Professor Aladdin Ayesh

CITP CEng FBCS, SFHEA

Personal Chair of Artificial Intelligence

Accepting PhDs

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Email Address: aladdin.ayesh@abdn.ac.uk
School/Department: School of Natural and Computing Sciences

Biography

Aladdin Ayesh, MSc (Essex, 1996), PhD (LJMU, 2000), is currently the Vice Dean for Joint Institute of Data Sciences and Artificial Intelligence at 51cg in UK. He also holds a Personal Chair as a Professor of Artificial Intelligence. Prior to his current role, he was a Professor of Artificial Intelligence at De Montfort University. His research focuses on computational cognition, machine learning and explainable AI. His research explored cognitive architectures, emotion modeling and recognition, and applied AI using variety of machine learning techniques including statistical approaches, e.g. Markov Models and Bayesian Networks, logic-based and symbolic approaches, e.g. Modal and Fuzzy Logics, and neural approaches, e.g. Self-Organizing Maps and Deep Learning Classifiers. He applies these techniques in three primary areas: Health Informatics, Sustainable Development, and Data Privacy. Prof. Ayesh has over 150 publications, supervised 24 PhD students to successful completions, and participated in 26 funded projects. He is a founding editor of four international journals and chaired several international conferences. He is also a member of two IEEE technical committees, several IEEE Standards working groups, and a contributor to IEEE 7010-2020 – IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being.

Memberships and Affiliations

Internal Memberships: School Executive.

Joint Institute Committees.
External Memberships: BCS Fellow

IEEE Senior Member

AHE Senior Fellow

IEEE Transactions on Affective Computing steering committee chair (2019 - 2024)

Research

Research Overview

My research focuses on computational cognition, machine learning and explainable AI. In various research projects, I have explored cognitive architectures, emotion modeling and recognition, and applied AI using variety of machine learning techniques including statistical approaches, e.g. Markov Models and Bayesian Networks, logic-based and symbolic approaches, e.g. Modal and Fuzzy Logics, and neural approaches, e.g. Self-Organizing Maps and Deep Learning Classifiers. My team and I applied these techniques in three primary areas: Health Informatics, Sustainable Development, and Data Privacy.

Research Areas

Accepting PhDs

I am currently accepting PhDs in Computing Science, Artificial Intelligence.

Please get in touch if you would like to discuss your research ideas further.

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Computing Science

Supervising
Accepting PhDs

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Artificial Intelligence

Supervising
Accepting PhDs

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Nutrition and Health

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Applied Health Sciences

Supervising

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Research Specialisms

Artificial Intelligence
Machine Learning
Cognitive Modelling
Natural Language Processing
Neural Computing

Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is , published under the licence.

Supervision

My current supervision areas are: Computing Science, Artificial Intelligence, Nutrition and Health, Applied Health Sciences.

I have supervised 25 PhD projects to successful completions of which 21 first supervision and 4 second supervision.

I have examined 23 PhD and MPhil projects of which 18 at National Universities: Hull, Essex, Manchester, Edinburgh, Leeds, Leicester, Huddersfield, Liverpool John Moores, and Bradford; and 5 at European Universities of: Le Havre, Rouen, Paris 8, and Granada.

Publications

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Biography

Memberships and Affiliations

Research Overview

Research Areas

Accepting PhDs

Computing Science

Artificial Intelligence

Nutrition and Health

Applied Health Sciences

Research Specialisms

Supervision

A Review of Supervised Learning Techniques for Dementia Detection from Cognitive Data

Deep learning‑based prediction of major page faults in cluster systems

Lightweight secure image encryption: a tent map chaos theory approach

A Privacy-Enhancing Image Encryption Algorithm for Securing Medical Images

Leveraging AI to Support Virtual Students in Intelligent Tutoring Systems

Deep learning-based prediction of reflection attacks using NetFlow data

Enhancing Data Security and Privacy Using Federated Learning: A Scalable Framework for Distributed Systems

Neural Networks Remember More: The Power of Parameter Isolation and Combination

Towards Assessing Generative AI Based Empathic Systems

Using Large Language Models to Integrate Virtual Students in Computerized Learning Platforms

Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models

User-Centric AI Analytics for Chronic Health Conditions Management

Multimodal motivation modelling and computing towards motivationally intelligent E-learning systems

A Transdisciplinary Framework for AI-driven Disaster Risk Reduction for Low-income Housing Communities in Kenya

Complementing privacy and utility trade-off with self-organising maps

Explaining the Principles to Practices Gap in AI

A functional BCI model by the P2731 working group: psychology

IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence

Gaze-based assessment of dyslexic students' motivation within an e-learning environment

Cognitive computing: Methodologies for neural computing and semantic computing in brain-inspired systems

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

IEEE 7010: A new standard for assessing the well-being implications of artificial intelligence

Investigating classification techniques with feature selection for intention mining from twitter feed

Principles to Practices for Responsible AI: Closing the Gap

Utility Promises of Self-Organising Maps in Privacy Preserving Data Mining

Behavior-based outlier detection for network access control systems

Cognitive Systems Approach to Smart Cities

On using EEG signals for emotion modeling and biometrics

Turing Test Revisited: A Framework for an Alternative

Users intention based on twitter features using text analytics

Intelligent intrusion detection systems using artificial neural networks

SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset

Class discovery from semi-structured EEG data for affective computing and personalisation

The effects of typing demand on learner's Motivation/Attitude-driven Behaviour (MADB) model with mouse and keystroke behaviours

Developing an intelligent filtering technique for bring your own device network access control

Combining supervised & unsupervised learning to discover emotional classes

The Effects of Task Demand and External Stimuli on Learner’s Stress Perception and Job Performance

Conceptual motivation modeling for students with dyslexia for enhanced assistive learning

Exploring direct learning instruction and external stimuli effects on learner's states and mouse/keystroke behaviours

The impact of using educational gamification in mobile computing course: A case study

The Motivation/Attitude-driven Behavior (MADB) model in e-learning and the effects on mouse dynamics

Towards psychologically based personalised modelling of emotions using associative classifiers

Experience with AOAB methodology in mobile computing module

Affective and behavioral assessment for adaptive intelligent tutoring systems

Models for Computational Emotions from Psychological Theories Using Type-II Fuzzy Logic

Energy efficient algorithm for swarmed sensors networks

Using mouse and keyboard dynamics to detect cognitive stress during mental arithmetic

Mobile cognitive systems: The business of connecting data to user-centred services with a smile

An extension to the FEEDB multimodal database of facial expressions and emotions

Preface: 29th Annual European Simulation and Modelling Conference 2015

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