1,720,953 research outputs found
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
Classifying ovarian cancer using machine learning methods
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. xii, 151 p.Ovarian cancer is one of the most fatal cancers for women nowadays. It is ranked as fifth
most common cancer deaths among women resulting more deaths than any other cancers
in female reproductive system. According to Canadian Cancer society that about 3000
ovarian cancer patients were detected, and among them 1950 patients died in 2022 which
indicating more than 50% of mortality rate. Ovarian cancer is mainly generated from
cancerous ovarian tumour. So, it is very important to classify cancerous tumour from
noncancerous tumour to prevent false positive for ovarian cancer. Moreover, if cancerous
tumour is diagnosed in early stage, it can be prevented from spreading and thus survival
rate for ovarian cancer can be increased. Also, by separating cancer patients from benign
tumour patients, it will be easier for doctors to know the stages of the cancer and know
patient’s prognosis and life expectancy.
The principal and initial objective of this thesis is building a feasible system using
Artificial Intelligence which is easy to use and compatible to classify ovarian cancer.
Proposed study will give a new non-conventional way to classify ovarian cancer from
ovarian tumour which will be affordable for the patients. Moreover, one of the primary
benefits of this study is that doctors/physicians can detect ovarian cancer with only blood
test/ serum test. There is no need to do any expensive tests such as: ultrasound, MRI or
CT-Scan. The main concept of this research is the application of several machine
learning techniques to correctly classify ovarian cancer and finding best technique
among those in terms of Accuracy, Precision, Sensitivity, and Specificity. Original
dataset is taken from website named Kaggle (https://www.kaggle.com/). This dataset is
screened, cleaned and normalized first and then expert’s advice has been taken to extract
the most important features to do the correct classification. Later, a correlation test has
been done for better understanding of the relations and independency among the input
features. 10 input features have been selected including age, menopause, CA-125, AFP,
NEU etc. From correlation test result 7 inputs were taken again and a comparison had
been made between 10 inputs and 7 inputs. And the output is TYPE which denotes 1 for
benign ovarian tumour and 0 for ovarian cancer. Four machine learning models have
been used for classification and they are, ANN, SVM, Naïve Bayes, and k-NN. Training
of each model is performed and after training, each algorithm is tested and hence
performance is calculated and compared. After analysing results, it is found that for the
problem considered, the Artificial Neural Network (ANN) is the best classifier in terms
of accuracy giving 85.91% accurate results on test data whereas SVM, NB and k-NN
gave accuracy of 76.05%, 83.09% and 76.06% respectively. In terms of sensitivity and
precision calculation, Naïve Bayes is best, and the ANN Classifier is second best
algorithm. Taking specificity into Consideration, the ANN is best with 87.50%.
Keywords: Machine Learning Classifier, Ovarian Cancer, Benign Ovarian Tumour,
Artificial Intelligence, Artificial Neural Network (ANN), Support Vector Machine
(SVM), Naïve Bayes (NB), k-nearest Neighbour (k-NN), Confusion Matrix.Studentye
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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