Although Deep Learning (DL) models have been introduced in various fields as effective prediction... more Although Deep Learning (DL) models have been introduced in various fields as effective prediction tools, they often do not care about uncertainty. This can be a barrier to their adoption in real-world applications. The current paper aims to apply and evaluate Monte Carlo (MC) dropout, a computationally efficient approach, to investigate the reliability of several skip connection-based Convolutional Neural Network (CNN) models while keeping their high accuracy. To do so, a high-dimensional regression problem is considered in the context of subterranean fluid flow modeling using 376,250 generated samples. The results demonstrate the effectiveness of MC dropout in terms of reliability with a Standard Deviation (SD) of 0.012–0.174, and of accuracy with a coefficient of determination (R2) of 0.7881–0.9584 and Mean Squared Error (MSE) of 0.0113–0.0508, respectively. The findings of this study may contribute to the distribution of pressure in the development of oil/gas fields.
Machine Learning (ML) and/or Deep Learning (DL) methods can be used to predict fluid flow in poro... more Machine Learning (ML) and/or Deep Learning (DL) methods can be used to predict fluid flow in porous media, as a suitable replacement for classical numerical approaches. Such data-driven approaches attempt to learn mappings between finite-dimensional Euclidean spaces. A novel neural framework, named Fourier Neural Operator (FNO), has been recently developed to act on infinite-dimensional spaces. A high proportion of the research available on the FNO has focused on problems with large-shape data. Furthermore, most published studies apply the FNO method to existing datasets. This paper applies and evaluates FNO to predict pressure distribution over a small, specified shape-data problem using 1700 Finite Element Method (FEM) generated samples, from heterogeneous permeability fields as the input. Considering FEM-calculated outputs as the true values, the configured FNO model provides superior prediction performance to that of a Convolutional Neural Network (CNN) in terms of statistical e...
A methodology for UN Sustainable Development Goal (SDG) attainment prediction is presented, the S... more A methodology for UN Sustainable Development Goal (SDG) attainment prediction is presented, the Sustainable Development Goals Correlation Attainment Predictions Extended framework SDG-CAP-EXT. Unlike previous SDG attainment methodologies, SDG-CAP-EXT takes into account the potential for a causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity) and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. A ensemble approach is presented that combines the results of a number of alternative causality relationship identification mechanisms. The identified relationships are used to build multi-variate time series prediction models that feed into a bottom-up SDG prediction taxonomy, which is used to make SDG attainment predictions and rank countries using a proposed Attainment Likelihood Index that reflects the likelihood of goal attainment. The fr...
The number of medical negligence claims filed in the UK each year has increased significantly ove... more The number of medical negligence claims filed in the UK each year has increased significantly over the past decade [NHS, 2018]. When filing a medical negligence claim, electronic health records act as a legally valid important source of evidence. Patients often undergo different and complex treatments over many months or years, easily resulting in hundreds of pages of electronically available medical records. Therefore, it is a non-trivial task to read all the related electronic health records and identify the supporting evidence to establish a legal case. Currently, the process of identifying evidence is carried out by humans who are experts in both medical negligence law and medicine. In this paper, we compare different methods of automatically extracting relevant statements from medical negligence claim texts, to move towards building a method for extracting relevant sections from electronic health records with the aim of expediting the litigation process and reducing the manual ...
The emergence of biometric technology provides enhanced security compared to the traditional iden... more The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the tre...
Although Deep Learning (DL) models have been introduced in various fields as effective prediction... more Although Deep Learning (DL) models have been introduced in various fields as effective prediction tools, they often do not care about uncertainty. This can be a barrier to their adoption in real-world applications. The current paper aims to apply and evaluate Monte Carlo (MC) dropout, a computationally efficient approach, to investigate the reliability of several skip connection-based Convolutional Neural Network (CNN) models while keeping their high accuracy. To do so, a high-dimensional regression problem is considered in the context of subterranean fluid flow modeling using 376,250 generated samples. The results demonstrate the effectiveness of MC dropout in terms of reliability with a Standard Deviation (SD) of 0.012–0.174, and of accuracy with a coefficient of determination (R2) of 0.7881–0.9584 and Mean Squared Error (MSE) of 0.0113–0.0508, respectively. The findings of this study may contribute to the distribution of pressure in the development of oil/gas fields.
Machine Learning (ML) and/or Deep Learning (DL) methods can be used to predict fluid flow in poro... more Machine Learning (ML) and/or Deep Learning (DL) methods can be used to predict fluid flow in porous media, as a suitable replacement for classical numerical approaches. Such data-driven approaches attempt to learn mappings between finite-dimensional Euclidean spaces. A novel neural framework, named Fourier Neural Operator (FNO), has been recently developed to act on infinite-dimensional spaces. A high proportion of the research available on the FNO has focused on problems with large-shape data. Furthermore, most published studies apply the FNO method to existing datasets. This paper applies and evaluates FNO to predict pressure distribution over a small, specified shape-data problem using 1700 Finite Element Method (FEM) generated samples, from heterogeneous permeability fields as the input. Considering FEM-calculated outputs as the true values, the configured FNO model provides superior prediction performance to that of a Convolutional Neural Network (CNN) in terms of statistical e...
A methodology for UN Sustainable Development Goal (SDG) attainment prediction is presented, the S... more A methodology for UN Sustainable Development Goal (SDG) attainment prediction is presented, the Sustainable Development Goals Correlation Attainment Predictions Extended framework SDG-CAP-EXT. Unlike previous SDG attainment methodologies, SDG-CAP-EXT takes into account the potential for a causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity) and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. A ensemble approach is presented that combines the results of a number of alternative causality relationship identification mechanisms. The identified relationships are used to build multi-variate time series prediction models that feed into a bottom-up SDG prediction taxonomy, which is used to make SDG attainment predictions and rank countries using a proposed Attainment Likelihood Index that reflects the likelihood of goal attainment. The fr...
The number of medical negligence claims filed in the UK each year has increased significantly ove... more The number of medical negligence claims filed in the UK each year has increased significantly over the past decade [NHS, 2018]. When filing a medical negligence claim, electronic health records act as a legally valid important source of evidence. Patients often undergo different and complex treatments over many months or years, easily resulting in hundreds of pages of electronically available medical records. Therefore, it is a non-trivial task to read all the related electronic health records and identify the supporting evidence to establish a legal case. Currently, the process of identifying evidence is carried out by humans who are experts in both medical negligence law and medicine. In this paper, we compare different methods of automatically extracting relevant statements from medical negligence claim texts, to move towards building a method for extracting relevant sections from electronic health records with the aim of expediting the litigation process and reducing the manual ...
The emergence of biometric technology provides enhanced security compared to the traditional iden... more The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the tre...
Uploads