ORCID Profile
0000-0003-4776-4932
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 23-01-2018
Publisher: Research Square Platform LLC
Date: 03-06-2022
DOI: 10.21203/RS.3.RS-1711503/V1
Abstract: Hierarchical clustering produces a cluster tree with different granularities. As a result, hierarchical clustering provides richer information and insight into a dataset than partitioning clustering. However, hierarchical clustering algorithms often have two weaknesses: scalability and the capacity to handle clusters of varying densities. This is because they rely on pairwise point-based similarity calculations and the similarity measure is independent of data distribution. In this paper, we aim to overcome these weaknesses and propose a novel efficient hierarchical clustering called StreaKHC that enables massive streaming data to be mined. The enabling factor is the use of a scalable point-set kernel to measure the similarity between an existing cluster in the cluster tree and a new point in the data stream. It also has an efficient mechanism to update the hierarchical structure so that a high-quality cluster tree can be maintained in real-time. Our extensive empirical evaluation shows that StreaKHC is more accurate and more efficient than existing hierarchical clustering algorithms.
Publisher: MDPI AG
Date: 03-05-2019
DOI: 10.3390/EN12091680
Abstract: Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer Science and Business Media LLC
Date: 02-07-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2030
DOI: 10.36227/TECHRXIV.21385929.V1
Abstract: Zero Trust Architecture (ZTA) is a paradigm shift in how we protect data, stay connected, and access resources. ZTA is non-perimeter based defense, which has been emerging as a promising revolution in the cybersecurity field. It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system. However, automation and orchestration ZTA, an essential direction towards its seamless deployments over real-word networks, have been poorly understood in literature. In this paper, we first identify the bottlenecks, discuss the background of ZTA and compare it with traditional perimeter-based security architectures. More importantly, we present a comprehensive direction towards the automation and orchestration of ZTA by employing AI techniques. In other words, the key potential and roles of celebrates AI techniques for the automation and orchestration of ZTA are demonstrated for further exploration. Overall, in this review paper, we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 27-10-2022
DOI: 10.36227/TECHRXIV.21385929
Abstract: Zero Trust Architecture (ZTA) is a paradigm shift in how we protect data, stay connected, and access resources. ZTA is non-perimeter based defense, which has been emerging as a promising revolution in the cybersecurity field. It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system. However, automation and orchestration ZTA, an essential direction towards its seamless deployments over real-word networks, have been poorly understood in literature. In this paper, we first identify the bottlenecks, discuss the background of ZTA and compare it with traditional perimeter-based security architectures. More importantly, we present a comprehensive direction towards the automation and orchestration of ZTA by employing AI techniques. In other words, the key potential and roles of celebrates AI techniques for the automation and orchestration of ZTA are demonstrated for further exploration. Overall, in this review paper, we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.
Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-02-2022
DOI: 10.36227/TECHRXIV.19111730
Abstract: Anomaly detection is a common and critical data mining task, it seeks to identify observations that differ significantly from others. Anomalies may indicate rare but significant events that require action. Market manipulation is an activity that undermines stock markets worldwide. This paper shares five large real-world, labelled data sets of anomalous stock market data where market manipulation is alleged to have occurred. Cutting edge deep learning techniques are then shown to successfully detect the anomalous periods. An LSTM based method with dynamic thresholding is particularly promising in this domain as it was able to identify contextual local anomalies in the data quickly, taking seconds to score two years of trading data for each stock, which can often be a challenge for deep learning approaches.
Publisher: MDPI AG
Date: 21-03-2019
DOI: 10.3390/EN12061094
Abstract: Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Hindawi Limited
Date: 05-2020
DOI: 10.1002/INT.22227
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 07-2022
Publisher: Wiley
Date: 05-01-2018
DOI: 10.1111/COIN.12156
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-02-2022
DOI: 10.36227/TECHRXIV.19111730.V1
Abstract: Anomaly detection is a common and critical data mining task, it seeks to identify observations that differ significantly from others. Anomalies may indicate rare but significant events that require action. Market manipulation is an activity that undermines stock markets worldwide. This paper shares five large real-world, labelled data sets of anomalous stock market data where market manipulation is alleged to have occurred. Cutting edge deep learning techniques are then shown to successfully detect the anomalous periods. An LSTM based method with dynamic thresholding is particularly promising in this domain as it was able to identify contextual local anomalies in the data quickly, taking seconds to score two years of trading data for each stock, which can often be a challenge for deep learning approaches.
Publisher: Elsevier BV
Date: 07-2020
Publisher: IEEE
Date: 10-2014
Publisher: Springer Science and Business Media LLC
Date: 24-04-2019
Publisher: Elsevier BV
Date: 09-2021
Publisher: Springer Science and Business Media LLC
Date: 29-04-2016
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer International Publishing
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 10-2022
Publisher: ACM
Date: 13-08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Wiley
Date: 24-10-2020
DOI: 10.1002/QRE.2789
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 12-2019
Location: United Kingdom of Great Britain and Northern Ireland
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