ORCID Profile
0000-0001-6353-1464
Current Organisations
Nicolaus Copernicus University
,
University of New South Wales
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Applied Statistics | Natural Resource Management | Statistics | Knowledge Representation and Machine Learning
Environmental Management Systems | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management |
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 04-2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 09-2018
Publisher: Copernicus GmbH
Date: 15-07-2019
Abstract: Abstract. The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal properties of any single mode become inadequate uncertainty measures, and s ling methods become inefficient for irregular posteriors or high-dimensional parameter spaces. 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 s ling 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. The inversion is performed using an updated version of the Obsidian distributed inversion software. We find that the posterior for this inversion has a complex local covariance structure, hindering the efficiency of adaptive s ling methods that adjust the proposal based on the chain history. Within the context of a parallel-tempered Markov chain Monte Carlo scheme for exploring high-dimensional multi-modal posteriors, a preconditioned Crank–Nicolson proposal outperforms more conventional forms of random walk. Aspects of the problem setup, such as priors on petrophysics and on 3-D geological structure, affect the shape and separation of posterior modes, influencing s ling performance as well as the inversion results. The use of uninformative priors on sensor noise enables optimal weighting among multiple sensors even if noise levels are uncertain.
Publisher: Public Library of Science (PLoS)
Date: 18-05-2023
DOI: 10.1371/JOURNAL.PONE.0285719
Abstract: Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. 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. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k -mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 09-2009
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 06-2017
Publisher: IEEE
Date: 07-2016
Publisher: Public Library of Science (PLoS)
Date: 07-2021
DOI: 10.1371/JOURNAL.PONE.0253217
Abstract: Recently, there has been much attention in the use of machine learning methods, particularly deep learning for stock price prediction. A major limitation of conventional deep learning is uncertainty quantification in predictions which affect investor confidence. Bayesian neural networks feature Bayesian inference for providing inference (training) of model parameters that provides a rigorous methodology for uncertainty quantification in predictions. Markov Chain Monte Carlo (MCMC) s ling methods have been prominent in implementing inference of Bayesian neural networks however certain limitations existed due to a large number of parameters and the need for better computational resources. Recently, there has been much progress in the area of Bayesian neural networks given the use of Langevin gradients with parallel tempering MCMC that can be implemented in a parallel computing environment. The COVID-19 pandemic had a drastic impact in the world economy and stock markets given different levels of lockdowns due to rise and fall of daily infections. It is important to investigate the performance of related forecasting models during the COVID-19 pandemic given the volatility in stock markets. In this paper, we use novel Bayesian neural networks for multi-step-ahead stock price forecasting before and during COVID-19. We also investigate if the pre-COVID-19 datasets are useful of modelling stock price forecasting during COVID-19. Our results indicate due to high volatility in the stock-price during COVID-19, it is more challenging to provide forecasting. However, we found that Bayesian neural networks could provide reasonable predictions with uncertainty quantification despite high market volatility during the first peak of the COVID-19 pandemic.
Publisher: Springer Science and Business Media LLC
Date: 09-10-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 06-2012
Publisher: Springer International Publishing
Date: 2016
Publisher: International Society for Environmental Information Science (ISEIS)
Date: 03-2009
Publisher: IEEE
Date: 08-2013
Publisher: Elsevier BV
Date: 07-2011
Publisher: IEEE
Date: 07-2014
Publisher: Informa UK Limited
Date: 29-08-2015
Publisher: MDPI AG
Date: 30-06-2023
DOI: 10.3390/INFO14070373
Abstract: Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can be utilised for the management and study of coral reef ecosystems. In this paper, we present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. We evaluate four major clustering approaches based on qualitative and visual assessments which include k-means, hierarchical clustering, Gaussian mixture model, and density-based clustering. We utilise remote sensing data featuring the One Tree Island reef in Australia’s Southern Great Barrier Reef. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters in reefs when compared with other studies. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects.
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 09-2020
Publisher: IEEE
Date: 05-2015
Publisher: MDPI AG
Date: 16-04-2020
DOI: 10.3390/RS12081261
Abstract: There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 07-2015
Publisher: Elsevier BV
Date: 10-2019
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 05-2015
Publisher: Public Library of Science (PLoS)
Date: 19-08-2021
DOI: 10.1371/JOURNAL.PONE.0255615
Abstract: Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. 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. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. We find that the optimistic, annoyed and joking tweets mostly dominate the monthly tweets with much lower portion of negative sentiments. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.
Publisher: Springer Science and Business Media LLC
Date: 05-08-2023
DOI: 10.1007/S10596-023-10223-4
Abstract: Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggle. We can incorporate efficient strategies such as surrogate-assisted optimisation to address the challenges however, implementing inter-process communication for surrogate-based model training is difficult. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. We first test the framework on a set of benchmark optimisation problems and then apply to a geoscientifc model that features landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computationally time while retaining optimisation solution accuracy through the use of surrogates in a parallel computing environment. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in better understanding of paleoclimate and geomorphology.
Publisher: Springer Science and Business Media LLC
Date: 24-12-2012
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 07-2014
Publisher: Elsevier BV
Date: 12-2016
Publisher: Oxford University Press (OUP)
Date: 16-05-2016
Publisher: Elsevier BV
Date: 09-2019
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 07-2018
Publisher: Springer International Publishing
Date: 2015
Publisher: Copernicus GmbH
Date: 08-07-2020
Abstract: Abstract. The complex and computationally expensive nature of landscape evolution models poses significant challenges to the inference and optimization of unknown model parameters. Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters. In our previous work, we developed parallel tempering Bayeslands as a framework for parameter estimation and uncertainty quantification for the Badlands landscape evolution model. Parallel tempering Bayeslands features high-performance computing that can feature dozens of processing cores running in parallel to enhance computational efficiency. Nevertheless, the procedure remains computationally challenging since thousands of s les need to be drawn and evaluated. In large-scale landscape evolution problems, a single model evaluation can take from several minutes to hours and in some instances, even days or weeks. Surrogate-assisted optimization has been used for several computationally expensive engineering problems which motivate its use in optimization and inference of complex geoscientific models. The use of surrogate models can speed up parallel tempering Bayeslands by developing computationally inexpensive models to mimic expensive ones. In this paper, we apply surrogate-assisted parallel tempering where the surrogate mimics a landscape evolution model by estimating the likelihood function from the model. We employ a neural-network-based surrogate model that learns from the history of s les generated. The entire framework is developed in a parallel computing infrastructure to take advantage of parallelism. The results show that the proposed methodology is effective in lowering the computational cost significantly while retaining the quality of model predictions.
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer International Publishing
Date: 2017
Publisher: MDPI AG
Date: 21-09-2022
DOI: 10.20944/PREPRINTS202209.0323.V1
Abstract: Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to quickly get safe, effective, and affordable COVID-19 treatment is to repurpose drugs that are already approved for other diseases as adjuvants along with the ongoing vaccine regime. The process of developing an accurate and standardized drug repurposing dataset requires a considerable level of resources and expertise due to the commercial availability of an extensive array of drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx platform. CoviRx is a user-friendly interface that provides access to the data, which is manually curated for COVID-19 drug repurposing data. Through CoviRx, the data curated has been made open-source to help advance drug repurposing research. CoviRx also encourages users to submit their findings after thoroughly validating the data, followed by merging it by enforcing uniformity and integ-rity-preserving constraints. This article discusses the various features of CoviRx and its design principles. CoviRx has been designed so that its functionality is independent of the data it dis-plays. Thus, in the future, this platform can be extended to include any other disease X beyond COVID-19. CoviRx can be accessed at www.covirx.org.
Publisher: Public Library of Science (PLoS)
Date: 28-01-2022
DOI: 10.1371/JOURNAL.PONE.0262708
Abstract: The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.
Publisher: IEEE
Date: 07-2016
Publisher: Springer Vienna
Date: 2010
Publisher: IEEE
Date: 11-2014
Publisher: IEEE
Date: 07-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 10-2011
Publisher: Elsevier BV
Date: 04-2023
Publisher: Elsevier BV
Date: 2022
Publisher: American Astronomical Society
Date: 03-08-2021
Abstract: We present a ground-based optical transmission spectrum for the warm Saturn-mass exoplanet WASP-110b from two transit observations made with the FOcal Reducer and Spectrograph on the Very Large Telescope. The spectrum covers the wavelength range from 4000–8333 Å, which is binned in 46 transit depths measured to an averaged precision of 220 parts per million (ppm) over an averaged 80 Å bin for a Vmag = 12.8 star. The measured transit depths are unaffected by a dilution from a close A-type field dwarf, which was fully resolved. The overall main characteristic of the transmission spectrum is an increasing radius with wavelength and a lack of the theoretically predicted pressure-broadened sodium and potassium absorption features for a cloud-free atmosphere. We analyze archival high-resolution optical spectroscopy and find evidence for low to moderate activity of the host star, which we take into account in the atmospheric retrieval analysis. Using the AURA retrieval code, we find that the observed transmission spectrum can be best explained by a combination of unocculted stellar faculae and a cloud deck. Transmission spectra of cloud-free and hazy atmospheres are rejected at a high confidence. With a possible cloud deck at its terminator, WASP-110b joins the increasing population of irradiated hot-Jupiter exoplanets with cloudy atmospheres observed in transmission.
Publisher: IEEE
Date: 07-2018
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 04-2017
Publisher: Elsevier BV
Date: 2022
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 03-2020
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 07-2015
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2017
Publisher: American Geophysical Union (AGU)
Date: 11-2019
DOI: 10.1029/2019GC008465
Publisher: IEEE
Date: 07-2018
Publisher: MDPI AG
Date: 18-11-2022
DOI: 10.3390/DATA7110164
Abstract: Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to get safe, effective, and affordable COVID-19 treatments quickly is to repurpose drugs that are already approved for other diseases. The process of developing an accurate and standardized drug repurposing dataset requires considerable resources and expertise due to numerous commercially available drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx.org platform. CoviRx is a user-friendly interface that allows analysis and filtering of large quantities of data, which is onerous to curate manually for COVID-19 drug repurposing. Through CoviRx, the curated data have been made open source to help combat the ongoing pandemic and encourage users to submit their findings on the drugs they have evaluated, in a uniform format that can be validated and checked for integrity by authenticated volunteers. This article discusses the various features of CoviRx, its design principles, and how its functionality is independent of the data it displays. Thus, in the future, this platform can be extended to include any other disease beyond COVID-19.
Publisher: Elsevier BV
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Geological Society of London
Date: 08-09-2020
Publisher: Informa UK Limited
Date: 11-10-2019
Publisher: IEEE
Date: 12-2009
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 12-2009
Publisher: Elsevier BV
Date: 06-2012
Publisher: Springer International Publishing
Date: 2016
Publisher: Cambridge University Press (CUP)
Date: 19-06-2014
DOI: 10.1017/S0263574714001362
Abstract: In this paper, a fast and efficient evolutional algorithm, called the G3-PCX has been implemented to solve the forward kinematics problem (FKP) of the general parallel manipulators being modeled by the 6-6 hexapod, constituted by a fixed and mobile platforms being non planar and non-symmetrical. The two platforms are connected by six linear actuators, each of which is located between one ball joint and one universal joint. Forward kinematics are formulated using Inverse Kinematics applying one position based equation system which is converted into an objective function by expressing the sum of squared error on kinematics chain lengths and mobile platform distances. In less than one second, the 16 unique real solutions are computed with improved accuracy when compared to previous methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 09-2012
Publisher: Elsevier BV
Date: 05-2022
DOI: 10.1016/J.COMPBIOMED.2022.105338
Abstract: In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this paper, we evaluate the performance of deep learning models for respiratory rate prediction. We consider three datasets from bio-sensors which include electrocardiogram (ECG), photoplethysmogram (PPG) data, and surface electromyogram (sEMG) data. The deep learning models include Long short-term memory (LSTM) networks, Bidirectional LSTM (Bi-LSTM), attention-based variants of LSTM, CNN-LSTM and Convolutional-LSTM networks. The deep learning models are evaluated for two separate windows which are 32 s and 64 s window. The models' performance is evaluated using mean absolute error (MAE). The 64 s window has more accurate prediction compared to the 32 s window. Our results indicate Bi-LSTM with Bahdanu Attention has the best performance for the bio-signals. LSTM performs best with one of the datasets, yielding an MAE of 0.70 ± 0.02. Bi-LSTM with Bahdanau attention showed best results with two of the three datasets with MAE of 0.51 ± 0.03 for sEMG based data and MAE of 0.24 ± 0.03 with PPG and ECG based data.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 31-03-2022
DOI: 10.1007/S00477-022-02204-3
Abstract: Hydrological extremes occupy a large spatial extent, with a temporal sequence, both of which can be influenced by a range of climatological and geographical phenomena. Understanding the key information in the spatial and temporal domain is essential to make accurate forecasts. The capabilities of deep learning methods can be applied in such instances due to their enhanced ability in learning complex relationships. Given its success in other domains, this study presents a framework that features a long short-term memory deep learning model for spatio temporal hydrological extreme forecasting in the South Pacific region. The data consists of satellite rainfall estimates and sea surface temperature (SST) anomalies. We use the satellite rainfall estimate to calculate the effective drought index (EDI), an indicator of hydrological extreme events. The framework is developed to forecast monthly EDI using three different approaches: (i) univariate (ii) multivariate with neighbouring spatial points (iii) multivariate with neighbouring spatial points and the eigenvector values of SST. Additionally, better identification of extreme wet events is noted with the inclusion of the eigenvector values of SST. By establishing the framework for the multivariate approach in two forms, it is evident that the model accuracy is contingent on understanding the dominant feature which influences precipitation regimes in the Pacific. The framework can be used to better understand linear and non-linear relationships within multi-dimensional data in other study regions, and provide long-term climate outlooks.
Publisher: Public Library of Science (PLoS)
Date: 08-2023
DOI: 10.1371/JOURNAL.PONE.0288681
Abstract: Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from the emergence (Alpha) to the Omicron variant in India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situations during the COVID-19 pandemic. We also find a strong correlation between the major topics with news media prevalent during the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 02-2020
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 07-2011
Publisher: MDPI AG
Date: 09-02-2022
DOI: 10.3390/RS14040819
Abstract: Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 24-10-2014
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 10-2024
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Public Library of Science (PLoS)
Date: 09-2022
DOI: 10.1371/JOURNAL.PONE.0273476
Abstract: The Upanishads are known as one of the oldest philosophical texts in the world that form the foundation of Hindu philosophy. The Bhagavad Gita is the core text of Hindu philosophy and is known as a text that summarises the key philosophies of the Upanishads with a major focus on the philosophy of karma. These texts have been translated into many languages and there exist studies about themes and topics that are prominent however, there is not much done using language models which are powered by deep learning. In this paper, we use advanced language models such as BERT to provide topic modelling of the Upanishads and the Bhagavad Gita. We then map those topics of the Bhagavad Gita and the Upanishads since it is well known that Bhagavad Gita summarizes the key messages in the Upanishads. We also analyse the distinct and overlapping topics amongst the texts and visualise the link of selected texts of the Upanishads with the Bhagavad Gita. Our results show very high similarity between the topics of these two texts with the mean cosine similarity of 73%. We find that out of the fourteen topics extracted from the Bhagavad Gita, nine of them have a cosine similarity of more than 70% with the topics of the Upanishads. We also find that topics generated by the BERT-based models show very high coherence when compared to the conventional models. Our best-performing model gives a coherence score of 73% on the Bhagavad Gita and 69% on the Upanishads. The visualization of the low-dimensional embeddings of these texts shows very clear overlapping themes among their topics adding another level of validation to our results.
Publisher: Elsevier BV
Date: 11-2023
Start Date: 08-2020
End Date: 08-2025
Amount: $3,973,202.00
Funder: Australian Research Council
View Funded Activity