scholarly journals Application of mathematical and machine learning techniques to analyse eye-tracking data enabling better understanding of children’s visual-cognitive behaviours

2018 ◽  
pp. 72-84
Author(s):  
Enrique Garcia Moreno-Esteva ◽  
Sonia L. J. White ◽  
Joanne M. Wood ◽  
Alex A. Black
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Machine Learning Techniques ◽  
Tracking Data ◽  
Learning Techniques ◽  
Cognitive Behaviours ◽  
Visual Cognitive

scholarly journals The Scientific Study of Passive Thinking: The Methodology of Mind Wandering Research

2020 ◽  
Author(s):  
Samuel Murray ◽  
Zachary Irving ◽  
Kristina Krasich
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Eye Tracking ◽  
Mind Wandering ◽  
Self Report ◽  
Machine Learning Techniques ◽  
Objective Measures ◽  
Learning Techniques ◽  
Self Reports ◽  
Direct Sensitivity

In this chapter, we survey methodological challenges in the empirical study of mind wandering and provide a metaphysical framework that begins to address these challenges. We argue that mind wandering is a passive manifestation of agency—passive because people cannot mind wander on command and a manifestation of agency because the onset, progression, and content of mind wandering often exhibits direct sensitivity to personal concerns and plans. To measure passive thinking, researchers must ask, “Is your mind wandering?” Worries about this self-report methodology have encouraged researchers to develop “objective” measures of mind wandering through eye tracking and machine learning techniques. These “objective” measures, however, are validated in terms of how well they predict self-reports, which means that purportedly objective measures of mind wandering retain a subjective core. To assuage worries about self-report (and, ultimately, vindicate objective measures of mind wandering), we offer a metaphysical account of mind wandering that generates several predictions about its causes and consequences. This account also justifies different methods for measuring mind wandering.

scholarly journals Diseño de un modelo de atención visual para campañas de comunicación. El caso de la Covid-19

2020 ◽  
Author(s):  
Juan-José Silva-Torres ◽  
Luz Martínez-Martínez ◽  
Ubaldo Cuesta-Cambra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Eye Tracking ◽  
Influenza A ◽  
Social Consequences ◽  
Machine Learning Techniques ◽  
Mathematical Tool ◽  
Learning Techniques ◽  
Mathematical Techniques

Health is one of the main concerns of society. Empirical evidence underscores the growing importance of prevention and health education as a fundamental instrument to improve the quality of public health. Recent health crises, such as Ebola, influenza A, SARS, and Covid-19, have highlighted the importance of communication. When designing communication campaigns during a crisis, the speed of the creation of messages and their effectiveness have relevant social consequences. The objective of this work is to design and develop a mathematical tool, based on Machine Learning techniques, to enable predictions of areas of visual attention quickly and accurately without the use of eye-tracking technology. The methodology combines deep learning algorithms, to extract the characteristics of the images, and supervised modeling mathematical techniques, to predict the areas of attention. Validation is carried out by analyzing various institutional communications from the Covid-19 campaign, comparing the results with the areas of attention obtained using an eye-tracking solution with proven accuracy. The results obtained using the tool in the investigated Covid-19 communication pieces are analyzed, resulting in conclusions of interest for the development of new campaigns. Resumen La salud es una de las principales preocupaciones de la sociedad. La evidencia empírica subraya la importancia creciente de la prevención y la educación para la salud (EpS) como instrumento fundamental para mejorar la calidad de la salud pública. Las recientes crisis sanitarias, como la del Ébola, la gripe A, el SARS o la Covid-19, han puesto de relieve la importancia de la comunicación. En el diseño de campañas de comunicación en tiempos de crisis, la rapidez en la creación de mensajes y la validación de su eficacia tiene consecuencias sociales relevantes. En este trabajo se ha diseñado y desarrollado una herramienta matemática basada en machine learning que permite predecir las áreas de atención visual de forma rápida y precisa, sin necesidad de utilizar tecnología de seguimiento ocular (eye-tracking). La metodología combina algoritmos de deep learning, para extraer las características de las imágenes, con técnicas de modelización supervisada para predecir las áreas de atención. La validación se ha realizado analizando varias comunicaciones institucionales de la campaña de Covid-19, comparando los resultados con las áreas de atención obtenidas con una solución de seguimiento ocular de validez contrastada. Se analizan los resultados obtenidos mediante la herramienta en las piezas de comunicación Covid-19 investigadas, planteando conclusiones de interés para el desarrollo de nuevas campañas.

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.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Sign in / Sign up

Export Citation Format

Share Document