1,720,958 research outputs found
Big data platforms: in the lens of selection and evaluation approach
No full textThe manifestation of big data has brought about firms’ intense desires for analytical value creation besides crucial challenges to handle data processing. Unlocking the potentials of big data analytics depends on devising and setting up platforms through the big data value chain. Achieving these appropriate platforms is indispensable and necessitates a selection and evaluation method. To that end, in this research, a new fuzzy superiority and inferiority ranking approach for selecting and evaluating big data platforms has been investigated; and introduced. In the proposed approach, both functional and non-functional criteria for the big data platform were gathered and customized. For demonstrating the applicability of the method, a numerical example provided; and the suggested approach was used in a company case that had been encountered with this platform acquisition problem. Simplifying assessment and selection of the big data platform, and feasible procedure are the main values of the offered approach. The proposed approach is beneficial for buyer firms, vendors and industries of big data, considering more accurate evaluations and making better acquisition decisions
Predicting Depression Status After Transcranial Direct Current Stimulation Treatment Using Machine Learning
No full textDepression is a serious medical illness that adversely affects how a person feels, thinks, and behaves. This illness can be treated with the aid of Transcranial Direct Current Stimulation (tDCS), which can help to reduce the symptoms of depression. The level of illness is typically evaluated using the Hamilton Depression Rating Scale (HDRS). The focus of this paper is the prediction of the HDRS score after a tDCS course. By predicting the result of tDCS, psychiatrists can provide better counseling to the patients about their future conditions after the treatment and decide wisely about the treatment method. We used different kinds of demographic information, treatment information, and the HDRS score before the treatment as predictors and supervised Machine Learning (ML) algorithms for the prediction. The analysis is conducted on 169 patients with depression. Our preliminary results show that the accuracy can be up to 63% when predicting the value of HDRS after tDCS treatment sessions as a binary variable using Gradient Boosting. This is encouraging on such a small data set. Moreover, our results provide insight into the predictors pivotal to this outcome. They show that the HDRS score at baseline, the age, and the gender of the subject are the three main predictors. The results suggest this methodology may yield very interesting results
Predicting Patient Sexual Function after Prostate Surgery Using Machine Learning
No full textA major health concern for men is prostate cancer. An accurate prediction of patients' conditions after surgery is essential for understanding their quality of life. For improving patient care, medical and healthcare professionals use machine learning for a variety of purposes. By using supervised machine learning algorithms, we aim to identify the most reliable predictors of patient sexual function one year after surgery. We used the EPIC-26 (Expanded Prostate Index Composite-26) questionnaire to assess the patient's quality of life and sexual function. An approximate 500 patient sample was used in our case study to test the effectiveness of this methodology. Based on demographic and clinical data collected prior to surgery, our model predicts patient self-Assessment of sexual function with high accuracy one year after surgery. In order to improve and enhance the quality of the patient experience, the methodology presented can support clinical decisions
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
Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning
No full textDuring the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions
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