Eyad Elyan
Robert Gordon University
Human–computer interactionDeep learningInstrumentation (computer programming)Machine learningUndersamplingArtificial intelligenceRandom forestSet (abstract data type)Pattern recognitionEngineering drawingKey (cryptography)Face (geometry)Context (language use)Task (project management)Facial recognition systemComputer visionField (computer science)Class (biology)Computer scienceEnsemble learningCluster analysisConvolutional neural network
67Publications
14H-index
755Citations
Publications 73
Newest
#1Faseela Abdullakutty (RGU: Robert Gordon University)H-Index: 1
#2Eyad Elyan (RGU: Robert Gordon University)H-Index: 14
Last. Pamela Johnston (RGU: Robert Gordon University)H-Index: 3
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Abstract Face Recognition is considered one of the most common biometric solutions these days and is widely used across a range of devices for various security purposes. The performance of FR systems has improved by orders of magnitude over the past decade. This is mainly due to the latest developments in computer vision and deep convolutional neural networks, and the availability of large training datasets. At the same time, these systems have been subject to various types of attacks. Presentat...
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A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders’ concerns and needs are collected and analyzed to obtain the most compromised decommissioning d...
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Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised usin...
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#1Andrew A. GumbsH-Index: 46
#2Isabella FrigerioH-Index: 19
Last. Eyad Elyan (RGU: Robert Gordon University)H-Index: 14
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Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous ac...
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#1Carlos Francisco Moreno-García (RGU: Robert Gordon University)H-Index: 9
#2Chrisina Jayne (Teesside University)H-Index: 13
Last. Eyad Elyan (RGU: Robert Gordon University)H-Index: 14
view all 3 authors...
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Jul 16, 2021 in INFOCOM (International Conference on Computer Communications)
#1Pattaramon Vuttipittayamongkol (MFU: Mae Fah Luang University)H-Index: 7
#2Aaron Tung (Aberd.: University of Aberdeen)H-Index: 2
Last. Eyad Elyan (RGU: Robert Gordon University)H-Index: 14
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Thousands of offshore oil and gas structures worldwide are approaching the end of their operating lifespan. Decommissioning processes are expensive and normally take years to finish as various options need to be analysed based on numerous stakeholders’ preferences. Despite recent and significant progress in machine learning and data-driven applications in the oil and gas industry, very little work has been done in the area of using machine learning to inform the decommissioning processes and ope...
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#1Truong Dang (RGU: Robert Gordon University)
#2Tien Thanh Nguyen (RGU: Robert Gordon University)H-Index: 13
Last. John McCall (RGU: Robert Gordon University)H-Index: 18
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#1Faseela Abdullakutty (RGU: Robert Gordon University)H-Index: 1
#2Eyad Elyan (RGU: Robert Gordon University)H-Index: 14
Last. Pamela Johnston (RGU: Robert Gordon University)H-Index: 3
view all 3 authors...
Face recognition has recently become widespread in security applications. Although advancing technology has improved the performance of these systems, they are still prone to various attacks, including spoofing. The inherent feature extraction capability of machine learning techniques and deep neural networks has facilitated more accurate performance in spoofing detection. However, challenges still remain in the generalisation of these methods. One significant challenge is training dataset limit...
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#1Adamu Ali-Gombe (RGU: Robert Gordon University)H-Index: 5
#2Eyad Elyan (RGU: Robert Gordon University)H-Index: 14
Last. Johan ZwiegelaarH-Index: 1
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Significant progress has been achieved in objects detection applications such as Face Detection. This mainly due to the latest development in deep learning-based approaches and especially in the computer vision domain. However, deploying deep-learning methods require huge computational power such as graphical processing units. These computational requirements make using such methods unsuitable for deployment on platforms with limited resources, such as edge devices. In this paper, we present an ...
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#1Scott Wares (RGU: Robert Gordon University)H-Index: 1
#2John P. Isaacs (RGU: Robert Gordon University)H-Index: 9
Last. Eyad Elyan (RGU: Robert Gordon University)H-Index: 14
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Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too fre...
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