Search Results for author:
Found 39 papers, 29 papers with code
Remote sensing framework for geological mapping via stacked autoencoders and clustering
In this study, we present an unsupervised machine learning framework for processing remote sensing data by utilizing stacked autoencoders for dimensionality reduction and k-means clustering for mapping geological units.
Self-supervised learning for skin cancer diagnosis with limited training data
Our framework is applicable to cancer image classification models in the low-labelled data regime.
Large language model for Bible sentiment analysis: Sermon on the Mount
Sentiment analysis provides a mechanism to study the emotions expressed in text.
A clustering and graph deep learning-based framework for COVID-19 drug repurposing
The dataset consists of 438 drugs, of which 224 are under clinical trials for COVID-19 (category A).
An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines
Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes.
Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams
In this paper, we present an analysis of the most effective architectures for the fusion of deep learning models for the prediction of company credit rating classes, by using structured and unstructured datasets of different types.
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data augmentation methods to address class imbalance problems.
Bayesian neural networks via MCMC: a Python-based tutorial
In the past three decades, MCMC sampling methods have faced some challenges in being adapted to larger models (such as in deep learning) and big data problems.
An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis
Sanskrit is known as the mother of languages such as Hindi and an ancient source of the Indo-European group of languages.
Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron
Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19.
Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing
Our framework compares different clustering methods for reef habitat mapping using remote sensing data.
Recursive deep learning framework for forecasting the decadal world economic outlook
Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes such as pandemics and wars.
Evolutionary bagging for ensemble learning
Random forest is a prominent example of bagging with additional features in the learning process.
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences.
Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita
The Bhagavad Gita is core text of Hindu philosophy and is known as a text that summarises the key philosophies of the Upanishads with major focus on the philosophy of karma.
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem
The resource constrained project scheduling problem (RCPSP) is an NP-Hard combinatorial optimization problem.
Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models
The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in a better understanding of paleoclimate and geomorphology.
Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework
Recent progress of language models powered by deep learning has enabled not only translations but a better understanding of language and texts with semantic and sentiment analysis.
SMOTified-GAN for class imbalanced pattern classification problems
The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets.
Bayesian graph convolutional neural networks via tempered MCMC
Bayesian inference provides a principled approach to uncertainty quantification of model parameters for deep learning models.
Revisiting Bayesian Autoencoders with MCMC
Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust uncertainty quantification remains a challenge.
Memory Capacity of Recurrent Neural Networks with Matrix Representation
We demonstrate the performance of this class of memory networks under certain algorithmic learning tasks such as copying and recall and compare it with Matrix RNNs.
COVID-19 sentiment analysis via deep learning during the rise of novel cases
In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India.
Evaluation of deep learning models for multi-step ahead time series prediction
The deep learning methods comprise simple recurrent neural networks, long short-term memory (LSTM) networks, bidirectional LSTM networks, encoder-decoder LSTM networks, and convolutional neural networks.
Ranked #1 on
Time Series Prediction
on Sunspot
(using extra training data)
A review of machine learning in processing remote sensing data for mineral exploration
As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits.
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown
Air pollution has a wide range of implications on agriculture, economy, road accidents, and health.
Deep learning via LSTM models for COVID-19 infection forecasting in India
Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences.
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
In this paper, we apply surrogate-assisted parallel tempering where that surrogate mimics a landscape evolution model by estimating the likelihood function from the model.
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo sampling to assess uncertainty, using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study.
Surrogate-assisted parallel tempering for Bayesian neural learning
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data.
Langevin-gradient parallel tempering for Bayesian neural learning
This not only provides point estimates of optimal set of weights but also the ability to quantify uncertainty in decision making using the posterior distribution.
Computer vision-based framework for extracting geological lineaments from optical remote sensing data
We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data.
Multi-core parallel tempering Bayeslands for basin and landscape evolution
The results show that PT in Bayeslands not only reduces the computation time over a multi-core architecture, but also provides a means to improve the sampling process in a multi-modal landscape.
Geophysics Distributed, Parallel, and Cluster Computing
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
The inference of unknown parameters is challenging due to the scarcity of data, sensitivity of the parameter setting and complexity of the Badlands model.
Stacked transfer learning for tropical cyclone intensity prediction
In this paper, we employ transfer stacking as a means of studying the effects of cyclones whereby we evaluate if cyclones in different geographic locations can be helpful for improving generalization performs.
Co-evolutionary multi-task learning for dynamic time series prediction
In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and co-evolutionary algorithms to address dynamic time series prediction.
Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks
A class imbalanced problem is encountered which makes it very challenging to achieve promising performance.
An affective computational model for machine consciousness
In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations.
Design of a Mobile Face Recognition System for Visually Impaired Persons
It is estimated that 285 million people globally are visually impaired.
