in 1946. At that point, Wiener presented the field of Cybernetics and Ashby, Von Forester, and McCulloch built up this field as a train. This Management Cybernetics was presented in 1959 by Beer is a blend of frameworks of control, and management sciences. Beer introduced VSM as an operational model here. Reviewing 1000 most cited publications demonstrates the presentation of this field achieved maturity and further advancement turned out to be generally under development. In addition, in view of investigated patterns, application of VSM model would now be able to be firmly alluring.

Clustering Chinese Documents Based on HybridDT and LDA Model

In traditional VSM model based text clustering approach, the abstract similarities among documents are represented by the cosine values between the corresponding space vectors. Although it is simple and intuitive, it ignores the semantic and polysemy information of the documents. Aiming to resolve these problems, a clustering framework is proposed in this paper. The framework contains two parts: (1) pre-training text by using Doc2vec to obtain document vector and combining it into TFIDF strategy based weight space vector, which is called HybridDT; (2) using Latent Dirichlet allocation to get the topic distribution and linearly mixing it as feature into HybridDT. The experimental results using the proposed framework on Fudan University Chinese corpus show that the performance of text clustering are improved significantly by combining HybridDT and LDA model.

Microblog Hot Topics Detection Based on VSM and HMBTM Model Fusion

Microblog Hot Topics Detection Based on VSM and HMBTM Model Fusion

Cluster Analysis for Internet Public Sentiment in Universities by Combining Methods

A clustering method based on the Latent Dirichlet Allocation and the VSM model to compute the text similarity is presented. The Latent Dirichlet Allocation subject models and the VSM vector space model weights strategy are used respectively to calculate the text similarity. The linear combination of the two results is used to get the text similarity. Then the k-means clustering algorithm is chosen for cluster analysis. It can not only solve the deep semantic information leakage problems of traditional text clustering, but also solve the problem of the LDA that could not distinguish the texts because of too much dimension reduction. So the deep semantic information is mined from the text, and the clustering efficiency is improved. Through the comparisons with the traditional methods, the result shows that this algorithm can improve the performance of text clustering.

Bag of meta-words: A novel method to represent document for the sentiment classification

It is crucial to represent the semantic information of a document in sentiment classification. Various semantic information representation models have been proposed, however existing approaches have their setbacks. Notable weaknesses among these are: (1) tradition VSM methods, completely ignore the semantic information; (2) averaging word embedding methods, cannot depict the synthetical semantic meaning of the given document; (3) neural network methods, require complex structure and are notoriously difficult to be trained. To overcome these limitations, we introduce a simple but novel method which we call bag of meta-words (BoMW). In our method, the semantic information of the document is indicated by a meta-words vector in which every single meta-word element denotes particular semantic information. Especially, these meta-words are extracted from pre-trained word embeddings through two different but complemental models, naive interval meta-words (NIM) and feature combination meta-words (FCM). In general, our new model BoMW is as simple as traditional VSM model but it can capture the synthetical semantic meanings of the document. Numerous experiments on two benchmarks (IMDB dataset and Pang’s dataset) are carried out to verify the effectiveness of the proposed method, and the results show that the performance of our method can exceed the traditional VSM methods and methods using pre-trained word embedding.

Electronic Book Recommendation Method Based on Group User Behavior Analysis

With the rapid development of the Internet, for-profit site need to analyze the user's behavior and provide more satisfactory service. Therefore, the classification of network behavior analysis and the further research based on it are more and more urgent on the agenda. In this paper, a method based on similar aggregation user behavior analysis algorithm is proposed. Addressing the recommendation of personalized books problems is solved by this method. Firstly, the user behavior is analyzed by using the RFM model. Secondly, the Apriori algorithm based on weight increment is applied to mining association rules between users in line with the recent habits of users. Similarity is calculated by Apriori algorithm with using VSM model. In this paper, readers’ browsing history of e-library which is provided by Harbin University of Science and Technology is used as experimental data. This method is compared with the method which does not use the weight increment and similar aggregation. Comparison of results showed that the method of our paper can meet the requirements of the Book Recommendation system.

Information Retrieval using Dempster-Shafer Theory

retrieval model focuses on the problem of retrieving documents relevant to a user's information need represented as a query. One of the major difficulty of information retrieval is to find the relevance of documents with respect to the user query or information need. The choice of similarity measure is decisive for improving search effectiveness of a IR model. Different similarity measures have been proposed to find most relevant documents with the given query. Vector space model is a popular model and is widely used for information retrieval. The judgment of the relevance between a query and a document is evaluated using cosine similarity between them. However, vector space model does not give reasonable results in terms of precision and recall value. Information retrieval model using Dempster-Shafer theory also known as evidence theory is used in this paper. In this model, each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster's rule of combination and the belief committed to the relevance is obtained which then ranked accordingly. To validate the feasibility of this approach, evidences for sample document collection i.e. TREC-9 filtering track i.e. OSHUMED dataset are considered and the results are compared with traditional VSM model in terms of precision and recall measures. It is found that Dempster Shafer Model's performance is better than VSM for information retrieval.

Text Clustering Based on Domain Ontology and Latent Semantic Analysis

One key step in text mining is the categorization of texts, i.e., to put texts of the same or similar contents into one group so as to distinguish texts of different contents. However, traditional word-frequency-based statistical approaches, such as VSM model, failed to reflect the complicated meaning in texts. This paper ushers in domain ontology and constructs new conceptual vector space model in the pre-processing stage of text clustering, substituting the initial matrix (lexicon-text matrix) in the latent semantic analysis with concept-text matrix. In the clustering analysis stage, this model adopts semantic similarity, partially overcoming the difficulty in accurately and effectively evaluating the degree of similarity of text due to simply taking into account the frequency of words and/or phrases in the text. Experimental results indicate that this method is helpful in improving the result of text clustering.

Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

The similarity between objects is the core research area of data mining. In order to reduce the interference of the uncertainty of nature language, a similarity measurement between normal cloud models is adopted to text classification research. On this basis, a novel text classifier based on cloud concept jumping up (CCJU-TC) is proposed. It can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers.

Semantic smoothing for text clustering

In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the representation of text documents. The basic hypothesis examined is that considering semantic relatedness between two text documents may improve the performance of the text document clustering task. For our experimental evaluation we analyze the performance of several semantic relatedness measures when embedded in the proposed (S-VSM) and present results with respect to different experimental conditions, such as: (i) the datasets used, (ii) the underlying knowledge sources of the utilized measures, and (iii) the clustering algorithms employed. To the best of our knowledge, the current study is the first to systematically compare, analyze and evaluate the impact of semantic smoothing in text clustering based on ‘wisdom of linguists’, e.g., WordNets, ‘wisdom of crowds’, e.g., Wikipedia, and ‘wisdom of corpora’, e.g., large text corpora represented with the traditional Bag of Words (BoW) model. Three semantic relatedness measures for text are considered; two knowledge-based (Omiotis[1] that uses WordNet, and WLM[2] that uses Wikipedia), and one corpus-based (PMI[3] trained on a semantically tagged SemCor version). For the comparison of different experimental conditions we use the BCubed F-Measure evaluation metric which satisfies all formal constraints of good quality cluster. The experimental results show that the clustering performance based on the S-VSM is better compared to the traditional VSM model and compares favorably against the standard GVSM kernel which uses word co-occurrences to compute the latent similarities between document terms.

VSM Model Algorithm Based on FAQ Optimization

VSM Model Algorithm Based on FAQ Optimization