文章基本信息

标题：Enhancement Of Eye Tracking Based On Deep Learning Using Manta Ray Foraging Optimization (MRFO)
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作者：Ahmed A. Salman ; Mohammed H. Ali
期刊名称：Webology
印刷版ISSN：1735-188X
出版年度：2022
卷号：19
期号：2
页码：8938-8956
语种：English
出版社：University of Tehran
摘要：Smart surveillance, decision making and automated response systems are trending topics nowadays, especially after performance’s growth for a wide range of control systems, which is a normal result of the noticeable evolution in the areas of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques made dramatically increasing in performance in terms of accuracy and results that have been reached for various systems and algorithms used for different applications in all fields. This development enabled human beings to make a lot of tasks that were done within manual manners much easier than before, not only easier but less time and power-consuming with reducing the numbers of essential operators. The produced application is Eye-tracking based on CNN; the dataset used for training was about 5568 images, gathered using the camera of laptop based on software designed as shown later. The maximum accuracy achieved by this model 97.57%. Many tools have been used for various types of necessary tasks which will be parts of the desired application such as: Python 3.7; which was used for building the essential algorithms, KERAS framework; which provided the deep learning algorithms, Visual studio code (VSC) as an Integrated Development Environment (IDE) and Anaconda navigator for downloading the different compatible libraries. The proposed models were trained on Processor Intel(R) Core (TM) i7-10750H CPU @ 2.60GHz 2.59 GHz, RAM 16.0 GB, 64-bit operating system, x64-based processor, NVIDIA GeForce GTX 1650 GPU.
关键词：Eye tracking;Artificial intelligence;Machine learning;Deep learning;CNN;MRFO