1,720,958 research outputs found
Parallel Query Processing on 2D Mesh and Linear Array Architectures
As the size of the web grows, it is necessary to parallelize the process of retrieving information from the web. Incorporating parallelism in search engines is one of the approaches towards achieving this aim. This paper presents an algorithm for query processing on the 2D mesh architecture and two algorithms for linear array architectures. We attempt to exploit the arrangement of processors and the communication pattern in both 2D mesh and linear array architectures to attain high speedup and efficiency for queries-keywords comparisons. A cost model is presented for each algorithm based on both processing and communication cost. Proposed algorithms are evaluated using speedup and efficiency performance metrics. For the same number of processors, 2D Mesh_QP outperforms both linear array algorithms (LA_QPAKP and LA_QPKE). Keywords: 2D Mesh, Linear Arrays, Parallel computing, Query processin
Going Beyond Counting First Authors in Author Co-citation Analysis
The 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
Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques
At the era of digital transformation, fraud has dramatically increased, notably in the banking industry. Annually, it now costs the world\u27s economies billions of dollars. Daily, news of financial fraud has a negative influence on the world economy. According to the harsh loss caused by fraud, effective strategies and methods for avoiding income statement fraud have to be implemented. Also, the procedure of identification should be applied. This is regarded as a result of the development of modern technology, modern invention, and the rapidity of global communications. Actually, deterrent technologies are most effective to reduce fraud and overcome cons. So, it is necessary to find ways to overcome such deterrence by depending on developed methods to identify fraud. Data mining techniques are currently the most widely used methods for the prevention and detection of financial fraud. The use of datasets for fraud detection complies with the norms of data mining, which include feature selection, representation, data gathering and management, pre-processing, comment, and summative evaluation. Methodologies for identifying fraud are essential if we want to catch criminals after fraud prevention has failed. The greatest fraud detection strategies for locating loan banking and financial fraud are compared in this article
Automated detection of fake news
During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance
Variations on the Author
“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
We 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
We 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
We 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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