1,720,964 research outputs found
ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT TEA TASTER-REVIEW
No full text. Tea is the most favorable beverage in the world after the pure water. Professional tea tasters categorize thequality of the tea in subjective manner by assessing the several parameters. The flavor, aroma and color of tea are themost important and considered parameters when professional tea tasters categorize and evaluate tea. The value of abovementioned parameters depends on the chemical composition of the tea. Basically, flavanols are major compounds whichaffect the quality of the tea. Therefore, it is possible to identify a correlation between flavanols composition of tea andprofessional tea taster’s valuation. The main purpose of this article is identifying above correlation and according to thatcorrelation design and implements “Artificial Neural Network (ANN) based Intelligent Tea Taster” to automate manualtea tasting process. This review is focused on training an artificial neural network according to identified correlationbetween flavanols composition and tea taster’s valuation and based on that trained artificial neural network After goingthrough successful training iterations and evaluations, a computer-based solution can be designed and implemented todefine the quality of tea according to its flavanols compound. The results show good correlation of estimated values oftheaflavins and thearubigins with the actual concentrations obtained by the system when we tested at laboratory. Thereview is based on ANN base Intelligent Tea Taster will automate the tea tasting process while improving efficiency,effectiveness and accuracy of the tea tasting process
ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT TEA TASTER-REVIEW
No full text. Tea is the most favorable beverage in the world after the pure water. Professional tea tasters categorize the
quality of the tea in subjective manner by assessing the several parameters. The flavor, aroma and color of tea are the
most important and considered parameters when professional tea tasters categorize and evaluate tea. The value of abovementioned parameters depends on the chemical composition of the tea. Basically, flavanols are major compounds which
affect the quality of the tea. Therefore, it is possible to identify a correlation between flavanols composition of tea and
professional tea taster’s valuation. The main purpose of this article is identifying above correlation and according to that
correlation design and implements “Artificial Neural Network (ANN) based Intelligent Tea Taster” to automate manual
tea tasting process. This review is focused on training an artificial neural network according to identified correlation
between flavanols composition and tea taster’s valuation and based on that trained artificial neural network After going
through successful training iterations and evaluations, a computer-based solution can be designed and implemented to
define the quality of tea according to its flavanols compound. The results show good correlation of estimated values of
theaflavins and thearubigins with the actual concentrations obtained by the system when we tested at laboratory. The
review is based on ANN base Intelligent Tea Taster will automate the tea tasting process while improving efficiency,
effectiveness and accuracy of the tea tasting process
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
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