scholarly journals Relative contributions of Shakespeare and Fletcher in Henry VIII: An analysis based on most frequent words and most frequent rhythmic patterns

2020 ◽  
Author(s):  
Petr Plecháč
Keyword(s):  
Machine Learning ◽  
William Shakespeare ◽  
Collaborative Work ◽  
Machine Learning Techniques ◽  
Henry Viii ◽  
John Fletcher ◽  
Combined Analysis ◽  
Learning Techniques ◽  
New Evidence

Abstract The versified play Henry VIII is nowadays widely recognized to be a collaborative work not written solely by William Shakespeare. We employ combined analysis of vocabulary and versification together with machine learning techniques to determine which other authors took part in the writing of the play and what were their relative contributions. Unlike most previous studies, we go beyond the attribution of particular scenes and use the rolling attribution approach to determine the probabilities of authorship of pieces of texts, without respecting the scene boundaries. Our results highly support the canonical division of the play between William Shakespeare and John Fletcher proposed by James Spedding, but also bring new evidence supporting the modifications proposed later by Thomas Merriam.

scholarly journals Adoption of machine learning techniques in Ecology and Earth Science

2016 ◽  
Author(s):  
Anne E Thessen
Keyword(s):  
Machine Learning ◽  
Earth Science ◽  
Collaborative Work ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Expert Annotation ◽  
Natural Scientists

The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

scholarly journals Collection, Processing and Analysis of Heterogeneous Data Coming from Spanish Hospitals in the Context of COVID-19

2021 ◽  
Author(s):  
Marta Barroso ◽  
Adrián Tormos ◽  
Raquel Pérez-Arnal ◽  
Sergio Alvarez-Napagao ◽  
Dario Garcia-Gasulla
Keyword(s):  
Machine Learning ◽  
Clinical Studies ◽  
Medical Records ◽  
Data Entry ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Publicly Funded ◽  
Combined Analysis ◽  
Learning Techniques

The COVID-19 pandemic has already caused more than 150,000,000 cases worldwide. In Spain this has lead to a massive and simultaneous saturation of all sanitary regions. Coherently, the quick and consistent understanding of the COVID-19 disease requires of the combined analysis of thousands of medical records generated by dozens of different institutions. In the context of the publicly funded CIBERES-UCI-COVID project, we have gathered, cleaned and preprocessed data from heterogeneous sources – more than 30 hospitals, with different data entry systems – in order to produce a unified database, of more than 6.000 patients, that is used in several clinical studies being carried by different multidisciplinary groups. In this paper, we identify the complexities we encountered, the solutions we applied, and we summarise the statistical and machine learning techniques we have applied for the studies.

scholarly journals Adoption of machine learning techniques in Ecology and Earth Science

2016 ◽  
Author(s):  
Anne E Thessen
Keyword(s):  
Machine Learning ◽  
Earth Science ◽  
Collaborative Work ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Expert Annotation ◽  
Natural Scientists

The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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