686 citations · 1.4k across the 12 of their papers we have counts for
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EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning
Mohammadreza Iman, John A. Miller, Khaled Rasheed +2
Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge. H…
The application of Evolutionary and Nature Inspired Algorithms in Data Science and Data Analytics
Farid Ghareh Mohammadi, Farzan Shenavarmasouleh, Khaled Rasheed +3
In the past 30 years, scientists have searched nature, including animals and insects, and biology in order to discover, understand, and model solutions for solving large-scale scie…
An Integrated Approach for Video Captioning and Applications
Soheyla Amirian, Thiab R. Taha, Khaled Rasheed +1
Physical computing infrastructure, data gathering, and algorithms have recently had significant advances to extract information from images and videos. The growth has been especial…
Generative Adversarial Network Applications in Creating a Meta-Universe
Soheyla Amirian, Thiab R. Taha, Khaled Rasheed +1
Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively ut…
A Review of Deep Transfer Learning and Recent Advancements
Mohammadreza Iman, Khaled Rasheed, Hamid R. Arabnia
Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two major constraints: dependency on extensive labeled data…