I am a senior lecturer and head of the division of Computing Science and Mathematics. My field of interest is artificial intelligence (AI), particularly data science, machine learning and computer vision.
Before coming to academia, I ran a software company which developed and sold neural network based software to customers in banking, insurance, marketing and sales.
I like to be innovative in teaching, and I'm expanding the ways we deliver knowledge and skills, including graduate apprenticeships, online degrees, micro-credits and overseas provision.
My research is mostly concerned with applying data science and AI to applications in health and wellbeing. My main research project is developing assistive technologies for the blind using computer vision. I've also worked on projects designed to optimise treatments, predict symptoms and manage scarce resources.
I also run a spin-out company for the University, which provides hardware and consultancy for data collection and analytics.
The main focus of my current research is a project called the Artificial Intelligence Sight Loss Assistant (AISLA). We are using computer vision, machine learning, natural language understanding and other AI tools to develop a system to help people with sight loss to live more independently. The project addresses many challenges in modern AI, as well as being potentially useful to many people.
The project web page is at https://www.aisla.org.uk/. I am keen to collaborate with other researchers with a similar interest as well as with charities and other organisations that help people with sight loss.
Digital Nutrition Assessment Tool
PI: Dr Jenni Connelly
Funded by: Economic and Social Research Council
–
INtelligent FOod Recognition and Monitoring for patient wellbeing
PI:
Funded by: Falcon Foodservice Equipment
–
Food recognition with computer vision
PI:
Funded by: Falcon Foodservice Equipment
–
Developing meta-ethnography reporting guidelines and standards for research (eMERGE)
PI: Dr Emma France
Funded by: National Institute for Health Research
–
Knowledge Transfer Partnership between University of Stirling and The Royal College of Midwives
PI:
Funded by: Royal College of Midwives and Technology Strategy Board
–
Knowledge Transfer Partnership between University of Stirling & ThinkAnalytics Limited
PI: Professor Kevin Swingler
Funded by: Technology Strategy Board and ThinkAnalytics Ltd
–
The Electronic Sales Engine - An artificial Intelligence based sales optimization system
PI: Professor Kevin Swingler
Funded by: Scottish Enterprise
–
Article
Text mining of veterinary forums for epidemiological surveillance supplementation
Munaf S, Swingler K, Brülisauer F, O’Hare A, Gunn G & Reeves A (2023) Text mining of veterinary forums for epidemiological surveillance supplementation. Social Network Analysis and Mining, 13 (1), Art. No.: 121 (2023). https://doi.org/10.1007/s13278-023-01131-7
Article
Johnston P, Nogueira K & Swingler K (2023) NS-IL: Neuro-Symbolic Visual Question Answering Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes. IEEE Access, 11, pp. 141406-141420. https://doi.org/10.1109/access.2023.3341007
Article
Johnston P, Nogueira K & Swingler K (2023) GMM-IL: Image Classification Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes. IEEE Access, 11, pp. 25492-25501. https://doi.org/10.1109/access.2023.3255795
Conference Proceeding
A Suite of Incremental Image Degradation Operators for Testing Image Classification Algorithms
Swingler K (2022) A Suite of Incremental Image Degradation Operators for Testing Image Classification Algorithms. In: Back T, van Stein B, Wagner C, Garibaldi J, Lam HK, Cottrell M, Doctor F, Filipe J, Warwick K & Kacprzyk J (eds.) 14th International Conference on Neural Computation Theory and Applications, Valletta, Malta, 24.10.2022-26.10.2022. Setubal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 262-272. https://doi.org/10.5220/0011511000003332
Conference Proceeding
A Haptic Interface for Guiding People with Visual Impairment using Three Dimensional Computer Vision
Swingler K & Grigson C (2022) A Haptic Interface for Guiding People with Visual Impairment using Three Dimensional Computer Vision. In: Back T, van Stein B, Wagner C, Garibaldi J, Lam HK, Cottrell M, Doctor F, Filipe J, Warwick K & Kaprzyk J (eds.) Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA. 14th International Conference on Neural Computation Theory and Applications, Valletta, Malta, 24.10.2022-26.10.2022. Setubal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 315-322. https://doi.org/10.5220/0011307800003332
Speech
Maltinsky W, Manuf S, Den Daas C, Ozakinci G, Gaitens H & Swingler K (2022) Using readily available social media data to describe sentiments towards transmission-reducing behaviours during the Covid pandemic., 23.08.2022-27.08.2022.
Conference Proceeding
Learning Spatial Relations with a Standard Convolutional Neural Network
Swingler K & Bath M (2020) Learning Spatial Relations with a Standard Convolutional Neural Network. In: Merelo JJ, Garibaldi J, Wagner C, Bäck T, Madani K & Warwick K (eds.) Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: NCTA. 12th International Conference on Neural Computation Theory and Applications, Budapest, Hungary, 02.11.2020-04.11.2020. Setubal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 464-470. https://doi.org/10.5220/0010170204640470
Article
Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples
Swingler K (2020) Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples. Evolutionary Computation, 28 (2), pp. 317-338. https://doi.org/10.1162/evco_a_00257
Conference Proceeding
A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter
Alqarafi A, Adeel A, Hawalah A, Swingler K & Hussain A (2018) A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter. In: Hussain A, Zhao H, Ren J, Zheng J, Liu C, Luo B & Zhao X (eds.) Advances in Brain Inspired Cognitive Systems. Lecture Notes in Computer Science, 10989. BICS 2018: 9th International Conference on Brain Inspired Cognitive Systems, Xi'an, China, 07.07.2018-08.07.2018. Cham, Switzerland: Springer International Publishing, pp. 589-596. https://doi.org/10.1007/978-3-030-00563-4_57
Conference Proceeding
High capacity content addressable memory with mixed order hyper networks
Swingler K (2017) High capacity content addressable memory with mixed order hyper networks. In: Merelo J, Rosa A, Cadenas J, Correia A, Mandani K, Ruano A & Filipe J (eds.) Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. Studies in Computational Intelligence, 669. Computational Intelligence International Joint Conference, IJCCI 2015, Lisbon, Portugal, 12.11.2015-14.11.2015. Cham, Switzerland: Springer, pp. 337-358. https://doi.org/10.1007/978-3-319-48506-5_17
Thesis
Mixed Order Hyper-Networks for Function Approximation and Optimisation
Swingler K (2016) Mixed Order Hyper-Networks for Function Approximation and Optimisation. Doctor of Philosophy. University of Stirling. http://hdl.handle.net/1893/25349
Conference Proceeding
Opening the Black Box: Analysing MLP Functionality Using Walsh Functions
Swingler K (2016) Opening the Black Box: Analysing MLP Functionality Using Walsh Functions. In: Merelo J, Rosa A, Cadenas J, Dourado A, Madani K & Filipe J (eds.) Computational Intelligence. Studies in Computational Intelligence, 620. International Joint Conference on Computational Intelligence (IJCCI) 2014, Rome, Italy, 22.10.2014-24.10.2014. Cham, Switzerland: Springer, pp. 303-323. https://doi.org/10.1007/978-3-319-26393-9_18
Article
Structure Discovery in Mixed Order Hyper Networks
Swingler K (2016) Structure Discovery in Mixed Order Hyper Networks. Big Data Analytics, 1 (1), Art. No.: 8. https://doi.org/10.1186/s41044-016-0009-x
Conference Proceeding
A Comparison of Learning Rules for Mixed Order Hyper Networks
Swingler K (2015) A Comparison of Learning Rules for Mixed Order Hyper Networks. In: Proceedings of the 7th International Joint Conference on Computational Intelligence. NCTA (IJCCI). Setubal, Portugal: Science and Technology Publications, pp. 17-27. http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220%2f0005588000170027; https://doi.org/10.5220/0005588000170027
Conference Proceeding
An analysis of the local optima storage capacity of Hopfield network based fitness function models
Swingler K & Smith L (2014) An analysis of the local optima storage capacity of Hopfield network based fitness function models. In: Nguyen N, Kowalczyk R, Fred A & Joaquim F (eds.) Transactions on Computational Collective Intelligence XVII. Lecture Notes in Computer Science, 8790. Berlin Heidelberg: Springer, pp. 248-271. http://link.springer.com/chapter/10.1007/978-3-662-44994-3_13; https://doi.org/10.1007/978-3-662-44994-3_13
Conference Proceeding
A walsh analysis of multilayer perceptron function
Swingler K (2014) A walsh analysis of multilayer perceptron function. In: Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2014). NCTA 2014: 6th International Conference on Neural Computation Theory and Applications, Rome, Italy, 22.10.2014-24.10.2014. Setubal, Portugal: Science and Technology Publications, pp. 5-14. http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0004974800050014; https://doi.org/10.5220/0004974800050014
Article
Training and making calculations with mixed order hyper-networks
Swingler K & Smith L (2014) Training and making calculations with mixed order hyper-networks. Neurocomputing, 141, pp. 65-75. https://doi.org/10.1016/j.neucom.2013.11.041
Article
Duncan E, Colver K, Dougall N, Swingler K, Stephenson J & Abhyankar P (2014) Consensus on items and quantities of clinical equipment required to deal with a mass casualties big bang incident: a national Delphi study. BMC Emergency Medicine, 14, Art. No.: 5. https://doi.org/10.1186/1471-227X-14-5
Website Content
Swingler K, Duncan E & Murray J (2013) CARE Measure. 2013. http://www.caremeasure.org/
Conference Proceeding
Mixed order associative networks for function approximation, optimisation and sampling
Swingler K & Smith L (2013) Mixed order associative networks for function approximation, optimisation and sampling. In: ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium, 24.04.2013-26.04.2013. ESANN, pp. 23-28. http://www.i6doc.com/en/livre/?GCOI=28001100131010
Conference Proceeding
On the Capacity of Hopfield Neural Networks as EDAs for Solving Combinatorial Optimisation Problems
Swingler K (2012) On the Capacity of Hopfield Neural Networks as EDAs for Solving Combinatorial Optimisation Problems. In: Rosa A, Correia A, Madani K, Filipe J & Kacprzyk J (eds.) IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence. 4th International Conference on Evolutionary Computation Theory and Applications, ECTA 2012, and the 4th International Joint Conference on Computational Intelligence, IJCCI 2012, Barcelona, Spain, 05.10.2012-07.10.2012. SciTePress, pp. 152-157.
Conference Proceeding
Swingler K (2011) The Perils of Ignoring Data Suitability: The Suitability of Data Used to Train Neural Networks Deserves More Attention. In: NCTA 2011 - International Conference on Neural Computation Theory and Application. International Conference on Neural Computation Theory and Application, Paris, France, 24.10.2011-26.10.2011. SciTePress Digital Library. http://www.ncta.ijcci.org/Abstracts/2011/NCTA_2011_Abstracts.htm
Software
SMILI - Development of a systematic Intrapartum observation Instrument
Swingler K & Ross-Davie M (2010) SMILI - Development of a systematic Intrapartum observation Instrument. (1.0) 2010.
Article
SUMS: A flexible approach to the teaching and learning of statistics
Swingler MV, Swingler K & Bishop P (2009) SUMS: A flexible approach to the teaching and learning of statistics. Psychology Learning and Teaching, 8 (1), pp. 39-45. http://www.psychology.heacademy.ac.uk/s.php?p=277
Article
Maguire R, Cowie J, Leadbetter C, McCall K, Swingler K, McCann LA & Kearney N (2009) The development of a side effect risk assessment tool (ASyMS©-SERAT) for use in patients with breast cancer undergoing adjuvant chemotherapy. Journal of Research in Nursing, 14 (1), pp. 27-40. https://doi.org/10.1177/1744987108099235
Conference Proceeding
The Effects of Mutation and Directed Intervention Crossover When Applied to Scheduling Chemotherapy
Godley PM, Cairns D, Cowie J, McCall J & Swingler K (2008) The Effects of Mutation and Directed Intervention Crossover When Applied to Scheduling Chemotherapy. In: Keijzer M (ed.) Proceedings of the 10th annual conference on Genetic and evolutionary computation (GECCO). ACM Genetic and Evolutionary Computation Conference (GECCO) 2008, Atlanta, Georgia, 12.07.2008-16.07.2008. New York, USA: Association for Computing Machinery (ACM), pp. 1105-1106. http://portal.acm.org/toc.cfm?id=1389095&type=proceeding&coll=GUIDE&dl=GUIDE&CFID=47644191&CFTOKEN=12932833; https://doi.org/10.1145/1389095.1389300
Conference Proceeding
Cowie J, Swingler K, Leadbetter C, Maguire R, McCall K & Kearney N (2008) ASYMS-SERAT: A Side-Effect Risk Assessment Tool to Predict Chemotherapy Related Toxicity in Patients with Cancer Receiving Chemotherapy. In: Azevedo L & Londral AR (eds.) Proceedings of the First International Conference on Health Informatics, HEALTHINF 2008, Funchal, Madeira, Portugal, January 28-31, 2008, Volume 2. HEALTHINF - International Conference on Health Informatics 2008, Madeira, Portugal, 28.01.2008-31.01.2008. Setubal, Portugal: INSTICC - Institute for Systems and Technologies of Information, Control and Communication, pp. 225-230. http://www.healthinf.biostec.org/Healthinf2008/
Article
Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients
Lukaszewski RA, Yates AM, Jackson MC, Swingler K, Scherer JM, Simpson AJH, Sadler P, McQuillan P, Titball RW, Brooks TJG & Pearce MJ (2008) Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients. Clinical and Vaccine Immunology, 15 (7), pp. 1089-1094. http://cdli.asm.org/cgi/content/abstract/15/7/1089; https://doi.org/10.1128/CVI.00486-07
Book Chapter
Making Decisions with Data Using Computational Intelligence within a Business Environment
Swingler K & Cairns D (2006) Making Decisions with Data Using Computational Intelligence within a Business Environment. In: Voges K & Pope N (eds.) Business applications and computational intelligence. Hershey PA and London: Idea Group, pp. 19-37. http://www.igi-global.com/chapter/making-decisions-data/6017
Authored Book
Applying Neural Networks, A Practical Guide
Swingler K (1996) Applying Neural Networks, A Practical Guide. London: Academic Press.
Article
Financial prediction: Some pointers, pitfalls and common errors
Swingler K (1996) Financial prediction: Some pointers, pitfalls and common errors. Neural Computing and Applications, 4 (4), pp. 192-197. https://doi.org/10.1007/BF01413817
Article
Producing a neural network for monitoring driver alertness from steering actions
Swingler K & Smith L (1996) Producing a neural network for monitoring driver alertness from steering actions. Neural Computing and Applications, 4 (2), pp. 96-104. https://doi.org/10.1007/BF01413745
Conference Proceeding
From steering to alertness: non-intrusive driver monitoring
Swingler K & Smith L (1995) From steering to alertness: non-intrusive driver monitoring. In: Proceedings of ICANN 95 (Industrial applications). ICANN 95: Neural Networks and their Applications (Industrial applications), Paris, France, 09.10.1995-13.10.1995. Lausanne, Switzerland: European Neural Network Society.
Conference Proceeding
The Filtered Activation Networks
Smith L & Swingler K (1993) The Filtered Activation Networks. In: ESANN 93: European Symposium on Artificial Neural Networks, Brussels, April 7-8-9, 1993. ESANN ' 93: European Symposium on Artificial Neural Networks, Brussels, 07.04.1993-09.04.1993. Brussels: D Facto Conference Services, pp. 165-170. https://groups.google.com/forum/#!msg/comp.ai.neural-nets/7RWb4AY14W0/cQxvTB1bRJkJ
Research Report
Dynamic Neural Networks for Sequence Recognition
Swingler K & Smith L (1991) Dynamic Neural Networks for Sequence Recognition. Consultancy Report. BT CONNEX Project.
I teach on a number of MSc. modules including databases, Python scripting and data analytics. I also develop material for the online masters programmes that we run.
I have an open door policy for students and I'm happy to help them with any aspect of their learning.
I believe in broad access to university level education, and that non-traditional courses like online degrees, graduate apprenticeships and micro credentials are really important.
