What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Agriculture
His primary scientific interests are in Mean squared error, Extreme learning machine, Statistics, Climatology and Multivariate statistics.
The concepts of his Mean squared error study are interwoven with issues in Convolutional neural network, Data mining and Pattern recognition.
His Extreme learning machine research is classified as research in Artificial neural network.
His Wavelet research extends to Statistics, which is thematically connected.
His Climatology research incorporates themes from Land cover, Climate extremes, Coefficient of determination and Decile.
His study focuses on the intersection of Autoregressive integrated moving average and fields such as Linear regression with connections in the field of Meteorology and Algorithm.
His most cited work include:
- An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction (235 citations)
- Impacts of land use/land cover change on climate and future research priorities (183 citations)
- A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset (176 citations)
What are the main themes of his work throughout his whole career to date?
Ravinesh C. Deo mainly investigates Mean squared error, Statistics, Artificial neural network, Artificial intelligence and Climatology.
In his research, Solar energy is intimately related to Meteorology, which falls under the overarching field of Mean squared error.
His work in the fields of Statistics, such as Correlation coefficient, Regression and Multivariate statistics, overlaps with other areas such as Mars Exploration Program.
His Artificial neural network study combines topics from a wide range of disciplines, such as Algorithm, Wavelet and Linear regression.
Ravinesh C. Deo has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His Climatology research incorporates elements of Global warming, Climate change and Precipitation.
He most often published in these fields:
- Mean squared error (20.00%)
- Statistics (18.43%)
- Artificial neural network (16.08%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (13.73%)
- Mean squared error (20.00%)
- Artificial neural network (16.08%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Mean squared error, Artificial neural network, Random forest and Statistics.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
The Mean squared error study combines topics in areas such as Tree, Hilbert–Huang transform, Coefficient of determination and Wavelet transform.
His Artificial neural network research includes themes of Wind power, Support vector machine, k-nearest neighbors algorithm, Solar energy and Renewable energy.
His Random forest study integrates concerns from other disciplines, such as Wind speed, Streamflow, Computational intelligence and Water resources.
The study incorporates disciplines such as Copula and Flood myth in addition to Statistics.
Between 2019 and 2021, his most popular works were:
- Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia (28 citations)
- Hybridized neural fuzzy ensembles for dust source modeling and prediction (20 citations)
- Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model (18 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Agriculture
Ravinesh C. Deo mainly focuses on Mean squared error, Artificial neural network, Random forest, Artificial intelligence and Hydrology.
His Mean squared error study results in a more complete grasp of Statistics.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Industrial engineering, Conjugate gradient method and k-nearest neighbors algorithm.
Ravinesh C. Deo focuses mostly in the field of Random forest, narrowing it down to topics relating to Computational intelligence and, in certain cases, Stage, Global warming and Feature selection.
His work on Drainage and Stormwater as part of general Hydrology research is frequently linked to Total suspended solids and Total phosphorus, bridging the gap between disciplines.
His Machine learning study incorporates themes from Grid and Climate change.
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