Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis

AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.

Regionalization of epididymal duct and epithelium in rats and mice by automatic computer-aided morphometric analysis

Aim： To establish a rat and mouse epididymal map based on the use of the Epiquatre automatic software for histologic image analysis. Methods： Epididymides from five adult rats and five adult mice were fixed in alcoholic Bouin＇s fixative and embedded in paraffin. Serial longitudinal sections through the medial aspect of the organ were cut at 10 jam and stained with hematoxylin and eosin. As determined from major connective tissue septa, nine subdivisions of the rat epididymis and seven for the mouse were determined, consisting of five sub-regions in the caput （rat and mouse）, one （mouse） or three （rat） in the corpus and one in the cauda （rat and mouse）. Using the Epiquatre software, several tubular, luminal and epithelial morphometric parameters were evaluated. Results： Statistical comparison of the quantitative parameters revealed regional differences （2-5 in the rat, 3-6 in the mouse, dependent on parameters） with caput regions 1 and 2 being largely distinguishable from the similar remaining caput and corpus, which were in turn recognizable from the cauda regions in both species. Conclusion： The use of the Epiquatre software allowed us to establish regression curves for different morphometric parameters that can permit the detection of changes in their values under different pathological or experimental conditions. （Asian J Androl 2005 Sep; 7： 267-275）

Study on text analysis and advanced English teaching

This paper is attempted to explore advanced English teaching from perspective of text analysis. It involves the introduction of culture background, the application of genre-based approach, the appreciation of writing style and the analysis of textual structure through sample studies.

The Probe of Significance of Text Analysis in Translation

This paper studies the significance of text analysis in translation in regard to the analysis both inside and outside the "text",discussing the weight of analyzing lexical units and stylistic scales in translation and examining the importance of analyzing the translator’s intention,the author’s intention and the target language(TL)readership.

Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

Text Sentiment Analysis Using Frequency-Based Vigorous Features

Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Sentiment Analysis of Short Texts Based on Parallel DenseNet

Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.

Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.

Text Analysis-Based Watermarking Approach for Tampering Detection of English Text

Due to the rapid increase in the exchange of text information via internet networks,the security and the reliability of digital content have become a major research issue.The main challenges faced by researchers are authentication,integrity verication,and tampering detection of the digital contents.In this paper,text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents.The proposed approach embeds and detects the watermark logically without altering the original English text document.Based on hidden Markov model(HMM),the fourth level order of the word mechanism is used to analyze the contents of the given English text to nd the interrelationship between the contexts.The extracted features are used as watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,the proposed approach has been implemented and validated with attacked English text.Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion,reorder,and deletion attacks.The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks.Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.

Interdiscursivity and Promotional Discourse:A Corpus-Assisted Genre Analysis of About Us Texts on Chinese University Websites

The debate on the marketization of discourse in higher education has sparked and sustained interest among researchers in discourse and education studies across a diversity of contexts.While most research in this line has focused on marketized discourses such as advertisements,little attention has been paid to promotional discourse in public institutions such as the About us texts on Chinese university websites.The goal of the present study is twofold:first,to describe the generic features of the university About us texts in China;and second,to analyze how promotional discourse is interdiscursively incorporated in the discourse by referring to the broader sociopolitical context.Findings have indicated five main moves:giving an overview,stressing historical status,displaying strengths,pledging political and ideological allegiance,and communicating goals and visions.Move 3,displaying strengths,has the greatest amount of information and can be further divided into six sub-moves which presents information on campus facilities,faculty team,talent cultivation,disciplinary fields construction,academic research,and international exchange.The main linguistic and rhetorical strategies used in these moves are analyzed and discussed.

Priorities for Social and Humanities Projects Based on Text Analysis

Purpose:Changes in the world show that the role,importance,and coherence of SSH(social sciences and the humanities)will increase significantly in the coming years.This paper aims to monitor and analyze the evolution(or overlapping)of the SSH thematic pattern through three funding instruments since 2007.Design/methodology/approach:The goal of the paper is to check to what extent the EU Framework Program(FP)affects/does not affect research on national level,and to highlight hot topics from a given period with the help of text analysis.Funded project titles and abstracts derived from the EU FP,Slovenian,and Estonian RIS were used.The final analysis and comparisons between different datasets were made based on the 200 most frequent words.After removing punctuation marks,numeric values,articles,prepositions,conjunctions,and auxiliary verbs,4,854 unique words in ETIS,4,421 unique words in the Slovenian Research Information System(SICRIS),and 3,950 unique words in FP were identified.Findings:Across all funding instruments,about a quarter of the top words constitute half of the word occurrences.The text analysis results show that in the majority of cases words do not overlap between FP and nationally funded projects.In some cases,it may be due to using different vocabulary.There is more overlapping between words in the case of Slovenia(SL)and Estonia(EE)and less in the case of Estonia and EU Framework Programmes(FP).At the same time,overlapping words indicate a wider reach(culture,education,social,history,human,innovation,etc.).In nationally funded projects(bottom-up),it was relatively difficult to observe the change in thematic trends over time.More specific results emerged from the comparison of the different programs throughout FP(top-down).Research limitations:Only projects with English titles and abstracts were analyzed.Practical implications:The specifics of SSH have to take into account—the one-to-one meaning of terms/words is not as important as,for example,in the exact sciences.Thus,even in co-word analysis,the final content may go unnoticed.Originality/value:This was the first attempt to monitor the trends of SSH projects using text analysis.The text analysis of the SSH projects of the two new EU Member States used in the study showed that SSH’s thematic coverage is not much affected by the EU Framework Program.Whether this result is field-specific or country-specific should be shown in the following study,which targets SSH projects in the so-called old Member States.

Topic prevalence and trends of metaverse in healthcare:a bibliometric analysis

Metaverse technology is an advanced form of virtual reality and augmented technologies. It merges the digital world with the real world, thus benefitting healthcare services. Medical informatics is promising in the metaverse. Despite the increasing adoption of the metaverse in commercial applications, a considerable research gap remains in the academic domain, which hinders the comprehensive delineation of research prospects for the metaverse in healthcare. This study employs text-mining methods to investigate the prevalence and trends of the metaverse in healthcare;in particular, more than 34,000 academic articles and news reports are analyzed. Subsequently, the topic prevalence, similarity, and correlation are measured using topic-modeling methods. Based on bibliometric analysis, this study proposes a theoretical framework from the perspectives of knowledge, socialization, digitization, and intelligence. This study provides insights into its application in healthcare via an extensive literature review. The key to promoting the metaverse in healthcare is to perform technological upgrades in computer science, telecommunications, healthcare services, and computational biology. Digitization, virtualization, and hyperconnectivity technologies are crucial in advancing healthcare systems. Realizing their full potential necessitates collective support and concerted effort toward the transformation of relevant service providers, the establishment of a digital economy value system, and the reshaping of social governance and health concepts. The results elucidate the current state of research and offer guidance for the advancement of the metaverse in healthcare.

Visualization of Special Features in “The Tale of Genji” by Text Mining and Correspondence Analysis with Clustering

In this paper, visualization of special features in “The Tale of Genji”, which is a typical Japanese classical literature, is studied by text mining the auxiliary verbs and examining the similarity in the sentence style by the correspondence analysis with clustering. The result shows that the text mining error in the number of auxiliary verbs can be as small as 15%. The extracted feature in this study supports the multiple authors of “The Tale of Genji”, which agrees well with the result by Murakami and Imanishi [1]. It is also found that extracted features are robust to the text mining error, which suggests that the classification error is less affected by the text mining error and the possible use of this technique for further statistical study in classical literatures.

Features of Suffering Perceived by Japanese Patients in a Stable Condition: A Text Analysis

Objectives: We performed a text analysis of telephone consultation content regarding features of suffering (thoughts that patients cannot express to nurses) perceived by Japanese patients in a stable condition. Methods: Semi-structured interviews were conducted by 8 telephone counselors who listened to patients’ suffering. Interview content was recorded verbatim, text was organized, and a text and association analysis was conducted (cluster analysis, bubble plot analysis, and a co-occurrence network analysis). Results: Seventy-two conversations were obtained and analyzed. It was confirmed that suffering as perceived by stable, Japanese patients had consistent concerns such as “lack of inference,” “privacy issues,” and “nurses’ not intervening on patients’ behalf.” Additionally, expectations of patients when patients are suffering are extremely diverse and were not characterized by specific tendencies. Conclusions: Emotions have a complicated influence in the context of Japanese patients’ suffering. It is necessary to consider the cultural background of expression in Japan to treat patients’ suffering.

Learning Outcomes Using Cooperative Learning in Communication Classes: Evaluation Using Text Analysis

Objectives: The study examined nursing students’ acquisition of good communication skills via text analysis of learning outcomes using cooperative learning. Methods: The study involved 90 first-year students enrolled in the nursing department of a Japanese university. Participants were asked to learn three learning tasks considered to heighten communicative ability through firsthand experience using the discussion-based technique of cooperative learning: 1) to engage in self-reflection, 2) to imagine something beyond your own experience, and 3) to accept something that does not fit within the scope of your own experience or thought. A questionnaire survey consisted of five items, including learning challenges 1) to 3) as well as 4) “Satisfaction with the exercises” and 5) “Students’ hopes.” These items were evaluated using text analysis. Results: A total of 79 survey questionnaires were collected (87.8% recovery rate) for analysis. “Self-reflection and self-realizations prompted by the communication exercise” was observed as a characteristic of Task 1, “becoming aware of ideas and opinions different than one’s own by listening to the opinions of others” as a characteristic of Task 2, “deepening relationships by learning about diverse ideas and values through interactions with others” as a characteristic of Task 3, and “the effects of communicating with student subjects” as a characteristic of Task 4. The responses to Task 5 were diverse;no common characteristics were found. The intervention was found to be useful for student engagement and the communication required of nurses. Conclusions: Using cooperative learning discussion in communication class was found to be effective. As nursing is an inherently interpersonal occupation, such effects include important elements.

Evolution and prospect of China's rural development policy: A policy text analysis of the No.1 Central Documents

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.

Reference in cohesion： From the perspective of text analysis

This paper attempts to explore how cohesion is realized by meanings of reference in text analysis. Through analyzing some aspects of reference, especially personal reference and demonstrative reference, we know that reference is a text characteristic beyond sentences. It contributes to the development of a text and makes the text more cohesive, communicative and accurate. Of course, reference in cohesion is not used separately, it is closely related with other aspects of text analysis, and they cooperate and restrain each other and perform functions together. So translators are required to have the competence of understanding and applying reference, ellipsis and other cohesive device from the viewpoint of texts level with the combination of other aspects in text analysis, then texts can be more cohesive, coherent and acceptable.

Inheritance and Development of Three Pre-Qin Classics of Confucianism——An Application of Topic Modeling in Classical Chinese Text Analysis

The Analects, Mengzi and Xunzi are the top-three classical works of pre-Qin Confucianism, which epitomized thoughts and ideas of Confucius, Mencius and XunKuang1. There have been lots of spirited and in-depth discussions on their ideological inheritance and development from all kinds of academics. This paper tries to cast a new light on these discussions through “machine reading2”.

A Linguistic and Stylistic Analysis of the Chinese Translation of Obama's Speech at the First Meeting of the Strategic and Economic Dialogue between the United States and China——Through the Lens of Reiss' s Text Type Theory

According to Reiss’s Text Type theory,a key part of the functionalist approach in translation studies,the source text can be assigned to a text type and to a genre.In making this assignment,the translator can decide on the hierarchy of postulates which has to be observed during target-text production(Mona,2005).This essay intends to conduct a linguistic and stylistic analysis of the Chinese translation of Obama’s speech to explore the general approach of the translator(if there is one),by comparing the respective results of the two analyses from the perspective of Katharina Reiss’s Text Type theory.In doing so,critical judgments will accordingly be made as to whether such an approach is justifiable or not.