Computational Scientometrics: Theory and applications

--Heart disease is one of the notable causes of death in worldwide everyday divination of heart disease is a critical challenge in area of medical data analysis. Machine learning (ML) has been shown to be successful in helping in making decision and prediction from huge quantity of data produced by medical health care industry. Medical data mining is an important area of data mining and considered as one of the important research field due to the application in medical health care domain. In medical data mining classification and prediction of medical dataset process challenges. It is difficult to medical practice to predict the heart attack as it complex task. The health sector at present contain the information that are hidden and which are important in making decision. Data mining algorithms such as Naive Bayes algorithm, decision tree algorithm and random forest are applied in the research for heart disease prediction the result show the comparison between the three algorithm and selecting the best one among three, the Random forest algorithm will provide the more accuracy can compared to all the three algorithms.

BackgroundThe Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications.ObjectiveDespite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years.MethodsPubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.ResultsA total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%).ConclusionsThe UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

Background and Aim: The COVID-19 pandemic has generated considerable knowledge in this regard in reputable international citation databases The purpose of this study is to analyze subjects clustering analysis and science mapping on published research to identify the current state of sciences in the field of COVID-19 in Scopus Methods: This was a quantitative and applied study conducted by scientometric methods including words cooccurrence and network analysis The study population was all published documents in the field of COVID-19 and SARS-CoV-2 in the Scopus citation database by 31 May, 2020 VOSviewer software was used to analyze the data Results: As of May 31, 2020, the United States (2,819), China (2,342), Italy (1,466), and the United Kingdom (1,264) had the highest number of scientific publications in the field of COVID-19 in Scopus Iran was ranked 12th with 318 scientific publications The three main clusters of research activities in the field of COVID-19 were health, basic science and clinical research clusters Conclusion: With the existing scientific capacities, the role of Iran and its medical universities in publishing scientific experiences related to the COVID-19 pandemic can be enhanced Accurate and comprehensive planning must be done in this regard Determining research needs and developing applied research in three main areas, including health and prevention, basic and clinical sciences, should be considered in the management and leadership of COVID-19 research © 2020 Baqiyatallah University of Medical Sciences All rights reserved

Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.

In recent years, discriminative learning methods have widely been used in various areas of Natural Language Processing (NLP). These methods achieve the best performance, when the set of training and testing samples have the same distribution. However, in many applications of NLP, the lack of labeled datasets for some domains is a serious challenge. In such conditions, we need to develop a model based on domains with rich labeled instances and apply it to the domain with no labeled instances. In this research, a method for sentiment classification of opinions into positive and negative groups, which represent the users' feelings, is offered based on multi-source transfer learning. The proposed method here employs Spectral Feature Alignment algorithm to adapt different domains. Furthermore, according to the Majority Voting, accuracy is assigned to classifications trained on different domains based on the Majority Voting Error. Ultimately, decisions are made for each classification based on the calculated error. The Amazon datasets for four different categories, each of which contains 1000 positive and 1000 negative samples, are exploited to train the proposed model. Meanwhile, each category includes unlabeled samples that are used to select pivot features. The accuracy values of 85.5%, 86.4%, 83.5% and 90.1% obtained for Electronics, DVD, Books and Kitchen domains respectively, show the effectiveness of the proposed method compared with similar methods.

The sand quarry activities in Cikalahang Village, District of Dukuhpuntang, Cirebon Regency had been illegally mined by hundreds of people. The land properties are owned by the Cikalahang Village. Those mine areas are close to the Telaga Remis (Remis Lake) , which is administratively included in the Kuningan Regency. The existence of illegal mining has disturbed both of local government and people community. In the regional government point of view, the illegal mining will not significantly contribute to generate regional income, whilst for many people their mining activities will produce a destruction to the environment. These can be seen from the objection of the people that request the government to stop all of mine activities because of disturbance to environmental condition of the Telaga Remis (Remis Lake) as one of the water resources for the Cirebon Regency. Based on the intensity of the people requirements, the government of Cirebon Regency had decided to close all of activities of the illegal mining in those area in 2006. According to the decision of Cirebon Regent and the Office of Environment, Forestry and Mining Cirebon Regency, it had prepared planning for rehabilitation and restoration to recover the natural and environmen- tal capability in the mined out areas with respect to the regional spatial planning, land utilization and community demand. The intention of study is to analyse the currently condition of environment in those area including people perception and then making further evaluation to obtain the properly environmental pic- tures for determining the real and strategic action. Data resources to support the study were collected through primary and secondary data. The primary data of physical-chemical component can be obtained by taking instantaneous samples of soil and surface water in the area of study. To identify the people perception, some questioners have been spreaded out to the various strata of people in order to obtain their perception and also direct interview will be applied in this study. The secondary data required for this study such as regional spatial planning, regional landuse and regional regulations will be taken from some institutions in Cirebon Regency. Result analyses showed that the soil characteristics in mined out area were poor because of lack of nutrient and high porousity and the water of Telaga Remis and mine out area could be used as fishing cultivation and household water. The final study shows that the mined out area can potentially be developed as tourism resort in accordance with people request. The other consideration this area is adjacent to Telaga Remis with the background of the beautiful panorama of Mount Ciremai.

In the article “Le competenze del bibliotecario digitale“ by Anna Maria Tammaro, the Seminar “The digital librarian competencies: DILL ten years after”, within the Conference of Stelline 2016 is described; it aimed to stimulate a reflection on the education of the digital librarian from the experience of the international Master Digital Library Learning DILL. On the occasion of the Seminar, a survey of the international community of DILL digital librarians (students, alumni, employers, and even foreign experts and teachers involved in the course), has been done to investigate the digital librarian competencies, from those identified by DILL in three areas: technology skills, research skills, librarianship skills. The paper describes the international debate, the educational objectives of DILL (competencies in the Bologna Process terminology) and analyze the survey results on the present and future digital librarian competencies.

The online availability of scientific-literature databases and natural-language-processing (NLP) algorithms has enabled large-scale bibliometric studies within the field of scientometrics. Using NLP techniques and Thomson ISI reports, an initial analysis of the role of Alzheimer's disease (AD) within the neurosciences as well as a summary of the various research foci within the AD scientific community are presented. Citation analyses and productivity filters are applied to post-1984, AD-specific subsets of the PubMed and Thomson ISI Web-of-Science literature bases to algorithmically identify a pool of the top AD researchers. From the initial pool of AD investigators, top-100 rankings are compiled to assess productivity and impact. One of the impact and productivity metrics employed is an AD-specific H-index. Within the AD-specific H-index ranking, there are many cases of multiple AD investigators with similar or identical H-indices. In order to facilitate differentiation among investigators with equal or near-equal H indices, two derivatives of the H-index are proposed: the Second-Tier H-index and the Scientific Following H-index. Winners of two prestigious AD-research awards are highlighted, membership to the Institute of Medicine of the US National Academy of Sciences is acknowledged, and an analysis of highly-productive, high-impact, AD-research collaborations is presented.

Ubiquitous access to internet has resulted in more and more people going online to get their daily dose of news. In a 2010 survey conducted by the Pew Project for Excellence in Journalism, 41% of the respondents said they get most of their news online, 10% more than those who said they got most of their news from a newspaper. A lot of socio-technical factors have contributed to this phenomenal rise in adoption of online news in recent years. One of the biggest reasons why people are increasingly reading news online is because it facilitates discussion with peers (Nguyen 2010), offering different viewpoints which aid in forming a rounded personal opinion about the news story. The Pew survey found that 37% of online news users (and 51% of 18-29 year olds) think that commenting on news stories is an important feature to have. A lot of people tend to shape their opinion by reading discussion comments, reflective articles, blogs and even tweets about the news. Hence, an increasing number of people rely on online sources of news – be it news websites or news aggregator services like Digg, Reddit, Google Reader, Flipboard, Pulse etc. The problem with these news websites and aggregators is that the only way people can gather public opinion is by actively searching through the endless stream of comments and feeds, filtering out spam (which is a growing problem) and then reading the relevant posts. A top trending story on Twitter will typically see multiple tweets per second, and keeping up with the rapid flow of incoming tweets is quite cumbersome and cognitively taxing. Hence it becomes increasingly difficult and time consuming for someone who wants to get the pulse of the people affected by a news story. Furthermore, in certain scenarios people might want to look at more fine grained opinions. Currently, there is no elegant way to extract geographic and demographic impact of a news story. What is the public sentiment in Indonesia about the Arab Spring? How did the public opinion about the Wikileaks disclosures change as the story unfolded during the course of a year? It is very difficult and tedious to observe such patterns using the currently available news providers. This work attempts to solve these problems by proposing a news aggregator platform which pulls news stories from various sources and also aggregates public responses, reflections, opinions and sentiments associated with those stories. This data is presented in ways that are easily understandable so readers can make better sense of the stories unfolding across the globe. Such a news aggregator platform that gathers and display public opinion and sentiments about a story, must deal with various challenges – 1. Opinions are very subjective. Different people feel about a story in different ways. With such an enormous amount of diverse opinions and subjectivity, how can we possibly aggregate the responses into something that makes sense as a whole? ________________________________ Acknowledgements: Prof. Yardi, S. for the guidance Sethi, P. (2013). Public opinion aggregation by annotation and tagging of online news stories. iConference 2013 Proceedings (pp. 891-894). doi:10.9776/13453 Copyright is held by the author. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 892 2. There isn’t really a unified web standard for expressing opinion (in textual form). Some people tweet in 140 characters, while others write elaborate blog posts. Some websites employ tags which a reader can use to define and classify their public opinion, while others rely on threaded comments and comment ranking systems. How can a platform be flexible enough to adapt to all these varied standards so that it can extract valuable data from various sources? Perhaps the platform can create a new standard of expression on the web which is flexible and comprehensive enough to be used to express diverse views about every news story in the world. 3. How to filter out spam while extracting public opinion? 4. Once the platform has access to the data it needs, how should it be displayed to the reader in a way that makes sense? What forms of visualizations, illustrations and graphical representations can be employed to give the reader a holistic view of how people feel about a story? 5. How can the platform determine and convey effects of geographical, demographic and temporal variations as the story unfolds? These are just a few out of possibly many issues which must be dealt with. Previous research on similar public opinion aggregation services has greatly focused on natural language processing, data mining and text categorization and clustering. Xiaojun (2010) proposed a framework for crawling the web for comments and applying various data mining algorithms on the data to extract relevant information. Diakopoulos and Shamma (2010) used tweets posted in conjunction with the live presidential debate between Barack Obama and John McCain to gauge public opinion. Brody and Diakopoulos (2011) studied the use of word lengthening to detect sentiment in microblogs. This research proposes a solution – The Opinionated Reader, which relies on sentiment tags and annotations associated with a news story. The essential idea is to create a commenting, discussion and sharing plug-in which can be used by news websites and aggregators as a commenting solution for their news pages. Users wanting to share or comment on a news story through the plug-in are asked to tag the news story with sentiment tags and annotate the story with their reaction (happy/positive or sad/negative). These tags and annotations are stored, aggregated and linked to each news story. A mobile application provides the front-end interface for users to access the news stories and the aggregated sentiment associated with each story. The basic architecture is explained as follows: The Opinionated Reader – Mobile/Tablet App The app fetches news articles from various web sources, based on the interests and preferences configured by the user. In every news article, a portion of the screen real estate is reserved for Opinions which shows graphical visualizations and illustrations of the public opinion surrounding the news story. These visualizations include: A Sentiment Graph indicating the popular tags associated with the story (E.g.: “Shocking”, “Inspiring”, “Amusing” etc.). See Figure 1 for example visualization. A Positivity Graph which plots the level of positivity associated with the story on a time scale from when the news broke. See Figure 2 for example visualization. Figure 1. Example Sentiment Graph Figure 2. Example Positivity Graph iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 893 The user can choose to see these visualizations for a particular time period in the evolution history of the news story, or for a specific country. The app also facilitates people to tag and annotate news articles from within its interface. The Opinionated Reader – Commenting and Sharing Web Plug-in These days, a common way of adding discussion and commenting functionality to news websites is by using 3 rd party services (like DISQUS). The Opinionated Reader is a similar service which can be embedded into the news articles of various news websites to enable commenting and sharing. When someone wishes to comment on an article, the comment is directed through this plug-in, which allows the users to annotate the article with the sentiment tags and reactions along with their comments. The Opinionated Reader saves this information along with the commenter’s location and date of comment (See Figure 3). Figure 3. The Opinionated Reader Web Plug-in 'Add Comment' dialog mockup The Opinionated Reader – Back-end The Back-end maintains a database of news items extracted from RSS feeds of various news websites. Each news article is linked with the sentiment tags and reaction/positivity annotations extracted from the comments and annotations gathered by the commenting plug-in. This data is used by the mobile/tablet app to generate visualizations (Sentiment Graph and Positivity Graph). The back-end also performs data mining on the tags and annotations for geographies and tracks the opinions across time. The back end system responds to queries received from the mobile app with the news story and associated tags and annotations, which are then rendered by the mobile app for the user. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 894 Discussion and Conclusion This design idea is still in a nascent state and has long hours of research, brainstorming, designing and development to go before it can be realized into something tangible. Twitter has grown exponentially in importance as a news source and it would be vastly valuable to integrate Twitter with The Opinionated Reader. Possibilities include use of special hash tags and natural language processing of tweets to extract public sentiment. The current design supports only two reaction annotations – positive and negative. Not every news story fits this annotation paradigm. Further research about human reactions to news stories might unveil interesting insights which would help zero in on a more robust annotation rubric. Lastly, since this service is envisioned to be non-curated and non-moderated, the value served by the app depends on the users themselves. Greater adoption will lead to more annotations and tags, which translates into a more accurate public opinion as presented to the user.

Impact of library holdings in terms of physical access of doctoral studies in six general universities of Karnataka (Bangalore University, University of Mysore, Mangalore University, Karnatak University, Gulbarga University and Kuvempu University) have been studied based on citation appended to the PhD dissertations in engineering and technology. It’s evident that some materials in libraries become outdated as time progresses. This can be referred to as ‘obsolescence’. Obsolescence studies are one of the foremost areas of citations, bibliometrics, scientometrics, and infometrics. Citation analysis has usually been generally study the obsolescence of materials, the existence of a core of heavily used sources, and so the sort of the foremost used materials. The problem most often studied in this fashion is that the obsolescence rate of the literature in many subjects of engineering. The study was done to grasp regarding the obsolescence of engineering literature cited in 137 doctoral dissertations of engineering and technology awarded during 1961 to 2008. The study reveals that, the overall of 7467 citations of periodical articles and 2014 book citations are scattered primarily among fifteen subjects. However, citations were derived from early 1990 to after 2001, 86 % of books cited by engineers and technologists in their PhD theses were of 9-39 years recent associated an outsized share of the journal citations (68.58 %) utilised by the researchers was published 20 years back or weres older than the recent ones. It together shows that extra journals literature cited during 1971-2000 equally in books literature cited at intervals of 1961-1990.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

The ever-increasing number of fielded Data Mining applications is evidence that the technology works and produces added value in a variety of business areas. Most of the research-lab generated algo...

This study aimed to determine the capability of top soil fertility which will be cuted from mining area and will be used as main material in land reclamation. This research conducted in term that there will be mining activity in Tanjung Baru, Bakong Village, Singkep Barat District, Lingga Regency of Riau Province, with total area 106.62 ha. The mining activity will cut the top soil 5,608,643 m3 or equal to 6,730,372 ton of soil. The cutted soil will used to be main material of land reclamation and become a substrat for revegetation plants. The result showed that the capacity of study field soil fertility was categorized as very low to low. In order to preserve the soil condition, it must be properly stored. The recommendation from this research is, it needs to re-analysis of soil condition before used to revegetation in order to test the land fertility status, and as the basic for determine the fertilizer dose that can be applied.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented. RECENT ADVANCES in natural language processing [NLP] have generated considerable interest within the Artificial Intelligence [AI] and Cognitive Science communities. Within NLP, researchers are trying to produce intelligent computer systems that can read, understand, and respond to various human-oriented texts. Terrorism stories, airline flight schedules, and how to fill ice cube trays are all domains that have been used for NLP programs. In order to understand these texts and others, some way of representing information is needed. A complete understanding of human-oriented prose requires the ability to combine the meanings of many readings in an intelligent manner. Learning through the process of generalization is one such mechanism. The integration of representation and generalization in the domain of NLP is the subject of this article. Physical object understanding is an area in which a variety of representation schemes and generalization methods have been used. In past years, researchers have devised various representation systems for objects that range from very simple

Twitter has provided a convenient platform to express feelings and opinions in different areas. Opinion mining in Twitter can be considered as studying the overall sentiment of a tweet. There are two general categories of sentiment analysis methods in the Persian language, linked-base methods and, content-based methods. In this study, we implement a new link-based method for improving opinion classification in the Persian language. To compare with the content-based method, we implement a content-based method using Naive Bayes Method with two different weighting Methods: TF/IDF and Chi-Square. The TF/IDF method has good results in previous Persian language studies. The Chi-Square method has not been used in the Persian language researches, but the accuracy is fairly good in English. The results show that the improvement in the language-independent methods is remarkable and is in accordance with this research, the precision of the proposed algorithm for positive and negative comments was 98.87% and 97.87%, and the recall value for positive and negative comments was 99.24% and 96.84% respectively. The results also show that because of complexities in Persian syntax and lack of proper natural language processing tools in Persian, content-based algorithms operate poorly compared to English.