Occurrence Of Terms Research Articles

Application of Robotic Recovery Techniques to Stroke Survivors-Bibliometric Analysis.

Stroke is a significant disability and death cause worldwide and is conventionally defined as a neurological impairment relating to the intense focal harm of the central nervous system (CNS) by vascular causative components. Although the applicability of robotic rehabilitation is a topic with considerable practical significance because it has produced noticeably higher improvements in motor function than regular (physical and occupational) therapy and exempted the therapists, most of the existing bibliometric papers were not focused on stroke survivors. Additionally, a modular system is designed by joining several medical end-effector devices to a single limb segment, which addresses the issue of potentially dangerous pathological compensatory motions. Searching the Web of Science database, 31,930 papers were identified, and using the VOSviewer software and science mapping technology, data were extracted on the most prolific countries, the connections between them, the most valuable journals according to certain factors, their average year of publication, the most influential papers, and the most relevant topical issues (bubble map of term occurrence). The most prolific country in the analyzed field and over the entire period evaluated (1975-2022) is the United States, and the most prolific journal is Neurorehabilitation and Neural Repair, observing a marked increase in the three periods of scientific interest for this field. The present paper assesses numerous scientific publications to provide, through statistical interpretation of the data, a detailed description of the use of robotic rehabilitation in stroke survivors. The findings may aid scientists, academics, and clinicians in establishing precise goals in the optimization of the management of stroke survivors via robotic rehabilitation, but also through easier access to scientifically validated literature.

Evaluation of Average Term Occurrences Weighting Technique for Arabic Textual Information Retrieval

Information retrieval of documents is an important process in the current time, and the vector space retrieval model uses a term weighting scheme as a basic method for matching queries with documents. Term frequency-Inverse document frequency is a widely used and famous term weighting scheme, and many studies proved its effectiveness in information retrieval. However, this term weighting scheme has some drawbacks like retrieving irrelevant documents, which sometimes reduces effectiveness. From this point, a new term weighting scheme called Term Frequency with Average Term Occurrence was proposed and experienced in the English language to minimize retrieving unnecessary documents. In this paper, an information retrieval system is built for the Arabic language, and Open-Source Arabic Corpora was used to complete experiments. Calculations were made using two schemes which are traditional Term frequency-inverse Document Frequency and proposed Term Frequency with Average Term Occurrence. After that, comparisons of results were made using evaluation measures. With all obtained queries, four case studies with two approaches (stop word removal and stemming) are implemented. In English experiments, stop word removal was applied with another discriminative approach, which calculates the centroid of documents. After the analysis of the results, it was found that the proposed scheme is applicable on Arabic text and applied approaches enhance IR effectiveness if they are both implemented. Furthermore, it was found that stop word removal has a favorable effect on both schemes which was also proved in English experiments.

ANALISIS PERFORMA DAN KECEPATAN KOMPUTASI ALGORITMA K-MEANS DAN K-MEDOIDS PADA TEXT CLUSTERING

The large number of theses will certainly make it difficult to find categories on thesis topics that have been written by students at a university. One of the uses of the Text Mining method is being able to group thesis objects into the number of clusters formed by the clustering algorithm. This study aims to compare 2 clustering algorithms, namely the K-Means and K-Medoids algorithms to obtain an accurate evaluation of the performance and computational time in the case of thesis clustering, so that relevant topics can be grouped and have better clustering accuracy. The evaluation parameter used is the Davies Bouldin Index (DBI) which is one of the testing techniques on clustering results, with the distribution of training data and testing data using cross validation using a repetition parameter of 10 folds iteration. From the results of the study with the Term Weighting condition used is Term Occurrences and using the N-Grams value is 2, it can be concluded that the K-Means algorithm has a better DBI value of -0.426. Meanwhile, the range of DBI values owned by K-Medoids with the same conditions has a DBI value of -1,631. However, from the visualization results using t-SNE with the same supporting parameters, there are options that can be used, namely the number of clusters is 6, and the DBI value is -1.110. For testing the computational time in the clustering process of 50 thesis documents, the K-Means algorithm has an average time of 2.5 seconds while the K-Medoids algorithm has an average time of 261.5 seconds. The computer specifications used are Asus ZenBook UX425EA.312 with the processor used is 11th Gen Intel® Core™ i5-1135G7 @ 2.40GHz @ 2.40GHz, the graphics card is Intel® Iris® Xe Graphics, the RAM used is 8GB, with storage of 512GB SSD.

L’épitomé des Actes de Thomas intitulé Dans le récit des voyages du saint apôtre Thomas (BHG 1836e)

The epitomized version of the Acts of Thomas (BHG 1800-1837z = CANT 245 II) is found in two manuscripts, the Parisinus Gr. 1554A (codex A, 14th cent.) and Hierosol. Sabait. 151 (codex B, 16th cent.). Their text (BHG 1836e), written in Late Antique koine, corresponds to chs. 17-29 of the Acts. The composition of the epitome, whose date is hard to determine, is most probably due to monastic circles, as shown by the occurrence of monastic-related terms. The edition and translation of the text are provided.

In-depth bibliometric analysis and current scientific mapping research in the context of rheumatoid arthritis pharmacotherapy

Rheumatoid arthritis (RA) is a chronic multifactorial disorder of autoimmune etiology with a complex pathophysiological mechanism that is not yet fully elucidated. RA pharmacotherapy includes active molecules of chemical or biological nature that offer symptomatic relief and a slowing of progression, but still in a context of incurability. Therefore, the development of continuous research and multidisciplinary collaboration is essential. Although the management of RA is a topic of significant scientific relevance, existing bibliometric analyses are insufficient to assess this vast field. Consequently, the present study examines numerous manuscripts indexed in the Web of Science database using the VOSviewer software to provide through statistical interpretation of the data a comprehensive description of RA pharmacotherapy in terms of scientific impact, current state of research, number and frequency of citations, most prolific journals, authors, and countries, along with their relationships and other useful data for the literature search/publication process. Furthermore, the use of bubble maps of term occurrence has applicability in identifying current research trends in the field of RA pharmacotherapy as well as their evolution over the years. The leader in this field in terms of published papers is the United States, and the most prolific journal is Annals of the rheumatic diseases. The global management of RA, which is characterized by extensiveness and depth due to many variables, suggests the need for a conceptual framework based on pharmacotherapy coupled with comprehensive bibliometric studies, and the results may be useful for researchers in setting specific objectives that contribute to improving RA outcomes.

Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model

BackgroundThis study aimed to use the hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict the long term occurrence of major adverse cardiac and cerebrovascular events (MACCE) of patients underwent percutaneous coronary intervention (PCI) with stent implantation.MethodThis retrospective cohort study included a total of 220 patients (69 women and 151 men) who underwent PCI in Ekbatan medical center in Hamadan city, Iran, from March 2009 to March 2012. The occurrence and non-occurrence of MACCE, (including death, CABG, stroke, repeat revascularization) were considered as a binary outcome. The predictive performance of ANFIS model for predicting MACCE was compared with ANFIS-PSO and logistic regression.ResultsDuring ten years of follow-up, ninety-six patients (43.6%) experienced the MACCE event. By applying multivariate logistic regression, the traditional predictors such as age (OR = 1.05, 95%CI: 1.02–1.09), smoking (OR = 3.53, 95%CI: 1.61–7.75), diabetes (OR = 2.17, 95%CI: 2.05–16.20) and stent length (OR = 3.12, 95%CI: 1.48–6.57) was significantly predicable to MACCE. The ANFIS-PSO model had higher accuracy (89%) compared to the ANFIS (81%) and logistic regression (72%) in the prediction of MACCE.ConclusionThe predictive performance of ANFIS-PSO is more efficient than the other models in the prediction of MACCE. It is recommended to use this model for intelligent monitoring, classification of high-risk patients and allocation of necessary medical and health resources based on the needs of these patients. However, the clinical value of these findings should be tested in a larger dataset.

Assessing Hospital Management Performance in Intensive Care Units (ICUs) During the COVID-19: A Study from the Pandemic Outbreak Perspective

In the early stages of the pandemic, both poor and developed nations lacked healthcare infrastructure capacity. ICUs had more patients than ordinary wards, and hospital resources for patients were minimal. The possibility of contamination and infection, as well as restricted resources, pose challenges to ICU staff. The circumstance posed a significant difficulty for ICU management to protect healthcare staff while providing healthcare services to patients. Similarly, technology participation in prevention and dissemination control was limited both within and outside of ICUs treating infected patients. The current study investigated the hospital management performance in intensive care units (ICUs) during the COVID-19. We used the PRISM statement 2020 to include and exclude the records in the study. In addition, the study used the VOS viewer software to identify key term occurrences and classification of literature. The major three categories find COVID-19, ICUs and performance management. The current study findings indicate that healthcare personnel such as physicians, nurses, and other support staff made significant contributions during the peak period of pandemic transmission. Nurses are the closest to the infected patients within the ICUs, and the findings show that a considerable percentage of nurses have been infected with the COVID-19 virus (Kramer et al., 2021). Aside from this, in ICUs, technology engagement and infrastructure are substantially lower than in pandemic control and management. Future pandemic damage control and minimising the strain on healthcare workers require advanced technologies and performance management mechanisms. Furthermore, AI and robotic technology can be utilised to address this challenge.

Comparative analysis of binary classifiers on an array of scientific publications

Binary classifiers are studies on balanced text samples. The samplings are formed from scientific publications in the field of Computer Science (Computer Science). The first class contains articles on «Text Data Mining» (the «TDM» class), the second one contains works on other topics of Computer Science (the «non-TDM» class). All the main stages of preliminary processing of text documents are considered, models of their presentation are analyzed. The problem of binary classification is formulated and the quality indicators used in the study are given. A method of sampling from the Russian digital library (Elibrary) is proposed. The generated sampling consists of bibliographic descriptions of documents (title, abstract and keywords). An exploratory analysis was carried out and the sampling structure was studied. «Term clouds» for two classes are constructed and analyzed, documents are visualized using the method of stochastic embedding of neighbors with t-distribution (t-SNE). Based on the review and analysis of known classifiers, the following methods were selected for the study: the K-nearest neighbor method, random forest, gradient boosting, logistic regression, and the support vector method. Profile methods based on the construction of a vector (profile) of the most informative terms determined by the frequency of occurrence of terms and classes are also used in the study. The parameters of the methods were configured using a five-fold cross-validation. The best quality of classification in our sampling demonstrated the methods using the ensemble (collective) decision-making principle (random forest, gradient boosting), as well as the support vector method. The best classifier, gradient boosting, had the proportion of correct answers (accuracy) about 0.98, recall and precision about 0.99. The other (simpler) methods used in the study also generally showed rather good quality of classification (for the least accurate k-nearest neighbor method accuracy, recall and precision were 0.90, 0.81, and 0.91, respectively).

Herbivore species coexistence in changing rangeland ecosystems: First high resolution national open-source and open-access ensemble models for Iceland

Rangeland ecosystems are changing worldwide with the abandonment of extensive pastoralism practices and greater interest for species coexistence. However, the lack of compiled data on current changes in the abundance and distribution of herbivores challenges rangeland management decisions. Here we gathered and made available for the first time the most extensive set of occurrence data for rangeland herbivores in Iceland in an Open Access framework for transparent and repeatable science-based decisions. We mapped fine scale species distribution overlap to identify areas at risk for wildlife-livestock conflict and overgrazing. Nationwide and long term (1861–2021) occurrence data from 8 independent datasets were used alongside 11 predictor raster layers (“Big Data”) to data mine and map the distribution of the domestic sheep (Ovis aries), feral reindeer (Rangifer tarandus tarandus), pink-footed geese (Anser brachyrhynchus), and rock ptarmigan (Lagopus muta islandorum) over the country during the summer. Using algorithms of Maxent in R, RandomForest, TreeNet (stochastic gradient boosting) and MARS (Splines) in Minitab-SPM 8.3, we computed 1 km pixel predictions from machine learning-based ensemble models. Our high-resolution models were tested with alternative datasets, and Area Under the Curve (AUC) values that indicated good (reindeer: 0.8817 and rock ptarmigan: 0.8844) to high model accuracy (sheep: 0.9708 and pink-footed goose: 0.9143). Whenever possible, source data and models are made available online and described with ISO-compliant metadata. Our results illustrate that sheep and pink-footed geese have the greatest overlap in distribution with potential implication for wildlife-livestock conflicts and continued ecosystem degradation even under diminishing livestock abundance at higher elevation. These nationwide models and data are a global asset and a first step in making available the best data for science-based sustainable decision-making about national herbivores affecting species coexistence and environmental management.

A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic

Under the influence of the health emergency triggered by the COVID-19 pandemic, many brands changed their communication strategy and included more or less explicit references to the principles of solidarity and fraternity in their TV commercials to boost the confidence and hope of Italian families during the lockdown. The traditional attitudes of the advertising format, which focused on product characteristics, were relegated to the background in order to reinforce the “brand image” through words, signs, hashtags and music that spread empathetic messages to all those who needed to regain hope and trust in a time of extreme emotional fragility. The objective of this paper is to identify the emotions and brand awareness during the lockdown using text mining techniques by measuring customer sentiment expressed on the Twitter social network. Our proposal starts from an unstructured corpus of 20,982 tweets processed with text data mining techniques to identify patterns and trends in people’s posts related to specific hashtags and TV ads produced during the COVID-19 pandemic. The innovations in the brand’s advertising among consumers seem to have triggered some sense of appreciation and gratitude, as well as a strong sense of belonging that was not present before, as the TV ads were perceived as a disruptive element in consumers’ tweets. Although this effect is clearly documented, in this paper we demonstrate its transitory nature, in the sense that the frequency of occurrence of terms associated with an emotional dimension peaks during the weeks of lockdown, and then gradually decreases.

The construction of an accurate Arabic sentiment analysis system based on resources alteration and approaches comparison

PurposeThis paper purposed a multi-facet sentiment analysis system.Design/methodology/approachHence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.FindingsThe proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.Originality/valueThe construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

Methods of Multi-Criteria Analysis in Technology Selection and Technology Assessment: A Systematic Literature Review

Abstract Technology assessment and selection problems have gained importance in recent decades as the used technology often determines the enterprises’ competitive advantage. Due to the extensive catalogue of criteria that should be considered and, on the other hand, the extensive catalogue of available technologies and solutions, the decision-making process of choosing a technology becomes a significant challenge for organisations and individuals. This study aims to identify the main research directions and trends in the scientific literature on applying multi-criteria analysis (MCA) in the context of technology assessment and/or technology selection. The author conducted a bibliometric analysis of publications indexed in the Web of Science and Scopus databases. The methodology of this study also included identifying the most productive authors, countries, organisations, and journals and analysing the occurrence and co-occurrence of terms. Final analyses included 380 publications retrieved from the Scopus database and 311 documents retrieved from the Web of Science repository. The analysis of the occurrence of terms and keywords allowed distinguishing two main research directions in using MCA methods in assessing and selecting industrial and health and medicine-related technologies. Some sub-areas have also been distinguished within these two areas: energy and renewable energy technologies, waste management, biomedical and medical technologies, and drug production technologies.

Matching Drug Metabolites from Non-Targeted Metabolomics to Self-Reported Medication in the Qatar Biobank Study.

Modern metabolomics platforms are able to identify many drug-related metabolites in blood samples. Applied to population-based biobank studies, the detection of drug metabolites can then be used as a proxy for medication use or serve as a validation tool for questionnaire-based health assessments. However, it is not clear how well detection of drug metabolites in blood samples matches information on self-reported medication provided by study participants. Here, we curate free-text responses to a drug-usage questionnaire from 6000 participants of the Qatar Biobank (QBB) using standardized WHO Anatomical Therapeutic Chemical (ATC) Classification System codes and compare the occurrence of these ATC terms to the detection of drug-related metabolites in matching blood plasma samples from 2807 QBB participants for which we collected non-targeted metabolomics data. We found that the detection of 22 drug-related metabolites significantly associated with the self-reported use of the corresponding medication. Good agreement of self-reported medication with non-targeted metabolomics was observed, with self-reported drugs and their metabolites being detected in a same blood sample in 79.4% of the cases. On the other hand, only 29.5% of detected drug metabolites matched to self-reported medication. Possible explanations for differences include under-reporting of over-the-counter medications from the study participants, such as paracetamol, misannotation of low abundance metabolites, such as metformin, and inability of the current methods to detect them. Taken together, our study provides a broad real-world view of what to expect from large non-targeted metabolomics measurements in population-based biobank studies and indicates areas where further improvements can be made.

Digitalisation in the Curricula of Vocational Schools: Text Mining as an Instrument of Curricula Analysis

AbstractThe influence of digitalisation on society and the workplace require stakeholders in school development to incorporate digital competencies into school curricula. This study examines the occurrence of digital components in 831 curricula of the vocational schools in Baden-Württemberg, Germany with a text mining approach and the analysis of keywords in context. In total, 200 curricula containing digital features could be identified. The occurrence of digital terms differs notably between the different types of vocational school. The distinct characteristics of digital competencies are represented to a varying degree within the curricula, with a strong focus on the usage of tools for profession-specific tasks and the deployment of the internet as a gateway to information. Ongoing reforms of school curricula need to widen their view on digital competencies and integrate these competencies more broadly into the different types of vocational schools.

Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.

Strategies for Cultural Transformation: Indonesian Debates of the 1970s and 1980s Revisited

This contribution investigates the discursive occurrence of key terms of the Indonesian debates about “modernization”, “cultural transformation”, and “pembaruan” (renewal). The sample consists of the book titles documented in Worldcat.org, from 1945 through 2020. This sample is gathered through various searches and then manually looked through one by one in order to omit duplicates. Selected titles are then further analyzed in a qualitative approach. The evidence generated by this method shows a close temporal link with the so-called “New Order” governments since 1966. This article can therefore serve to further analyze individual elements of New Order discourses on modernization discourse and modernization theory.

Termos metalinguísticos e operações de natureza gramatical na escrita colaborativa dos alunos do ensino básico

Collaborative writing holds the potential to bringing references to language units and operations of language (re)construction to the surface in conversational interactions. The present study aims to trace the surfacing of metalinguistic terms during collaboration for textual production and to identify the nature of the operations performed. Methodologically, data were collected through audio-visual recording within the classroom with the same participants in two stages of schooling (grades 2 and 4). The analyses focused on the occurrence of metalinguistic terms and the domains to which they categorically belonged as well as on the discursive or grammatical nature of the operations of language re(construction). The results show that in both stages of schooling, the domains of spelling and punctuation were most prominent, along with the discursive operations. Also, a larger number of metalinguistic terms was identified in grade 4. Despite this predominance, the results also reveal the potential of collaborative writing to activating the explanation of metalinguistic knowledge, in association with operations of language (re)construction. Such potential may be reinforced by the teacher's clarifications during the writing process and by activities that review the grammatical choices and decisions made during the writing process.

Comparison of Latent Semantic Analysis and Vector Space Model for Automatic Identification of Competent Reviewers to Evaluate Papers

The assignment of reviewers to papers is one of the most important and challenging tasks in organizing scientific events. A major part of it is the correct identification of proper reviewers. This article presents a series of experiments aiming to test whether the latent semantic analysis (LSA) could be reliably used to identify competent reviewers to evaluate submitted papers. It also compares the performance of the LSA, the vector space model (VSM) and the method of explicit document description by a taxonomy of keywords, in computing accurate similarity factors between papers and reviewers. All the three methods share the same input datasets, taken from real-life conferences and the produced paper-reviewer similarities are evaluated with the same evaluation methods, allowing a fair and objective comparison between them. Experimental results show that in most cases LSA outperforms VSM and could even slightly outperform the explicit document description by a taxonomy of keywords, if the term-document matrix is composed of TF-IDF values, rather than the raw number of term occurrences.

Построение терминологических профилей научных сотрудников на основе публикаций в цифровой библиотеке eLIBRARY.RU

The terminological profiles of specialists are drawn up based on publications from the digital library eLIBRARY. By averaging the individual terminological profiles, it is possible to draw up a generalized profile (“portrait”) of a small research team (a department, laboratory, or sector). Comparison of individual profiles with the use of proximity measures (for example, a cosine measure) makes it possible to group similar profiles and identify groups of employees who conduct research in the same subject area. This helps determine the research team specialization by means of Text Mining tools without using subjective expert assessments. The results obtained using the profile approach are confirmed by constructing graphs of co-authorship and a graph of terms in the Gephi computer program. The compilation of terminological profiles was also used in the development of personalized scientific activity support systems. This system is intended for helping the user (a specialist in a subject area) in choosing relevant scientific conferences and searching for useful (pertinent as far as possible) publications. For describing text documents, a vector model is used, and the weights of terms are determined by calculating the term occurrence frequency (or the tfc-weighting formula). At the preprocessing stage, the stop-words and rarely encountered words are removed, and lemmatization is carried out. The developed profile approach has been approbated on the example of a small research team specializing in computer science. The terminological profiles were constructed and analyzed, based on which the areas in which the team members specialize have been identified, and a personalized scientific activity support system has been developed, that tracks, in an automated mode, publications in the eLIBRARY in one of the relevant areas (Data Mining).

Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing.

BackgroundOnline media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication.ObjectiveThe goal of this study was to perform a longitudinal analysis of the COVID-19–related content on online media based on natural language processing.MethodsWe collected a data set of news articles published by Croatian online media during the first 13 months of the pandemic. First, we tested the correlations between the number of articles and the number of new daily COVID-19 cases. Second, we analyzed the content by extracting the most frequent terms and applied the Jaccard similarity coefficient. Third, we compared the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we applied named entity recognition to extract the most frequent entities and tracked the dynamics of changes during the observation period.ResultsThe results showed no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there were high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second waves. Finally, the findings indicate that the most influential entities have lower overlaps for the identified people and higher overlaps for locations and institutions.ConclusionsOur study shows that online media have a prompt response to the pandemic with a large number of COVID-19–related articles. There was a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well-covered.