1,720,954 research outputs found
A Novel Big Data Approach Using Fuzzy Rule Based Multilayer Perceptrons
Full text linkThey are faced with immense quantities and high velocity of data with complicated structures in the big data era. Social networks, sensors, online and offline transactions, and our daily lives can all produce data. When big data is processed correctly, it can lead to relevant, helpful and useful decisions being made in a number of areas, including government, business, management, and medicine and healthcare. Large amounts of data on healthcare have the ability to significantly enhance patient outcomes, predict epidemics, provide insightful information, prevent diseases that may be prevented, reduce the cost of healthcare delivery, and generally increase life. Big data is made up of patient data that is gathered for remote healthcare applications that differs in terms of volume, velocity, variety, veracity, and value. Healthcare data classification presents a number of challenges for big data since it gathers huge quantities of data. Processing a heterogeneous collection of this size requires a specialized approach, making it one of the most difficult challenges. The paper presents a novel big data approach using fuzzy rule-based multilayer perceptrons to address these problems. Big data offers the ability to accumulate, analyze, manage, and integrate large amounts of disparate, structured, and unstructured information generated by the healthcare systems of currently. A FRCNN (Fuzzy Region based Convolutional Neural Network) classifier is designed to perform normal and disease classification. Accuracy, precision, recall, and F1-score are only some of the performance criteria used to evaluate this model
CLINICAL DECISION SUPPORT SYSTEM ON COPD PREDICTION USING BIG DATA ANALYTICS WITH IMPROVED PATIENT MATCHING
No full textBig data analytics is a fast developing area that plays a vital part in research and health care practice advancements. Clinical Decision Support systems need patient identification and matching of their information residing in different systems for making better diagnosis and treatments at the right time. The COPD (Chronic Obstructive Pulmonary Disease) was a major cause of mortality and morbidity global outcome in social and financial burdens which was increasing significantly. In this paper Clinical Decision Support (CDS) system on COPD prediction with improved Patient matching utilizing big data analytics is presented. As the healthcare organizations share different documents of patients from different systems such as pharmacy, laboratory, claim systems, etc. they are required to be link with correct patient records for guarantying the better decisions by clinicians and minimized duplicate information and overheads. The Fuzzy Matching algorithm & Map Reduce are introduced in this work for addressing the issue of patient’s record matching from various systems to support clinical decision greatly. Then utilizing the data mining application of big data, Decision Tree (DT) model is applied to obtain best approach in the detection of COPD in independent patients. The result analysis show that this system has scalability and flexibility utilizing any fuzzy algorithm and handling the data source exhibits greater accuracy in COPD patient diagnosis with better efficiency
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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