1,721,081 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
Forecasting the electricity demand using machine learning algorithms
No full textForecasting the electricity demand has been the key task to reach the long-term goals of
European Union. Inadequacy of accurate measurement of electricity demand which further
causes the under generation or over generation of electricity and may also results in huge
investments on energy resources. Predictive analysis and time series forecasting methods are
used to overcome these difficulties. The aim of this paper is to short-term forecast the electricity
demand using the seasonal auto-regressive Integrated moving average (SARIMA) and compare
the results with the Multi-layer perceptron (MLP) model. The half-hourly dataset of London
city is used for analyzing the electricity demand which is collected from UK power networks
along with weather and holidays in the same city from November 2011 to February 2014. The
forecasting plots has made based on the maximum, minimum and average consumption and
then compare the mean absolute error (MAE), mean absolute percentage error (MAPE), mean
square error (MSE) and root mean square error (RMSE) of SARIMA with the MLP model.
During the evaluation MLP outperformed the SARIMA model and the prediction graphs are
displayed using User Interface
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
Predicting rainfall for agriculture in India using regression
No full textIn recent years the machine learning has been proven as a powerful tool for predicting the rainfall
that could be useful in many sectors. This study will focus on predicting rainfall for the agriculture
sector. Indian climatic conditions vary in terms of rainfall, which can be divided and observed
on the basis of states. In addition, if rainfall is categorized state-wise; the constant & highest
trend can be observed in the state called Meghalaya. While the lowest rainfall can be observed
in both of the following states - Leh and Rajasthan. Agriculture is the crucial player in the econ-
omy of India, and it is highly dependent on agriculture and forestry, which are a ected by rainfall
[Krishna Kumar et al., 2004].
Disaster due to heavy rainfall like
oods leads to the destruction of crops which a ects the farming
sectors. If the prediction for rainfall is made by taking monthly and seasonal data of the crop into
consideration; then it would be bene cial for the agriculture sector. This study will be applying the
regression algorithms by di erent models, which can help in predicting the rainfall. To achieve such
results, this study will be using ve various regression models and select the best one among - Mul-
tiple linear regression, KNN regression, SVM(Support Vector Machine) regression, DTR(Decision
tree regression), RFE(Random forest regression. The aim is to develop a model that can predict
the rainfall that will help the agriculture sector, so that rainfall doesn't become a barrier for the
agricultural production
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
Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain and Yield Predictions
No full textAgriculture is a critical part of the world’s food production, being a vital aspect of all societies.
Procedures need to be adjusted to their specific environment because of their climate and field
condition disparity. Existing research has demonstrated the potential of grain yield predictions on
Norwegian farms. However, this research is limited to regional analytics, which is unable to acquire
sufficient plant growth factors influenced by field conditions and farmers’ decisions. One factor
critical for yield prediction is the crop type planted on a per-field basis.
This research effort proposes a novel approach for improving crop yield predictions using a hybrid
deep neural network utilizing temporal satellite imagery from a remote sensing system. Additionally, We apply a variety of data, including grain production, meteorological data, and geographical
data. The crop yield prediction system is supported by a field-based crop type classification model,
which supplies features related to crop type and field area. Our crop classification system takes
advantage of both raw satellite images as well as carefully chosen vegetation indices. Further, we
propose a multi-class attention-based deep multiple instance learning model to utilize semi-labeled
datasets, fully benefiting Norwegian data acquisition.
Our best crop classification model, which consists of a time distributed network and a gated recurrent unit, classifies crop types with an accuracy of 70% and is currently state-of-the-art for
country-wide crop type mapping in Norway. Lastly, our yield prediction system enables realistic
in-season early predictions that could benefit actors in real-life scenarios
Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain Identification and Grain Yield Predictions
Get PDFAgriculture is a critical part of the world's food production, being a vital aspect of all societies. Procedures need to be adjusted to their specific environment because of their climate and field condition disparity. Existing research has demonstrated the potential of grain yield predictions on Norwegian farms. However, this research is limited to regional analytics, which is unable to acquire sufficient plant growth factors influenced by field conditions and farmers' decisions. One factor critical for yield prediction is the crop type planted on a per-field basis.
This research effort proposes a novel approach for improving crop yield predictions using a hybrid deep neural network utilizing temporal satellite imagery from a remote sensing system. Additionally, We apply a variety of data, including grain production, meteorological data, and geographical data. The crop yield prediction system is supported by a field-based crop type classification model, which supplies features related to crop type and field area. Our crop classification system takes advantage of both raw satellite images as well as carefully chosen vegetation indices. Further, we propose a multi-class attention-based deep multiple instance learning model to utilize semi-labeled datasets, fully benefiting Norwegian data acquisition.
Our best crop classification model, which consists of a time distributed network and a gated recurrent unit, classifies crop types with an accuracy of 70\% and is currently state-of-the-art for country-wide crop type mapping in Norway. Lastly, our yield prediction system enables realistic in-season early predictions that could benefit actors in real-life scenarios
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