686 citations · 1.4k across the 12 of their papers we have counts for
15 papers
Students Success Modeling: Most Important Factors
Sahar Voghoei, James M. Byars, Scott Jackson King +4
The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. The present study, motivated…
Deep Learning in Healthcare: An In-Depth Analysis
Farzan Shenavarmasouleh, Farid Ghareh Mohammadi, Khaled M. Rasheed +1
Deep learning (DL) along with never-ending advancements in computational processing and cloud technologies have bestowed us powerful analyzing tools and techniques in the past deca…
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…
DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM
Farzan Shenavarmasouleh, Farid Ghareh Mohammadi, M. Hadi Amini +3
Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges i…
Automatic Generation of Descriptive Titles for Video Clips Using Deep Learning
Soheyla Amirian, Khaled Rasheed, Thiab R. Taha +1
Over the last decade, the use of Deep Learning in many applications produced results that are comparable to and in some cases surpassing human expert performance. The application d…
The Use of Video Captioning for Fostering Physical Activity
Soheyla Amirian, Abolfazl Farahani, Hamid R. Arabnia +2
Video Captioning is considered to be one of the most challenging problems in the field of computer vision. Video Captioning involves the combination of different deep learning mode…
A Concise Review of Transfer Learning
Abolfazl Farahani, Behrouz Pourshojae, Khaled Rasheed +1
The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where…
A Brief Review of Domain Adaptation
Abolfazl Farahani, Sahar Voghoei, Khaled Rasheed +1
Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perfor…
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
Mohammadreza Iman, Amy Giuntini, Hamid Reza Arabnia +1
People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symp…
GELATO and SAGE: An Integrated Framework for MS Annotation
Khalifeh AlJadda, Rene Ranzinger, Melody Porterfield +6
Several algorithms and tools have been developed to (semi) automate the process of glycan identification by interpreting Mass Spectrometric data. However, each has limitations when…
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz +7
Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Baye…