Recently researchers are using Google scholar widely to find out the research articles and the relevant experts in their domain. But it is unable to find out all experts in a relevant research area from a specific country by a quick search. Basically the custom search technique is not available in the current Google scholar’s setup. The authors have combined custom search with domain-specific search and named as domain specific custom search in this research. First time this research introduces a domain specific custom search technique using new search methodology called n-paged-m-items partial crawling algorithm. This algorithm is a real-time faster crawling algorithm due to the partial crawling technique. It does not store anything in the database, which can be shown later on to the user. The proposed algorithm is implemented on a new domain scholar.google.com to find out the scholars or experts quickly. Finally the authors observe the better performance of the proposed algorithm comparing with Google scholar.

Social media is one of the many sources of data that can be extracted to obtain various information. In R Programming, there are thousands of packages that can be used to crawl social media data, both the default packages and the developer versions. Difficulty in extracting data will result in less than optimal research, so there is a need for adequate tools to support research. R Programming is able to become a bridge for researchers to explore existing tools, both by utilizing packages and building simple algorithms in R programming. Twitter data crawling is done through an API connection on Twitter with R Programming, so that we can get data by entering keywords according to the desired search both in realtime and at certain times as needed. With these packages it makes it easy to research in crawling social media data such as Twitter and other social media

The goal of the article is to find out and interpret the effect of giving a workshop on basic statistics with the R Programming Language at SMA ERENOS Tangerang. Researchers used descriptive statistics and basic inferential statistics to analyse student performances. The sample was taken as seventy-two students from SMA Erenos, where the actual number of students was ninety-six for grades X and XI majoring in science and social studies. The score of students was carried out directly from the Quizziz website and it is divided into two sessions, namely the Pre-Test and Post-Test. The results of this study obtained a real influence on learning basic statistics before and after the workshop. Therefore it can be concluded that when students have been given the right method to learn statistics or any other subject, it would be increased the performance of the students.

Programming a computer is an activity that can be very beneficial to undergraduate students in terms of improving their mental capabilities, collaborative attitudes and levels of engagement in learning. Despite the initial difficulties that typically arise when learning to program, there are several well-known strategies to overcome them, providing a very high benefit-cost ratio to most of the students. Moreover, the use of a programming language usually raises the interest of students to learn any specific concept, which has caused that many teachers around the world employ a programming language as a learning environment to treat almost every possible topic. Particularly, mathematics can be taught and learnt while using a suitable programming language. The R programming language is endowed with a wide range of capabilities that allow its use to learn different kind of concepts while programming. Therefore, complex subjects such as mathematics could be learnt with the help of this powerful programming language. In addition, since the R language provides numerous graphical functions, it could be very useful to acquire simultaneously basic plane geometry and programming knowledge at the undergraduate level. This paper describes the LearnGeom R package, a novel pedagogical tool, which contains multiple functions to learn geometry in R at different levels of difficulty, from the most basic geometric objects to high-complexity geometric constructions, while developing numerous programming skills.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.

The key programming language used in the field of data science is R programming, because of the freeavailability and ability to handle huge data. Packages developed in the R are used widely by scientists toobtain solutions for various real-world problems in several disciplines like healthcare, agriculture, andinformation technology. Next-generation sequencing data requires various bioinformatics analyses likequality checking, differential gene expression studies, and annotation which is facilitated by R packages andsoftware. Bioconductorprojectremains a hub of R packages that are open access and move the researchers tothe comfort zone for various kinds of analysis. In this review, we briefly discussed various packages usefulfor NGS data analysis and also explained how to use the packages for basic level analysis that benefitsresearchers having less exposure to R programming.

Decision support systems based on machine learning models should be able to help users identify opportunities and threats. Popular model-agnostic explanation models can identify factors that support various predictions, answering questions such as “What factors affect sales?” or “Why did sales decline?”, but do not highlight what a person should or could do to get a more desirable outcome. Counterfactual explanation approaches address intervention, and some even consider feasibility, but none consider their suitability for real-time applications, such as question answering. Here, we address this gap by introducing a novel model-agnostic method that provides specific, feasible changes that would impact the outcomes of a complex Black Box AI model for a given instance and assess its real-world utility by measuring its real-time performance and ability to find achievable changes. The method uses the instance of concern to generate high-precision explanations and then applies a secondary method to find achievable minimally-contrastive counterfactual explanations (AMCC) while limiting the search to modifications that satisfy domain-specific constraints. Using a widely recognized dataset, we evaluated the classification task to ascertain the frequency and time required to identify successful counterfactuals. For a 90% accurate classifier, our algorithm identified AMCC explanations in 47% of cases (38 of 81), with an average discovery time of 80 ms. These findings verify the algorithm’s efficiency in swiftly producing AMCC explanations, suitable for real-time systems. The AMCC method enhances the transparency of Black Box AI models, aiding individuals in evaluating remedial strategies or assessing potential outcomes.

his review article explains the prediction of automatic learning of Wikipedia time series data using r programming. Although many time series forecast researchers have been analyzed the time series could not cover the gap between chart interpretation and time series analysis of the Internet database directly. Its main objective is to explain the simplest way to time model series whose data structure was different using R programming, the result was sufficiently summarized with different forecast models. The simplest form of analysis with graphical interpretation to obtain conclusions from the time search Cristiano_Ronaldo of Wikipedia, a best player in euro football team. Whose trend and prediction is analyzed for next 2020 from the past records trend. Therefore, this document presents the simplest way to predict time series data and its strengths for data analysis using R programming.

Purpose – This paper discusses how University of Maryland University College (UMUC) librarians customized EBSCO Discovery Service (EDS) to allow for searching across librarian-selected sets of discipline-specific databases. Discipline-specific searching results in a smaller and more relevant set of search results, which can make research more efficient and effective. Design/methodology/approach – This paper describes the collaboration between systems and reference and instruction librarians to develop, test, launch, promote, and assess discipline-specific searching in EDS in support of effective teaching and learning. Findings – Customization of a discovery tool to allow researchers to run searches across pre-selected sets of discipline-specific databases is beneficial to the researchers since it enables them to find a smaller and more relevant set of search results than they would otherwise receive if they searched across all databases available in the discovery tool. Originality/value – This paper provides detailed instructions regarding customization of EDS to allow for discipline-specific searching and discusses ways in which this enhancement can be brought to researchers' attention during reference and instruction interactions. This paper should be of interest to technical librarians as well as to reference and instruction librarians.

<p> </p> <p>Having quality instruments is essential in ensuring data integrity. Indiscriminately application and over-dependency on Cronbach alpha index for multiple measured items (ordinal scale) and usage of SPSS software, which produce spurious estimation, have been a subject of technical debates in the literature. This debate toes the path of fulfilling stringent underlying assumptions of Cronbach alpha, such as uni-dimensionality, tau-equivalent, etc. However, modern approaches like ordinal alpha, Omega coefficient, GLB, Guttman Lambda, and Revelle Beta have been suggested with precise estimates and confidence intervals via R programming language. Thus, this paper examined the performance of alternative approaches to Cronbach alpha and documented practical step by step of establishing it. Non-experimental design of scale development research was adopted, and a multi-stage sampling procedure was used to sample N = 883 subjects that participated in the study. Findings showed that the instrument is multidimensional, in which Cronbach alpha is not apt for its estimation. Also, other forms of reliability methods produced better and more precise estimates, though their performance differs among themselves. The authors concluded that estimation of Cronbach Alpha using SPSS when the instrument is ordinal is absolutely not sufficient. Therefore, it is recommended that researchers explore and shift their paradigm from traditional reliability estimates through SPSS to modern approaches using an R programming language.</p>