Weights Of Keywords Research Articles

SQL Injection Attack Detection Based on Similarity Matching Between Vectors Extracted From Design Time and Run-Time Queries

Everyone uses web-based applications to carry out daily business and personal tasks. These programmes are vulnerable to attack by hackers, who may also misuse the data. The most serious attack with the greatest damaging potential on digital platforms is the structured query language injection attack (SQLiA). The backend databases could be corrupted or destroyed by SQLiA if it manages to breach security protections. Using SQLiA tactics, hackers can get unauthorized access, steal important data, and take over the network completely or partially. An automatic SQL injection prevention and detection technique is needed to safeguard web-based applications from SQLiA. This research suggests a novel similarity-matching algorithm of vectors extracted from design time and run-time queries. This technique allocates the weights of different SQL keywords used in design time and run-time queries and further design time and run-time vectors have been created from respective queries. The similarity between the design time and run time vector is determined by calculating the angle between these two vectors. The angle of deviation between the design time vector and run time vector is calculated and if the angle of deviation is zero, then it is concluded as no SQL injection otherwise, it indicates the existence of SQLiA vulnerability. The proposed algorithm is validated against the GitHub dataset. In the first dataset, out of 1300 injected queries, the proposed method identifies 1219 injected queries; out of 300 normal queries, it identifies 290 normal queries with 93.76% and 96.66% detection accuracy, respectively. Similarly, for the second dataset, out of 10489 injected queries, it identifies 10280 injected queries and out of 301 normal queries, it identifies 280 normal queries with 98.01% and 93.02% detection accuracy, respectively.

Improved literature recommendation system through the fusion mode of conformity bias elimination and keyword preference

Authors are often influenced by historical experience and personal habits when selecting keywords in composing papers, and tend to choose specific keywords, resulting in a phenomenon of conformity bias. In order to improve the accuracy of literature recommendation services, it is crucial to effectively identify and eliminate the adverse effects of conformity bias. Based on the definition of a measurement for the degree of impact of conformity bias using conformity width, this paper designs an effective method for eliminating conformity bias based on one-class implicit access information. At the same time, this method further utilizes the author's own keyword preference information based on explicit weight of keywords and achieves the comprehensive fusion of various effective user access information through the design of a model. The experimental results validated the phenomenon of conformity bias in the composing process of scholars, and comprehensively analyzed and compared the characteristics and advantages of the algorithm proposed in this paper. The code is available at https://github.com/EdwardSig/CBEWC_ESWA.

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Abstract E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.

텍스트 마이닝을 이용한 청소년의 희망 관련 연구 동향 분석

This study analyzes research trends related to hope by applying text-mining analysis. The subjects of this study were journals related to hope-related research on adolescents, including upper elementary school students registered in KCI from 2001 to 2022. According to the analysis of keyword frequency (TF) and keyword weight (TF-IDF), “academic” and “strength” showed the highest values. In the centrality analysis of keywords, “psychology” was the highest value, and “stress” was the highest value in mediation centrality. Applying topic modeling analysis classified it into five topics: (1) adaptation-related area, (2) relationship area, (3) effectiveness area, (4) career-related area, and (5) academic-related area. The periods were divided into the first period (2001-2010), the second period (2011-2015), and the third period (2016-2022). As a result of the analysis, the effectiveness area appeared in the second period. The proportion increased toward the third period, and the proportion of the adaptation and academic areas decreased. The proportion of the relationship area increased from the first to the third period. This study is meaningful because it examined the research trends related to hope by journals using text mining analysis.

키워드 네트워크 분석을 활용한 청소년 게임중독 연구동향 분석

The purpose of this study is to suggest the direction of future studies on the prevention and treatment of gaming addiction by analyzing the trends in research related to gaming addiction for the last 10 years. To accomplish this purpose, this study analyzed publication trends in research related to gaming addiction by examining 175 studies published between 2013 and 2023. Particularly, a keyword network analysis was conducted using keywords extracted from these studies. The main results are as follows. First, the results of the keyword frequency analysis indicate that the most frequently occurring terms in the 175 publications are, in descending order, ‘adolescent’, ‘gaming addiction, ‘Internet gaming addiction’, ‘depression’, and ‘aggression.’ Second, the results of the TF-IDF analysis demonstrate that keyword weights of 42 keywords were high, listed in the following order: ‘Internet gaming addiction’, ‘gaming addiction’, ‘adolescent’, ‘aggression’, and ‘depression.’ Third, the results of the keyword network centrality analysis indicate that the keywords with high degree centrality, closeness centrality, betweenness centrality were ranked as follows: ‘gaming addiction’, ‘adolescent’, ‘Internet gaming addiction’, ‘depression.’ Based upon these results, this study proposes directions for future research on the prevention and treatment of gaming addiction.

Modernization of Intellectual Property Discipline Governance Based on Multivariate Statistical Analysis Towards the Construction of a Powerful Intellectual Property Country

Abstract In this paper, after using the TF-IDF algorithm to calculate the weights of intellectual property keywords of digital content, the weights of fused word multi-features are improved to obtain the proportion of intellectual property keywords. Based on intellectual property keywords, combining adversarial fingerprint generation and intellectual property authentication, an intellectual property protection model based on RCNN is constructed. From the perspective of strong nation-building, the research on intellectual property affecting the artificial intelligence industry is designed, and multivariate statistical analysis of scientific governance of intellectual property in the context of strong nation-building is carried out. The results show that the SSIM values obtained by the method of this paper are much better than those obtained by the comparison method in terms of model analysis. Except for the anti-quantization attack, the rest of the indicators are all greater than 0.9. It confirms that the method presented in this paper has excellent robustness in protecting intellectual property. In the empirical analysis of the impact of intellectual property rights on the artificial intelligence industry, except for RDF, which is significant at the 5% level, the regression coefficients of the other explanatory variables are all significant at the 1% level, 2 R amounting to 0.860771 and with a DW value of 1.667387 in the range of 1.5-2.5, that is, it indicates that the level of China’s intellectual property rights protection and the competitiveness of the AI industry are positively correlated. It is positively correlated. This study promotes the improvement of China’s innovation ability and the formation of innovation mechanisms from the aspect of intellectual property protection so as to realize the comprehensive catching up of technology and economy of late-developed countries.

A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis.

Skin cancers (SCs) arise due to the proliferation of atypical cells that have the potential to infiltrate or metastasize to different areas of the body. There is a lack of understanding regarding the country-based collaborations among authors (CBCA) and article themes on SCs. A clustering algorithm capable of categorizing CBCA and article themes on skin cancer is required. This study aimed to apply a follower-leading clustering algorithm to classify CBCA and article themes and present articles that deserve reading in recent ten years. Between 2013 and 2022, a total of 6526 articles focusing on SC were extracted from the Web of Science core collection. The descriptive, diagnostic, predictive, and prescriptive analytics model was employed to visualize the study results. Various visualizations, including 4-quadrant radar plots, line charts, scatter plots, network charts, chord diagrams, and impact beam plots, were utilized. The category, journal, authorship, and L-index score were employed to assess individual research achievements. Diagnostic analytics were used to cluster the CBCA and identify common article themes. Keyword weights were utilized to predict article citations, and noteworthy articles were highlighted in prescriptive analytics based on the 100 most highly cited articles on SC (T100SC). The primary entities contributing to SC research include the United States, the University of California, San Francisco in US, dermatology department, and the author Andreas Stang from Germany, who possess higher category, journal, authorship, and L-index scores. The Journal of the American Academy of Dermatology has published the highest number of articles (n = 336, accounting for 5.16% of the total). From the T100SC, 7 distinct themes were identified, with melanoma being the predominant theme (92% representation). A strong correlation was observed between the number of article citations and the keyword weights (F = 81.63; P < .0001). Two articles with the highest citation counts were recommended for reading. By applying the descriptive, diagnostic, predictive, and prescriptive analytics model, 2 noteworthy articles were identified and highlighted on an impact beam plot. These articles are considered deserving of attention and could potentially inspire further research in the field of bibliometrics, focusing on relevant topics related to melanoma.

Feature selection algorithm for substation main equipment defect text mining based on natural language processing

AbstractThe dimension of relevant text feature space and feature weight of substation main equipment defect information is high, so it is difficult to accurately select mining features. The Natural Language Processing (NLP) medium and short‐term neural network model is used to realise the defect information text feature word segmentation in the log. After extracting the text features of defect information of main substation equipment with high categories to form the feature space; the TF‐IDF algorithm is designed to calculate the importance weight of text keywords, judge the criticality of defect information text feature vocabulary, accurately locate defect information text features, and realise defect information text feature mining. Experiments show that the algorithm has high precision for specific word segmentation of massive substation main equipment log information.

A Curriculum Learning Approach for Multi-Domain Text Classification Using Keyword Weight Ranking

Text classification is a well-established task in NLP, but it has two major limitations. Firstly, text classification is heavily reliant on domain-specific knowledge, meaning that a classifier that is trained on a given corpus may not perform well when presented with text from another domain. Secondly, text classification models require substantial amounts of annotated data for training, and in certain domains, there may be an insufficient quantity of labeled data available. Consequently, it is essential to explore methods for efficiently utilizing text data from various domains to improve the performance of models across a range of domains. One approach for achieving this is through the use of multi-domain text classification models that leverage adversarial training to extract domain-shared features among all domains as well as the specific features of each domain. After observing the varying distinctness of domain-specific features, our paper introduces a curriculum learning approach using a ranking system based on keyword weight to enhance the effectiveness of multi-domain text classification models. The experimental data from Amazon reviews and FDU-MTL datasets show that our method significantly improves the efficacy of multi-domain text classification models adopting adversarial learning and reaching state-of-the-art outcomes on these two datasets.

Retracted: Research on the Relationship between Intelligent Analysis and Weight of Keywords in English Test Questions

Retracted: Research on the Relationship between Intelligent Analysis and Weight of Keywords in English Test Questions

A comprehensive retrieval method of social media information based on fuzzy mathematics

In order to solve the problems of low retrieval accuracy and long retrieval time of traditional information comprehensive retrieval methods, a social media information comprehensive retrieval method based on fuzzy mathematics is proposed in this paper. Firstly, the social media information data is collected by hash function; Then, the cross filter is used to remove similar social media data. Finally, according to the fuzzy triangle number in the fuzzy mathematics method, the keyword weight in social media information is calculated. The comprehensive retrieval of social media information is completed by determining the attribute of social media information and reducing the word frequency range of social media information. The experimental results show that the minimum detection error of this method is about 0.15%, and the shortest information retrieval time is about 1.0 s. The retrieval effect of the proposed method is good.

Scalable Fuzzy Keyword Ranked Search Over Encrypted Data on Hybrid Clouds

Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold d, the storage cost will grow exponentially or the precision of search results will greatly decrease as <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$d$</tex></formula> increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this paper, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over the encrypted database, in which our scheme achieves fuzzy keyword search for any similarity threshold d with constant storage size and accurate search results. Furthermore, we design a two-factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.

Construction of Intelligent Recommendation Retrieval Model of FuJian Intangible Cultural Heritage Digital Archives Resources

In order to improve the consistency between the recommended retrieval results and user needs, improve the recommendation efficiency, and reduce the average absolute deviation of resource retrieval, a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed. The TG-LDA (Tag-granularity LDA) model is proposed on the basis of the standard LDA (Linear Discriminant Analysis) model. The model is used to mine archive resource topics. The Pearson correlation coefficient is used to measure the relevance between topics. Based on the measurement results, the FastText deep learning model is used to achieve archive resource classification. According to the classification results, TF-IDF (term frequency–inverse document frequency) algorithm is used to calculate the weight of resource retrieval keywords to achieve resource retrieval, and a recommendation model of intangible cultural heritage digital archives resources is built through the knowledge map to achieve comprehensive and personalized recommendation of resources. The experimental results show that the recommendation and retrieval results of the proposed method are more in line with users’ needs, can provide users with personalized digital archive resources, and the average absolute deviation of resource retrieval is low, the recommendation efficiency is high, and the utilization effect of archive resources is effectively improved.

Ontology-based hierarchical retrieval model for digital English teaching information

In order to overcome the low recall and precision of traditional English teaching information retrieval model, this paper designs a hierarchical retrieval model of digital English teaching information based on ontology. TF-IDF and simhash algorithm are used to judge the similarity of digital English teaching database documents and calculate the weight of English teaching information retrieval keywords. Using the relationship between different retrieval concepts to build a semantic network diagram, a hierarchical retrieval model of digital English teaching information was built, the retrieval results according to the user's interest were adjusted, and more accurate retrieval results are obtained. The experimental results show that the recall rate of the model is more than 94%, the precision rate is more than 96%, and the average retrieval time is only 0.44s, which shows that the recall rate and precision rate of the design model are higher, and the retrieval time is shorter.

Research on the Relationship between Intelligent Analysis and Weight of Keywords in English Test Questions

With the rapid development of educational informationization, Internet of Things, and other technologies, English education has been paid special attention to, and all aspects such as educational model, learning behavior, teaching philosophy, and teaching evaluation have been greatly influenced by educational informationization. Based on the experience of practical education, this paper explores and studies the connotation and characteristics of English test questions and its influence and application on modern English test questions. This paper constructs a systematic method to extract keywords from English test questions. It perfects the fair and reasonable index of English keywords, establishes the weight system, discusses the relationship of keywords, and conducts academic research on vocabulary, word frequency and word position, emphatically adopts BayesNet algorithm to extract keywords, and realizes the evaluation system of English test keywords based on intelligent analysis and weight relationship. The results show that (1) selecting the calculation method and weight relationship suitable for the text system to carry out intelligent analysis, the weight ratio exceeds 65%; that is, the text keyword retrieval is successful. (2) The average accuracy (%), average recall (%) and average F -measure (%) in weighted names are almost less than 70%. Only the BayesNet algorithm has 72.3% weight analysis in keyword extraction in reading comprehension. (3) KEA algorithm, PAT TERR algorithm, and BayesNet algorithm take 0-2.8 s, 0-2.6 s, and 0-2.1 s, respectively, and the BayesNet algorithm takes the shortest time. The calculation time of users is greatly saved. (4) According to the calculation results of CPT model, the sum of the weights of the three algorithms is equal to 1, and the BayesNet algorithm is dominant in extracting keywords with a weight analysis of 0.529 in verb translation.

Web Page Classification Algorithm Based on Deep Learning.

Transmit and process information to establish a learning mechanism and realize the processing of image data and sound data. However, the current research on Web page classification algorithm (WPCA) based on deep learning (DL) is not in-depth. Therefore, the main research of this article is the research of WPCA based on DL. This article first uses the keyword weight calculation method to reduce the impact of a small number of high-frequency words in the web page document on the weight calculation and reduces the value of the low-frequency word weights so that the WPCA is more accurate in the calculation process; second, the use of Chinese web pages: the classification method calculates the similarity between the text to be classified and all the class templates and then determines the category of all texts according to the similarity and certain classification rules; finally, in order to improve the learning rate of DL, consider using adaptive parameters. The optimization algorithm automatically adjusts the size of the learning rate, making the research of WPCA based on DL more efficient. After comparing the DL-based WPCA with the traditional algorithm, the data shows that in terms of time expenditure, the DL WPCA is 354 s, the traditional algorithm is 2436 s; in terms of memory overhead, the DL WPCA is 6.35 s, the traditional algorithm is 186.25 s. The experimental results show that WPCA based on DL are faster and more efficient than traditional algorithms and consume less system memory.

News keyword extraction algorithm based on semantic clustering and word graph model

The internet is an abundant source of news every day. Thus, efficient algorithms to extract keywords from the text are important to obtain information quickly. However, the precision and recall of mature keyword extraction algorithms need improvement. TextRank, which is derived from the PageRank algorithm, uses word graphs to spread the weight of words. The keyword weight propagation in TextRank focuses only on word frequency. To improve the performance of the algorithm, we propose Semantic Clustering TextRank (SCTR), a semantic clustering news keyword extraction algorithm based on TextRank. Firstly, the word vectors generated by the Bidirectional Encoder Representation from Transformers (BERT) model are used to perform k-means clustering to represent semantic clustering. Then, the clustering results are used to construct a TextRank weight transfer probability matrix. Finally, iterative calculation of word graphs and extraction of keywords are performed. The test target of this experiment is a Chinese news library. The results of the experiment conducted on this text set show that the SCTR algorithm has greater precision, recall, and F1 value than the traditional TextRank and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms.

Comparison and Transition of Research Focus on Application of IT in Education

The application of Information Technology (IT) in education has opened new scenarios for this ancient process. With rapidly changing field of IT, the adoption of IT in education has been changed drastically. It is quite difficult for researchers to keep pace with changing research trends. An analysis based on the keywords could provide a synopsis on the use of IT in education. The keywords can be extracted and clustered to draw a sketch of trend changes over time. In this paper, we propose two empirical methods based on classic TF/IDF, i.e., overall rating (OR) and dynamic character (DC) of a keyword for in-depth keyword analysis to examine changing trends in research. The method help in disclosing time based changes in research focuses by comparing TF/IDF weights of keywords in different years. A total of 8131 scholarly articles from 12 well recognized journals were used in this analysis. The analysis shows that proposed methods provide sufficient insight into the research trends of application of IT in education in 11 years, i.e., 2007-2017.

Tatt‐BiLSTM: Web service classification with topical attention‐based BiLSTM

AbstractWith the rapid growth of the number of Web services on the Internet, how to classify Web services correctly and efficiently become particularly important in service management tasks, such as service discovery, service selection, service ranking, and service recommendation. Existing functionality‐based service classification techniques have some drawbacks: (1) the keyword order and context information are not considered; (2) the embedding features of keywords are taken as equal importance to learn the classification model; (3) the topic number is hard to determine manually. Due to these drawbacks, the accuracy of service classification needs to be improved further. At present, deep learning techniques show the strong power in modeling complex and nonlinear function relationship. Thus, to address the problems above, this paper exploits attention mechanism to combine the local implicit state vector of Bidirectional Long Short‐Term Memory Network (BiLSTM) and the global hierarchical Dirichlet process (HDP) topic vector, and proposes a Web service classification approach with topical attention‐based BiLSTM. Specifically, BiLSTM is used to automatically learn the keyword feature representations of Web services. Then, the topic vectors of Web service documents are obtained with HDP by offline training, and topic attention mechanism is adopted to strengthen the feature representation by discriminating the importance or weight of different keywords in Web service documents. Finally, the enhanced Web service feature representation is used as the input of a softmax neural network layer to perform the classification prediction for Web services. Extensive experiments are conducted to validate the effectiveness of the proposed approach.

Web service discovery based on maximum weighted bipartite graphs

Web service discovery is the process that service requestor or user finds services to meet their requirements, its core technologies include Web service description language and the service matching algorithm. Duo to services with more ambiguity, using service-oriented computing in the process of finding service without strong restriction can cause some error services which have the same background but not satisfy the initial request returned. In order to eliminate the effect of this kind of situation in service-oriented computing, we present a novel approach for Web service retrieval based on the evaluation of similarity between Web service interfaces. The algorithm automatically distributes the keyword weights for the request service, in order to weaken the weight of some keywords in request service have the same background in services set, which is to help the request service find the more accurate results. The experiments show that our solution outperforms some searching methods.