scholarly journals Data-driven classification of the certainty of scholarly assertions

2019 ◽  
Author(s):  
Mario Prieto ◽  
Helena Deus ◽  
Anita De Waard ◽  
Erik Schultes ◽  
Beatriz García-Jiménez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Cross Validation ◽  
Classification Systems ◽  
Data Driven ◽  
Formal Ontology ◽  
Machine Learning Model ◽  
Linguistic Cues ◽  
Grammatical Structures

The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation - a Nanopublication - where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.

scholarly journals Data-driven classification of the certainty of scholarly assertions

2019 ◽  
Author(s):  
Mario Prieto ◽  
Helena Deus ◽  
Anita De Waard ◽  
Erik Schultes ◽  
Beatriz García-Jiménez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Cross Validation ◽  
Classification Systems ◽  
Data Driven ◽  
Formal Ontology ◽  
Machine Learning Model ◽  
Linguistic Cues ◽  
Grammatical Structures

The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation - a Nanopublication - where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.

scholarly journals Data-driven classification of the certainty of scholarly assertions

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8871
Author(s):  
Mario Prieto ◽  
Helena Deus ◽  
Anita de Waard ◽  
Erik Schultes ◽  
Beatriz García-Jiménez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Cross Validation ◽  
Classification Systems ◽  
Data Driven ◽  
Formal Ontology ◽  
Machine Learning Model ◽  
Linguistic Cues ◽  
Grammatical Structures

The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation—a Nanopublication—where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.

scholarly journals Data-driven classification of the certainty of scholarly assertions

2019 ◽  
Author(s):  
Mario Prieto ◽  
Helena Deus ◽  
Anita De Waard ◽  
Erik Schultes ◽  
Beatriz García-Jiménez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Significant Degree ◽  
Classification Systems ◽  
Data Driven ◽  
Formal Ontology ◽  
Machine Learning Model ◽  
Linguistic Cues ◽  
Grammatical Structures

The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty. However, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three categories of certainty perceived by readers: one level of high certainty, and two levels of lower certainty that are somewhat less distinct, but nevertheless show a significant degree of inter-annotator agreement. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation - a Nanopublication - where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.

scholarly journals Speech-Based Emotion Recognition using Neural Networks and Information Visualization

2020 ◽  
Author(s):  
Jumana Almahmoud ◽  
Kruthika Kikkeri
Keyword(s):  
Machine Learning ◽  
Information Visualization ◽  
Healthcare Professionals ◽  
Health Assessment ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Emotion Classification ◽  
Machine Learning Model ◽  
Speech Data

Emotions recognition is commonly employed for health assessment. However, the typical metric for evaluation in therapy is based on patient-doctor appraisal. This process can fall into the issue of subjectivity, while also requiring healthcare professionals to deal with copious amounts of information. Thus, machine learning algorithms can be a useful tool for the classification of emotions. While several models have been developed in this domain, there is a lack of userfriendly representations of the emotion classification systems for therapy. We propose a tool which enables users to take speech samples and identify a range of emotions (happy, sad, angry, surprised, neutral, clam, disgust, and fear) from audio elements through a machine learning model. The dashboard is designed based on local therapists' needs for intuitive representations of speech data in order to gain insights and informative analyses of their sessions with their patients.

scholarly journals Breath can discriminate tuberculosis from other lower respiratory illness in children

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carly A. Bobak ◽  
Lili Kang ◽  
Lesley Workman ◽  
Lindy Bateman ◽  
Mohammad S. Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lower Respiratory Tract ◽  
Cross Validation ◽  
Pediatric Patients ◽  
Respiratory Illness ◽  
Health Crisis ◽  
Machine Learning Model ◽  
Childhood Tb ◽  
Pediatric Tb ◽  
Pediatric Tuberculosis

AbstractPediatric tuberculosis (TB) remains a global health crisis. Despite progress, pediatric patients remain difficult to diagnose, with approximately half of all childhood TB patients lacking bacterial confirmation. In this pilot study (n = 31), we identify a 4-compound breathprint and subsequent machine learning model that accurately classifies children with confirmed TB (n = 10) from children with another lower respiratory tract infection (LRTI) (n = 10) with a sensitivity of 80% and specificity of 100% observed across cross validation folds. Importantly, we demonstrate that the breathprint identified an additional nine of eleven patients who had unconfirmed clinical TB and whose symptoms improved while treated for TB. While more work is necessary to validate the utility of using patient breath to diagnose pediatric TB, it shows promise as a triage instrument or paired as part of an aggregate diagnostic scheme.

Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test

2021 ◽  
pp. 190-200
Author(s):  
Lesia Mochurad ◽  
Yaroslav Hladun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Real Data ◽  
Finger Tapping ◽  
Similar Distribution ◽  
Model Learning ◽  
Machine Learning Model ◽  
Finger Tapping Test

The paper considers the method for analysis of a psychophysical state of a person on psychomotor indicators – finger tapping test. The app for mobile phone that generalizes the classic tapping test is developed for experiments. Developed tool allows collecting samples and analyzing them like individual experiments and like dataset as a whole. The data based on statistical methods and optimization of hyperparameters is investigated for anomalies, and an algorithm for reducing their number is developed. The machine learning model is used to predict different features of the dataset. These experiments demonstrate the data structure obtained using finger tapping test. As a result, we gained knowledge of how to conduct experiments for better generalization of the model in future. A method for removing anomalies is developed and it can be used in further research to increase an accuracy of the model. Developed model is a multilayer recurrent neural network that works well with the classification of time series. Error of model learning on a synthetic dataset is 1.5% and on a real data from similar distribution is 5%.

A Machine Learning Model for Automated Classification of Sleep Stages Using Polysomnography Signals

2021 ◽  
pp. 209-222
Author(s):  
Santosh Kumar Satapathy ◽  
Hari Kishan Kondaveeti ◽  
D. Loganathan ◽  
S. Sharathkumar
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Sleep Stages ◽  
Automated Classification ◽  
Machine Learning Model

scholarly journals Deep learning based DNA:RNA triplex forming potential prediction

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yu Zhang ◽  
Yahui Long ◽  
Chee Keong Kwoh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cross Validation ◽  
Roc Curves ◽  
Triplex Dna ◽  
Triplex Formation ◽  
Machine Learning Model ◽  
Non Coding Rnas ◽  
High Level ◽  
Integrated Program

Abstract Background Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: (1) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and (2) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. Results In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the fivefold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. Conclusions The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

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