Amir Hussain

Amir Hussain
Edinburgh Napier University · School of Computing

BEng (1st Class Hons) PhD

About

619
Publications
168,341
Reads
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14,098
Citations
Introduction
Amir Hussain is currently Professor and founding Head of the Cognitive Big Data and Cybersecurity (CogBiD) Research Lab at Edinburgh Napier University, U.K. He is founding Editor-in-Chief of two leading journals: Cognitive Computation (Springer Nature), and BMC Big Data Analytics (BioMed Central), and of the Springer Book Series on Socio-Affective Computing, and Cognitive Computation Trends. He has also been appointed Ass. Editor for a number of prestigious journals, including IEEE Trans. on Neural Networks & Learning Systems, IEEE Trans. Emerging Topics in Computational Intelligence, (Elsevier) Information Fusion & IEEE Computational Intelligence. Amongst other distinguished roles, he is General Chair for IEEE WCCI 2020 - the world's largest technical event in Computational Intelligence.
Additional affiliations
June 2000 - September 2018
University of Stirling
Position
  • Professor (Full)
Description
  • Head of Cognitive Big Data Informatics (CogBID) Research Laboratory

Publications

Publications (619)
Article
Full-text available
The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the target. However, these technologies have well-known limitations of occlusion and ambient lighting with...
Article
Representation learning impacts the performance of Machine Learning (ML) models. Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform efficiently on a specific task, in terms of: (1) high accuracy, (2) large short-term memory and (3) low execution time...
Article
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Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an increment of the different tensions underlying politics, as has been reported after many other politic...
Article
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Robust understanding of the lane position and type is essential for changing lanes in autonomous vehicles. However, accomplishing this task in real time with high level of precision is not trivial. In this paper, we propose a novel cascaded deep neural network (DNet-CNet) for real-time end-to-end lane detection (DNet) and classification (CNet). The...
Article
Full-text available
Background Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical...
Conference Paper
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, such approaches do not deliver required levels of speech intelligibility in everyday noisy environment...
Conference Paper
A flexible meander line monopole antenna (MMA) is presented in this paper. The antenna can be worn for on-and off-body applications. The overall dimension of the MMA is 37 mm x 50 mm x 2.37 mms, The MMA was manufactured and measured, and the results matched with simulation results. The MMA design shows a bandwidth of up to 1282.4 (450.5) MHz and pr...
Conference Paper
A button sensor antenna (BSA) for wireless medical body area networks (WMBAN) is presented, which works through the IEEE 802.11b/g/n standard. Due to strong interaction between the sensor antenna and the body, a new robust system is designed with a small footprint that can serve on- and off-body healthcare applications. The measured and simulated r...
Conference Paper
Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLF...
Conference Paper
Sign language is a means of communication between the deaf community and normal hearing people who use hand gestures, facial expressions, and body language to communicate. It has the same level of complexity as spoken language, but it does not employ the same sentence structure as English. The motions in sign language comprise a range of distinct h...
Article
Full-text available
Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that...
Article
Full-text available
Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are extracted by solving inverse problem through beamforming. EEG sources epochs are used as source-time maps input to...
Article
With the growth of social platforms in recent years and the rapid increase in the means of communication through these platforms, a significant amount of textual data is available that contains an abundance of individuals’ opinions. Sentiment analysis is a task that supports companies and organizations to evaluate this textual data with the intenti...
Article
Full-text available
Traditional approaches tend to cause classier bias in the imbalanced data set, resulting in poor classification performance for minority classes. In particular, there are many imbalanced data in financial fraud, network intrusion, and fault detection, where recognition rate of minority classes is pertinent than the classification performance of maj...
Article
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Aspect-based sentiment analysis (ABSA), a popular research area in NLP, has two distinct parts—aspect extraction (AE) and labelling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesizes that transferring knowledge from a pre-trained AE model can benefit the performance...
Article
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Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual tasks can enhance each other via the joint modeling. Before conducting research on jointly modeling...
Preprint
Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity...
Preprint
p>Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter problems caused by a growing number of unethical online social activities. This study presents a new Quantum Beta-Distributed Multi-Objective Particle Swarm Optimization (QBD-MOPSO) system to detect fake accounts on Twitter. The proposed system aims to min...
Preprint
p>Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter problems caused by a growing number of unethical online social activities. This study presents a new Quantum Beta-Distributed Multi-Objective Particle Swarm Optimization (QBD-MOPSO) system to detect fake accounts on Twitter. The proposed system aims to min...
Article
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Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using...
Article
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Heart disease is one of the leading causes of death in the world today. Prediction of heart disease is a prominent topic in the clinical data processing. To increase patient survival rates, early diagnosis of heart disease is an important field of research in the medical field. There are many studies on the prediction of heart disease, but limited...
Article
Full-text available
A button sensor antenna for on-body monitoring in wireless body area network (WBAN) systems is presented. Due to the close coupling between the sensor antenna and the human body, it is highly challenging to design sensor antenna devices. In this paper, a mechanically robust system is proposed that integrates a dual-band button antenna with a wirele...
Article
Full-text available
This paper studies the issues of coexistence in a homogenous network environment that constitutes a wireless body area network (WBANs). IEEE 802.15.6 specially designed a network for the human body. IEEE 802.15.6 provide three different types of techniques to avoid the interference issue. These techniques do not provide a proper solution for avoidi...
Preprint
Sign language is a mean of communication between the deaf community and hearing people, who use hand gestures, facial expressions, and body language to communicate. It has the same level of complexity as spoken language, but it does not employ the same sentence structure as English. The motions in sign language are made up of a range of distinct ha...
Preprint
Full-text available
In this paper, we focus on tackling the precise keypoint coordinates regression task. Most existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice. To overcome this limitation, we develop a small yet discrimicative model called STair Network, which can be si...
Preprint
Full-text available
Sign language is a mean of communication between the deaf community and hearing people, who use hand gestures, facial expressions, and body language to communicate. It has the same level of complexity as spoken language, but it does not employ the same sentence structure as English. The motions in sign language are made up of a range of distinct ha...
Preprint
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, such approaches do not deliver required levels of speech intelligibility in everyday noisy environment...
Preprint
Full-text available
Multimodal hearing aids (HAs) aim to deliver more intelligible audio in noisy environments by contextually sensing and processing data in the form of not only audio but also visual information (e.g. lip reading). Machine learning techniques can play a pivotal role for the contextually processing of multimodal data. However, since the computational...
Preprint
Full-text available
This paper proposes a novel multimodal self-supervised architecture for energy-efficient AV speech enhancement by integrating graph neural networks with canonical correlation analysis (CCA-GNN). This builds on a state-of-the-art CCA-GNN that aims to learn representative embeddings by maximizing the correlation between pairs of augmented views of th...
Preprint
Full-text available
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are generally trained to minimise the distance between clean and enhanced speech features. These often result in improved speech quality however they suffer from a lack of generalisation and may not deliver the required speech intelligibility in...
Preprint
Full-text available
In acoustic signal processing, the target signals usually carry semantic information, which is encoded in a hierarchal structure of short and long-term contexts. However, the background noise distorts these structures in a nonuniform way. The existing deep acoustic signal enhancement (ASE) architectures ignore this kind of local and global effect....
Article
The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-...
Preprint
Particle swarm optimization system based on the distributed architecture over multiple sub-swarms has shown its efficiency for static optimization and has not been studied to solve dynamic multi-objective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challengin...
Article
Full-text available
Sentic computing is a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and commonsense computing, which exploits both computer and social sciences to better recognize, interpret, and process opinions and sentiments over the Web. In the last ten years, many different models (such as the Hourglass of Emo...
Article
Full-text available
Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related documents. The QAS system is composed of three main modules; question analysis, passage retrieval, and ans...
Article
Road extraction from synthetic aperture radar (SAR) images has attracted much attention in the field of remote sensing image processing. General road extraction algorithms, affected by shadows of buildings and trees, are prone to producing fragmented road segments. To improve the accuracy and completeness of road extraction, we propose a neural net...
Article
Full-text available
This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical correlation analysis (CCA-GNN). The proposed approach lays its foundations on a state-of-the-art CCA-GNN that learns representative embeddings by maximizing the correlatio...
Article
Full-text available
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES aff...
Preprint
Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study propos...
Preprint
Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study propos...
Preprint
The human brain contextually exploits heterogeneous sensory information to efficiently perform cognitive tasks including vision and hearing. For example, during the cocktail party situation, the human auditory cortex contextually integrates audio-visual (AV) cues in order to better perceive speech. Recent studies have shown that AV speech enhanceme...
Article
Full-text available
Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity...
Preprint
Full-text available
Existing deep learning (DL) based speech enhancement approaches are generally optimised to minimise the distance between clean and enhanced speech features. These often result in improved speech quality however they suffer from a lack of generalisation and may not deliver the required speech intelligibility in real noisy situations. In an attempt t...
Article
Full-text available
With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various data as services in a shared and automated environment has become a reality with the advent of cloud...
Article
Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: (1) current label generation techniques are mostly empirical and lack theoretical support, discouraging elaborate label design; and (2) as a result, most methods rely he...
Conference Paper
Full-text available
One of the most challenging problems in the telecommunications industry is predicting customer churn (CCP). Decision-makers and business experts stressed that acquiring new clients is more expensive than maintaining current ones. From current churn data, business analysts must identify the causes for client turnover and behavior trends. This study...
Article
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practical utility using Light Detection and Ranging (LiDAR) camera fusion networks. In particular, we develo...
Article
Full-text available
Instance segmentation methods for synthetic aperture radar (SAR) ship imaging have certain unsolved problems: (1) Most of the anchor-based detection algorithms encounter difficulties in tuning the anchor-related parameters and high computational costs. (2) Different tasks share the same features without considering the differences between tasks, le...
Preprint
Full-text available
The main challenge in the clinical assessment of Psychogenic Non-Epileptic Seizures (PNES) is the lack of an electroencephalographic marker in the electroencephalography (EEG) readout. Although decades of EEG studies have focused on detecting cortical brain function underlying PNES, the principle of PNES remains poorly understood. To address this p...
Article
Full-text available
Generative Adversarial Networks (GANs) have drawn great attention recently since they are the powerful models to generate high-quality images. Although GANs have achieved great success, they usually suffer from unstable training and consequently may lead to the poor generations in some cases. Such drawback is argued mainly due to the difficulties i...
Conference Paper
Full-text available
Education plays a crucial role in individual life as well as for the whole nation. Many students are dropped out yearly in different academic courses. This study investigates the contribution of students’ demographic attributes to their academic achievements. The Random Forest classification model is used to predict students’ final exam performance...
Conference Paper
Full-text available
Heart disease is the world’s leading cause of increasing death rates. Although there is a lot of research in the medical sector, an efficient and reliable model to predict this disease at an early stage is still required. So, early diagnosis of heart disease is the most promising strategy for effective treatment. In this paper, we utilize the Genet...
Article
Synthetic aperture radar (SAR) image recognition is an important stage of SAR image interpretation. The standard convolutional neural network (CNN) has been successfully applied in the SAR image recognition due to its powerful feature extraction capability. Nevertheless, the CNN requires numerous labeled samples for satisfactory recognition perform...
Article
In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs)...
Article
Background: The roll-out of vaccines for SARS-CoV-2 in the United Kingdom, started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalisations and deaths in vaccinated individuals. However, vaccine hesitancy remains a concern, in particular relating to adverse effects following immunisation (AE...
Preprint
BACKGROUND The rollout of vaccines for COVID-19 in the United Kingdom started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalizations, and deaths among vaccinated individuals. However, vaccine hesitancy remains a concern, in particular relating to adverse effects following immunization (AEFI...
Article
Full-text available
Introduction Virtual reality (VR) and augmented reality (AR) technologies are increasingly being used in undergraduate medical education. We aim to evaluate the effectiveness of VR and AR technologies for improving knowledge and skills in medical students. Methods and analysis Using Best Evidence in Medical Education (BEME) collaboration guideline...