ML-based classification of eye movement patterns during reading using eye tracking data from an Apple iPad device: Perspective machine learning algorithm needed for reading quality analytics app on an iPad with built-in eye tracking

2021 ◽  
Author(s):  
V. Anisimov ◽  
K. Chernozatonsky ◽  
A. Pikunov ◽  
K. Shedenko ◽  
D. Zhigulskaya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Eye Movement ◽  
Learning Algorithm ◽  
Movement Patterns ◽  
Machine Learning Algorithm ◽  
Tracking Data ◽  
Apple Ipad

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

Detecting Cognitive Distraction Using Random Forest by Considering Eye Movement Type

2018 ◽  
pp. 1587-1599
Author(s):  
Hiroaki Koma ◽  
Taku Harada ◽  
Akira Yoshizawa ◽  
Hirotoshi Iwasaki
Keyword(s):  
Machine Learning ◽  
Eye Movements ◽  
Random Forest ◽  
Decision Trees ◽  
Eye Movement ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Still Images ◽  
Cognitive Distraction ◽  
Movement Type

Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

An Analysis of Machine Learning Algorithm for the Classification of Emotion Recognition

2021 ◽  
pp. 399-408
Author(s):  
Aditi Sakalle ◽  
Pradeep Tomar ◽  
Harshit Bhardwaj ◽  
Divya Acharya ◽  
Arpit Bhardwaj
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals Image Classification using CNN and Machine Learning

2019 ◽  
pp. 575-580
Author(s):  
G. Keerthi Devipriya ◽  
E. Chandana ◽  
B. Prathyusha ◽  
T. Seshu Chakravarthy
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Models ◽  
Classification Of Images ◽  
Respective Category

Here by in this paper we are interested for classification of Images and Recognition. We expose the performance of training models by using a classifier algorithm and an API that contains set of images where we need to compare the uploaded image with the set of images available in the data set that we have taken. After identifying its respective category the image need to be placed in it. In order to classify images we are using a machine learning algorithm that comparing and placing the images.

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