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School of Science and Technology – Vice-Chancellor’s Researcher Development Scheme Broad Area of Research – Computer Science Computational Intelligence, Computational Neuroscience, Data Mining, Machine Learning and Computational Modelling. Important note: Projects within the School of Science and Technology may have the option of joining a new, University Alliance-sponsored, national Doctoral Training Alliance in Applied Biosciences for Health, in which students will be part of an exciting multidisciplinary cohort of postgraduate students and supervisors, and benefit from regular networking and training opportunities through attendance at regular national meetings, including residential summer schools, throughout their PhD studies. Project Titles (descriptions below) 1. Prof Ahmad Lotfi – Fuzzy Transfer Learning in Human Activity Recognition. 2. Dr Georgina Cosma – Development of Novel Computational Intelligence Approaches to Cancer Prognosis and Diagnosis. 3. Prof TM McGinnity – Computational Modelling of Biological Learning, Adaptation and Cognition Utilising Integrated Neural Systems. 1. Prof Ahmad Lotfi – Fuzzy Transfer Learning in Human Activity Recognition The aim of this research is to combine three concepts; Transfer Learning (TL), Fuzzy System (FS) and Activity Recognition (AR) to address the problem of learning and recognising Activities of Daily Living (ADL) in an Ambient Assisted Living environment. ADL is a term used in healthcare to refer to people's daily self-care activities. They are defined as "the things we normally do, such as feeding ourselves, bathing, dressing, grooming, work, homemaking, and leisure. Transfer learning (aka inductive transfer), is a research hypothesis in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. Human learners appear to have inherent ways to transfer knowledge between tasks. For example, a carer who is looking after one patient could easily adapt learned knowledge to another patient. Standard machine learning approaches including Artificial Neural Networks focus on a need for large training data for models to be developed from the same domain as the target task. This dependency in data could be reduced if tools and concept suitable for handling uncertainty are used. Fuzzy systems uses the concept of fuzzy membership, linguistic variable and conditional rules to represent the degree of uncertainty and this could be utilised to reduce the dependency to numerical data. Healthcare professionals use a person's ability or inability to perform ADLs as a measurement of their functional status, particularly in regard to people with disabilities and the elderly. Activity recognition aims to identify activities as they occur based on data collected by sensors. Environment sensors such as PIR motion detectors or door entry magnetic sensors are used to gather information about more complex activities such as cooking, sleeping, and eating. These recognised activities are representing the ADL. Considering the chaotic nature of the human activities, application of Bayesian framework, Hidden Markov Model (HMM) and other statistical techniques are already investigated. Non-statistical techniques including data mining and machine learning algorithms are also being used to model different human activities using a large training dataset. The proposed fuzzy transfer learning for human activity recognition is an alternative approach to traditional supervised or unsupervised learning techniques to recognise and transfer learned activities from one scenario to another. This will be expanded to transfer the model from one user to another user. As part of our ongoing research, different techniques in human activity recognition are already investigated. The project supervisory team has extensive knowledge and experience in Ambient Assisted Living environment, machine learning techniques including fuzzy systems and human activity recognition. Specific qualifications/subject areas required of the applicants for this project: A first class or upper second class UK BSc (Hons) degree (or equivalent) or Master’s degree in Computer Science, Computer Systems Engineering or Mathematics. Good mathematical skills for algorithm development and evaluation are essential. For informal discussion regarding the project, please contact: [email protected] 2. Dr Georgina Cosma – Development of Novel Computational Intelligence Approaches to Cancer Prognosis and Diagnosis An important challenge for primary and secondary care physicians remains the accurate evaluation of the risk of cancer occurrence and prediction of progression, both of which are essential for determining the optimum treatment and management. Predictive modelling in medicine involves deriving a mathematical model for the prediction of a future outcome for patients. Predictive tools can help during the complex decision-making processes, and provide individualised, evidence-based estimates for cancer patients. The predictive models can be based on statistical or computational intelligence techniques. Computational intelligence is a relatively new term, for which there is currently no formal definition. Computational intelligence algorithms are considered by some researchers to involve only evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these. However, others consider a more broad definition of computational intelligence to include the above mentioned, as well as paradigms such as Bayesian belief networks, multi-agent systems, case-based reasoning and so on. Many computational intelligence approaches, such as artificial neural networks and support vector machines, are known to increase accuracy in cancer prognosis and diagnosis because these approaches are capable of dealing with the complexity which is typically found in clinical datasets. However, a majority of these approaches do not provide qualitative reasoning behind the derived prediction. The aim of the project is to identify the factors which are predictors of cancer prognosis and diagnosis tasks (prostate cancer and lung cancer), and to develop a new computational intelligence approach which can be used for cancer prediction, and which can achieve higher predictive accuracy than the existing approaches. The new computational intelligence method should be able to provide qualitative reasoning behind the prediction, and therefore provide the combinations of factors and their weightings which have derived the prediction. The derived method will be integrated in mobile apps and tablets. These apps will be capable of collecting and storing ‘big data’ which could be used for making cancer risk predictions in the future. Such data could include ‘conventional’ information on diet, physical activity, smoking, exposure to pollution, and aspirin use, and be enhanced on the basis of additional biomarkers such as peripheral blood phenotypic and other ‘liquid biopsy’ derived data. It is expected that the new approaches that will be developed will improve existing approaches for predicting cancer risk and the delivery of personalized approaches for the management and treatment of patients with cancer that are focused on reducing risk and recurrence. Students will work with a subset of data extracted from The Health Improvement Network (THIN) database which is a large UK primary care database; and with data from the British Association of Urological Surgeons (BAUS). Specific qualifications/subject areas required of the applicants for this project: BSc and/or MSc in subject areas: computer science, computational intelligence, machine learning, data science, statistics and optimization, operational research. For informal discussion regarding the project, please contact: [email protected] 3. Prof TM McGinnity – Computational Modelling of Biological Learning, Adaptation and Cognition Utilising Integrated Neural Systems. Specific qualifications/subject areas required of the applicants for this project: 2.1 honours degree or above in STEM-related subjects such as Computer Science, Mathematics, Physics or Computational Neuroscience. This project would suit a student with good programming/modelling skills (e.g. in Matlab) and a background in one of the following: computer science, physics, mathematics, computational neuroscience or a closely related discipline, together with a strong interest in multi-disciplinary modelling of brain signal processing, neural networks or cognition. Brain information processing, learning and cognition are dependent on neural connections formed during development and modified during life. The structural complexity, scale, extensive and substantially unknown connectivity, and limited accessibility to neurons, complicate the study of the dynamics of brain networks. Despite important advances having been made by biologists and neuroscientists/computational neuroscientists, the exact ways in which neuronal circuits interconnect and their precise information processing and dysfunction in health and disease are still active areas of research. Two main approaches have been utilised by scientists to understand the fundamental (i.e. non-psychological) processes of learning and cognition. In the first (more biological) approach, the cellular and molecular mechanisms involved in learning and memory have been analysed in simple animals and in circuits of mammalian brains. The second (more computational) approach involves algorithmic modelling of cells and axonal/synaptic interconnections, at various network scales on powerful computational platforms. This latter approach is entirely dependent on the accuracy and fidelity of the neuronal and synaptic models. The ideal research platform would be a hybrid system, that has available a controllable and fully observable bio-computational, 3D structured and electronically interconnected matrix of in-vitro biological cells, with direct computational control of stimulation and read-back of intercellular communication signals. This would allow for the possibility of computational modelling using accurate (real) cells under precise experimental control. This PhD project is one component of an integrated effort to develop and exploit a controllable and fully observable platform of real biological cells for computational modelling. A cross-disciplinary team of experts in nanotechnology, biology and computational modelling is driving the overall effort and establishing reliable, biologically relevant and well-characterised multi-layer nanofiber-based 3D tissue scaffolds. These scaffold systems will have a range of applications (e.g. studies of neural development and function) but are particularly applicable to computational modelling of brain signal processing for learning and adaptation. The project will build upon our recent developments of novel bio-modified nanofiber lattices that can be electrically stimulated. The PhD Project This PhD project will be focused on the development of approaches for computational modelling of inter-cell communication, where biological cells are deposited on a hybrid, nanostructured lattice (other members of the team will focus on the nanotechnology and cell deposition aspects). The PhD student will: Explore whether bioactive nanofiber lattices, with co-cultures of distinct physiologically active populations of cells found in the nervous system, can be interconnected (bio-electronically) to produce simple models of stimulus-response signalling events in brain tissue; Develop algorithms which address the computationally controlled formation of synaptic interconnect by guided growth, utilising graphene-based interconnect incorporated into the nanofiber-based 3D tissue scaffolds; Research computational approaches for studying synaptic modification utilising computational neuroscience learning algorithms; Explore the potential of the platform for studies of larger scale neural development. Specific qualifications/subject areas required of the applicants for this project: 2.1 honours degree or above in STEM-related subjects such as Computer Science, Mathematics, Physics or Computational Neuroscience. For informal discussion regarding the project, please contact: [email protected]