1,720,955 research outputs found
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
Application of artificial intelligence to variable rate technology in agriculture
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electronic Systems Engineering, University of Regina. xvii, 192 p.Variable Rate Application (VRA) plays a pivotal role in enhancing agricultural
profitability by optimizing the use of resources and promoting consistent crop growth.
This also helps mitigate the negative environmental impact of farming practices.
However, the implementation of VRA is heavily reliant on data. An effective VRA
prescription involves an agronomist’s in-depth knowledge of the soil and crop conditions
within Homogenized Management Zones (HMZs). Certain soil attributes like
electrical conductivity, elevation, and soil moisture are measured using proximal sensors
installed on farm machinery. However, other soil properties like soil texture
and Soil Organic Matter (SOM) measurements require soil sampling and laboratorybased
testing. Similarly, crop and weed information is gathered via manual scouting.
The collected SOM, soil texture, and crop information based on limited sampling
may not be representative of whole field conditions resulting in low spatio-temporal
resolution of information. Our research seeks to bridge these gaps by proposing costeffective
and scalable solutions that improve spatio-temporal resolution. We suggest
installing RGB sensors on farm machinery to monitor crop and weed growth, categorize
soil texture, and estimate SOM. This high spatio-temporal information gathered
is subsequently processed to investigate if improved HMZs can be identified. We
develop crop and weed-specific semantic segmentation methods to detect, localize
and quantify crops/weeds, yielding a mean Intersection Over Union (mIOU) up to
83%. These semantic segmentation models are customized to handle agricultural
image data, minimizing memory usage and computational costs during training and
inference. Through this adaptation, we observe a 6% performance improvement in
crop and weed semantic segmentation. The efficiency of binary semantic segmentation
models is further enhanced by up to 12% using ensemble learning methods. We
recognize the strong correlation between soil properties and crop/weed densities and
thus use this relationship to our advantage. We train machine learning models to
predict crop and weed densities based on soil properties and satellite data. To accurately
predict SOM and soil texture from RGB images, we employ a hybrid approach
that combines deep learning and conventional image-processing techniques to overcome
the challenges posed by uncontrolled field conditions data. Lastly, we explore
the potential for identifying HMZs based on resultant high-resolution crop and soil
information. Parts of this research are successfully commercialized under the product
name ”SWATCAM”.Studentye
Estimation of Weed Densities for Variable Rate Herbicide Application
No full textA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Electronic Systems Engineering, University of Regina. xiii, 79 p.Use of herbicides is rising globally to maximize crop yield and profitability. Herbicides
negatively impact environmental health and biosphere. To lessen its negative
effects, herbicides have to be applied judiciously on crops. Precision agriculture
practices suggest adoption of site specific weed management techniques by exploiting
patchy nature of weed distribution in the fields which requires accurate weed mapping.
Despite recent technical advancement and growing awareness about environment protection, site specific weed management has not got traction in farmer community. In
this thesis, endeavours are made to develop relatively simple site specific weed control
method using weed density based variable rate herbicide application.
Soil, Water and Topography (SWAT) maps are being used by farmers for variable
rate seeding and fertilizer in prairie lands of Canada. In this work, we investigate
relationship between weeds and SWAT zones and present a new method for variable
rate herbicide application which combines deep learning and SWAT maps. Average
weed densities are estimated in each SWAT zone through deep learning based semantic segmentation in order to help agronomist develop variable rate herbicide
prescription. The study simplifies the weed detection system with the objective to
enhance savings of herbicide quantities less costs involved in site specific weed control.
Manual labeling bottleneck in semantic segmentation is addressed by labeling only
weed pixels. Consequently, trained semantic models zeros out crop pixel along with
background pixels. The developed model has the advantage to detect new types of
weeds. Binary classification of images based on weeds is also studied in this thesis to
compare deep learning models.
By investigating SWAT zones and weed density relationship, it is found that the
zones with higher salinity, organic matter and water content contain higher density
of weeds while the driest zones like eroded hill tops have few or no weeds at all. The
crop specific semantic segmentation models have shown MIOU values greater than
80% and FWIOU values more than 97%. The trained models also show robustness
in detecting unseen weeds. For binary classification problem of detecting weeds in
Canola field, VGG19 has shown 100% accuracy compared to other deep learning
architectures.Studentye
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
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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