1,720,955 research outputs found
Optimización de una matriz de sensores de gas para clasificar tres etapas de maduración comestible de dátiles mediante aprendizaje automático y selección de características (estudio de caso: cv. Shahani)
No full textAim of study: This study aimed to analyze aroma changes in date fruits (Phoenix dactylifera L.) across three ripening stages (Khalal, Rutab, and Tamr) using an e-nose as a non-destructive monitoring method for fruit quality management. The study also evaluated feature selection methods to optimize the sensor array for a repeatable model based on external data.
Area of study: This study was carried out at a date palm garden cultivated by cv. Shahani in Jahrom, west-south of Iran.
Material and methods: An e-nose profiled the aroma of date fruits from a date palm garden across three ripening stages over two years. Classification models, developed using one year’s data as internal data, were externally validated using data from the other year as external data. Feature selection optimized the sensor array, improving the prediction accuracy on the external dataset. Main results: The classification models had similar test accuracy, but their predictive performance varied on external data. Using F-test and principal component analysis (PCA) for feature selection to optimize the sensor array, with support vector machine (SVM) as the classification algorithm, resulted in a highly repeatable model. This study suggests that e-noses are a promising tool for monitoring aroma changes during date fruit ripening in artificial ripening or storage processes.
Research highlights: Aroma profiles from edible ripening stages in data fruits were classified with high accuracy using e-nose; Feature selection methods can affect linear discriminant analysis (LDA) and SVM algorithms differently on external data; In SVM, some feature selection methods decreased prediction accuracy on external data compared to using the full sensor array; PCA effectively determined the optimal number of features for optimizing the sensor array.Objetivo del estudio: Este estudio tuvo como objetivo analizar los cambios en el aroma de los dátiles (Phoenix dactylifera L.) en tres etapas de maduración (Khalal, Rutab y Tamr) utilizando una “nariz electrónica” (e-nose) como método de monitoreo no destructivo para la gestión de la calidad de la fruta. El estudio también evaluó métodos de selección de características para optimizar la matriz de sensores en un modelo repetible basado en datos externos.
Área de estudio: Este estudio se llevó a cabo en un huerto de palmeras datileras cultivadas con el cultivar Shahani, en Jahrom, al suroeste de Irán.
Materiales y métodos: Un e-nose perfiló el aroma de los dátiles de un huerto de palmeras datileras en tres etapas de madu-ración durante dos años. Los modelos de clasificación, desarrollados utilizando los datos de un año como datos internos, fueron validados externamente utilizando los datos del otro año como datos externos. La selección de características optimizó la matriz de sensores, mejorando la precisión de la predicción en el conjunto de datos externos.
Resultados principales: Los modelos de clasificación tuvieron una precisión de prueba similar, pero su rendimiento predic-tivo varió en datos externos. El uso de la prueba F y el análisis de componentes principales (PCA) para la selección de carac-terísticas con el fin de optimizar la matriz de sensores, utilizando la máquina de soporte vectorial (SVM) como algoritmo de clasificación, resultó en un modelo altamente repetible. Este estudio sugiere que la e-nose es una herramienta prometedora para monitorear los cambios de aroma durante la maduración de los dátiles en procesos de maduración artificial o almacenamiento. Aspectos destacados de la investigación: los perfiles de aroma de las etapas de maduración comestibles en los dátiles, fueron clasificados con alta precisión utilizando e-nose; los métodos de selección de características pueden afectar de manera diferente al análisis discriminante lineal (LDA) y a los algoritmos SVM en datos externos; en SVM, algunos métodos de se-lección de características disminuyeron la precisión de la predicción en datos externos en comparación con el uso de toda la matriz de sensores; PCA determinó de manera efectiva el número óptimo de características para optimizar la matriz de sensores
Optimizing gas sensor array to classify three edible ripening stages of date fruit using machine learning and feature selection (case study: cv. Shahani.)
No full textAim of study: This study aimed to analyze aroma changes in date fruits (Phoenix dactylifera L.) across three ripening stages (Khalal, Rutab, and Tamr) using an e-nose as a non-destructive monitoring method for fruit quality management. The study also evaluated feature selection methods to optimize the sensor array for a repeatable model based on external data. Area of study: This study was carried out at a date palm garden cultivated by cv. Shahani in Jahrom, west-south of Iran.Material and methods: An e-nose profiled the aroma of date fruits from a date palm garden across three ripening stages over two years. Classification models, developed using one year’s data as internal data, were externally validated using data from the other year as external data. Feature selection optimized the sensor array, improving the prediction accuracy on the external dataset.Main results: The classification models had similar test accuracy, but their predictive performance varied on external data. Using F-test and principal component analysis (PCA) for feature selection to optimize the sensor array, with support vector machine (SVM) as the classification algorithm, resulted in a highly repeatable model. This study suggests that e-noses are a promising tool for monitoring aroma changes during date fruit ripening in artificial ripening or storage processes.Research highlights: Aroma profiles from edible ripening stages in data fruits were classified with high accuracy using e-nose; Feature selection methods can affect linear discriminant analysis (LDA) and SVM algorithms differently on external data; In SVM, some feature selection methods decreased prediction accuracy on external data compared to using the full sensor array; PCA effectively determined the optimal number of features for optimizing the sensor array.Objetivo del estudio: Este estudio tuvo como objetivo analizar los cambios en el aroma de los dátiles (Phoenix dactylifera L.) en tres etapas de maduración (Khalal, Rutab y Tamr) utilizando una “nariz electrónica” (e-nose) como método de monitoreo no destructivo para la gestión de la calidad de la fruta. El estudio también evaluó métodos de selección de características para optimizar la matriz de sensores en un modelo repetible basado en datos externos.Área de estudio: Este estudio se llevó a cabo en un huerto de palmeras datileras cultivadas con el cultivar Shahani, en Jahrom, al suroeste de Irán. Materiales y métodos: Un e-nose perfiló el aroma de los dátiles de un huerto de palmeras datileras en tres etapas de madu-ración durante dos años. Los modelos de clasificación, desarrollados utilizando los datos de un año como datos internos, fueron validados externamente utilizando los datos del otro año como datos externos. La selección de características optimizó la matriz de sensores, mejorando la precisión de la predicción en el conjunto de datos externos.Resultados principales: Los modelos de clasificación tuvieron una precisión de prueba similar, pero su rendimiento predic-tivo varió en datos externos. El uso de la prueba F y el análisis de componentes principales (PCA) para la selección de carac-terísticas con el fin de optimizar la matriz de sensores, utilizando la máquina de soporte vectorial (SVM) como algoritmo de clasificación, resultó en un modelo altamente repetible. Este estudio sugiere que la e-nose es una herramienta prometedora para monitorear los cambios de aroma durante la maduración de los dátiles en procesos de maduración artificial o almacenamiento.Aspectos destacados de la investigación: los perfiles de aroma de las etapas de maduración comestibles en los dátiles, fueron clasificados con alta precisión utilizando e-nose; los métodos de selección de características pueden afectar de manera diferente al análisis discriminante lineal (LDA) y a los algoritmos SVM en datos externos; en SVM, algunos métodos de se-lección de características disminuyeron la precisión de la predicción en datos externos en comparación con el uso de toda la matriz de sensores; PCA determinó de manera efectiva el número óptimo de características para optimizar la matriz de sensores
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
- …
