Prof David Windridge

Prof David Windridge
NameProf David Windridge
Job titleProfessor of Data Science and Machine Learning
Research institute
Primary appointmentComputer Science
Email addressd.windridge@mdx.ac.uk
ORCIDhttps://orcid.org/0000-0001-5507-8516
Contact categoryAcademic staff

Biography

Biography

Professor of Data Science & Machine Learning, Dept. of Computing,

Associate Professor, Dept. of Computing, Middlesex University .

Senior Research Fellow/Research Fellow, University of Surrey (Centre for Vision Speech & Signal Processing, CVSSP)

PhD Bristol University (Cosmology/Astrophysics)  [EPSRC ROPA funded scholarship]

Teaching

I am the originator & Programme Lead for the Data Science M.Sc. Programme.  I also lead the Foundations of Machine Learning Module CST4050, and co-lead the Business Informatics Module CST2321.

Education and qualifications

Grants

Projects

  • Judicial Decision Data Gathering, Encoding and Sharing

Prizes and Awards

External activities

Editorial Board Springer Quantum Machine Intelligence
External committee
Member of Editorial Board
Springer
Springer Quantum Machine Intelligence (Impact Factor 5.1)
https://link.springer.com/journal/42484
01 Jan 2019

Search amongst my papers with the word 'quantum' in the abstract on Google Scholar.

Steering Board of Multi-disciplinary Trends in Artificial Intelligence Conference (MIWAI)
External committee
Springer

MIWAI is currently in its 17th year and is the foremost conference for interdisciplinarity in AI.

I will act as host for the 18th MIWAI Conference to be held at Middlesex University in 2025.

Search amongst my papers with the word 'kernel', 'fusion', or 'classifier combination' in the abstract on Google Scholar.

Research outputs

A language-guided latent diffusion approach to visual attribution in medical imaging

Siddiqui, A. A., Tirunagari, S., Zia, T. and Windridge, D. 2024. A language-guided latent diffusion approach to visual attribution in medical imaging. Scientific Reports. https://doi.org/10.1038/s41598-024-81646-x

Quantum enhanced knowledge distillation

Simone, P., Lavagna, L., De Falco, F,, Ceschini, A., Rosato, A., Windridge, D. and Panella, M. 2024. Quantum enhanced knowledge distillation. Quantum Techniques in Machine Learning 2024 Conference. Melbourne, Australia 24 - 29 Nov 2024

Faithful Counterfactual Visual Explanations (FCVE)

Khan, B., Tariq, S., Zia, T., Ahsan, M. and Windridge, D. 2024. Faithful Counterfactual Visual Explanations (FCVE). Knowledge-Based Systems. 294. https://doi.org/10.1016/j.knosys.2024.111668

An evolutionary approach to automated class-specific data augmentation for image classification

Marc, S., Belavkin, R., Windridge, D. and Gao, X. 2024. An evolutionary approach to automated class-specific data augmentation for image classification. Moosaei, H., Hladík, M. and Pardalos, P. (ed.) 6th International Conference on the Dynamics of Information Systems. Prague, Czech Republic 03 - 06 Dec 2023 Springer. pp. 170–185 https://doi.org/10.1007/978-3-031-50320-7_12

A topological features based quantum kernel

Incudini, M., Martini, F., Di Pierro, A. and Windridge, D. 2023. A topological features based quantum kernel. 7th International Conference on Quantum Techniques in Machine Learning. CERN, Geneva 19 - 24 Nov 2023

Resource saving via ensemble techniques for quantum neural networks

Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. 2023. Resource saving via ensemble techniques for quantum neural networks. Quantum Machine Intelligence. 5 (2). https://doi.org/10.1007/s42484-023-00126-z

Visual attribution using Adversarial Latent Transformations

Zia, T., Wahab, A., Windridge, D., Tirunagari, S. and Bhatti, N. 2023. Visual attribution using Adversarial Latent Transformations. Computers in Biology and Medicine. 166. https://doi.org/10.1016/j.compbiomed.2023.107521

Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures

Towakel, P., Windridge, D. and Nguyen, H. 2023. Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures. IEEE Transactions on Instrumentation and Measurement. 72, pp. 1-15. https://doi.org/10.1109/TIM.2023.3307768

Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability

Balla, Y., Tirunagari, S. and Windridge, D. 2023. Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability. Indian Pediatrics. 60 (7), pp. 561-569. https://doi.org/10.1007/s13312-023-2936-8

Interpretable chronic kidney disease risk prediction from clinical data using machine learning

Chennareddy, V., Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Interpretable chronic kidney disease risk prediction from clinical data using machine learning. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_63

Multi-disciplinary Trends in Artificial Intelligence: 16th International Conference, MIWAI 2023, Hyderabad, India, July 21–22, 2023, Proceedings

Morusupalli, R., Dandibhotla, T., Atluri, V., Windridge, D., Lingras, P. and Komati, V. (ed.) 2023. Multi-disciplinary Trends in Artificial Intelligence: 16th International Conference, MIWAI 2023, Hyderabad, India, July 21–22, 2023, Proceedings. Springer.

Addressing challenges in healthcare big data analytics

Tirunagari, S., Mohan, S., Windridge, D. and Balla, Y. 2023. Addressing challenges in healthcare big data analytics. 16th Multi-Disciplinary International Conference on Artificial Intelligence (MIWAI 2023). Hyderabad, India 21 2023 - 22 Jul 2024 Springer. https://doi.org/10.1007/978-3-031-36402-0_70

The quantum path kernel: A generalized neural tangent kernel for deep quantum machine learning

Incudini, M., Grossi, M., Mandarino, A., Vallecorsa, S., Di Pierro, A. and Windridge, D. 2023. The quantum path kernel: A generalized neural tangent kernel for deep quantum machine learning. IEEE Transactions on Quantum Engineering. 4. https://doi.org/10.1109/TQE.2023.3287736

Resource saving via ensemble techniques for quantum neural networks

Incudini, M., Grossi, M., Ceschini, A., Mandarino, A., Panella, M., Vallecorsa, S. and Windridge, D. 2023. Resource saving via ensemble techniques for quantum neural networks. https://doi.org/10.48550/arXiv.2303.11283

The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning

Incudini, M., Grossi, M., Mandarino, A., Vallecorsa, S., Di Pierro, A. and Windridge, D. 2022. The quantum path kernel: A generalized quantum neural tangent kernel for deep quantum machine learning. https://doi.org/10.48550/arXiv.2212.11826

Discriminator-based adversarial networks for knowledge graph completion

Tubaishat, A., Zia, T., Faiz, R., Al Obediat, F., Shah, B. and Windridge, D. 2022. Discriminator-based adversarial networks for knowledge graph completion. Neural Computing and Applications. https://doi.org/10.1007/s00521-022-07680-w

A user-guided personalization methodology to facilitate new smart home occupancy

Ali, S.M.M., Augusto, J., Windridge, D. and Ward, E. 2023. A user-guided personalization methodology to facilitate new smart home occupancy. Universal Access in the Information Society. 22 (3), pp. 869-891. https://doi.org/10.1007/s10209-022-00883-x

Reducing the dependency of having prior domain knowledge for effective online information retrieval

Zammit, O., Smith, S., Windridge, D. and De Raffaele, C. 2023. Reducing the dependency of having prior domain knowledge for effective online information retrieval. Expert Systems. 40 (4). https://doi.org/10.1111/exsy.13014

A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk

Nwegbu, N., Tirunagari, S. and Windridge, D. 2022. A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk. Scientific Reports. 12 (1), pp. 1-16. https://doi.org/10.1038/s41598-022-08757-1

VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging

Zia, T., Murtaza, S., Bhatti, N., Windridge, D. and Nisar, Z. 2022. VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging. Pattern Recognition Letters. 156, pp. 112-118. https://doi.org/10.1016/j.patrec.2022.02.005

A generative adversarial network for single and multi-hop distributional knowledge base completion

Zia, T. and Windridge, D. 2021. A generative adversarial network for single and multi-hop distributional knowledge base completion. Neurocomputing. 461, pp. 543-551. https://doi.org/10.1016/j.neucom.2021.04.128

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

Navidan, H., Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D. 2021. Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation. Computer Networks. 194, pp. 1-21. https://doi.org/10.1016/j.comnet.2021.108149

Multi-view convolutional recurrent neural networks for lung cancer nodule identification

Naeem Abid, M., Zia, T., Ghafoor, M. and Windridge, D. 2021. Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing. 453, pp. 299-311. https://doi.org/10.1016/j.neucom.2020.06.144

Exposing students to new terminologies while collecting browsing search data (best technical paper)

Zammit, O., Smith, S., Windridge, D. and De Raffaele, C. 2020. Exposing students to new terminologies while collecting browsing search data (best technical paper). 40th SGAI International Conference on Artificial Intelligence, AI 2020. Cambridge, UK 15 - 17 Dec 2020 Cham Springer. https://doi.org/10.1007/978-3-030-63799-6_1

A genetic deep learning model for electrophysiological soft robotics

Pandey, H. and Windridge, D. 2021. A genetic deep learning model for electrophysiological soft robotics. Balas, V., Jain, L., Balas, M. and Shahbazova, S. (ed.) 8th International Workshop on Soft Computing Applications. Arad, Romania 13 - 15 Sep 2018 Cham Springer. https://doi.org/10.1007/978-3-030-51992-6_12

A low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems

Sazdar, A.M., Ghorashi, S.A., Moghtadaiee, V., Khonsari, A. and Windridge, D. 2020. A low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems. Journal of Information Security and Applications. 53. https://doi.org/10.1016/j.jisa.2020.102515

Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems

Pandey, H., Bessis, N., Das, S., Windridge, D. and Chaudhary, A. 2020. Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems. Neural Computing and Applications. 32 (12), pp. 7743-7745. https://doi.org/10.1007/s00521-020-04903-w

Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns

Ramanna, S., Tirunagari, S. and Windridge, D. 2020. Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns. Health and Technology. 10 (3), pp. 699-709. https://doi.org/10.1007/s12553-019-00395-4

On the utility of dreaming: a general model for how learning in artificial agents can benefit from data hallucination

Windridge, D., Svensson, H. and Thill, S. 2021. On the utility of dreaming: a general model for how learning in artificial agents can benefit from data hallucination. Adaptive Behavior. 29 (3), pp. 267-280. https://doi.org/10.1177/1059712319896489

A generative adversarial strategy for modeling relation paths in knowledge base representation learning

Zia, T., Zahid, U. and Windridge, D. 2019. A generative adversarial strategy for modeling relation paths in knowledge base representation learning. KR2ML - Knowledge Representation and Reasoning Meets Machine Learning Workshop, NeurIPS 2019, Thirty-third Conference on Neural Information Processing Systems. Vancouver, Canada 09 - 14 Dec 2019

Improving the adaptation process for a new smart home user

Ali, S., Augusto, J. and Windridge, D. 2019. Improving the adaptation process for a new smart home user. Bramer, M. and Petridis, M. (ed.) 39th SGAI International Conference on Artificial Intelligence. Cambridge, UK 17 - 19 Dec 2019 Springer. https://doi.org/10.1007/978-3-030-34885-4_32

Revisiting direct neuralisation of first-order logic

Gunn, I. and Windridge, D. 2018. Revisiting direct neuralisation of first-order logic. NeSy 2018 : Thirteenth International Workshop on Neural-Symbolic Learning and Reasoning. Prague, Czech Republic 23 - 24 Aug 2018

Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI

Spiteri, M., Guillemaut, J., Windridge, D., Avula, S., Kumar, R. and Lewis, E. 2020. Fully-automated identification of imaging biomarkers for post-operative cerebellar mutism syndrome using longitudinal paediatric MRI. Neuroinformatics. 18 (1), pp. 151-162. https://doi.org/10.1007/s12021-019-09427-w

An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning

Attfield, S., Fields, B., Windridge, D. and Xu, K. 2019. An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning. Conrad, J., Pickens, J., Jones, A., Baron, J. and Henseler, H. (ed.) First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019).. Montreal, Canada 17 Jun 2019 CEUR Workshop Proceedings. pp. 7-11

A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation

Ali, M., Augusto, J. and Windridge, D. 2019. A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation. Applied Artificial Intelligence. 33 (8), pp. 747-774. https://doi.org/10.1080/08839514.2019.1603784

A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration

Brown, M., Windridge, D. and Guillemaut, J. 2019. A family of globally optimal branch-and-bound algorithms for 2D–3D correspondence-free registration. Pattern Recognition. 93, pp. 36-54. https://doi.org/10.1016/j.patcog.2019.04.002

Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics

Windridge, D. and Kammueller, F. 2020. Edit distance Kernelization of NP theorem proving for polynomial-time machine learning of proof heuristics. FICC 2019: Future of Information and Communications Conference. San Francisco, USA 14 - 15 Mar 2019 Springer. pp. 271-283 https://doi.org/10.1007/978-3-030-12385-7_22

Data governance in the health industry: investigating data quality dimensions within a big data context

Juddoo, S., George, C., Duquenoy, P. and Windridge, D. 2018. Data governance in the health industry: investigating data quality dimensions within a big data context. Applied System Innovation. 1 (4), pp. 1-16. https://doi.org/10.3390/asi1040043

A comprehensive classification of deep learning libraries

Pandey, H. and Windridge, D. 2019. A comprehensive classification of deep learning libraries. Yang, X.S., Sherratt, S., Dey, N. and Joshi, A. (ed.) 3rd International Congress on Information and Communication Technology. London, UK 27 - 28 Feb 2018 Singapore Springer. pp. 427-435 https://doi.org/10.1007/978-981-13-1165-9_40

Quantum error-correcting output codes

Windridge, D., Mengoni, R. and Nagarajan, R. 2018. Quantum error-correcting output codes. International Journal of Quantum Information. 16 (8). https://doi.org/10.1142/S0219749918400038

Representational fluidity in embodied (artificial) cognition

Windridge, D. and Thill, S. 2018. Representational fluidity in embodied (artificial) cognition. Biosystems. 172, pp. 9-17. https://doi.org/10.1016/j.biosystems.2018.07.007

An improved block matching algorithm for motion estimation in video sequences and application in robotics

Bhattacharjee, K., Kumar, S., Pandey, H., Pant, M., Windridge, D. and Chaudhary, A. 2018. An improved block matching algorithm for motion estimation in video sequences and application in robotics. Computers & Electrical Engineering. 68, pp. 92-106. https://doi.org/10.1016/j.compeleceng.2018.03.045

Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease

Spanos, C., Maldonado, E., Fisher, C., Leenutaphong, P., Oviedo-Orta, E., Windridge, D. and Salguero, F. 2018. Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease. Proteome Science. 16 (1). https://doi.org/10.1186/s12953-018-0131-y

Hamming distance kernelisation via topological quantum computation

Di Pierro, A., Mengoni, R., Nagarajan, R. and Windridge, D. 2017. Hamming distance kernelisation via topological quantum computation. Martín-Vide C., Neruda R. and Vega-Rodríguez M. (ed.) 6th International Conference on the Theory and Practice of Natural Computing. Prague, Czech Republic 18 - 20 Dec 2017 Springer. pp. 269-280 https://doi.org/10.1007/978-3-319-71069-3_21

Emergent intentionality in perception-action subsumption hierarchies

Windridge, D. 2017. Emergent intentionality in perception-action subsumption hierarchies. Frontiers in Robotics and AI. 4. https://doi.org/10.3389/frobt.2017.00038

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Tirunagari, S., Poh, N., Wells, K., Bober, M., Gorden, I. and Windridge, D. 2017. Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition. Machine Vision and Applications. 28 (3-4), pp. 393-407. https://doi.org/10.1007/s00138-017-0835-5

Quantum Bootstrap Aggregation

Windridge, D. and Nagarajan, R. 2017. Quantum Bootstrap Aggregation. de Barros, J., Coecke, B. and Pothos, E. (ed.) Quantum Interaction. San Francisco, CA, USA 20 - 22 Jul 2016 Springer. pp. 115-121 https://doi.org/10.1007/978-3-319-52289-0_9

Addressing VAST 2016 mini challenge 2 with POLAR kermode, classifier, excel on a power wall and data timelines

Attfield, S., Hewitt, D., Xu, K., Passmore, P., Wagstaff, A., Phillips, G., Windridge, D., Dash, G., Chapman, R. and Mason, L. 2016. Addressing VAST 2016 mini challenge 2 with POLAR kermode, classifier, excel on a power wall and data timelines. IEEE VAST Challenge 2016. Baltimore, MD, USA 23 Oct 2016

Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration

Avula, S., Spiteri, M., Kumar, R., Lewis, E., Harave, S., Windridge, D., Ong, C. and Pizer, B. 2016. Post-operative pediatric cerebellar mutism syndrome and its association with hypertrophic olivary degeneration. Quantitative Imaging in Medicine and Surgery. 6 (5), pp. 535-544. https://doi.org/10.21037/qims.2016.10.11

A generalised framework for saliency-based point feature detection

Brown, M., Windridge, D. and Guillemaut, J. 2017. A generalised framework for saliency-based point feature detection. Computer Vision and Image Understanding. 157, pp. 117-137. https://doi.org/10.1016/j.cviu.2016.09.008

Can DMD obtain a scene background in color?

Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2016. Can DMD obtain a scene background in color? 2016 International Conference on Image, Vision and Computing (ICIVC). Portsmouth, UK 03 - 05 Aug 2016 pp. 46-50 https://doi.org/10.1109/ICIVC.2016.7571272

Criminal pattern identification based on modified K-means clustering

Aljrees, T., Shi, D., Windridge, D. and Wong, B. 2016. Criminal pattern identification based on modified K-means clustering. 2016 International Conference on Machine Learning and Cybernetics (ICMLC). 2, pp. 799-806. https://doi.org/10.1109/ICMLC.2016.7872990

Criminal pattern identification based on modified K-means clustering

Aljrees, T., Shi, D., Windridge, D. and Wong, B. 2016. Criminal pattern identification based on modified K-means clustering. 2016 International Conference on Machine Learning and Cybernetics. Jeju, South Korea 10 - 13 Jul 2016 IEEE. pp. 799-806 https://doi.org/10.1109/ICMLC.2016.7872990

Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT

Juneja, P., Evans, P., Windridge, D. and Harris, E. 2016. Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT. BMC Medical Imaging. 16 (1). https://doi.org/10.1186/s12880-016-0107-2

Globally optimal 2D-3D registration from points or lines without correspondences

Brown, M., Windridge, D. and Guillemaut, J. 2015. Globally optimal 2D-3D registration from points or lines without correspondences. 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile 07 - 13 Dec 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 2111-2119 https://doi.org/10.1109/ICCV.2015.244

A generalisable framework for saliency-based line segment detection

Brown, M., Windridge, D. and Guillemaut, J. 2015. A generalisable framework for saliency-based line segment detection. Pattern Recognition. 48 (12), pp. 3993-4011. https://doi.org/10.1016/j.patcog.2015.06.015

Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics

Tirunagari, S., Poh, N., Bober, M. and Windridge, D. 2015. Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics. 2015 IEEE International Workshop on Information Forensics and Security (WIFS). Rome, Italy 16 - 19 Nov 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-6 https://doi.org/10.1109/WIFS.2015.7368599

Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI

Spiteri, M., Windridge, D., Avula, S., Kumar, R. and Lewis, E. 2015. Identifying quantitative imaging features of posterior fossa syndrome in longitudinal MRI. Journal of Medical Imaging. 2 (4), p. 044502. https://doi.org/10.1117/1.JMI.2.4.044502

Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children

Spiteri, M., Lewis, E., Windridge, D. and Avula, S. 2015. Longitudinal MRI assessment: the identification of relevant features in the development of posterior fossa syndrome in children. Medical imaging 2015: Computer-Aided Diagnosis. Orlando, Florida, United States 21 Feb 2015 Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2081591

A novel Markov logic rule induction strategy for characterizing sports video footage

Windridge, D., Kittler, J., De Campos, T., Yan, F., Christmas, W. and Khan, A. 2015. A novel Markov logic rule induction strategy for characterizing sports video footage. IEEE MultiMedia. 22 (2), pp. 24-35. https://doi.org/10.1109/MMUL.2014.36

Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses

Tirunagari, S., Poh, N., Hu, G. and Windridge, D. 2015. Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses. https://doi.org/10.48550/arXiv.1503.06316

Breast cancer data analytics with missing values: a study on ethnic, age and income groups

Tirunagari, S., Poh, N., Abdulrahman, H., Nemmour, N. and Windridge, D. 2015. Breast cancer data analytics with missing values: a study on ethnic, age and income groups. ArXiv e-prints: Quantitative Biology > Quantitative Methods. https://doi.org/10.48550/arXiv.1503.03680

Kernel combination via debiased object correspondence analysis

Windridge, D. and Yan, F. 2016. Kernel combination via debiased object correspondence analysis. Information Fusion. 27, pp. 228-239. https://doi.org/10.1016/j.inffus.2015.02.002

On the intrinsic limits to representationally-adaptive machine-learning

Windridge, D. 2015. On the intrinsic limits to representationally-adaptive machine-learning. ArXiv e-prints.

Detection of face spoofing using visual dynamics

Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N. and Ho, A. 2015. Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security. 10 (4), pp. 762-777. https://doi.org/10.1109/TIFS.2015.2406533

Challenges in designing an online healthcare platform for personalised patient analytics

Poh, N., Tirunagari, S. and Windridge, D. 2014. Challenges in designing an online healthcare platform for personalised patient analytics. 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD). Orlando, FL., USA 09 - 12 Dec 2014 IEEE. pp. 1-6 https://doi.org/10.1109/CIBD.2014.7011526

Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses

Tirunagari, S., Poh, N., Aliabadi, K., Windridge, D. and Cooke, D. 2014. Patient level analytics using self-organising maps: a case study on type-1 diabetes self-care survey responses. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Orlando, FL., USA 09 - 12 Dec 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 304-309 https://doi.org/10.1109/CIDM.2014.7008682

Automatic annotation of tennis games: an integration of audio, vision, and learning

Yan, F., Kittler, J., Windridge, D., Christmas, W., Mikolajczyk, K., Cox, S. and Huang, Q. 2014. Automatic annotation of tennis games: an integration of audio, vision, and learning. Image and Vision Computing. 32 (11), pp. 896-903. https://doi.org/10.1016/j.imavis.2014.08.004

Multilevel Chinese takeaway process and label-based processes for rule induction in the context of automated sports video annotation

Khan, A., Windridge, D. and Kittler, J. 2014. Multilevel Chinese takeaway process and label-based processes for rule induction in the context of automated sports video annotation. IEEE Transactions on Cybernetics. 44 (10), pp. 1910-1923. https://doi.org/10.1109/TCYB.2014.2299955

Supervised selective kernel fusion for membrane protein prediction

Tatarchuk, A., Sulimova, V., Torshin, I., Mottl, V. and Windridge, D. 2014. Supervised selective kernel fusion for membrane protein prediction. Comin, M., Käll, L., Marchiori, E., Ngom, A. and Rajapakse, J. (ed.) 9th IAPR International Conference Pattern Recognition in Bioinformatics (PRIB 2014). Stockholm, Sweden 21 - 23 Aug 2014 Springer. pp. 98-109 https://doi.org/10.1007/978-3-319-09192-1_9

Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs

Chernousova, E., Levdik, P., Tatarchuk, A., Mottl, V. and Windridge, D. 2014. Non-enumerative cross validation for the determination of structural parameters in feature-selective SVMs. 22nd International Conference on Pattern Recognition ICPR 2014. Stockholm, Sweden 24 - 28 Aug 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 3654-3659 https://doi.org/10.1109/ICPR.2014.628

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

Da Lio, M., Biral, F., Bertolazzi, E., Galvani, M., Bosetti, P., Windridge, D., Saroldi, A. and Tango, F. 2015. Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems. IEEE Transactions on Intelligent Transportation Systems. 16 (1), pp. 244-263. https://doi.org/10.1109/TITS.2014.2330199

A kernel-based framework for medical big-data analytics

Windridge, D. and Bober, M. 2014. A kernel-based framework for medical big-data analytics. in: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges Springer. pp. 197-208

A saliency-based framework for 2D-3D registration

Brown, M., Guillemaut, J. and Windridge, D. 2014. A saliency-based framework for 2D-3D registration. 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014). Lisbon, Portugal 05 - 08 Jan 2014 SCITEPRESS - Science and Technology Publications. pp. 265-273 https://doi.org/10.5220/0004675402650273

Domain anomaly detection in machine perception: a system architecture and taxonomy

Kittler, J., Christmas, W., De Campos, T., Windridge, D., Yan, F., Illingworth, J. and Osman, M. 2014. Domain anomaly detection in machine perception: a system architecture and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36 (5), pp. 845-859. https://doi.org/10.1109/TPAMI.2013.209

Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection

Chernousova, E., Razin, N., Krasotkina, O., Mottl, V. and Windridge, D. 2014. Linear regression via elastic net: non-enumerative leave-one-out verification of feature selection. in: Aleskerov, F., Goldengorin, B. and Pardalos, P. (ed.) Clusters, Orders, and Trees: Methods and Applications: In Honor of Boris Mirkin's 70th Birthday New York Springer.

High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP)

Abbey, C., Hope, C., Sterr, A., Elangovan, P., Geades, N., Windridge, D., Young, K., Wells, K. and Mello-Thoms, C. 2013. High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP). SPIE Proceedings Vol. 8673. https://doi.org/10.1117/12.2007557

Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene

Taya, S., Windridge, D. and Osman, M. 2012. Looking to score: the dissociation of goal influence on eye movement and meta-attentional allocation in a complex dynamic natural scene. PLoS ONE. 7 (6). https://doi.org/10.1371/journal.pone.0039060

Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution

Poh, N., Windridge, D., Mottl, V., Tatarchuk, A. and Eliseyev, A. 2010. Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution. IEEE Transactions on Information Forensics and Security. 5 (3), pp. 461-469. https://doi.org/10.1109/TIFS.2010.2053535
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