A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed docume...A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.展开更多
By combing 20 documents of the Central Committee on the historical evolution of rural development policies since 1982, we hold that historical evolution has undergone reforms, adjustments, modernization developments a...By combing 20 documents of the Central Committee on the historical evolution of rural development policies since 1982, we hold that historical evolution has undergone reforms, adjustments, modernization developments and new ideas, and the path of reform experienced economic recovery, industrial nurturing agriculture, agriculture modernization and rural revitalization. The study found that: farmers' income has always been the focus of attention; agricultural production has shifted from total demand to green ecology; urban and rural resource elements are not well-organized, resulting in internal contradictions. The implementation of the rural revitalization strategy is an important measure to fundamentally solve the rural development problems in the new era.展开更多
This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on Twitter data. The uniqueness of this work is establi...This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on Twitter data. The uniqueness of this work is established by determining the overall sentiment of a politician’s tweets based on TF-IDF values of terms used in their published tweets. By calculating the TF-IDF value of terms from the corpus, this work displays the correlation between TF-IDF score and polarity. The results of this work show that calculating the TF-IDF score of the corpus allows for a more accurate representation of the overall polarity since terms are given a weight based on their uniqueness and relevance rather than just the frequency at which they appear in the corpus.展开更多
Signature verification involves vague situations in which a signature could resemble many reference samples ormight differ because of handwriting variances. By presenting the features and similarity score of signature...Signature verification involves vague situations in which a signature could resemble many reference samples ormight differ because of handwriting variances. By presenting the features and similarity score of signatures from thematching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy,a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertaintiesand ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values,which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1neutrosophic representation is also unable to adjust to various degrees of uncertainty. The proposed work exploresthe type-2 neutrosophic logic to enable additional flexibility and granularity in handling ambiguity, indeterminacy,and uncertainty, hence improving the accuracy of signature verification systems. Because type-2 neutrosophiclogic allows the assessment of many sources of ambiguity and conflicting information, decision-making is moreflexible. These experimental results show the possible benefits of using a type-2 neutrosophic engine for signatureverification by demonstrating its superior handling of uncertainty and variability over type-1, which eventuallyresults in more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) verification results. In acomparison analysis using a benchmark dataset of handwritten signatures, the type-2 neutrosophic similaritymeasure yields a better accuracy rate of 98% than the type-1 95%.展开更多
This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such...This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such as blogs,forums,emails and online archives.The discussion highlights the role of AI in different stages of the research process,including question generation,sample and design definition,ethical considerations,data analysis,and results dissemination,emphasizing how AI can automate complex tasks and enhance research design.The paper also reports on practical experiences using AI tools,specifically ChatGPT-4,in conducting web-mediated documentary analysis and shares some ideas for the integration of AI in social research.展开更多
BACKGROUND Imipenem is a highly effective carbapenem antibiotic,which is widely used in the treatment of many serious bacterial infections.At the same time,it can also cause some adverse reactions,mental abnormalities...BACKGROUND Imipenem is a highly effective carbapenem antibiotic,which is widely used in the treatment of many serious bacterial infections.At the same time,it can also cause some adverse reactions,mental abnormalities are the most concerned central nervous system adverse reactions.Different patients respond differently to imipenem,and the effect of imipenem on psychiatric disorders is unclear.Therefore,meta-analysis summarizing the results of multiple previous studies can provide stronger evidence support for clinical guidelines to guide clinical rational use of imipenem to minimize risks.After reviewing the literature published between 2003 and 2017,seven controlled trials with a total of 550 patients were included,with 273 and 277 patients in the control and experimental groups,respectively.The sample size of the study ranged from a minimum of 30 cases to a maximum of 61 cases.Patients in the experimental group were treated with imipenem while the control group was treated with conventional drugs.Meta-analysis showed that the incidence of mental disorders in the experimental group was higher than that in the control group(odds ratio=3.66,95%confidence interval:1.11-12.11,P=0.030);however,there was no significant difference in the incidence of adverse reactions between the two groups(odds ratio=0.05,95%confidence interval:0.00 to 0.10,P=0.060).Funnel diagrams showed that the scattered points of each study were symmetrical and distributed in an inverted funnel shape;therefore,there was no publication bias.CONCLUSION Imipenem can cause mental disorders in patients.However,the low quality of the included literature may have affected the final results.Therefore,it is necessary to conduct a high-quality randomized controlled study with multiple samples to further confirm the mechanism of imipenem-induced mental disorders and provide effective guidance for clinical treatment.展开更多
Laterally with the birth of the Internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal writings.Twi...Laterally with the birth of the Internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal writings.Twitter is micro-blogging short text and social networking services,with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andfields.This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event.Although sentiment examination of these texts is useful for a variety of reasons,it is typically seen as a difficult undertaking due to the fact that these messages are frequently short,informal,loud,and rich in linguistic ambiguities such as polysemy.Furthermore,most contemporary sentiment analysis algorithms are based on clean data.In this paper,we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and Inverse Document Frequency(TF-IDF)and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing noise.We also utilise the Random Forest network to detect the emotion of a tweet.To authenticate the proposed approach performance,we conduct extensive tests on publically accessible datasets,and thefindings reveal that the suggested technique significantly outperforms sentiment classification in multi-class emotion text data.展开更多
The theory of proximization is an effective discourse strategy to study the speaker’s ability to achieve his own legitimacy or reinforce the other’s illegitimacy,and its superiority can be maximized by means of quan...The theory of proximization is an effective discourse strategy to study the speaker’s ability to achieve his own legitimacy or reinforce the other’s illegitimacy,and its superiority can be maximized by means of quantitative and comparative analysis.In this study,we collected reports on Trump’s and Biden’s policies on China to build two small corpora,with a total of 11,030 words in the Trump corpus and 17,566 words in the Biden corpus.The critical discourse analysis is combined with proximization theory.With the help of BFSU Qualitative Coder 1.2,Antconc 3.5.7,and Log-Likelihood and Chi-Square Calculator 1.0,a critical cognitive score of the relevant discourse was conducted from the perspective of proximization theory.It has been found that:(1)Both Trump and Biden administrations resort to a large number of spatial proximization strategies to build ODCs converging to IDCs with China as the ODC,posing a threat to internal physical IDCs;(2)in the use of temporal proximization strategy,both administrations use primarily modal verbs and various entities to construct ODCs that extend indefinitely into the present and future,emphasizing the urgency and the threat of the effect and reinforcing the legitimacy of their actions;(3)in terms of axiological proximization strategy,the two administrations differ greatly from each other,indicating that there are still discursive biases.展开更多
The greatest benefit is realized from failure mode, effect and criticality analysis (FMECA) when it is done early in the design phase and tracks product changes as they evolve; design changes can then be made more eco...The greatest benefit is realized from failure mode, effect and criticality analysis (FMECA) when it is done early in the design phase and tracks product changes as they evolve; design changes can then be made more economically than if the problems are discovered after the design has been completed. However, when the discovered design flaws must be prioritized for corrective actions, precise information on their probability of occurrence, the effect of the failure, and their detectability often are not availabe. To solve this problem, this paper described a new method, based on fuzzy sets, for prioritizing failures for corrective actions in a FMCEA. Its successful application to the container crane shows that the proposed method is both reasonable and practical.展开更多
To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PC...To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.展开更多
Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion.The data that is...Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion.The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity.In social behavior,sentiment can be thought of as a latent variable.Measuring and comprehending this behavior could help us to better understand the social issues.Because sentiments are domain specific,sentimental analysis in a specific context is critical in any real-world scenario.Textual sentiment analysis is done in sentence,document level and feature levels.This work introduces a new Information Gain based Feature Selection(IGbFS)algorithm for selecting highly correlated features eliminating irrelevant and redundant ones.Extensive textual sentiment analysis on sentence,document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection algorithm.The analysis is done based on the datasets from Cornell and Kaggle repositories.When compared to existing baseline classifiers,the suggested Information Gain based classifier resulted in an increased accuracy of 96%for document,97.4%for sentence and 98.5%for feature levels respectively.Also,the proposed method is tested with IMDB,Yelp 2013 and Yelp 2014 datasets.Experimental results for these high dimensional datasets give increased accuracy of 95%,96%and 98%for the proposed Information Gain based classifier for document,sentence and feature levels respectively compared to existing baseline classifiers.展开更多
Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ...Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.展开更多
This paper selects 998 articles as its data sources from four Chinese core journals in the field of Library and Information Science from 2003 to 2007.Some pertinent aspects of reference citations particularly from web...This paper selects 998 articles as its data sources from four Chinese core journals in the field of Library and Information Science from 2003 to 2007.Some pertinent aspects of reference citations particularly from web resources are selected for a focused analysis and discussion.This includes primarily such items as the number of web citations,web citations per each article,the distribution of domain names of web citations and also certain aspects about the institutional and/or geographical affiliations of the author.The evolving situation of utilizing online networked academic information resources in China is the central thematic discussion of this study.The writing of this paper is augmented by the explicatory presentation of 3 graphic figures,6 tables and 18 references.展开更多
Objective To explore the rules and characteristics of the adverse drug reactions(ADRs)of three Chinese patent medicines and three herbal formulas for the treatment of COVID-19,and to provide a reference for clinical s...Objective To explore the rules and characteristics of the adverse drug reactions(ADRs)of three Chinese patent medicines and three herbal formulas for the treatment of COVID-19,and to provide a reference for clinical safe medication.Methods The cases and ADR reports of the three Chinese patent medicines and three herbal formulas in PubMed,Web of Science,Springer Link,CNKI,Wanfang and VIP database were retrieved from December 2019 to May 2021.Then we extracted and analyzed the effective information included in the literature.Results and Conclusion According to the pre-developed retrieval plan,a total of 136 documents were obtained,and a total of 6 documents met the inclusion criteria finally.553 patients used three Chinese patent medicines and three herbal formulas,and there were 133 cases of adverse reactions.The adverse reactions of patients taking the three Chinese patent medicines and three herbal formulas can all be explained under the theory of traditional Chinese medicine,and the adverse reactions can be eliminated by adding or subtracting the flavor of the medicine or stopping the medicine.展开更多
An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the...An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.展开更多
Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text ...Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture;and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.展开更多
Background Document images such as statistical reports and scientific journals are widely used in information technology.Accurate detection of table areas in document images is an essential prerequisite for tasks such...Background Document images such as statistical reports and scientific journals are widely used in information technology.Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction.However,because of the diversity in the shapes and sizes of tables,existing table detection methods adapted from general object detection algorithms,have not yet achieved satisfactory results.Incorrect detection results might lead to the loss of critical information.Methods Therefore,we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections.To better deal with table areas of different shapes and sizes,we added a dualbranch context content attention module(DCCAM)to high-dimensional features to extract context content information,thereby enhancing the network's ability to learn shape features.For feature fusion at different scales,we replaced the original 3×3 convolution with a multilayer residual module,which contains enhanced gradient flow information to improve the feature representation and extraction capability.Results We evaluated our method on public document datasets and compared it with previous methods,which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score.https://github.com/Yong Z-Lee/TD-DCCAM.展开更多
基金This research was supported and funded by KAU Scientific Endowment,King Abdulaziz University,Jeddah,Saudi Arabia.
文摘A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.
文摘By combing 20 documents of the Central Committee on the historical evolution of rural development policies since 1982, we hold that historical evolution has undergone reforms, adjustments, modernization developments and new ideas, and the path of reform experienced economic recovery, industrial nurturing agriculture, agriculture modernization and rural revitalization. The study found that: farmers' income has always been the focus of attention; agricultural production has shifted from total demand to green ecology; urban and rural resource elements are not well-organized, resulting in internal contradictions. The implementation of the rural revitalization strategy is an important measure to fundamentally solve the rural development problems in the new era.
文摘This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on Twitter data. The uniqueness of this work is established by determining the overall sentiment of a politician’s tweets based on TF-IDF values of terms used in their published tweets. By calculating the TF-IDF value of terms from the corpus, this work displays the correlation between TF-IDF score and polarity. The results of this work show that calculating the TF-IDF score of the corpus allows for a more accurate representation of the overall polarity since terms are given a weight based on their uniqueness and relevance rather than just the frequency at which they appear in the corpus.
文摘Signature verification involves vague situations in which a signature could resemble many reference samples ormight differ because of handwriting variances. By presenting the features and similarity score of signatures from thematching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy,a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertaintiesand ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values,which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1neutrosophic representation is also unable to adjust to various degrees of uncertainty. The proposed work exploresthe type-2 neutrosophic logic to enable additional flexibility and granularity in handling ambiguity, indeterminacy,and uncertainty, hence improving the accuracy of signature verification systems. Because type-2 neutrosophiclogic allows the assessment of many sources of ambiguity and conflicting information, decision-making is moreflexible. These experimental results show the possible benefits of using a type-2 neutrosophic engine for signatureverification by demonstrating its superior handling of uncertainty and variability over type-1, which eventuallyresults in more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) verification results. In acomparison analysis using a benchmark dataset of handwritten signatures, the type-2 neutrosophic similaritymeasure yields a better accuracy rate of 98% than the type-1 95%.
文摘This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such as blogs,forums,emails and online archives.The discussion highlights the role of AI in different stages of the research process,including question generation,sample and design definition,ethical considerations,data analysis,and results dissemination,emphasizing how AI can automate complex tasks and enhance research design.The paper also reports on practical experiences using AI tools,specifically ChatGPT-4,in conducting web-mediated documentary analysis and shares some ideas for the integration of AI in social research.
基金Supported by the Education Research Program Project of Zhejiang Province,No.Y202043224.
文摘BACKGROUND Imipenem is a highly effective carbapenem antibiotic,which is widely used in the treatment of many serious bacterial infections.At the same time,it can also cause some adverse reactions,mental abnormalities are the most concerned central nervous system adverse reactions.Different patients respond differently to imipenem,and the effect of imipenem on psychiatric disorders is unclear.Therefore,meta-analysis summarizing the results of multiple previous studies can provide stronger evidence support for clinical guidelines to guide clinical rational use of imipenem to minimize risks.After reviewing the literature published between 2003 and 2017,seven controlled trials with a total of 550 patients were included,with 273 and 277 patients in the control and experimental groups,respectively.The sample size of the study ranged from a minimum of 30 cases to a maximum of 61 cases.Patients in the experimental group were treated with imipenem while the control group was treated with conventional drugs.Meta-analysis showed that the incidence of mental disorders in the experimental group was higher than that in the control group(odds ratio=3.66,95%confidence interval:1.11-12.11,P=0.030);however,there was no significant difference in the incidence of adverse reactions between the two groups(odds ratio=0.05,95%confidence interval:0.00 to 0.10,P=0.060).Funnel diagrams showed that the scattered points of each study were symmetrical and distributed in an inverted funnel shape;therefore,there was no publication bias.CONCLUSION Imipenem can cause mental disorders in patients.However,the low quality of the included literature may have affected the final results.Therefore,it is necessary to conduct a high-quality randomized controlled study with multiple samples to further confirm the mechanism of imipenem-induced mental disorders and provide effective guidance for clinical treatment.
文摘Laterally with the birth of the Internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal writings.Twitter is micro-blogging short text and social networking services,with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andfields.This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event.Although sentiment examination of these texts is useful for a variety of reasons,it is typically seen as a difficult undertaking due to the fact that these messages are frequently short,informal,loud,and rich in linguistic ambiguities such as polysemy.Furthermore,most contemporary sentiment analysis algorithms are based on clean data.In this paper,we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and Inverse Document Frequency(TF-IDF)and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing noise.We also utilise the Random Forest network to detect the emotion of a tweet.To authenticate the proposed approach performance,we conduct extensive tests on publically accessible datasets,and thefindings reveal that the suggested technique significantly outperforms sentiment classification in multi-class emotion text data.
文摘The theory of proximization is an effective discourse strategy to study the speaker’s ability to achieve his own legitimacy or reinforce the other’s illegitimacy,and its superiority can be maximized by means of quantitative and comparative analysis.In this study,we collected reports on Trump’s and Biden’s policies on China to build two small corpora,with a total of 11,030 words in the Trump corpus and 17,566 words in the Biden corpus.The critical discourse analysis is combined with proximization theory.With the help of BFSU Qualitative Coder 1.2,Antconc 3.5.7,and Log-Likelihood and Chi-Square Calculator 1.0,a critical cognitive score of the relevant discourse was conducted from the perspective of proximization theory.It has been found that:(1)Both Trump and Biden administrations resort to a large number of spatial proximization strategies to build ODCs converging to IDCs with China as the ODC,posing a threat to internal physical IDCs;(2)in the use of temporal proximization strategy,both administrations use primarily modal verbs and various entities to construct ODCs that extend indefinitely into the present and future,emphasizing the urgency and the threat of the effect and reinforcing the legitimacy of their actions;(3)in terms of axiological proximization strategy,the two administrations differ greatly from each other,indicating that there are still discursive biases.
基金National Natural Science Foundation ofChina! under the Contract Number:594 750 4 3
文摘The greatest benefit is realized from failure mode, effect and criticality analysis (FMECA) when it is done early in the design phase and tracks product changes as they evolve; design changes can then be made more economically than if the problems are discovered after the design has been completed. However, when the discovered design flaws must be prioritized for corrective actions, precise information on their probability of occurrence, the effect of the failure, and their detectability often are not availabe. To solve this problem, this paper described a new method, based on fuzzy sets, for prioritizing failures for corrective actions in a FMCEA. Its successful application to the container crane shows that the proposed method is both reasonable and practical.
基金National Natural Science Foundation of China(No.60872065)the Key Laboratory of Textile Science&Technology,Ministry of Education,China(No.P1111)+1 种基金the Key Laboratory of Advanced Textile Materials and Manufacturing Technology,Ministry of Education,China(No.2010001)the Priority Academic Program Development of Jiangsu Higher Education Institution,China
文摘To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.
文摘Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion.The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity.In social behavior,sentiment can be thought of as a latent variable.Measuring and comprehending this behavior could help us to better understand the social issues.Because sentiments are domain specific,sentimental analysis in a specific context is critical in any real-world scenario.Textual sentiment analysis is done in sentence,document level and feature levels.This work introduces a new Information Gain based Feature Selection(IGbFS)algorithm for selecting highly correlated features eliminating irrelevant and redundant ones.Extensive textual sentiment analysis on sentence,document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection algorithm.The analysis is done based on the datasets from Cornell and Kaggle repositories.When compared to existing baseline classifiers,the suggested Information Gain based classifier resulted in an increased accuracy of 96%for document,97.4%for sentence and 98.5%for feature levels respectively.Also,the proposed method is tested with IMDB,Yelp 2013 and Yelp 2014 datasets.Experimental results for these high dimensional datasets give increased accuracy of 95%,96%and 98%for the proposed Information Gain based classifier for document,sentence and feature levels respectively compared to existing baseline classifiers.
文摘Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.
基金supported by National Social Science Fund of China(Grant No.08CTQ015)
文摘This paper selects 998 articles as its data sources from four Chinese core journals in the field of Library and Information Science from 2003 to 2007.Some pertinent aspects of reference citations particularly from web resources are selected for a focused analysis and discussion.This includes primarily such items as the number of web citations,web citations per each article,the distribution of domain names of web citations and also certain aspects about the institutional and/or geographical affiliations of the author.The evolving situation of utilizing online networked academic information resources in China is the central thematic discussion of this study.The writing of this paper is augmented by the explicatory presentation of 3 graphic figures,6 tables and 18 references.
文摘Objective To explore the rules and characteristics of the adverse drug reactions(ADRs)of three Chinese patent medicines and three herbal formulas for the treatment of COVID-19,and to provide a reference for clinical safe medication.Methods The cases and ADR reports of the three Chinese patent medicines and three herbal formulas in PubMed,Web of Science,Springer Link,CNKI,Wanfang and VIP database were retrieved from December 2019 to May 2021.Then we extracted and analyzed the effective information included in the literature.Results and Conclusion According to the pre-developed retrieval plan,a total of 136 documents were obtained,and a total of 6 documents met the inclusion criteria finally.553 patients used three Chinese patent medicines and three herbal formulas,and there were 133 cases of adverse reactions.The adverse reactions of patients taking the three Chinese patent medicines and three herbal formulas can all be explained under the theory of traditional Chinese medicine,and the adverse reactions can be eliminated by adding or subtracting the flavor of the medicine or stopping the medicine.
文摘An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.
文摘Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture;and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.
文摘Background Document images such as statistical reports and scientific journals are widely used in information technology.Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction.However,because of the diversity in the shapes and sizes of tables,existing table detection methods adapted from general object detection algorithms,have not yet achieved satisfactory results.Incorrect detection results might lead to the loss of critical information.Methods Therefore,we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections.To better deal with table areas of different shapes and sizes,we added a dualbranch context content attention module(DCCAM)to high-dimensional features to extract context content information,thereby enhancing the network's ability to learn shape features.For feature fusion at different scales,we replaced the original 3×3 convolution with a multilayer residual module,which contains enhanced gradient flow information to improve the feature representation and extraction capability.Results We evaluated our method on public document datasets and compared it with previous methods,which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score.https://github.com/Yong Z-Lee/TD-DCCAM.
