1 citations · 3 across the 4 of their papers we have counts for
5 papers
Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization
Shamima Nasrin Tumpa, Kehelwala Dewage Gayan Maduranga
This study explores the use of Recurrent Neural Networks (RNN) for real-time cryptocurrency price prediction and optimized trading strategies. Given the high volatility of the cryp…
Optimization of Actuarial Neural Networks with Response Surface Methodology
Belguutei Ariuntugs, Kehelwala Dewage Gayan Madurang
In the data-driven world of actuarial science, machine learning (ML) plays a crucial role in predictive modeling, enhancing risk assessment and pricing strategies. Neural networks,…
HomOpt: A Homotopy-Based Hyperparameter Optimization Method
Sophia J. Abraham, Kehelwala D. G. Maduranga, Jeffery Kinnison +3
Machine learning has achieved remarkable success over the past couple of decades, often attributed to a combination of algorithmic innovations and the availability of high-quality…
Symmetry Structured Convolutional Neural Networks
Kehelwala Dewage Gayan Maduranga, Vasily Zadorozhnyy, Qiang Ye
We consider Convolutional Neural Networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions. Such networks arise in modeling pairwise relationships fo…
Complex Unitary Recurrent Neural Networks using Scaled Cayley Transform
Kehelwala D. G. Maduranga, Kyle E. Helfrich, Qiang Ye
Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well known difficulty in using RNNs is the \textit{vanishing or explodin…