Ravinesh Deo

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Professor

University of Southern Queensland

Australia

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About

Ravinesh Deo is a Professor at the University of Southern Queensland, Australia. His research areas include deep learning applications in medical diagnostics, particularly for glaucoma detection and Alzheimer's disease, as well as advanced modeling for electricity price prediction and demand uncertainty. He also explores intelligent systems for driver distraction recognition and asthma detection using sound signals. His recent work emphasizes explainable AI frameworks and data-driven insights in thermodynamic efficiency.

Recent Grants

Grant: Close

Exploring decision-support systems to address current uncertainties in electrical energy demand, pricing, security and renewable energy integration via data analytics and predictive models in Australian and Japanese competitive energy markets

Open Date: 2017-12-01

Close Date: 2018-01-01

Grant: Close

Modelling impacts of vegetation cover change on regional climate

Open Date: 2006-01-01

Close Date: 2009-01-01

Grant: Close

Large and Small-Scale Mixing in Turbulent Planar Jets

Open Date: 2001-01-01

Close Date: 2005-01-01

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Collaborators (15)

Jorge Pérez-Aracil

Universidad de Alcalá

SPAIN

Ravinesh Deo

Research Interests

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About

Recent Grants

Articles (29)

Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images

Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals

Novel deep hybrid model for electricity price prediction based on dual decomposition

Explainable deep learning hybrid modeling framework for total suspended particles concentrations prediction

Electricity demand uncertainty modeling with Temporal Convolution Neural Network models

NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals

Intelligent modeling and analysis of hybrid organic Rankine plants: Data-driven insights into thermodynamic efficiency and economic viability

Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)

Explainable hybrid deep learning framework for enhancing multi-step solar ultraviolet-B radiation predictions

Very short-term solar ultraviolet-A radiation forecasting system with cloud cover images and a Bayesian optimized interpretable artificial intelligence model

Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model

Probabilistic-based electricity demand forecasting with hybrid convolutional neural network-extreme learning machine model

Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach

Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery

Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model

Electricity demand error corrections with attention bi-directional neural networks

End-to-end learning of adaptive coded modulation schemes for resilient wireless communications

Deep Learning Based Over-the-Air Training of Wireless Communication Systems without Feedback

Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites

Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression

A fuzzy-based cascade ensemble model for improving extreme wind speeds prediction

Enhanced joint hybrid deep neural network explainable artificial intelligence model for 1-hr ahead solar ultraviolet index prediction

Downscaling Surface Albedo to Higher Spatial Resolutions With an Image Super-Resolution Approach and PROBA-V Satellite Images

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)

Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012–2022)

Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach

Near real-time wind speed forecast model with bidirectional LSTM networks

Developing a novel hybrid method based on dispersion entropy and adaptive boosting algorithm for human activity recognition

Collaborators (15)

Jorge Pérez-Aracil

Houshyar Asadi

Sancho Salcedo-Sanz

Prabal Datta Barua

Bradley Carter

Thong Nguyen-Huy

Adam Jaffe

Seyyed Ali Pourmousavi Kani

Jeffrey Soar

U. Rajendra Acharya

Shahab Abdulla

Toan Dinh

David Casillas-Pérez

Oliver Faust

César Peláez-Rodríguez

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