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Objectives: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field., Methods: We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019., Results: Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation., Conclusions: The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart.)

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines., Competing Interests: ZB, TG No competing interests declared, AC runs the cell bank in Australia and heads the ICLAC consortium. MM, AB heads the RRID project, and founded SciCrunch, a company that supports the RRID project. AB develops the Cellosaurus database. IO works as a consultant for SciCrunch., (© 2019, Babic et al.)

In the era of informatization and digitization, the rapid increase in the number of scientific papers has given rise to various challenges, such as lengthy articles, difficulty in information extraction and high time costs associated with reading. Literature reading challenges for researchers are increasingly tedious and time-consuming. By utilizing the language models, the assited reading system of scientific papers has been designed to address these challenges. By adopting machine reading comprehension technology as the core, the system parses scientific texts and offers some common questions to achieve automated response capabilities. By fully utilizing the pre-trained language model PERT, the system enhances its capabilities in semantic understanding and information extraction, effectively resolving various challenges in reading scientific papers and helping readers improve the efficiency of scientific literature review. [ABSTRACT FROM AUTHOR]

PurposeIdentifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.Design/methodology/approachThis study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.FindingsResearch showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.Originality/valueThis study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines.

During graduate education, postgraduates have to spend considerable time finding papers to explore the development branches of their field. However, existing paper recommendation methods focus on several attributes (title, author, keyword, venue, etc.). The network schema constructed by these attributes is extremely sparse, which easily causes the loss of important semantic paths between attributes. This results in a lack of correlations among relevant papers, which affects paper recommendation efficiency. Moreover, the relationships between multiple semantic paths can be found through common homogeneous and heterogeneous attributes. These relationships can establish many correlations among relevant papers. To address the above problems, this paper proposes a new approach to fuse multi-semantic paths into a heterogeneous educational network (HEN) for personalized paper recommendation. After data processing, a new HEN schema is built by enriching nodes and edges in heterogeneous networks. Then, different semantic meta-paths are generated by projection sub-nets. Next, a new HEN embedding method is proposed by multi-semantic path fusion to generate rich HEN node sequences. Finally, personalized paper recommendation for postgraduates by targeted path similarity. The proposed method was performed on two paper datasets in the fields of educational intergenerational mobility from 1987 to 2021 and data mining and intelligent media from 1997 to 2021. Substantial experiments demonstrate that the proposed approach is effective. [ABSTRACT FROM AUTHOR]

Keyword extraction is the task of identifying important terms or phrase that are most representative of the source document. Although the process of automatic extraction of keywords from title is an old method, it was mainly for extraction from a single web document. Our approach differs from previous research works on keyword extraction in several aspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purpose of this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturing research trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools. More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how far significant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientific article corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract the results. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-experts of that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be a solution to reduce the labour-intensive manual indexing process. [ABSTRACT FROM AUTHOR]

In this era, research and development are becoming a continuous and accelerating process because technology changes rapidly with a short lifecycle. As a result, various methodologies are being developed to monitor these rapidly changing research trends; In particular, clustering method-related studies in science and technology documents are being developed with a variety of approaches. However, previous studies on document clustering methods focus on a specific field or language but do not take into consideration certain important pieces of information in science and technology documents. Therefore, this study proposes an embedding methodology that uses important content from scientific and technical documents. We took into consideration the importance of information containing core structures in science and technology documents and proposed a clustering methodology that analyzes structured and unstructured data, such as textual information, author information, and citation information. The proposed method combines both textual and structural data from the paper, using a method that focuses on screening important information by sections in science and technology documents. Then, Girvan-Newman clustering and Louvain clustering models are applied to generate embedding vectors and show evaluation results through the clustering indices. As a practical example, we applied the proposed methodology using paper data from the field of hydrogen cell vehicles. The results of this study will be effective in identifying gaps in technology for new technological development, identifying technology trends, and presenting directional information for future technology development.

Background: In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks. Methods: We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1). Results: In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s. Conclusion: In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline. [ABSTRACT FROM AUTHOR]

In education quality assurance, the accuracy of test data is crucial. However, there is still a problem regarding to the possibility of incorrect data filled by test taker during paper-based test. On the contrary, this problem does not appear in computer-based test. In this study, a method was proposed in order to analyze the accuracy of answer sheet filling out in paper-based test using data mining technique. A single layer of data comprehension was added within the method instead of raw data. The results of the study were a web-based program for data pre-processing and decision tree models. There were 374 instances which were analyzed. The accuracy of answer sheet filling out attained 95.19% while the accuracy of classification varied from 99.47% to 100% depend on evaluation method chosen. This study could motivate the administrators for test improvement since it preferred computer-based test to paper-based.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Keeping up with the rapidly growing literature has become virtually impossible for most scientists. This can have dire consequences. First, we may waste research time and resources on reinventing the wheel simply because we can no longer maintain a reliable grasp on the published literature. Second, and perhaps more detrimental, judicious (or serendipitous) combination of knowledge from different scientific disciplines, which would require following disparate and distinct research literatures, is rapidly becoming impossible for even the most ardent readers of research publications. Text mining - the automated extraction of information from (electronically) published sources - could potentially fulfil an important role - but only if we know how to harness its strengths and overcome its weaknesses. As we do not expect that the rate at which scientific results are published will decrease, text mining tools are now becoming essential in order to cope with, and derive maximum benefit from, this information explosion. In genomics, this is particularly pressing as more and more rare disease-causing variants are found and need to be understood. Not being conversant with this technology may put scientists and biomedical regulators at a severe disadvantage. In this review, we introduce the basic concepts underlying modern text mining and its applications in genomics and systems biology. We hope that this review will serve three purposes: (i) to provide a timely and useful overview of the current status of this field, including a survey of present challenges; (ii) to enable researchers to decide how and when to apply text mining tools in their own research; and (iii) to highlight how the research communities in genomics and systems biology can help to make text mining from biomedical abstracts and texts more straightforward.

Background: The accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself resides beyond titles or abstracts. For emerging science, with a limited set of known papers of interest and an incomplete information model, this is of pressing concern. A timely example in retrospect is the identification of immune signatures (coherent sets of biomarkers) driving differential SARS-CoV-2 infection outcomes. Implementation: We built a classifier to identify papers containing domain-specific information from the document embeddings of the title and abstract. To train this classifier with limited data, we developed an iterative process leveraging pre-trained SPECTER document embeddings, SVM classifiers and web-enabled expert review to iteratively augment the training set. This training set was then used to create a classifier to identify papers containing domain-specific information. Finally, information was extracted from these papers through a semi-automated system that directly solicited the paper authors to respond via a web-based form. Results: We demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. The type of immune signature (e.g., gene expression vs. other types of profiling) was also identified with a positive predictive value of 74%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. Conclusions: Our results demonstrate the efficacy of using a SVM classifier with document embeddings of the title and abstract, to retrieve papers with domain-specific information, even when that information is rarely present in the abstract. Targeted author engagement based on classifier predictions offers a promising pathway to build a semi-structured representation of such information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems. [ABSTRACT FROM AUTHOR]

In the digital era like today the growth of data in the database is very rapid, all things related to technology have a large contribution to data growth as well as social media, financial technology and scientific data. Therefore, topics such as big data and data mining are topics that are often discussed. Data mining is a method of extracting information through from big data to produce an information pattern or data anomaly

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work. [ABSTRACT FROM AUTHOR]

Life Cycle Assessment (LCA) has been used to evaluate the life-cycle Greenhouse Gas (GHG) emissions of pulp and paper production, and most previous studies rely on process-based models for specific product types (e.g., printing paper), industry-average data, or information from a few mills. In this work, a data-mining approach is used to quantify GHG emissions intensities of different paper products manufactured by the U.S. mills. Facility-level emission data collected from publically available governmental databases and mill-level production data collected from the private sector were integrated to track the GHG emissions for different product lines and paper products in mills (in total, 165 mills were matched and analyzed). The results highlight the ranges of GHG emissions intensities by different product groups and categories, and can be used as a transparent data source for LCA practitioners, policymakers, and the pulp and paper industry to perform further analysis on carbon accounting and strategic planning for GHG mitigation. [ABSTRACT FROM AUTHOR]

This paper introduces CORE, a widely used scholarly service, which provides access to the world's largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE's continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services. [ABSTRACT FROM AUTHOR]

Abstract: There is a large value in making Pulp and Paper mills more energy efficient. ABB has developed an energy assessment service where opportunities to save energy in the paper machine are identified. The energy assessment is done by quantifying energy flows, benchmarking energy users, data mining and steam sensitivity analysis and by experiments and additional measurements at the paper machine. Energy quantification helped in identifying main energy consumer, benchmarking was useful to assess the gap between operating performance and best performance whereas data mining and steam sensitivity analysis helped in studying the impact of key operating variables on performance of paper machines. After the assessment an action plan was presented to the mill for energy efficiency improvement together with a return on investment. [Copyright &y& Elsevier]

Representing a strong instrument of control and feedback used by top management executives, regulators institutions or independent bodies, the audit, its methods and techniques incite the interest of specialists, professionals, professors and researchers from all socio-economic activities. The way domain experts write about audit itself is often reflected in the manner in which they choose the keywords for the title and for the article. This study is a detailed analysis of assignment to the specific thematic areas of articles published in "Financial Audit" journal, for all public appearances in electronic format from the period 2003-2015. The study is different from other similar researches by the methodology and the type of information extracted addressed. The main purpose is to identify the most used keywords in the title and content of articles published over time and insight traceability to future research directions. The conclusions of the analysis from this article give a comprehensive picture of audit multidisciplinary, thus providing researchers, on several economic fields, an image about the content of the publication, quality information for readers, authors and future authors.

Waste paper recycling is an important way to realize the environmental protection development of the papermaking industry. The quality of the pulp will affect the pulp sales of the secondary fiber paper mills. The waste paper pulp can be adjusted by controlling the pulping process working conditions, but the working conditions of the waste paper pulping process have too many parameters. And the parameters are coupled with each other, it is difficult to control. In order to find the best working conditions and improve the quality of the pulp, this study uses the association rules algorithm to optimize the parameters for the waste paper pulping process. These parameters are power of refiner, waste paper concentration of refiner, the volume of slurry that enters deinked process, deinking agent amount, deinking time, deinking temperature, bleaching agent amount, bleaching time, and bleaching temperature. The test results show that the qualified rate of the pulp produced under the improved working conditions is 92.56%, an increase of 6.93%, and the average electricity consumption per ton of pulp is reduced by 5.76 kWh/t. In addition to potential economic benefits, this method can reduce carbon emissions. [ABSTRACT FROM AUTHOR]

This document is an editorial note introducing a special issue of the journal Electronics on the topic of "Trends and Applications in Information Systems and Technologies." The note highlights the importance of information technologies in reshaping business models and improving operational efficiency. It also discusses the challenges associated with information systems, such as cybersecurity issues and data-centric decision-making processes. The special issue includes fourteen selected papers covering topics such as the adoption of blockchain technology, digital business solutions, ICT penetration, processing and information extraction in information systems, and innovative applications of information systems. The note concludes by expressing the hope that readers will find the special issue informative and inspiring for further research. [Extracted from the article]

Citations are references used by researchers to recognize the contributions of researchers in their articles. Citations can be used to discover hidden patterns in the research domain, and can also be used to perform various analyses in data mining. Citation analysis is a quantitative method to identify knowledge dissemination and influence papers in any research area. Citation analysis involves multiple techniques. One of the most commonly used techniques is Main Path Analysis (MPA). According to the specific use of MPA, it has evolved into various variants. Currently, MPA is carried out in different domains, but deep learning in the field of remote sensing has not yet been considered. In this paper, we have used three centrality attributes which are Degree, Betweenness and Closeness centrality to automatically identify important papers by applying clustering method based on machine learning (i.e., K-means). In addition, the main path is drawn from important papers and compared with existing manual methods. In order to conduct experiments, a data set from Web of Science (WOS) has been established, which contains 538 papers in the field of deep learning. Compared with existing works, our method provides the most relevant papers on the main path. [ABSTRACT FROM AUTHOR]

Continual development necessitates innovation. One must discover seeds of innovation and then concentrate resources on these seeds. To do so, one must recognize technology trends and then adopt and execute appropriate innovation strategies. This study used advanced change point detection method to investigate leading indicators that represent changes in technology trends. We examine patents, papers, and newspaper articles in Japan and US for 55 technologies. Results suggest that patents can be more appropriate as leading indicators than either papers or newspapers. This result can contribute to appropriate innovation strategies for planning and updating, and can provide tools that are useful to decision-makers. [ABSTRACT FROM AUTHOR]

Copyright of Acta Infologica is the property of Acta Infologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract  In this paper a model-driven decision support system related to paper making is introduced. The intention is to emphasize the necessity of coupling different modeling techniques, multiobjective optimization, and software engineering in order to make the end user application realistic, practical and usable. Firstly the paper making process and selected aspects concerning its mathematical modeling, numerical simulation, and multiobjective optimization are introduced, then the related computerized system, called a virtual paper making line, is described. In addition, the associated decision support system, which provides a suitable level of automation to improve the quality of decision making and support the user’s expertise is discussed. Finally, an example presents different ways of using such a software tool. [ABSTRACT FROM AUTHOR]

Representing a strong instrument of control and feedback used by top management executives, regulators institutions or independent bodies, the audit, its methods and techniques incite the interest of specialists, professionals, professors and researchers from all socio-economic activities. The way domain experts write about audit itself is often reflected in the manner in which they choose the keywords for the title and for the article. This study is a detailed analysis of assignment to the specific thematic areas of articles published in "Financial Audit" journal, for all public appearances in electronic format from the period 2003-2015. The study is different from other similar researches by the methodology and the type of information extracted addressed. The main purpose is to identify the most used keywords in the title and content of articles published over time and insight traceability to future research directions. The conclusions of the analysis from this article give a comprehensive picture of audit multidisciplinary, thus providing researchers, on several economic fields, an image about the content of the publication, quality information for readers, authors and future authors. [ABSTRACT FROM AUTHOR]

This paper summarizes librarian research on information visualization as well as general trends in the broader field, highlighting the most recent trends, important journals, and which subject disciplines are most involved with information visualization. By comparing librarian research to the broader field, the paper identifies opportunities for libraries to improve their information visualization support services. [ABSTRACT FROM AUTHOR]

The optimal predictive rule is the conditional response function Graph HT ht . Start with a set of I n i training samples, and divide them into I K i evenly sized and non-overlapping folds Graph HT ht . We have access to a stream independent and identically distributed samples Graph HT ht with Graph HT ht and Graph HT ht . [Extracted from the article]

Nowadays, tourism management is a universal concern in the world. It is important for generating tourism characteristics for travelers, so as to digitally facilitate tourism business scheduling. Currently, there is still a lack of technologies that are competent in managing tourism business affairs. Therefore, in this paper a smart tourism management system is designed through multisource data visualization-based knowledge discovery. Firstly, this work presents the total architecture of a tourism management system with respect to three modules: data collection, data visualization, and knowledge discovery. Then, multisource business data are processed with the use of visualization techniques so as to output statistical analysis results for different individuals. On this basis, characterized knowledge can be found from previous visualization results and demonstrated for travelers or administrators. In addition, a case study on real data is conducted to test running performance of the proposed tourism management system. The main body of public service tourism is the government or other social organizations that do not regard profit as the main purpose; public service tourism a general term for products and services with obvious public nature. The testing results show that user preferences can be mined and corresponding travelling plans can be suggested via multisource data visualization-based knowledge discovery means. [ABSTRACT FROM AUTHOR]

The traditional teaching model cannot adapt to the teaching needs of the era of smart teaching. Based on this, this paper combines data mining technology to carry out teaching reforms, constructs a computer-aided system based on data mining, and constructs teaching system functions based on actual conditions. The constructed system can carry out multisubject teaching. Moreover, this paper uses a data mining system to mine teaching resources and uses spectral clustering methods to integrate multiple teaching resources to improve the practicability of data mining algorithms. In addition, this paper combines digital technology to deal with teaching resources. Finally, after building the system, this paper designs experiments to verify the performance of the system. From the research results, it can be seen that the system constructed in this paper has certain teaching and practical effects, and it can be applied to a larger teaching scope in subsequent research. [ABSTRACT FROM AUTHOR]

In Today's era the word Big Data has a very useful meaning in every aspects. The meaning of Big data is to use and analysis of the data that is big in size. Now-a-days Big Data[1] is becoming very popular and there is so many research and analysis going on in this field. Researcher uses the big data to analysis the practical data and to find out its use and application based on the analysis. Data analysis of the given data is done through various modes that is known as an unstructured data as we can easily find out now a days on the Google in the form of posts that has to be taken from social media sites[2] and images. Our research paper will give a an idea about Big Data and its application in daily life. We have also includes the challenges that has been faced by every data science engineer while analysis of the data. Our paper also talks about the Hadoop and gives a basic understanding of Hadoop. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of nonopen access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, "to what extent metadata-based features can perform in contrast to content-based approaches." In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-of-the-art techniques in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a data-based mining and hesitant fuzzy C-canopy-K mean clustering degree algorithm and uses it in the parameter assessment model of teaching quality indicators. Simulation and training are carried out through data class mining, and information input, followed by combining the hesitant fuzzy K-mean classification assessment method, which involves a hesitant fuzzy type evaluation system, a neural network identification and prediction system, and an application system for module identity verification. The simulation results show that the results of the six simulation conditions are consistent with the actual results, with only slight differences in some amplitudes, and a high degree of consistency in the overall trend, the change rule, and the average peak value. Through the prediction model processing in this paper, the teaching quality index parameter assessment has high accuracy and can reach more than 95.0%, in addition, the development of the law also fits very well. a, b, c, d four kinds of teaching quality parameter assessment of the average calculation of the assessment speed increased by 52.5%. In addition, the assessment test after the integrated design of the module shows that the system can effectively identify the four clustering identification processes that can be seen as excellent, good, medium, and poor; at the same time, the test data show that the system class effectively for teaching quality indicator parameter assessment. [ABSTRACT FROM AUTHOR]

This paper comprehends the global research scenario about incorporating cultural components in contemporary urban planning practice. Various urban cultural elements have been analysed under the umbrella of 'Urban Cultural Dynamics' (UCD). This study aims to identify hot spots and explore influential sources through a bibliometric investigation of the evolutionary patterns of cultural dynamics in urban planning research. Data was collected from the Web of Science core collection (1965-2021). The data mining tools VOSviewer, Biblioshiny, KNIME, and WordStat were used to analyse hot topics, keywords, influential journals, core papers, top authors, and cooperation networks of countries and institutions. The Analytical Hierarchy Process (AHP) is employed in journal analysis. The analysis showed that publications on UCD increased exponentially between 2016 and 2022. Italy (32), China (26), and Spain (18) are the top countries in total publications. Technical universities of Dresden and Munich have the most significant influence on UCD's research, with 103 and 101 total citations, respectively. Landscape and Urban Planning, Journal of Cultural Heritage, and Tourism Geographies are the top three consistently influential journals. The top ten core papers were identified. The keywords and topic analysis charted out the evolution of multidisciplinary UCD research topics. The hotspot analysis revealed the significance of cultural dynamics in sustainable urban development. The results highlighted the need for intercontinental cooperation between developed countries and the third world. The future scope of research on UCD topics is discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields. [ABSTRACT FROM AUTHOR]

Background: Named Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts. In a traditional concept-recognition pipeline, these tasks are combined in a serial way, which is inherently prone to error propagation from NER to NEN. We propose a parallel architecture, where both NER and NEN are modeled as a sequence-labeling task, operating directly on the source text. We examine different harmonisation strategies for merging the predictions of the two classifiers into a single output sequence., Results: We test our approach on the recent Version 4 of the CRAFT corpus. In all 20 annotation sets of the concept-annotation task, our system outperforms the pipeline system reported as a baseline in the CRAFT shared task, a competition of the BioNLP Open Shared Tasks 2019. We further refine the systems from the shared task by optimising the harmonisation strategy separately for each annotation set., Conclusions: Our analysis shows that the strengths of the two classifiers can be combined in a fruitful way. However, prediction harmonisation requires individual calibration on a development set for each annotation set. This allows achieving a good trade-off between established knowledge (training set) and novel information (unseen concepts)., (© 2022. The Author(s).)

Text deals with data mining used in libraries. It focuses on „physical“ libraries as they can use data mining in many processes but chooses its scope opposite to digital libraries where data mining has different variety in usage. This is why papers concerning digital libraries are excluded from this summarizing study. All the papers were selected based on concern with Library and Information Science and were divided into several groups. Most of the studies come from academic environment. Papers from czech and slovak environment are described separately. Time limitation for inclusion of the papers is up to year 2015. [ABSTRACT FROM AUTHOR]