1,720,959 research outputs found
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
AI-Driven Pattern Recognition and Object Detection : Emphasis on Precision Agriculture
No full textPrecision agriculture utilizes Artificial Intelligence (AI) and remote sensing to address challenges in sustainable food production. A significant obstacle is effective weed management, particularly for species like black-grass (Alopecurus myosuroides), which visually resembles wheat. This thesis introduces AI-driven methods using Unmanned Aerial Vehicle (UAV)-based multispectral image analysis to improve weed detection, addressing key challenges such as limited datasets, sensor misalignment, and the need for robust algorithms. The first part of the thesis addresses data scarcity using synthetic data augmentation. A study using a Mask Region-based Convolutional Neural Network (Mask R-CNN) model for black-grass detection shows that scaling foreground objects is a particularly effective technique for improving performance, providing a basis for adapting models to new field conditions with less data. The second part focuses on multispectral data quality, where sensor misalignment can corrupt Vegetation Indices (VIs) like Normalized Difference Vegetation Index (NDVI). A method for sensor calibration and image registration is presented to correct for these errors. The study then evaluates various VIs, finding that models integrating indices like Triangular Greenness Index (TGI) and Excess Green Index (ExG) show improved detection performance compared to using only the basic spectral bands, particularly between crop rows. Finally, the third part draws parallels to biometrics to explore the robustness of different network architectures. By evaluating triplet-based and Siamese networks for fingerprint recognition, it is demonstrated that clustering-based approaches using contrastive loss offer better performance with incomplete and variable data. This insight highlights broader principles for creating robust recognition systems. Overall, this thesis contributes methods for addressing key obstacles in precision agriculture, spanning data augmentation, sensor calibration, and the optimization of deep learning models. The findings of this work aim to contribute to the ongoing development of more effective agricultural practices.Paper III is excluded from the attached file because of being submitted for publication in a journal.</p
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
AI-Driven Pattern Recognition and Object Detection : Emphasis on Precision Agriculture
No full textPrecision agriculture utilizes Artificial Intelligence (AI) and remote sensing to address challenges in sustainable food production. A significant obstacle is effective weed management, particularly for species like black-grass (Alopecurus myosuroides), which visually resembles wheat. This thesis introduces AI-driven methods using Unmanned Aerial Vehicle (UAV)-based multispectral image analysis to improve weed detection, addressing key challenges such as limited datasets, sensor misalignment, and the need for robust algorithms. The first part of the thesis addresses data scarcity using synthetic data augmentation. A study using a Mask Region-based Convolutional Neural Network (Mask R-CNN) model for black-grass detection shows that scaling foreground objects is a particularly effective technique for improving performance, providing a basis for adapting models to new field conditions with less data. The second part focuses on multispectral data quality, where sensor misalignment can corrupt Vegetation Indices (VIs) like Normalized Difference Vegetation Index (NDVI). A method for sensor calibration and image registration is presented to correct for these errors. The study then evaluates various VIs, finding that models integrating indices like Triangular Greenness Index (TGI) and Excess Green Index (ExG) show improved detection performance compared to using only the basic spectral bands, particularly between crop rows. Finally, the third part draws parallels to biometrics to explore the robustness of different network architectures. By evaluating triplet-based and Siamese networks for fingerprint recognition, it is demonstrated that clustering-based approaches using contrastive loss offer better performance with incomplete and variable data. This insight highlights broader principles for creating robust recognition systems. Overall, this thesis contributes methods for addressing key obstacles in precision agriculture, spanning data augmentation, sensor calibration, and the optimization of deep learning models. The findings of this work aim to contribute to the ongoing development of more effective agricultural practices.Paper III is excluded from the attached file because of being submitted for publication in a journal.</p
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
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