13 citations · 29 across the 13 of their papers we have counts for
13 papers
EVOTS: Evolutionary Transformer Search for Time Series Forecasting
AbdElRahman ElSaid, Damir Pulatov
Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored, with most approaches relying on fixed Transformer architectures despite subs…
Mobile Health Text Misinformation Identification Using Mobile Data Mining
Wen-Chen Hu, Sanjaikanth E Vadakkethil Somanathan Pillai, Abdelrahman Ahmed ElSaid
More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to k…
Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization
Abdelrahman Elsaid
Crafting neural network architectures manually is a formidable challenge often leading to suboptimal and inefficient structures. The pursuit of the perfect neural configuration is…
Backpropagation-Free 4D Continuous Ant-Based Neural Topology Search
AbdElRahman ElSaid, Karl Ricanek, Zeming Lyu +2
Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (A…
Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics
Aizaz Ul Haq, Niranjana Deshpande, AbdElRahman ElSaid +2
Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or ac…
Continuous Ant-Based Neural Topology Search
AbdElRahman ElSaid, Joshua Karns, Zimeng Lyu +2
This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), whic…
An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution
Zimeng Lyu, AbdElRahman ElSaid, Joshua Karns +2
Weight initialization is critical in being able to successfully train artificial neural networks (ANNs), and even more so for recurrent neural networks (RNNs) which can easily suff…
Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation
AbdElRahman ElSaid, Joshua Karns, Alexander Ororbia +3
Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset. While this has been shown to be qui…
Improving Neuroevolution Using Island Extinction and Repopulation
Zimeng Lyu, Joshua Karns, AbdElRahman ElSaid +1
Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is through the use of islands, w…
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks
AbdElRahman A. ElSaid, Alexander G. Ororbia, Travis J. Desell
Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, we propose a…
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution
Travis J. Desell, AbdElRahman A. ElSaid, Alexander G. Ororbia
Neuro-evolution and neural architecture search algorithms have gained increasing interest due to the challenges involved in designing optimal artificial neural networks (ANNs). Whi…
Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution
Alexander Ororbia, Ahmed Ahmed Elsaid, Travis Desell
This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variet…
Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration
AbdElRahman ElSaid, Travis Desell, Fatima El Jamiy +2
This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM)…