1,720,962 research outputs found
Predictive data modelling for biomedical data and imaging
No full textIn this book, we embark on a journey into the realm of predictive data modeling for biomedical data and imaging in healthcare. It explores the potential of predictive analytics in the field of medical science through utilizing various tools and techniques to unravel insights and enhance patient care. This volume creates a medium for an interchange of knowledge from expertise and concerns in the field of predictive data modeling. In detail, the research work on this will include the effective use of predictive data modeling algorithms to run image analysis tasks for understanding. Predictive Data Modelling for Biomedical Data and Imaging is divided into three sections, namely Section I - Beginning of Predictive Data Modeling for Biomedical Data and Imaging/Healthcare, Section II - Data Design and Analysis for Biomedical Data and Imaging/Healthcare, and Section III - Case Studies of Predictive Analytics for Biomedical Data and Imaging/Healthcare. We hope this book will inspire further research and innovation in the field of predictive data modeling for biomedical data and imaging in healthcare. By exploring diverse case studies and methodologies, this book contributes to the advancement of healthcare practices, ultimately improving patient outcomes and well-being
Classification and clustering algorithms for medical data
No full textIn recent years, there has been a substantial increase in the amount of attention paid to the use of machine learning strategies within the area of medical data. Classification and clustering algorithms are two major kinds of machine learning algorithms that are frequently utilized for a variety of tasks in medical data analysis. These algorithms are employed in a variety of different ways. Clustering algorithms are used to group data points that are similar together based on their similarity or distance metrics, while classification algorithms are used to predict the class or category of a new data point based on the patterns learned from labeled data. Classification methods may be found in machine learning software. We present an overview of classification and clustering algorithms for medical data in this article, covering its uses, problems, and future approaches, among other things
Detection of brain tumor on MRI images using comparison analysis of deep learning techniques
No full textThe process of diagnosing brain tumors typically involves the use of scans created with magnetic resonance imaging (MRI). MRI scans have the ability to localize precisely in the brain an area that is exhibiting abnormal growth of tissue. A number of research articles have been written on the topic of locating brain tumors with the assistance of machine learning and deep learning algorithms. When combined with MRI scans, these algorithms enable a quicker and more accurate detection of brain tumors, which in turn makes it easier to treat patients who have the condition. The radiologist can more easily decide what course of action to take with the help of these projections. In the proposed chapter, self-made algorithms called Random Forest (RF) and Support Vector Machine (SVM) are used to search for brain tumors, and 312their performance is analyzed. Both of these algorithms were developed by the authors of the chapter. The authors are responsible for the development of both of these algorithms. The performance of these classifiers, which determine whether or not a brain image is normal, is evaluated based on a variety of criteria, including sensitivity, specificity, and accuracy, amongst others. These classifiers are responsible for determining whether or not a brain image is normal. It has a significance of 0.001, which is lower than the p value, and it has the capability of giving an accuracy that ranges between 94.1220 and 96.8940. As a direct consequence of this, its statistical significance is indisputable
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
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