Dhiya Al-jumeily | Liverpool John Moores University (original) (raw)

Papers by Dhiya Al-jumeily

2019 12th International Conference on Developments in eSystems Engineering (DeSE)

In this study, we propose and compare neural network models that use unsupervised layers for the ... more In this study, we propose and compare neural network models that use unsupervised layers for the prediction of financial time series. We compare the novel FL-RBM and FL-SMIA-RMB models that integrate a Restricted Boltzmann Machine (RBM) and the self-organizing layer of the Selforganized Multi-Layer Network using the Immune Algorithm (SMIA) with the FL-SMIA network and a standard MLP. We aim to investigate the performance of unsupervised learning in comparison to purely supervised and other mixed models. The FL-RBM model combines the products of raw input features (the Functional Link, FL), with the Restricted Boltzmann Machine RBM as a self-organizing first hidden layer, while the FL-SMIA model uses the Immune Algorithm on the first layer. The FL-SMIA-RBM model, combines both self-organizing layers with a back-propagation network. The results show that the FL-SMIA model outperforms the FL-RBM, the FL-SMIA-RBM and the MLP as measured by Annualized Return (AR) in one-day-ahead prediction on exchange rates time series. In terms of volatility, the FL-SMIA and MLP perform similarly.

IOP Conference Series: Materials Science and Engineering, 2021

A significant number of researches pointed to the serious environmental and health effects of the... more A significant number of researches pointed to the serious environmental and health effects of the Ordinary Portland Cement (OPC), including the harmful emissions and alkaline wastewaters. Therefore, the development of eco-friendly alternatives for the OPC is one of the priorities of nowadays studies. However, the suggested eco-friendly alternatives to the OPC might possess negative influences on the properties of the concrete. This research aims at investigating the applicability of by-product materials, such as cement kiln dust (CKD) and silica fume (SF), as an alternative to OPC in the cement mortars. The mortar specimens were mixed with 0 to70% CKD with SF (equal values) as a partial replacement for cement. The hardening samples have been tested by the UPV test at ages 1 week to 4 weeks. The results indicated that high ratios of CKD and SF replacements result in a slight decrease in the pulse velocity of specimens, while small replacement ratios show improvement in these properti...

2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017

Biometric fingerprints are one of the most broadly used form of biometric identification. Everyon... more Biometric fingerprints are one of the most broadly used form of biometric identification. Everyone is known to have unique, immutable fingerprints. In this area, the most challenging task is fingerprint recognition and identification system. This filed of the biometric data is significantly depends on the major quality data (input and tested images). A latent intelligent model is proposed in this paper. Our approach relies on develop an alternative approach to solve the localization problem based on Swarm Intelligence (SI) methodology for a robustness rotational and spatially invariant fingerprint recognition and verification model. In our latent model, a group of partial local features is extracted from fingerprint based on swarm-intelligence methodology such as Particle Swarm Optimization Algorithm (PSO) as a first one, and Firefly Optimized Algorithm (FOA) Algorithms as a second algorithm. The search strategy in swarm-intelligence methodology is an iteratively process which is gu...

2018 1st Annual International Conference on Information and Sciences (AiCIS), 2018

The principal aim of this study was to develop and verify a new Artificial Intelligence model to ... more The principal aim of this study was to develop and verify a new Artificial Intelligence model to predict the hyperbolic soil stress-strain parameter, namely the modulus exponent (n). To achieve the planned aim, artificial neural network was developed and trained, additionally, it targeted to provide an appropriate empirical model to predict the parameter n with high efficiency. A database of laboratory measurements encompasses total of (83) case records for modulus exponent (n). Four input parameters namely: Dry unit weight, Plasticity index, Confining stress, and Water content, are considered to have the most substantial influence on the nonlinear soil stress-train relationship parameter, which are used as individual input parameters to the developed the proposed model. Multi-layer perceptron class trained using back propagation approach in this work. The effect of several issues in relation to the proposed model construction such as artificial neural network geometry and internal ...

The proposed learner environment presented in this paper is based on constructive perspective (le... more The proposed learner environment presented in this paper is based on constructive perspective (learner focus) learning according to Hadjerrouit (2007). This model offers an environment where focus is on the learner and encourages them to construct new ideas by testing theory through the solving of problems. Associated pedagogy with this model is: the provision of an interactive environment for the building of knowledge and problem solving ability, provision of activities that promote experimentation and discovery and allow evaluation and reflection. On the other hand, this model allows a teacher to maximize the pedagogy of a rich/dynamic learning environment, increase student participation, and provide back-up learning materials. This is because the theory of learning encourages a learning environment where instructions are learner centered and teachers are only facilitators. In this theory, knowledge and skills are gained by the interaction between study materials. Therefore, the r...

2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017

Nowadays, the internet is considered as one of the key building blocks of modern communities and ... more Nowadays, the internet is considered as one of the key building blocks of modern communities and a primary function to every aspect of our daily life activities. The internet is widespread and progress in the realm of the information and communication technologies demonstrated great improvements that can be utilized by government entities that strive towards achieving sustainable excellence and utmost performance. In the meantime, government organizations should maintain the delivery of high quality of services and continuously monitor and measure their performance based on appropriate approaches namely, organizing the map of corporate strategy, set of organizational strategic objectives and formulating key performance indicators. The quality levels of organizations operations can be witnessed and improved by identifying an effective planning procedure which indicate areas of enhancements and effective process for decision making to respond to dynamical changes. This paper aims to develop an interactive system to enhance strategic planning processes and quality of aviation operations using balanced scorecard. The proposed system will be integrated with the balanced scorecard approach and shall be evaluated for its effectiveness and usefulness in the aviation operations.

Technology for Smart Futures, 2017

The risk assessment of any network or security systems has a high level of uncertainties because ... more The risk assessment of any network or security systems has a high level of uncertainties because usually probability and statistics were used to evaluate the security of different cybersecurity systems. In this book chapter, we will use Shannon entropy to represent the uncertainty of information utilised to calculate systems risk and entropy weight method since the weight of the object index is normally used and points to the significant components of the index. We evaluate the risk of security systems in terms of different vulnerabilities and protections existing in each host. A new methodology was developed to present an attack graph with a dynamic cost metric based on a Dynamic Vulnerability Scoring System (DVSS), and also a novel methodology to estimate and represent the cost-centric approach for each host’s states was followed up.

2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), 2018

Billions of devises are expected to be connected to the Internet of Things network in the near fu... more Billions of devises are expected to be connected to the Internet of Things network in the near future, therefore, a considerable amount of data will be generated, and gathered every second. The current network paradigm, which relies on centralised data-centres (a.k.a. Cloud computing), becomes impractical solution for IoT data due to the long distance between the data source and designated data-center. In other words, the amount of time taken by data to travel to a data-centre makes the importance of the data vanished. Therefore, the network topology have been evolved to permit data processing at the edge of the network, introducing what so-called "Fog computing". The later will obviously lead to improvements in quality of service via efficient and quick responding to sensors requests. In this paper, we are proposing a fog computing architecture and framework to enhance QoS via request offloading method. The proposed method employ a collaboration strategy among fog nodes in order to permit data processing in a shared mode, hence satisfies QoS and serves largest number of IoT requests. The proposed framework could have the potential in achieving sustainable network paradigm and highlights significant benefits of fog computing into the computing ecosystem.

2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017

Disc herniation is considered as the main cause for lower back pain (LBP), a health issue that af... more Disc herniation is considered as the main cause for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves a visual examination of a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic detection of the lumbar disc herniation will reduce the time to diagnose and detect the cause of LBP by the orthopedist. There has been very limited progress towards automatic detection of disc herniation and all of the proposed techniques still require substantial manual intervention in many of the stages. Our analysis of the problem suggests that using the axial view of the MRI could potentially improve the outcome as opposed to the sagittal view used by these techniques. In this paper, we propose using the Centroid Distance Function as a shape feature of a segmented disc MRI taken from the axial view. Visual observation of the feature indicates that the feature could be used as a suitable indicator of the presence of herniation in the lumbar disc

2018 11th International Conference on Developments in eSystems Engineering (DeSE), 2018

This article considers how teachers and university administrators can use a significant amount of... more This article considers how teachers and university administrators can use a significant amount of data stored in the information systems of institutions. Intelligent analysis of these learning processes is of great use in the higher education system. The use of learning analytics (LA) by a large number of higher educational institutions shows the interest and participation of universities in this matter. Learning analytics can tell a lot about the progress of students and the environment in which learning takes place. Intellectualization of educational analytics will help provide predictive models that can serve as a basis for quality assurance and quality improvement. This article gives an idea of the current level of LA development at the international level. The article also draws conclusions about the problems and limitations associated with learning analytics (LA). The existing experience has been studied, and the conclusion have been made about the existing limitations that prevent the wider use of LA.

2018 11th International Conference on Developments in eSystems Engineering (DeSE), 2018

The contextual compendium analysis presented in this paper focuses on the Industry 4.0 and health... more The contextual compendium analysis presented in this paper focuses on the Industry 4.0 and healthcare services innovation that relate to it. The appraisal discerns the specific components of Industry 4.0 and their related innovations or contribution in the healthcare industry. The first component, Cyber-physical systems, has led to Medical Cyber-physical systems applied in different circumstance to improve the efficiency of service provision. The second component, Internet of Things, has brought with it expanded networks, biosensors, smart pharmaceuticals, and other artificial organs. The final component has inspired the integrated of Natural Language Processing model as a calm-system operating in the background to complete a host of the process that improves diagnoses among other service provision and assistance functions. Additionally, the paper discusses Cognitive Computing, mHealth, and eHealth as emerging medical fields that can benefit from Industry 4.0.

BMC Medical Informatics and Decision Making, 2020

An amendment to this paper has been published and can be accessed via the original article.

Monthly Notices of the Royal Astronomical Society, 2018

Period estimation is an important task in the classification of many variable astrophysical objec... more Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimized for the processing of survey data with spurious and aliased artefacts. It uses a Bayesian Generalized Lomb-Scargle (BGLS) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope. We construct a set of simulated light curves using both regular and Skycam survey cadence with four types of signal: sinusoidal, sawtooth, symmetric eclipsing binary, and eccentric eclipsing binary. We apply GRAPE and a BGLS periodogram to this data and show that the performance of GRAPE is superior to the periodogram on sinusoidal and sawtooth light curves with relative hit rate improvement of 18.2 per cent and 6.4 per cent, respectively. The symmetric and eccentric eclipsing binary light curves have similar performance on both methods. We show the Skycam cadence is sufficient to correctly estimate the period for all of the sinusoidal shape light curves although this degrades with increased non-sinusoidal shape with sawtooth, symmetric binary, and eccentric binary light curves down by 20 per cent, 30 per cent, and 35 per cent, respectively. The runtime of GRAPE demonstrates that light curves with more than 500-1000 data points achieve similar performance in less computing time. The GRAPE performance can be matched by a frequency spectrum with an oversampled fine-tuning grid at the cost of almost doubling the runtime. Finally, we propose improvements which will extend this method to the detection of quasi-periodic signals and the use of multiband light curves.

IEEE Access, 2019

We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through... more We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through semantic segmentation and delineation of magnetic resonance imaging (MRI) scans of the lumbar spine using deep learning. Our dataset contains MRI studies of 515 patients with symptomatic back pains. Each study is annotated by expert radiologists with notes regarding the observed characteristics and condition of the lumbar spine. We have developed a ground truth dataset, containing image labels of four important regions in the lumbar spine, to be used as the training and test images to develop classification models for segmentation. We developed two novel metrics, namely confidence, and consistency, to assess the quality of the ground truth dataset through a derivation of the Jaccard Index. We experimented with semantic segmentation of our dataset using SegNet. Our evaluation of the segmentation and the delineation results show that our proposed methodology produces a very good performance as measured by several contourbased and region-based metrics. In addition, using the Cohen's kappa and frequency-weighted confidence metrics, we can show that 1) the model's performance is within the range of the worst and the best manual labeling results and 2) the ground-truth dataset has an excellent inter-rater agreement score. We also presented two representative delineation results of the worst and best segmentation based on their BF-score to show visually how accurate and suitable the results are for computer-aided-diagnosis purposes.

2015 International Conference on Systems, Signals and Image Processing (IWSSIP), 2015

Floods are common natural disasters that cause severe devastation of any country. They are common... more Floods are common natural disasters that cause severe devastation of any country. They are commonly caused by precipitation and runoff of rivers, particularly during periods of excessively high rainy season. Due to global warming issues and extreme environmental effects, flood has become a serious problem to the extent of bringing about negative impact to the mankind and infrastructure. To date, sensor network technology has been used in many areas including water level fluctuation. However, efficient flood monitoring and real time notification system still a crucial part because Information Technology enabled applications have not been employed in this sector in a broad way. This research presents a description of an alert generating system for flood detection with a focus on determining the current water level using sensors technology. The system then provides notification message about water level sensitivity via Global Communication and Mobile System modem to particular authorise person. Besides the Short Message Service, the system instantaneously uploads and broadcast information through web base public network. Machine-learning algorithms were conducted to perform the classification process. Four experiments were carried out to classify flood data from normal and at risk condition in which 99.5% classification accuracy was achieved using Random Forest algorithm. Classification using Bagging, Decision Tree and HyperPipes algorithms achieved accuracy of 97.7 %, 94.6% and 89.8 %, respectively.

2016 9th International Conference on Developments in eSystems Engineering (DeSE), 2016

There has been an increase of survival rates among children born premature. The rate of survival ... more There has been an increase of survival rates among children born premature. The rate of survival differs globally from developed countries to developing countries. Some follow-up studies of children born prematurely are concerned of the neurodevelopmental impact on children as they progress from infant through adulthood and they indicated the range of deficits that comes with premature children. This research addresses the gap of how the education of children born premature is affected. A survey methodology was utilized and questionnaires were distributed to teachers in five special needs schools and one normal school in the North West of the United Kingdom. The results of the special needs schools were compared to normal school with respect to awareness of children born premature in classes, the technologies used by the impaired children, and the educational improvement of impaired children using the technology. The results showed that 100% of teachers in both schools were unaware ...

Sensors

Three-dimensional (3D) image and medical image processing, which are considered big data analysis... more Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects...

Journal of Cloud Computing

The world of the Internet and networking is exposed to many cyber-attacks and threats. Over the y... more The world of the Internet and networking is exposed to many cyber-attacks and threats. Over the years, machine learning models have progressed to be integrated into many scenarios to detect anomalies accurately. This paper proposes a novel approach named cloud-based anomaly detection (CAD) to detect cloud-based anomalies. CAD consist of two key blocks: ensemble machine learning (EML) model for binary anomaly classification and convolutional neural network long short-term memory (CNN-LSTM) for multiclass anomaly categorization. CAD is evaluated on a complex UNSW dataset to analyze the performance of binary anomaly detection and categorization of multiclass anomalies. Furthermore, the comparison of CAD with other machine learning conventional models and state-of-the-art studies have been presented. Experimental analysis shows that CAD outperforms other studies by achieving the highest accuracy of 97.06% for binary anomaly detection and 99.91% for multiclass anomaly detection.

IEEE Access

Image pattern classification is considered a significant step for image and video processing. Alt... more Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOMs). Therefore, finding a fast PET classification method that accurately classify image pattern is crucial. To this end, this paper proposes a new scheme for accurate and fast image pattern classification using an efficient DOM. To reduce the computational complexity of feature extraction, an election mechanism is proposed to reduce the number of processed block patterns. In addition, support vector machine is used to classify the extracted features for different block patterns. The proposed scheme is evaluated by comparing the accuracy of the proposed method with the accuracy achieved by state-of-the-art methods. In addition, we compare the performance of the proposed method based on different DOMs to get the robust one. The results show that the proposed method achieves the highest classification accuracy compared with the existing methods in all the scenarios considered. INDEX TERMS Image patterns, image properties, orthogonal polynomials, orthogonal moments, support vector machine.

PLOS ONE

The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, gove... more The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, governmental, and industrial attention in recent years. Since the IoT specifications are quite different from what the Internet can deliver today, many groundbreaking techniques, such as Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSN), have gradually been integrated into IoT. The Routing Protocol for Low power and Lossy network (RPL) is the de-facto IoT routing protocol in such networks. Unfortunately, it is susceptible to numerous internal attacks. Many techniques, such as cryptography, Intrusion Detection System (IDS), and authorization have been used to counter this. The large computational overhead of these techniques limits their direct application to IoT nodes, especially due to their low power and lossy nature. Therefore, this paper proposes a Trust-based Hybrid Cooperative RPL protocol (THC-RPL) to detect malicious Sybil nodes in an RPL-based IoT network. The propo...

2019 12th International Conference on Developments in eSystems Engineering (DeSE)

In this study, we propose and compare neural network models that use unsupervised layers for the ... more In this study, we propose and compare neural network models that use unsupervised layers for the prediction of financial time series. We compare the novel FL-RBM and FL-SMIA-RMB models that integrate a Restricted Boltzmann Machine (RBM) and the self-organizing layer of the Selforganized Multi-Layer Network using the Immune Algorithm (SMIA) with the FL-SMIA network and a standard MLP. We aim to investigate the performance of unsupervised learning in comparison to purely supervised and other mixed models. The FL-RBM model combines the products of raw input features (the Functional Link, FL), with the Restricted Boltzmann Machine RBM as a self-organizing first hidden layer, while the FL-SMIA model uses the Immune Algorithm on the first layer. The FL-SMIA-RBM model, combines both self-organizing layers with a back-propagation network. The results show that the FL-SMIA model outperforms the FL-RBM, the FL-SMIA-RBM and the MLP as measured by Annualized Return (AR) in one-day-ahead prediction on exchange rates time series. In terms of volatility, the FL-SMIA and MLP perform similarly.

IOP Conference Series: Materials Science and Engineering, 2021

A significant number of researches pointed to the serious environmental and health effects of the... more A significant number of researches pointed to the serious environmental and health effects of the Ordinary Portland Cement (OPC), including the harmful emissions and alkaline wastewaters. Therefore, the development of eco-friendly alternatives for the OPC is one of the priorities of nowadays studies. However, the suggested eco-friendly alternatives to the OPC might possess negative influences on the properties of the concrete. This research aims at investigating the applicability of by-product materials, such as cement kiln dust (CKD) and silica fume (SF), as an alternative to OPC in the cement mortars. The mortar specimens were mixed with 0 to70% CKD with SF (equal values) as a partial replacement for cement. The hardening samples have been tested by the UPV test at ages 1 week to 4 weeks. The results indicated that high ratios of CKD and SF replacements result in a slight decrease in the pulse velocity of specimens, while small replacement ratios show improvement in these properti...

2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017

Biometric fingerprints are one of the most broadly used form of biometric identification. Everyon... more Biometric fingerprints are one of the most broadly used form of biometric identification. Everyone is known to have unique, immutable fingerprints. In this area, the most challenging task is fingerprint recognition and identification system. This filed of the biometric data is significantly depends on the major quality data (input and tested images). A latent intelligent model is proposed in this paper. Our approach relies on develop an alternative approach to solve the localization problem based on Swarm Intelligence (SI) methodology for a robustness rotational and spatially invariant fingerprint recognition and verification model. In our latent model, a group of partial local features is extracted from fingerprint based on swarm-intelligence methodology such as Particle Swarm Optimization Algorithm (PSO) as a first one, and Firefly Optimized Algorithm (FOA) Algorithms as a second algorithm. The search strategy in swarm-intelligence methodology is an iteratively process which is gu...

2018 1st Annual International Conference on Information and Sciences (AiCIS), 2018

The principal aim of this study was to develop and verify a new Artificial Intelligence model to ... more The principal aim of this study was to develop and verify a new Artificial Intelligence model to predict the hyperbolic soil stress-strain parameter, namely the modulus exponent (n). To achieve the planned aim, artificial neural network was developed and trained, additionally, it targeted to provide an appropriate empirical model to predict the parameter n with high efficiency. A database of laboratory measurements encompasses total of (83) case records for modulus exponent (n). Four input parameters namely: Dry unit weight, Plasticity index, Confining stress, and Water content, are considered to have the most substantial influence on the nonlinear soil stress-train relationship parameter, which are used as individual input parameters to the developed the proposed model. Multi-layer perceptron class trained using back propagation approach in this work. The effect of several issues in relation to the proposed model construction such as artificial neural network geometry and internal ...

The proposed learner environment presented in this paper is based on constructive perspective (le... more The proposed learner environment presented in this paper is based on constructive perspective (learner focus) learning according to Hadjerrouit (2007). This model offers an environment where focus is on the learner and encourages them to construct new ideas by testing theory through the solving of problems. Associated pedagogy with this model is: the provision of an interactive environment for the building of knowledge and problem solving ability, provision of activities that promote experimentation and discovery and allow evaluation and reflection. On the other hand, this model allows a teacher to maximize the pedagogy of a rich/dynamic learning environment, increase student participation, and provide back-up learning materials. This is because the theory of learning encourages a learning environment where instructions are learner centered and teachers are only facilitators. In this theory, knowledge and skills are gained by the interaction between study materials. Therefore, the r...

2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017

Nowadays, the internet is considered as one of the key building blocks of modern communities and ... more Nowadays, the internet is considered as one of the key building blocks of modern communities and a primary function to every aspect of our daily life activities. The internet is widespread and progress in the realm of the information and communication technologies demonstrated great improvements that can be utilized by government entities that strive towards achieving sustainable excellence and utmost performance. In the meantime, government organizations should maintain the delivery of high quality of services and continuously monitor and measure their performance based on appropriate approaches namely, organizing the map of corporate strategy, set of organizational strategic objectives and formulating key performance indicators. The quality levels of organizations operations can be witnessed and improved by identifying an effective planning procedure which indicate areas of enhancements and effective process for decision making to respond to dynamical changes. This paper aims to develop an interactive system to enhance strategic planning processes and quality of aviation operations using balanced scorecard. The proposed system will be integrated with the balanced scorecard approach and shall be evaluated for its effectiveness and usefulness in the aviation operations.

Technology for Smart Futures, 2017

The risk assessment of any network or security systems has a high level of uncertainties because ... more The risk assessment of any network or security systems has a high level of uncertainties because usually probability and statistics were used to evaluate the security of different cybersecurity systems. In this book chapter, we will use Shannon entropy to represent the uncertainty of information utilised to calculate systems risk and entropy weight method since the weight of the object index is normally used and points to the significant components of the index. We evaluate the risk of security systems in terms of different vulnerabilities and protections existing in each host. A new methodology was developed to present an attack graph with a dynamic cost metric based on a Dynamic Vulnerability Scoring System (DVSS), and also a novel methodology to estimate and represent the cost-centric approach for each host’s states was followed up.

2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), 2018

Billions of devises are expected to be connected to the Internet of Things network in the near fu... more Billions of devises are expected to be connected to the Internet of Things network in the near future, therefore, a considerable amount of data will be generated, and gathered every second. The current network paradigm, which relies on centralised data-centres (a.k.a. Cloud computing), becomes impractical solution for IoT data due to the long distance between the data source and designated data-center. In other words, the amount of time taken by data to travel to a data-centre makes the importance of the data vanished. Therefore, the network topology have been evolved to permit data processing at the edge of the network, introducing what so-called "Fog computing". The later will obviously lead to improvements in quality of service via efficient and quick responding to sensors requests. In this paper, we are proposing a fog computing architecture and framework to enhance QoS via request offloading method. The proposed method employ a collaboration strategy among fog nodes in order to permit data processing in a shared mode, hence satisfies QoS and serves largest number of IoT requests. The proposed framework could have the potential in achieving sustainable network paradigm and highlights significant benefits of fog computing into the computing ecosystem.

2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017

Disc herniation is considered as the main cause for lower back pain (LBP), a health issue that af... more Disc herniation is considered as the main cause for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves a visual examination of a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic detection of the lumbar disc herniation will reduce the time to diagnose and detect the cause of LBP by the orthopedist. There has been very limited progress towards automatic detection of disc herniation and all of the proposed techniques still require substantial manual intervention in many of the stages. Our analysis of the problem suggests that using the axial view of the MRI could potentially improve the outcome as opposed to the sagittal view used by these techniques. In this paper, we propose using the Centroid Distance Function as a shape feature of a segmented disc MRI taken from the axial view. Visual observation of the feature indicates that the feature could be used as a suitable indicator of the presence of herniation in the lumbar disc

2018 11th International Conference on Developments in eSystems Engineering (DeSE), 2018

This article considers how teachers and university administrators can use a significant amount of... more This article considers how teachers and university administrators can use a significant amount of data stored in the information systems of institutions. Intelligent analysis of these learning processes is of great use in the higher education system. The use of learning analytics (LA) by a large number of higher educational institutions shows the interest and participation of universities in this matter. Learning analytics can tell a lot about the progress of students and the environment in which learning takes place. Intellectualization of educational analytics will help provide predictive models that can serve as a basis for quality assurance and quality improvement. This article gives an idea of the current level of LA development at the international level. The article also draws conclusions about the problems and limitations associated with learning analytics (LA). The existing experience has been studied, and the conclusion have been made about the existing limitations that prevent the wider use of LA.

2018 11th International Conference on Developments in eSystems Engineering (DeSE), 2018

The contextual compendium analysis presented in this paper focuses on the Industry 4.0 and health... more The contextual compendium analysis presented in this paper focuses on the Industry 4.0 and healthcare services innovation that relate to it. The appraisal discerns the specific components of Industry 4.0 and their related innovations or contribution in the healthcare industry. The first component, Cyber-physical systems, has led to Medical Cyber-physical systems applied in different circumstance to improve the efficiency of service provision. The second component, Internet of Things, has brought with it expanded networks, biosensors, smart pharmaceuticals, and other artificial organs. The final component has inspired the integrated of Natural Language Processing model as a calm-system operating in the background to complete a host of the process that improves diagnoses among other service provision and assistance functions. Additionally, the paper discusses Cognitive Computing, mHealth, and eHealth as emerging medical fields that can benefit from Industry 4.0.

BMC Medical Informatics and Decision Making, 2020

An amendment to this paper has been published and can be accessed via the original article.

Monthly Notices of the Royal Astronomical Society, 2018

Period estimation is an important task in the classification of many variable astrophysical objec... more Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimized for the processing of survey data with spurious and aliased artefacts. It uses a Bayesian Generalized Lomb-Scargle (BGLS) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope. We construct a set of simulated light curves using both regular and Skycam survey cadence with four types of signal: sinusoidal, sawtooth, symmetric eclipsing binary, and eccentric eclipsing binary. We apply GRAPE and a BGLS periodogram to this data and show that the performance of GRAPE is superior to the periodogram on sinusoidal and sawtooth light curves with relative hit rate improvement of 18.2 per cent and 6.4 per cent, respectively. The symmetric and eccentric eclipsing binary light curves have similar performance on both methods. We show the Skycam cadence is sufficient to correctly estimate the period for all of the sinusoidal shape light curves although this degrades with increased non-sinusoidal shape with sawtooth, symmetric binary, and eccentric binary light curves down by 20 per cent, 30 per cent, and 35 per cent, respectively. The runtime of GRAPE demonstrates that light curves with more than 500-1000 data points achieve similar performance in less computing time. The GRAPE performance can be matched by a frequency spectrum with an oversampled fine-tuning grid at the cost of almost doubling the runtime. Finally, we propose improvements which will extend this method to the detection of quasi-periodic signals and the use of multiband light curves.

IEEE Access, 2019

We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through... more We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through semantic segmentation and delineation of magnetic resonance imaging (MRI) scans of the lumbar spine using deep learning. Our dataset contains MRI studies of 515 patients with symptomatic back pains. Each study is annotated by expert radiologists with notes regarding the observed characteristics and condition of the lumbar spine. We have developed a ground truth dataset, containing image labels of four important regions in the lumbar spine, to be used as the training and test images to develop classification models for segmentation. We developed two novel metrics, namely confidence, and consistency, to assess the quality of the ground truth dataset through a derivation of the Jaccard Index. We experimented with semantic segmentation of our dataset using SegNet. Our evaluation of the segmentation and the delineation results show that our proposed methodology produces a very good performance as measured by several contourbased and region-based metrics. In addition, using the Cohen's kappa and frequency-weighted confidence metrics, we can show that 1) the model's performance is within the range of the worst and the best manual labeling results and 2) the ground-truth dataset has an excellent inter-rater agreement score. We also presented two representative delineation results of the worst and best segmentation based on their BF-score to show visually how accurate and suitable the results are for computer-aided-diagnosis purposes.

2015 International Conference on Systems, Signals and Image Processing (IWSSIP), 2015

Floods are common natural disasters that cause severe devastation of any country. They are common... more Floods are common natural disasters that cause severe devastation of any country. They are commonly caused by precipitation and runoff of rivers, particularly during periods of excessively high rainy season. Due to global warming issues and extreme environmental effects, flood has become a serious problem to the extent of bringing about negative impact to the mankind and infrastructure. To date, sensor network technology has been used in many areas including water level fluctuation. However, efficient flood monitoring and real time notification system still a crucial part because Information Technology enabled applications have not been employed in this sector in a broad way. This research presents a description of an alert generating system for flood detection with a focus on determining the current water level using sensors technology. The system then provides notification message about water level sensitivity via Global Communication and Mobile System modem to particular authorise person. Besides the Short Message Service, the system instantaneously uploads and broadcast information through web base public network. Machine-learning algorithms were conducted to perform the classification process. Four experiments were carried out to classify flood data from normal and at risk condition in which 99.5% classification accuracy was achieved using Random Forest algorithm. Classification using Bagging, Decision Tree and HyperPipes algorithms achieved accuracy of 97.7 %, 94.6% and 89.8 %, respectively.

2016 9th International Conference on Developments in eSystems Engineering (DeSE), 2016

There has been an increase of survival rates among children born premature. The rate of survival ... more There has been an increase of survival rates among children born premature. The rate of survival differs globally from developed countries to developing countries. Some follow-up studies of children born prematurely are concerned of the neurodevelopmental impact on children as they progress from infant through adulthood and they indicated the range of deficits that comes with premature children. This research addresses the gap of how the education of children born premature is affected. A survey methodology was utilized and questionnaires were distributed to teachers in five special needs schools and one normal school in the North West of the United Kingdom. The results of the special needs schools were compared to normal school with respect to awareness of children born premature in classes, the technologies used by the impaired children, and the educational improvement of impaired children using the technology. The results showed that 100% of teachers in both schools were unaware ...

Sensors

Three-dimensional (3D) image and medical image processing, which are considered big data analysis... more Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects...

Journal of Cloud Computing

The world of the Internet and networking is exposed to many cyber-attacks and threats. Over the y... more The world of the Internet and networking is exposed to many cyber-attacks and threats. Over the years, machine learning models have progressed to be integrated into many scenarios to detect anomalies accurately. This paper proposes a novel approach named cloud-based anomaly detection (CAD) to detect cloud-based anomalies. CAD consist of two key blocks: ensemble machine learning (EML) model for binary anomaly classification and convolutional neural network long short-term memory (CNN-LSTM) for multiclass anomaly categorization. CAD is evaluated on a complex UNSW dataset to analyze the performance of binary anomaly detection and categorization of multiclass anomalies. Furthermore, the comparison of CAD with other machine learning conventional models and state-of-the-art studies have been presented. Experimental analysis shows that CAD outperforms other studies by achieving the highest accuracy of 97.06% for binary anomaly detection and 99.91% for multiclass anomaly detection.

IEEE Access

Image pattern classification is considered a significant step for image and video processing. Alt... more Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOMs). Therefore, finding a fast PET classification method that accurately classify image pattern is crucial. To this end, this paper proposes a new scheme for accurate and fast image pattern classification using an efficient DOM. To reduce the computational complexity of feature extraction, an election mechanism is proposed to reduce the number of processed block patterns. In addition, support vector machine is used to classify the extracted features for different block patterns. The proposed scheme is evaluated by comparing the accuracy of the proposed method with the accuracy achieved by state-of-the-art methods. In addition, we compare the performance of the proposed method based on different DOMs to get the robust one. The results show that the proposed method achieves the highest classification accuracy compared with the existing methods in all the scenarios considered. INDEX TERMS Image patterns, image properties, orthogonal polynomials, orthogonal moments, support vector machine.

PLOS ONE

The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, gove... more The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, governmental, and industrial attention in recent years. Since the IoT specifications are quite different from what the Internet can deliver today, many groundbreaking techniques, such as Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSN), have gradually been integrated into IoT. The Routing Protocol for Low power and Lossy network (RPL) is the de-facto IoT routing protocol in such networks. Unfortunately, it is susceptible to numerous internal attacks. Many techniques, such as cryptography, Intrusion Detection System (IDS), and authorization have been used to counter this. The large computational overhead of these techniques limits their direct application to IoT nodes, especially due to their low power and lossy nature. Therefore, this paper proposes a Trust-based Hybrid Cooperative RPL protocol (THC-RPL) to detect malicious Sybil nodes in an RPL-based IoT network. The propo...