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Adams, A. A., Murata, K., Orito, Y. and Parslow, P. (2011) Emerging social norms in the UK and Japan on privacy and revelation in SNS. IRIE: the International Review of Information Ethics, 16. pp. 20-26. ISSN 1614-1687

Adams, A. A. and Ferryman, J. (2013) The future of video analytics for surveillance and its ethical implications. Security Journal. pp. 1-18. ISSN 1743-4645 doi: https://doi.org/10.1057/sj.2012.48

Adams, S. V., Ford, R. W., Hambley, M., Hobson, J.M., Kavčič, I., Maynard, C. M., Melvin, T., Müller, E. H., Porter, A. R., Rezny, M., Shipway, B. J. and Wong, W. (2019) LFRic: meeting the challenges of scalability and performance portability in weather and climate models. Journal of Parallel and Distributed Computing, 132. pp. 383-396. ISSN 0743-7315 doi: https://doi.org/10.1016/j.jpdc.2019.02.007

Adedoyin-Olowe, M., Gaber, M. M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2013) TRCM: a methodology for temporal analysis of evolving concepts in Twitter. Lecture Notes in Computer Science, 7895. pp. 135-145. ISSN 0302-9743 doi: https://doi.org/10.1007/978-3-642-38610-7_13 (Proceedings, Part II. 12th International Conference on Artificial Intelligence and Soft Computing)

Adedoyin-Olowe, M., Gaber, M. M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2014) A survey of data mining techniques for social media analysis. Journal of Data Mining & Digital Humanities, 2014. ISSN 2416-5999

Adedoyin-Olowe, M., Gaber, M. M., Dancausa, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2016) A rule dynamics approach to event detection in Twitter with its application to sports and politics. Expert Systems with Applications, 55. pp. 351-360. ISSN 0957-4174 doi: https://doi.org/10.1016/j.eswa.2016.02.028

Adetoye, A. O. and Badii, A. (2009) A policy model for secure information flow. Lecture Notes in Computer Science, 5511. pp. 1-17. ISSN 0302-9743 doi: https://doi.org/10.1007/978-3-642-03459-6

Aitken, D.L.P., Bishop, J.M., Mitchell, R. J. and Pepper, S.E. (1989) Pattern separation in digital learning nets. Electronics Letters, 25 (11). pp. 685-686. ISSN 0013-5194 doi: https://doi.org/10.1049/el:19890463

Al Ghamdi, S. and Di Fatta, G. (2019) Efficient clustering techniques on Hadoop and Spark. International Journal of Big Data Intelligence, 6 (3/4). pp. 269-290. ISSN 2053-1389 doi: https://doi.org/10.1504/IJBDI.2019.10018592

Al-Azawei, A. and Badii, A. (2014) State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss). International Journal of Computer Science & Information Technology, 6 (3). pp. 1-19. ISSN 0975-3826 doi: https://doi.org/10.5121/ijcsit.2014.6301

Al-Azawei, A., Parslow, P. and Lundqvist, K. (2016) Barriers and opportunities of e-learning implementation in Iraq: a case of public universities. The International Review of Research in Open and Distributed Learning, 17 (5). pp. 126-146. ISSN 1492-3831 doi: https://doi.org/10.19173/irrodl.v17i5.2501

Al-Azawei, A., Parslow, P. and Lundqvist, K. (2015) A psychometric analysis of reliability and validity of the index of learning styles (ILS). International Journal of Psychological Studies, 7 (3). pp. 46-57. ISSN 1918-722X doi: https://doi.org/10.5539/ijps.v7n3p46

Al-Khafajiy, M. ORCID: https://orcid.org/0000-0001-6561-0414, Baker, T., Al-Libawy, H., Maamar, Z., Aloqaily, M. and Jararweh, Y. (2019) Improving fog computing performance via Fog-2-Fog collaboration. Future Generation Computer Systems, 100. pp. 266-280. ISSN 0167-739X doi: https://doi.org/10.1016/j.future.2019.05.015

Al-Khafajiy, M. ORCID: https://orcid.org/0000-0001-6561-0414, Baker, T., Asim, M., Guo, Z., Ranjan, R., Longo, A., Puthal, D. and Taylor, M. (2020) COMITMENT: a fog computing trust management approach. Journal of Parallel and Distributed Computing, 137. pp. 1-16. ISSN 0743-7315 doi: https://doi.org/10.1016/j.jpdc.2019.10.006

Al-Khafajiy, M. ORCID: https://orcid.org/0000-0001-6561-0414, Baker, T., Chalmers, C., Asim, M., Kolivand, H., Fahim, M. and Waraich, A. (2019) Remote health monitoring of elderly through wearable sensors. Multimedia Tools and Applications, 78 (17). pp. 24681-24706. ISSN 1573-7721 doi: https://doi.org/10.1007/s11042-018-7134-7

Al-Khafajiy, M. ORCID: https://orcid.org/0000-0001-6561-0414, Kolivand, H., Baker, T., Tully, D. and Waraich, A. (2019) Smart hospital emergency system via mobile-based requesting services. Multimedia Tools and Applications, 78 (14). pp. 20087-20111. ISSN 1573-7721 doi: https://doi.org/10.1007/s11042-019-7274-4

Al-Khafajiy, M. ORCID: https://orcid.org/0000-0001-6561-0414, Otoum, S., Baker, T., Asim, M., Maamar, Z., Aloqaily, M., Taylor, M. and Randles, M. (2021) Intelligent control and security of fog resources in healthcare systems via a cognitive fog model. ACM Transactions on Internet Technology, 21 (3). pp. 1-23. ISSN 1533-5399 doi: https://doi.org/10.1145/3382770

Almutairi, M., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Bramer, M. (2021) ReG-Rules: an explainable rule-based ensemble learner for classification. IEEE Access, 9. pp. 52015-52035. ISSN 2169-3536 doi: https://doi.org/10.1109/ACCESS.2021.3062763

Alowayr, A. and Badii, A. (2014) Review of monitoring tools for e-learning platforms. International Journal of Computer Science & Information Technology, 6 (3). pp. 79-86. ISSN 0975-3826 doi: https://doi.org/10.5121/ijcsit.2014.6306

Alsaffar, E., Sun, L. and Kabeil, M. (2010) Impact of e-procurement systems on UAE government services. Systemics and Informatics World Network (SIWN), 8. pp. 102-107. ISSN 2044-7272

Alsaffar, E., Sun, L. and Kabeil, M.M. (2009) Effective e-procurement systems to support UAE government service provision. International Transactions on Systems Science and Applications. ISSN 1751-1461

Alsultan, A., Warwick, K. and Wei, H. (2016) Free-text keystroke dynamics authentication for Arabic language. IET Biometrics, 5 (3). pp. 164-169. ISSN 2047-4938 doi: https://doi.org/10.1049/iet-bmt.2015.0101

Alsultan, A., Warwick, K. and Wei, H. (2018) Improving the performance of free-text keystroke dynamics authentication by fusion. Applied Soft Computing, 70 (9). pp. 1024-1033. ISSN 1568-4946 doi: https://doi.org/10.1016/j.asoc.2017.11.018

Alsultan, A., Warwick, K. and Wei, H. (2017) Non-conventional keystroke dynamics for user authentication. Pattern Recognition Letters, 89 (1). pp. 53-59. ISSN 0167-8655 doi: https://doi.org/10.1016/j.patrec.2017.02.010

Alzubi, S., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gaber, M. M. (2021) Towards intrusion detection of previously unknown network attacks. Communications of the ECMS, 35 (1). pp. 35-41. ISSN 2522-2414 doi: https://doi.org/10.7148/2021-0035

Amaradio, M. N., Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, Jansen, G., Gulisano, M., Costanza, J. and Nicosia, G. (2022) Pareto optimal metabolic engineering for the growth-coupled overproduction of sustainable chemicals. Biotechnology and Bioengineering, 119 (7). pp. 1890-1902. ISSN 0006-3592 doi: https://doi.org/10.1002/bit.28103

Anderson, J. A. D. W. (2019) Foundations of transmathematics. Transmathematica. ISSN 2632-9212 doi: https://doi.org/10.36285/tm.v0i0.23

Anderson, J. A. D. W. (1997) Representing geometrical knowledge. Philsophical Transactions of the Royal Society B, 352 (1358). pp. 1129-1139. ISSN 1471-2970 doi: https://doi.org/10.1098/rstb.1997.0096

Anderson, J. A. D. W. (1994) The programming language standards scene, ten years on paper 19: Pop. Computer Standards & Interfaces, 16 (5-6). pp. 547-553. ISSN 0920-5489 doi: https://doi.org/10.1016/0920-5489(94)90039-6

Anderson, J. A. D. W., Baker, K.D. and Sullivan, G.D. (1986) ‘Model’: a POPLOG package to support model-based vision. Image and Vision Computing, 4 (2). pp. 115-119. ISSN 0262-8856 doi: https://doi.org/10.1016/0262-8856(86)90030-2

Anderson, J.A.D.W. (2005) Perspex Machine IV: Spatial Properties of Computation. AISB Quarterly (121). p. 7.

Ashlam, A. A., Badii, A. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2022) WebAppShield: an approach exploiting machine learning to detect SQLi attacks in an application layer in run-time. International Journal of Computer and Information Engineering, 16 (8). pp. 294-302. ISSN 1307-6892 doi: https://doi.org/10.5281/zenodo.6983905

Ayers, D. ORCID: https://orcid.org/0000-0002-5667-8174, Lau, J., Amezcua, J. ORCID: https://orcid.org/0000-0002-4952-8354, Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 (2023) Supervised machine learning to estimate instabilities in chaotic systems: estimation of local Lyapunov exponents. Quarterly Journal of the Royal Meteorological Society, 149 (753). pp. 1236-1262. ISSN 1477-870X doi: https://doi.org/10.1002/qj.4450

Badii, A., Etxeberria, I., Huijnen, C., Maseda, M., Dittenberger, S., Hochgatterer, A., Thiemert, D. and Rigaud, A.-S. (2009) CompanionAble- graceful integration of mobile robot companion with a smart home environment. Gerontechnology, 8 (3). p. 185. doi: https://doi.org/10.4017/gt.2009.08.03.008.00

Badii, A., Etxeberria, I., Huijnen, C., Maseda, M., Dittenberger, S., Hochgatterer, A., Thiemert, D. and Rigaud, A.-S. (2009) CompanionAble- mobile robot companion and smart home system for people with mild cognitive impairment. Journal of Nutrition, Health and Aging, 13 (Supplement). S113. doi: https://doi.org/10.1007/s12603-009-0093-y

Badii, A., Khan, A. and Fuschi, D. (2009) One’s own soundtrack: music affectivisation. CALLAS Newsletter, 2 (2). pp. 3-7.

Badii, A. and Khan, W. (2019) Pathological gait abnormality detection and segmentation by processing the hip joints motion data to support mobile gait rehabilitation. Research in Medical & Engineering Sciences, 7 (3). pp. 754-762. ISSN 2576-8816

Badii, A. and Thiemert, D. (2010) A framework towards a multi-modal fingerprinting scheme for multimedia assets. International Journal of Business Information Systems, 6 (1). pp. 133-149. doi: https://doi.org/10.1504/..034009

Badii, A., Al-Obaidi, A., Einig, M. and Ducournau, A. (2013) Holistic privacy impact assessment framework for video privacy filtering technologies. Signal & Image Processing: An International Journal, 4 (6). pp. 13-32. ISSN 2229 - 3922 doi: https://doi.org/10.5121/SIPIJ.2013.4602

Badii, A., Khan, A., Raval, R., Oudi, H., Ayora, R., Khan, W., Jaidi, A. and Viswanathan, N. (2014) Situation assessment through multi-modal sensing of dynamic environments to support cognitive robot control. Factas Universitatis: Mechanical Engineering, 12 (3). pp. 251-260. ISSN 2335-0164

Badii, A., Lallah, C., Kolomiyets, O., Zhu, M. and Crouch, M. (2008) KAIFIA: Knowledge assisted intelligent framework for information access. Lecture Notes in Artificial Intelligence, 4999. pp. 226-236. ISSN 0302-9743 978-3-540-70873-5

Badii, A., Lallah, C., Zhu, M. and Crouch, M. (2009) Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks. Signal Processing-Image Communication, 24 (9). pp. 759-773. ISSN 0923-5965 doi: https://doi.org/10.1016/j.image.2009.05.003

Balaji, V., Taylor, K. E., Juckes, M., Lawrence, B. N. ORCID: https://orcid.org/0000-0001-9262-7860, Durack, P. J., Lautenschlager, M., Blanton, C., Cinquini, L., Denvil, S., Elkington, M., Guiglielmo, F., Guilyardi, E., Hassell, D. ORCID: https://orcid.org/0000-0001-5106-7502, Kharin, S., Kindermann, S., Nikonov, S., Radhakrishnan, A., Stockhause, M., Weigel, T. and Williams, D. (2018) Requirements for a global data infrastructure in support of CMIP6. Geoscientific Model Development, 11 (9). pp. 3659-3680. ISSN 1991-9603 doi: https://doi.org/10.5194/gmd-11-3659-2018

Bamford, C. D. and Mitchell, R. J. (2011) Modular non-computational-connectionist-hybrid neural network approach to robotic systems. Paladyn. Journal of Behavioral Robotics, 2 (3). pp. 126-133. ISSN 2081-4836 doi: https://doi.org/10.2478/s13230-012-0003-6 (special issue: Cybernetic Approaches to Robotics)

Bartels, M. and Wei, H. (2007) Pattern recognition in photogrammetry and remote sensing: PRRS 2006 in conjunction with ICPR 2006. The Photogrammetric Record, 22 (117). pp. 97-99. ISSN 1477-9730 doi: https://doi.org/10.1111/j.1477-9730.2007.00420.x

Bartels, M. and Wei, H. (2010) Threshold-free object and ground point separation in LIDAR data. Pattern Recognition Letters, 31 (10). pp. 1089-1099. ISSN 0167-8655 doi: https://doi.org/10.1016/j.patrec.2010.03.007

Becker, R., Betzold, K., Boness, K. D., Collins, R., Holt, D.C. and Simkins, J. (1986) The modelling of electrical current NDT methods and its application to weld testing. British Journal of Non-Destructive Testing, 28 (6). pp. 361-370. ISSN 0007-1137

Becker, R., Betzold, K., Boness, K. D., Collins, R.C. and Holt, C.C. (1986) The modelling of electrical current NDT methods and its application to weld testing. British Journal of Non-Destructive Testing, 28 (5). pp. 286-294. ISSN 0007-1137

Berrar, D., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Silva, C., Rui Rodrigues, J., Brito, R. M. M. and Dubitzky, W. (2005) Towards data warehousing and mining of protein unfolding simulation data. Journal of Clinical Monitoring and Computing, 19 (4-5). pp. 307-317. ISSN 1573-2614 doi: https://doi.org/10.1007/s10877-005-0676-z

Bielik, M., Schneider, S., Kuliga, S., Griego, D., Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, König, R., Schmitt, G. and Donath, D. (2019) Examining trade-offs between social, psychological, and energy potential of urban form. ISPRS International Journal of Geo-Information, 8 (2). 52. ISSN 2220-9964 doi: https://doi.org/10.3390/ijgi8020052

Billings, S. A. and Hong, X. (1998) Dual-orthogonal radial basis function networks for nonlinear time series prediction. Neural Networks, 11 (3). pp. 479-493. ISSN 0893-6080 doi: https://doi.org/10.1016/S0893-6080(97)00132-9

Bishop, J. M. and Mitchell, R. J. (1994) Auto-associative memory using n-tuple techniques. Intelligent Systems Engineering, 3 (4). pp. 222-230. ISSN 0963-9640

Bonacin, R., Baranauskas, M.C.C., Liu, K. and Sun, L. (2009) Norms-based simulation for personalised service provision. Semiotica, 2009 (175). 403–428. ISSN 1613-3692 doi: https://doi.org/10.1515/semi.2009.055

Boness, K., Finkelstein, A. and Harrison, R. (2011) A method for assessing confidence in requirements analysis. Information and Software Technology, 53 (10). pp. 1084-1096. ISSN 0950-5849 doi: https://doi.org/10.1016/j.infsof.2011.05.003

Boness, K. D. and Harrison, R. (2010) Goal sketching from a concise business case. The International Journal on Advances in Software , 3 (1-2). pp. 90-99. ISSN 1942-2628

Boness, K. D. and Midgley, D. (1977) Dipole modes on open periodic structures — an experimental study. Interrnational Journal of Electronics, 42 (3). pp. 273-286. ISSN 1362-3060 doi: https://doi.org/10.1080/00207217708900640

Boness, K. D., Finkelstein, A. and Harrison, R. (2008) A lightweight technique for assessing risks in requirements analysis. IET Software, 2 (1). pp. 46-57. doi: https://doi.org/10.1049/iet-sen:20070068

Boness, K. D., Harrison, R. and Liu, K. (2008) Goal sketching: an agile approach to clarifying requirements. The International Journal on Advances in Software, 1 (1). pp. 1-13. ISSN 1942-2628

Boukadi, K., Faci, N., Maamar, Z., Ugljanin, E., Sellami, M., Baker, T. and Al-Khafajiy, M. ORCID: https://orcid.org/0000-0001-6561-0414 (2019) Norm-based and commitment-driven agentification of the Internet of Things. Internet of Things, 6. 100042. ISSN 2542-6605 doi: https://doi.org/10.1016/j.iot.2019.02.002

Bowen, J. W., Owen, T., Jackson, J. B., Walker, G. C., Roberts, J. F., Martos-Levif, D., Lascourrèges, P., Giovannacci, D. and Detalle, V. (2015) Cyclododecane as a contrast improving substance for the terahertz imaging of artworks. IEEE Transactions on Terahertz Science and Technology, 5 (6). pp. 1005-1011. ISSN 2156-342X doi: https://doi.org/10.1109/TTHZ.2015.2476666

Boyle, J. ORCID: https://orcid.org/0000-0002-5785-8046, Nawaz, T. and Ferryman, J. ORCID: https://orcid.org/0000-0003-2194-4871 (2022) Using Deep Siamese networks for trajectory analysis to extract motion patterns in videos. Electronics Letters. ISSN 0013-5194 doi: https://doi.org/10.1049/ell2.12460

Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J. M., Steketee, R. M.E., Orellana, C. M., Meijboom, R., Pinto, M., Meireles, J. R., Garrett, C., Bastos-Leite, A. J., Abdulkadir, A., Ronneberger, O., Amoroso, N., Bellotti, R., Cárdenas-Peña, D., Álvarez-Meza, A. M., Dolph, C. V., Iftekharuddin, K. M., Eskildsen, S. F., Coupé, P., Fonov, V. S., Franke, K., Gaser, C., Ledig, C., Guerrero, R., Tong, T., Gray, K. R., Moradi, E., Tohka, J., Routier, A., Durrleman, S., Sarica, A., Di Fatta, G., Sensi, F., Chincarini, A., Smith, G. M., Stoyanov, Z. V., Sørensen, L., Nielsen, M., Tangaro, S., Inglese, P., Wachinger, C., Reuter, M., van Swieten, J. C., Niessen, W. J. and Klein, S. (2015) Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge. NeuroImage, 111. pp. 562-579. ISSN 1053-8119 doi: https://doi.org/10.1016/j.neuroimage.2015.01.048

Burghouts, G. J., Schutte, K., ten Hove, R. J.-M., van den Broek, S. P., Baan, J., Rajadell, O., van Huis, J. R., van Rest, J., Hanckmann, P., Bouma, H., Sanroma, G., Evans, M. and Ferryman, J. (2014) Instantaneous threat detection based on a semantic representation of activities, zones and trajectories. Signal, Image and Video Processing, 8 (S1). pp. 191-200. ISSN 1863-1703 doi: https://doi.org/10.1007/s11760-014-0672-1

Byne, J.H.M. and Anderson, J. A. D. W. (1998) A CAD-based computer vision system. Image and Vision Computing, 16 (8). pp. 533-539. ISSN 0262-8856 doi: https://doi.org/10.1016/S0262-8856(98)00100-0

Camara, C., Subramaniyam, N. P., Warwick, K., Parkkonen, L., Aziz, T. and Pereda, E. (2019) Non-linear dynamical analysis of resting tremor for demand-driven deep brain stimulation. Sensors, 19 (11). 2507. ISSN 1424-8220 doi: https://doi.org/10.3390/s19112507

Cao, Y., Wei, H., Zhao, H. and Li, N. (2012) An effective approach for land-cover classification from airborne lidar fused with co-registered data. International Journal of Remote Sensing, 33 (18). pp. 5927-5953. ISSN 0143-1161 doi: https://doi.org/10.1080/01431161.2012.676746

Cau, A., Hale, R., Dimitrov, J., Zedan, H., Moszkowski, B., Manjunathaiah, M. and Spivey, M. (2002) A compositional framework for hardware/software co-design. Design Automation for Embedded Systems, 6 (4). pp. 367-399. ISSN 1572-8080 doi: https://doi.org/10.1023/A:1016507527035

Chen, H., Gong, Y. and Hong, X. (2013) Online modeling with tunable RBF network. IEEE Transactions on Cybernetics, 43 (3). pp. 935-947. ISSN 2168-2267 doi: https://doi.org/10.1109/TSMCB.2012.2218804

Chen, H., Gong, Y. and Hong, X. (2016) A new adaptive multiple modelling approach for non-linear and non-stationary systems. International Journal of Systems Science, 47 (9). pp. 2100-2110. ISSN 0020-7721 doi: https://doi.org/10.1080/00207721.2014.973926

Chen, H., Gong, Y., Hong, X. and Chen, S. (2014) Adaptive nonlinear equalizer using a mixture of gaussians based on-line density estimator. IEEE Transactions on Vehicular Technology, 63 (9). pp. 4265-4276. ISSN 0018-9545 doi: https://doi.org/10.1109/TVT.2014.2313458

Chen, H., Gong, Y., Hong, X. and Chen, S. (2016) A fast adaptive tunable RBF network for nonstationary systems. IEEE Transactions on Cybernetics, 46 (12). pp. 2683-2692. ISSN 2168-2267 doi: https://doi.org/10.1109/TCYB.2015.2484378

Chen, L., Wei, H. and Ferryman, J. (2014) ReadingAct RGB-D action dataset and human action recognition from local features. Pattern Recognition Letters, 50. pp. 159-169. ISSN 0167-8655 doi: https://doi.org/10.1016/j.patrec.2013.09.004

Chen, L., Wei, H. and Ferryman, J. (2013) A survey of human motion analysis using depth imagery. Pattern Recognition Letters, 34 (15). pp. 1995-2006. ISSN 0167-8655 doi: https://doi.org/10.1016/j.patrec.2013.02.006

Chen, S., Harris, C. J., Hong, X. and Luk, B. L. (2009) Nonlinear system identification using particle swarm optimisation tuned radial basis function models. International Journal of Bio-Inspired Computation, 1 (4). pp. 246-257. ISSN 1758-0374 doi: https://doi.org/10.1504/IJBIC.2009.024723

Chen, S., Hong, X. and Harris, C. J. (2011) Grey-box radial basis function modelling. Neurocomputing, 74 (10). pp. 1564-1571. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2011.01.023

Chen, S., Hong, X. and Harris, C. J. (2004) Kernel density construction using orthogonal forward regression. Lecture Notes in Computer Science, 3177. pp. 586-592. ISSN 0302-9743 3-540-22881-0

Chen, S., Hong, X. and Harris, C. J. (2005) Orthogonal forward selection for constructing the radial basis function network with tunable nodes. Lecture Notes in Computer Science, 3644. pp. 777-786. ISSN 0302-9743

Chen, S., Hong, X. and Harris, C. J. (2010) Particle swarm optimization aided orthogonal forward regression for unified data modeling. IEEE Transactions on Evolutionary Computation, 14 (4). pp. 477-499. ISSN 1089-778X doi: https://doi.org/10.1109/TEVC.2009.2035921

Chen, S., Hong, X. and Harris, C. J. (2010) Probability density estimation with tunable kernels using orthogonal forward regression. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 40 (4). pp. 1101-1114. ISSN 1083-4419 doi: https://doi.org/10.1109/TSMCB.2009.2034732

Chen, S., Hong, X. and Harris, C. J. (2010) Regression based D-optimality experimental design for sparse kernel density estimation. Neurocomputing, 73 (4-6). pp. 727-739. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2009.11.002

Chen, S., Hong, X. and Harris, C. J. (2004) Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 34 (4). pp. 1708-1717. ISSN 1083-4419 doi: https://doi.org/10.1109/tsmcb.2004.828199

Chen, S., Hong, X. and Harris, C. J. (2003) Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design. IEEE Transactions on Automatic Control, 48 (6). pp. 1029-1036. ISSN 0018-9286 doi: https://doi.org/10.1109/tac.2003.812790

Chen, S., Hong, X. and Harris, C. J. (2003) Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design. IEE Proceedings-Control Theory and Applications, 150 (2). pp. 139-146. ISSN 1350-2379 doi: https://doi.org/10.1049/ip-cta:20030253

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Chen, S., Hong, X., Gao, J. and Harris, C.J. (2014) Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems. IEEE Transactions on Neural Networks and Learning Systems, 25 (9). pp. 1673-1685. ISSN 2162-237X doi: https://doi.org/10.1109/TNNLS.2014.2298535

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Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2020) The 17th Top Chess Engine Championship: TCEC17. ICGA Journal, 42 (2-3). pp. 181-191. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-200152

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2020) The 18th Top Chess Engine Championship: TCEC18. ICGA Journal, 42 (2-3). pp. 211-222. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-200160

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2021) The 19th Top Chess Engine Championship, TCEC19. ICGA Journal, 42 (4). pp. 295-305. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-200172

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2019) TCEC Cup 1. ICGA Journal, 41 (1). pp. 31-38. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190099

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2019) TCEC Cup 2. ICGA Journal, 41 (2). pp. 100-107. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190104

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2019) TCEC Cup 3. ICGA Journal, 41 (3). pp. 168-173. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190114

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2020) TCEC Cup 4. ICGA Journal, 41 (4). pp. 259-263. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190124

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2020) TCEC Cup 5. ICGA Journal, 42 (2-3). pp. 207-210. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-200150

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2020) TCEC Cup 6. ICGA Journal, 42 (2-3). pp. 237-241. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-200159

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2020) TCEC Cup 7. ICGA Journal, 42 (4). pp. 321-325. ISSN 1389-6911

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2018) TCEC10: the 10th Top Chess Engine Championship. ICGA Journal, 40 (2). pp. 113-118. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-180045

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2018) TCEC11: the 11th top chess engine championship. ICGA Journal, 40 (3). pp. 216-223. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190081

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2019) TCEC12: the 12th Top Chess Engine Championship. ICGA Journal, 41 (1). pp. 24-30. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190090

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2019) TCEC13: the 13th Top Chess Engine Championship. ICGA Journal, 41 (2). pp. 92-99. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190103

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2019) TCEC14: the 14th top chess engine championship. ICGA Journal, 41 (3). pp. 143-151. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190117

Haworth, G. ORCID: https://orcid.org/0000-0001-9896-1448 and Hernandez, N. (2019) TCEC15: the 15th Top Chess Engine Championship. ICGA Journal, 41 (3). pp. 153-163. ISSN 1389-6911 doi: https://doi.org/10.3233/ICG-190115

Haworth, G. M. ORCID: https://orcid.org/0000-0001-9896-1448, Regan, K. and Di Fatta, G. (2010) Performance and prediction: Bayesian modelling of fallible choice in chess. Lecture Notes in Computer Science, 6048. pp. 99-110. ISSN 0302-9743 doi: https://doi.org/10.1007/978-3-642-12993-3_10

Hoefler, T. ORCID: https://orcid.org/0000-0002-1333-9797, Stevens, B. ORCID: https://orcid.org/0000-0003-3795-0475, Prein, A. F. ORCID: https://orcid.org/0000-0001-6250-179X, Baehr, J. ORCID: https://orcid.org/0000-0003-4696-8941, Schulthess, T., Stocker, T. F. ORCID: https://orcid.org/0000-0003-1245-2728, Taylor, J. ORCID: https://orcid.org/0000-0001-9003-4076, Klocke, D. ORCID: https://orcid.org/0000-0001-7405-013X, Manninen, P., Forster, P. M., Kölling, T., Gruber, N. ORCID: https://orcid.org/0000-0002-2085-2310, Anzt, H., Frauen, C. ORCID: https://orcid.org/0000-0002-8615-5702, Ziemen, F. ORCID: https://orcid.org/0000-0001-7095-5740, Klöwer, M. ORCID: https://orcid.org/0000-0002-3920-4356, Kashinath, K., Schär, C., Fuhrer, O. and Lawrence, B. N. ORCID: https://orcid.org/0000-0001-9262-7860 (2023) Earth Virtualization Engines: a technical perspective. Computing in Science & Engineering, 25 (3). pp. 50-59. ISSN 1521-9615 doi: https://doi.org/10.1109/MCSE.2023.3311148

Hong, S., YAo, F., Zhang, F., Ding, Y. and Yang, S.-H. (2023) LPWC: long preamble wake-up communication protocol for a LoRa network. Internet of Things, 22. 100787. ISSN 2542-6605 doi: https://doi.org/10.1016/j.iot.2023.100787

Hong, S., Yao, F., Ding, Y. and Yang, S.-H. (2022) A hierarchy-based energy-efficient routing protocol for LoRa-Mesh network. IEEE Internet of Things, 9 (22). pp. 23836-22849. ISSN 2327-4662 doi: https://doi.org/10.1109/JIOT.2022.3185619

Hong, S., Yao, F., Zhang, F., Ding, Y. and Yang, S.-H. (2023) Reinforcement learning approach for SF allocation in LoRa network. IEEE Internet of Things, 10 (20). pp. 18259-18272. ISSN 2327-4662 doi: https://doi.org/10.1109/JIOT.2023.3279429

Hong, X., Brown, M. and Chen, S. (2004) Backward elimination methods for associative memory network pruning. International Journal of Hybrid Intelligent Systems, 1 (1-2). pp. 90-98.

Hong, X., Brown, M., Harris, C.J. and Chen, S. (2003) An orthogonal forward regression algorithm combined with basis pursuit and D-optimality. IEEE SMC UK&RI Chapter Workshop: Cybernetic Intelligence-Challenges and Advances. pp. 57-62.

Hong, X., Chen, S. and Sharkey, P. M. (2004) Automatic Kernel Regression Modelling using Combined Leave-One-Out Test Score and Regularised Orthogonal Least Squares. International Journal of Neural Systems, 14 (1). pp. 27-37.

Hong, X. (2007) Modified radial basis function neural network using output transformation. IET Control Theory and Applications, 1 (1). pp. 1-8. ISSN 1751-8644 doi: https://doi.org/10.1049/iet-cta:20050039

Hong, X. (2006) A fast identification algorithm for Box-Cox transformation based radial basis function neural network. IEEE Transactions on Neural Networks, 17 (4). pp. 1064-1069. ISSN 1045-9227 doi: https://doi.org/10.1109/tnn.2006.875986

Hong, X. and Billings, S. A. (1999) Parameter estimation based on stacked regression and evolutionary algorithms. IEE Proceedings-Control Theory and Applications, 146 (5). pp. 406-414. ISSN 1350-2379 doi: https://doi.org/10.1049/ip-cta:19990505

Hong, X. and Billings, S. A. (1999) Time series multistep-ahead predictability estimation and ranking. Journal of Forecasting, 18 (2). pp. 139-149. ISSN 0277-6693 doi: https://doi.org/10.1002/(SICI)1099-131X(199903)18:2<139::AID-FOR710>3.0.CO;2-W

Hong, X. and Billings, S.A. (1997) Givens rotation based fast backward elimination algorithm for RBF neural network pruning. IEE Proceedings-Control Theory and Applications, 144 (5). pp. 381-384. ISSN 1350-2379 doi: https://doi.org/10.1049/ip-cta:19971436

Hong, X. and Chen, S. (2015) Elastic net orthogonal forward regression. Neurocomputing, 148. pp. 551-560. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2014.07.008

Hong, X. and Chen, S. (2005) M-estimator, and D-optimality model construction using orthogonal forward regression. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 35 (1). pp. 155-162. ISSN 1083-4419 doi: https://doi.org/10.1109/tsmcb.2004.839910

Hong, X. and Chen, S. (2011) Modeling of complex-valued Wiener systems using B-spline neural network. IEEE Transactions on Neural Networks, 22 (5). pp. 818-825. ISSN 1045-9227 doi: https://doi.org/10.1109/TNN.2011.2119328

Hong, X. and Chen, S. (2017) Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion. International Journal of Automation and Computing, 14 (4). pp. 442-449. ISSN 1476-8186 doi: https://doi.org/10.1007/s11633-017-1087-6

Hong, X. and Chen, S. (2008) A minimum approximate-BER beamforming approach for PSK modulated wireless systems. International Journal of Automation and Computing, 5 (3). pp. 284-289. ISSN 1476-8186 doi: https://doi.org/10.1007/s11633-008-0284-8

Hong, X. and Chen, S. (2009) A new RBF neural network with boundary value constraints. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 39 (1). pp. 298-303. ISSN 1083-4419 doi: https://doi.org/10.1109/tsmcb.2008.2005124

Hong, X. and Chen, S. (2012) The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization. Neurocomputing, 82. pp. 216-223. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2011.11.016

Hong, X. and Gao, J. (2021) Estimating the square root of probability density function on Riemannian manifold. Expert Systems: International Journal of Knowledge Engineering, 38 (7). e12266. ISSN 1468-0394 doi: https://doi.org/10.1111/exsy.12266

Hong, X. and Harris, C. J. (2003) Experimental design and model construction algorithms for radial basis function networks. International Journal of Systems Science, 34 (14-15). pp. 733-745. ISSN 0020-7721 doi: https://doi.org/10.1080/00207720310001640223

Hong, X. and Harris, C. J. (2001) Neurofuzzy design and model construction of nonlinear dynamical processes from data. IEE Proceedings-Control Theory and Applications, 148 (6). pp. 530-538. ISSN 1350-2379 doi: https://doi.org/10.1049/ip-cta:20010704

Hong, X. and Harris, C. J. (2002) Nonlinear model structure design and construction using orthogonal least squares and D-optimality design. IEEE Transactions on Neural Networks, 13 (5). pp. 1245-1250. ISSN 1045-9227 doi: https://doi.org/10.1109/TNN.2002.1031959

Hong, X. and Harris, C. J. (2001) Nonlinear model structure detection using optimum experimental design and orthogonal least squares. IEEE Transactions on Neural Networks, 12 (2). pp. 435-439. ISSN 1045-9227 doi: https://doi.org/10.1109/72.914539

Hong, X. and Harris, C. J. (2001) Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks. IEEE Transactions on Fuzzy Systems, 9 (1). pp. 88-101. ISSN 1063-6706 doi: https://doi.org/10.1109/91.917117

Hong, X. and Harris, C. J. (2002) A mixture of experts network structure construction algorithm for modelling and control. Applied Intelligence, 16 (1). pp. 59-69. ISSN 1573-7497 doi: https://doi.org/10.1023/A:1012869427428

Hong, X. and Harris, C. J. (2003) A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition. IEEE Transactions on Fuzzy Systems, 11 (4). pp. 528-541. ISSN 1063-6706 doi: https://doi.org/10.1109/tfuzz.2003.814842

Hong, X. and Harris, C.J. (2000) Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition. IEEE Transactions on Neural Networks, 11 (4). pp. 889-902. ISSN 1045-9227 doi: https://doi.org/10.1109/72.857770

Hong, X. and Harwin, W.S. ORCID: https://orcid.org/0000-0002-3928-3381 (2006) Finding the point on Bezier Curves with the normal vector passing an external point. International Journal of Modelling Identification and Control, 1 (4). doi: https://doi.org/10.1504/IJMIC.2006.012620

Hong, X. and Mitchell, R. J. (2007) Backward elimination model construction for regression and classification using leave-one-out criteria. International Journal of Systems Science, 38 (2). pp. 101-113. ISSN 0020-7721 doi: https://doi.org/10.1080/00207720601051463

Hong, X. and Mitchell, R. J. (2007) Hammerstein model identification algorithm using Bezier-Bernstein approximation. IET Control Theory and Applications, 1 (4). pp. 1149-1159. ISSN 1751-8644 doi: https://doi.org/10.1049/iet-cta:20060018

Hong, X., Brown, M., Chen, S. and Harris, C. J. (2004) Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality. IEE Proceedings-Control Theory and Applications, 151 (4). pp. 491-498. ISSN 1350-2379 doi: https://doi.org/10.1049/ip-cta:20040693

Hong, X., Chen, S. and Becerra, V. (2016) Sparse density estimator with tunable kernels. Neurocomputing, 173 (3). pp. 1976-1982. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2015.08.021

Hong, X., Chen, S. and Harris, C. (2012) Using zero-norm constraint for sparse probability density function estimation. International Journal of Systems Science, 43 (11). pp. 2107-2113. ISSN 0020-7721 doi: https://doi.org/10.1080/00207721.2011.564673

Hong, X., Chen, S. and Harris, C. J. (2008) A-optimality orthogonal forward regression algorithm using branch and bound. IEEE Transactions on Neural Networks, 19 (11). pp. 1961-1967. ISSN 1045-9227 doi: https://doi.org/10.1109/tnn.2008.2003251

Hong, X., Chen, S. and Harris, C. J. (2013) Elastic-net prefiltering for two-class classification. IEEE Transactions on Cybernetics, 43 (1). pp. 286-295. ISSN 2168-2267 doi: https://doi.org/10.1109/TSMCB.2012.2205677

Hong, X., Chen, S. and Harris, C. J. (2006) Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate. Lecture Notes in Computer Science, 4113. pp. 106-114. ISSN 0302-9743 9783540372714 doi: https://doi.org/10.1007/11816157_11

Hong, X., Chen, S. and Harris, C. J. (2008) A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate. International Journal of Systems Science, 39 (2). pp. 119-125. ISSN 0020-7721 doi: https://doi.org/10.1080/00207720701727822

Hong, X., Chen, S. and Harris, C. J. (2008) A forward-constrained regression algorithm for sparse kernel density estimation. IEEE Transactions on Neural Networks, 19 (1). pp. 193-198. ISSN 1045-9227 doi: https://doi.org/10.1109/tnn.2007.908645

Hong, X., Chen, S. and Harris, C.J. (2007) A kernel-based two-class classifier for imbalanced data sets. IEEE Transactions on Neural Networks, 18 (1). pp. 28-41. ISSN 1045-9227 doi: https://doi.org/10.1109/TNN.2006.882812

Hong, X., Chen, S., Gao, J. and Harris, C. J. (2015) Nonlinear identification using orthogonal forward regression with nested optimal regularization. IEEE Transactions on Cybernetics, 45 (12). pp. 2925-2936. ISSN 2168-2267 doi: https://doi.org/10.1109/TCYB.2015.2389524

Hong, X., Chen, S., Gong, Y. and Harris, C. J. (2014) Nonlinear equalization of Hammerstein OFDM systems. IEEE Transactions on Signal Processing, 62 (21). pp. 5629-5639. ISSN 1053-587X doi: https://doi.org/10.1109/TSP.2014.2355773

Hong, X., Chen, S., Guo, Y. and Gao, J. (2017) l1-norm penalized orthogonal forward regression. International Journal of Systems Science, 48 (10). pp. 2195-2201. ISSN 0020-7721 doi: https://doi.org/10.1080/00207721.2017.1311383

Hong, X., Chen, S., Harris, C. J. and Khalaf, E. F. (2014) Single-carrier frequency domain equalisation for hammerstein communication systems using complex-valued neural networks. IEEE Transactions on Signal Processing, 62 (17). pp. 4467-4478. ISSN 1053-587X doi: https://doi.org/10.1109/TSP.2014.2333555

Hong, X., Chen, S., Qatawneh, A., Daqrouq, K., Sheikh, M. and Morfeq, A. (2013) Sparse probability density function estimation using the minimum integrated square error. Neurocomputing, 114. pp. 122-129. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2013.02.003

Hong, X., Chen, S., Qatawneh, A., Daqrouq, K., Sheikh, M. and Morfeq, A. (2014) A radial basis function network classifier to maximise leave-one-out mutual information. Applied Soft Computing, 23. pp. 9-18. ISSN 1568-4946 doi: https://doi.org/10.1016/j.asoc.2014.06.003

Hong, X., Gao, J. and Chen, S. (2020) Semi-blind joint channel estimation and data detection on sphere manifold for MIMO with high-order QAM signaling. Journal of the Franklin Institute, 357 (9). pp. 5680-5690. ISSN 0016-0032 doi: https://doi.org/10.1016/j.jfranklin.2020.04.009

Hong, X., Gao, J. and Chen, S. (2017) Zero attracting recursive least squares algorithms. IEEE Transactions on Vehicular Technology, 66 (1). 213 -221. ISSN 0018-9545 doi: https://doi.org/10.1109/TVT.2016.2533664

Hong, X., Gao, J., Chen, S. and Harris, C. J. (2013) Particle swarm optimisation assisted classification using elastic net prefiltering. Neurocomputing, 122. pp. 210-220. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2013.06.030

Hong, X., Gao, J., Chen, S. and Zia, T. (2015) Sparse density estimation on the multinomial manifold. IEEE Transactions on Neural Networks and Learning Systems, 26 (11). pp. 2972-2977. ISSN 2162-237X doi: https://doi.org/10.1109/TNNLS.2015.2389273

Hong, X., Gao, J., Jiang, X. and Harris, C. J. (2014) Estimation of Gaussian process regression model using probability distance measures. Systems Science & Control Engineering, 2. pp. 655-663. ISSN 2164-2583 doi: https://doi.org/10.1080/21642583.2014.970731

Hong, X., Gao, J., Jiang, X. and Harris, C. J. (2014) Fast identification algorithms for Gaussian process model. Neurocomputing, 133. pp. 25-31. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2013.11.035

Hong, X., Gao, J., Wei, H., Xiao, J. and Mitchell, R. (2023) Two-step scalable spectral clustering algorithm using landmarks and probability density estimation. Neurocomputing, 519. pp. 173-186. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2022.11.063

Hong, X., Harris, C. J. and Chen, S. (2004) Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 34 (1). pp. 598-608. ISSN 1083-4419 doi: https://doi.org/10.1109/tsmcb.2003.817089

Hong, X., Harris, C. J. and Wilson, P. A. (1999) Neurofuzzy state identification using prefiltering. IEE Proceedings-Control Theory and Applications, 146 (2). pp. 234-240. ISSN 1350-2379 doi: https://doi.org/10.1049/ip-cta:19990121

Hong, X., Harris, C. J., Chen, S. and Sharkey, P. M. (2003) Robust nonlinear model identification methods using forward regression. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans, 33 (4). pp. 514-523. ISSN 1083-4427 doi: https://doi.org/10.1109/tsmca.2003.809217

Hong, X., Iplikci, S., Chen, S. and Warwick, K. (2014) A model-based PID controller for Hammerstein systems using B-spline neural networks. International Journal of Adaptive Control and Signal Processing, 28 (3-5). pp. 412-428. ISSN 0890-6327 doi: https://doi.org/10.1002/acs.2293

Hong, X., Mitchell, R. and Chen, S. (2012) Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm. International Journal of Systems Science, 43 (10). pp. 1976-1984. ISSN 0020-7721 doi: https://doi.org/10.1080/00207721.2011.564320

Hong, X., Mitchell, R. and Di Fatta, G. (2019) Simplex basis function based sparse least squares support vector regression. Neurocomputing, 330. pp. 394-402. ISSN 0925-2312 doi: https://doi.org/10.1016/j.neucom.2018.11.025

Hong, X., Mitchell, R. J. and Chen, S. (2013) System identification of Wiener systems with B-spline functions using De Boor recursion. International Journal of Systems Science, 44 (9). pp. 1666-1674. ISSN 0020-7721 doi: https://doi.org/10.1080/00207721.2012.669863

Hong, X., Mitchell, R. J., Chen, S., Harris, C. J., Li, K. and Irwin, G. W. (2008) Model selection approaches for non-linear system identification: a review. International Journal of Systems Science, 39 (10). pp. 925-946. ISSN 0020-7721 doi: https://doi.org/10.1080/00207720802083018

Hong, X., Sharkey, P. M. and Warwick, K. (2003) Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic. IEE Proceedings-Control Theory and Applications, 150 (3). pp. 245-254. ISSN 1350-2379 doi: https://doi.org/10.1049/ip-cta:20030311

Hong, X., Sharkey, P. M. and Warwick, K. (2003) A robust nonlinear identification algorithm using PRESS statistic and forward regression. IEEE Transactions on Neural Networks, 14 (2). pp. 454-458. ISSN 1045-9227 doi: https://doi.org/10.1109/tnn.2003.809422

Hu, P., Yang, F., Wei, H., Ji, L. and Liu, D. (2019) A multi-algorithm block fusion method based on set-valued mapping for dual-modal infrared images. Infrared Physics & Technology, 102. 102977. ISSN 1350-4495 doi: https://doi.org/10.1016/j.infrared.2019.102977

Hu, P., Yang, F., Wei, H., Ji, L. and Wang, X. (2019) Research on constructing difference-features to guide the fusion of dual-modal infrared images. Infrared Physics & Technology, 102 (1). 102994. ISSN 1350-4495 doi: https://doi.org/10.1016/j.infrared.2019.102994

Hunneman, R. and Hawkins, G. J. (1996) Infrared filters and dichroics for the advanced along track scanning radiometer. Applied Optics, 35 (28). pp. 5524-5528. ISSN 1559-128X doi: https://doi.org/10.1364/AO.35.005524

Hussey, R., Williams, S. and Mitchell, R. (2011) Automated categorisation of e-journals by synonym analysis of n-grams. The International Journal on Advances in Software, 4 (3-4). pp. 532-542. ISSN 1942-2628

Hussey, R., Williams, S., Mitchell, R. and Field, I. (2012) A comparison of automated keyphrase extraction techniques and of automatic evaluation vs. human evaluation. International Journal on Advances in Life Sciences, 4 (3 and 4). pp. 136-153. ISSN 1942-2660

Idrees, M. M., Minku, L. L., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Badii, A. (2020) A heterogeneous online learning ensemble for non-stationary environments. Knowledge-Based Systems, 188. 104983. ISSN 0950-7051 doi: https://doi.org/10.1016/j.knosys.2019.104983

Idrees, M. M. ORCID: https://orcid.org/0000-0002-2572-3251, Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Badii, A. (2022) Adaptive learning with extreme verification latency in non-stationary environments. IEEE Access, 10. pp. 127345-127364. ISSN 2169-3536 doi: https://doi.org/10.1109/access.2022.3225225

Ihmeida, M. ORCID: https://orcid.org/0009-0009-8728-6868 and Shahzad, M. (2023) Enhanced change detection performance based on deep despeckling of synthetic aperture radar images. IEEE Access. p. 1. ISSN 2169-3536 doi: https://doi.org/10.1109/access.2023.3307208

Jia, G., Wang, X., Wei, H. and Zhang, Z. (2013) Modeling image motion in airborne Three-Line-Array (TLA) push-broom cameras. Photogrammetric Engineering and Remote Sensing, 79 (1). pp. 67-78. ISSN 0099-1112

Juckes, M., Taylor, K. E., Durack, P. J., Lawrence, B. ORCID: https://orcid.org/0000-0001-9262-7860, Mizielinski, M. S., Pamment, A., Peterschmitt, J.-Y., Rixen, M. and Sénési, S. (2020) The CMIP6 Data Request (DREQ, version 01.00.31). Geoscientific Model Development, 13 (1). pp. 201-224. ISSN 1991-9603 doi: https://doi.org/10.5194/gmd-13-201-2020

Jumah, N., Kunkel, J. M., Zängl, G., Yashiro, H., Dubos, T. and Meurdesoif, T. (2017) GGDML: icosahedral models language extensions. Journal of Computer Science Technology Updates, 4 (1). pp. 1-10. ISSN 2410-2938 doi: https://doi.org/10.15379/2410-2938.2017.04.01.01

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Adderley, R., Badii, A. and Wu, C. (2008) The automatic identification and prioritisation of criminal networks from police crime data. In: OrtizArroyo, D., Larsen, H.L., Zeng, D.D., Hicks, D. and Wagner, G. (eds.) Intelligence and security informatics, proceedings. Springer-Verlag, Berlin, 5–14. ISBN 9783540898993

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Wrench, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Le, T., Di Fatta, G., Karthikeyan, V. and Nauck, D. (2016) A method of rule induction for predicting and describing future alarms in a telecommunication network. In: Thirty-sixth SGAI International Conference on Artificial Intelligence, 13-15 December 2016, Cambridge, UK, pp. 309-323.

Xu, W. and Wei, H. (2010) Development of a multi-channel wireless medical data acquisition system. In: SICE 2010: International Conference on Instrumentation, Control and Information Technology, 18-21 Aug 2010, Taipei, Taiwan.

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Zhang, W., Xu, M., Evans, K., Norman, M., Morales-Hernandez, M., Mahajan, S., Hill, A., Manners, J., Shipway, B. and Maynard, C. (2021) Progress towards accelerating the unified model on hybrid multi-core systems. In: PASC '21: Platform for Advanced Scientific Computing ConferencE, 5 - 9 July 2021, Geneva, Switzerland. doi: https://doi.org/10.1145/3468267.3470612

Zhou, M. and Wei, H. (2009) Constructing week learner and performance evaluation in AdaBoost. In: The 1st International Conference on Information Science and Engineering, Nanjing , China.

Zhou, M. and Wei, H. (2009) Facial feature extraction and selection by Gabor wavelets and boosting. In: The 2nd International Congress on Image and Signal Processing, Tianjin, China. doi: https://doi.org/10.1109/CISP.2009.5304650

Zhou, M. and Wei, H. (2010) Multi-class AdaBoost learning of facial feature selection through grid computing. In: 9th IEEE International Conference on Cybernetic Intelligent Systems, 1-2 Sept 2010, Reading, UK, p. 130.

Zhou, M., Wei, H. and Maybank, S.J. (2006) Gabor wavelets and AdaBoost in feature selection for face verification. In: In Proceedings of Applications of Computer Visions 2006 workshop in conjunction with ECCV 2006, Graz, Austria, 101 - 109.

Zhu, M. and Badii, A. (2008) Semantic-level visual content descriptor combining visual and textual cues as collaterals. In: University of Reading School of Systems Engineering Annual Research Conference (SSE Conf08), Reading, UK.

Zichuan, X., Jiangkai, W., Qiufen, X., Pan, Z., Jiankang, R. and Liang, H. (2020) Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds. In: ACM multi-media, 12-16 Oct 2020, Seattle, United States, pp. 3265-3273. doi: https://doi.org/10.1145/3394171.3414048

Zjavka, L., Snásel, V., Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 and Pedrycz, W. (2016) A substitution of the general partial differential equation with extended polynomial networks. In: International Joint Conference on Neural Networks (IJCNN), 25-29 Jul 2016, Vancouver, Canada, pp. 4819-4826. doi: https://doi.org/10.1109/IJCNN.2016.7727833

Zong, N. and Hong, X. (2004) On improvement of classification accuracy for stochastic discrimination-multi-class Classification. In: Proc. Int. Conf. on Computing, Communications and Control Technologies, Austin, Texas.

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Book

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Thesis

Abbo, D. (2017) The analysis of cyber security: the extended Cartesian method approach with innovative study models. PhD thesis, University of Reading.

Abd Aziz, A. Z. B. (2017) Vision-based spoofing face detection using polarised light. PhD thesis, University of Reading.

Al Ghamdi, S. (2018) Efficient clustering techniques for big data. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00084926

Al-Azawei, A. H. S. (2017) Modelling e-learning adoption: the influence of learning style and universal learning theories. PhD thesis, University of Reading.

Al-Dabbagh, A. (2019) The distributed p-median problem in computer networks. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00084947

Alghamdi, H. A. (2018) A service driven method for evaluating business and IT alignment. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00084956

Almutairi, M. K. (2022) Expressive and explainable rule-based classification. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00114127

Alowayr, A. S. (2018) Effectiveness of avatars representing teachers in M-learning in Saudi Arabia higher education. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00079819

Alzubi, S. M. Y. (2022) Network intrusion detection system for detecting unknown network attacks using machine learning methods. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00110386

Anwer, M. S. (2017) Improving TCP behaviour to non-invasively share spectrum with safety messages in VANET. PhD thesis, University of Reading.

Ayiad, M. (2020) Asynchronous epidemic algorithms for consistency in large-scale systems. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00089406

Badrul, N. A. B. (2016) Analysis of obligatory disclosure regarding individual’s privacy. PhD thesis, University of Reading.

Boness, K. (2011) Goal-refinement appraisal of soft projects (GRASP). PhD thesis, University of Reading.

Cane, T. A. (2019) Class-agnostic maritime object detection. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00088725

Cavalcanti das Neves Valle, L. H. (2019) Dialogues in cybernetics: a model for understanding critical thinking construction in the digital age. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00088337

Ellis, A.-L. (2012) An eye for an eye: robust motion segmentation by applying the latest in human vision research. PhD thesis, University of Reading.

Galea, D. (2022) Meteorological data reduction for tropical cyclones using deep learning techniques. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00108656

Idrees, M. M. (2024) Online semi-supervised learning in non-stationary environments. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00114864

Katti, A. (2016) Epidemic failure detection and consensus for message passing interface (MPI). PhD thesis, University of Reading.

Le, D. T. (2019) Expressive and modular rule-based classifier for data streams. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00085883

Mitchell, R. J. (1987) Multi-microprocessor control of processes with pure time delay. PhD thesis, University of Reading.

Nath, R. (2017) Sparse hierarchical models of vision. PhD thesis, University of Reading.

Osei-Brefo, E. (2024) Advances in machine learning algorithms for financial risk management. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00115168

Otto, F. (2016) Exploring social collaborative e-learning in higher education: a study of two universities in Uganda. PhD thesis, University of Reading.

Pravin, C. (2023) Fragility, robustness, and antifragility in deep neural networks. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00112630

Raoult, B. (2020) Fast retrieval of weather analogues in a multi-petabyte meteorological archive. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00093430

Raval, R. K. (2018) Content driven energy efficiency analysis of hardware accelerated spatial filters for digital image processing. PhD thesis, University of Reading.

Tennant, M. (2018) A parallel data stream classification technique for high velocity data streams. PhD thesis, University of Reading.

Varzandian, A. (2023) Machine learning based biomarkers for neurodegenerative disease classification. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00111984

Vizcaino, N. (2017) Software evolution: hypergraph based model of solution space andhmeta-search. PhD thesis, University of Reading.

Wattanapanich, C. (2019) Investigation of gait representations and partial body gait recognition. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00084979

Wrench, C. (2019) Expressive prediction and forecasting of alarms within a national telecommunications network. PhD thesis, University of Reading. doi: https://doi.org/10.48683/1926.00086163

Patent

The University of Reading (2010) Processors. GB2462770. doi: GB2462770

United Kingdom Atomic Energy Authority (1991) Eddy current testing program with scanning probe head having parallel and normal sensing coils. US5019777. doi: US5019777

Schlumberger Holdings Limited; University of Reading (2016) Temperature invariant infrared filter. .

The University of Reading (2010) Processors (WO 2010/082067 A2). WO 2010/082067 A2. doi: WO 2010/082067 A2

Norman P. Whitehead; Kenneth D. Boness; Roger Martin (1983) Information processing: US Patent 4,409,617. US Patent No 4,409,617. doi: US Patent No 4,409,617

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