Faculty of Science and Technology
| Title | Faculty of Science and Technology |
|---|---|
| Alternative | S&T |
| University | Middlesex University London |
| Campus | London |
Latest research outputs
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Offline signature verification using deep neural network with application to computer vision
Sharma, N., Gupta, S., Mehta, P., Cheng, X., Shankar, A., Singh, P. and Nayak, S. 2022. Offline signature verification using deep neural network with application to computer vision. Journal of Electronic Imaging (JEI). 31 (4). https://doi.org/10.1117/1.JEI.31.4.041210Article
Intrusion detection and prevention system for an IoT environment
Kumar, A., Abhishek, K., Ghalib, M., Shankar, A. and Cheng, X. 2022. Intrusion detection and prevention system for an IoT environment. Digital Communications and Networks. 8 (4), pp. 540-551. https://doi.org/10.1016/j.dcan.2022.05.027Article
An empirical study on Retinex methods for low-light image enhancement
Rasheed, M., Guo, G., Shi, D., Khan, H. and Cheng, X. 2022. An empirical study on Retinex methods for low-light image enhancement. Remote Sensing. 14 (18). https://doi.org/10.3390/rs14184608Article
Information requirements of a decision support system for severe accident management in nuclear power plants
Salim, S., Choi, E. and Ham, D. 2022. Information requirements of a decision support system for severe accident management in nuclear power plants. Applied Sciences. 12 (8). https://doi.org/10.3390/app12083803Article
A pilot study for satellite-tracking Gadwalls Mareca strepera wintering in the Lea Valley, UK
Spencer, R., Roper, P., Hill, T. and Scott, S. 2022. A pilot study for satellite-tracking Gadwalls Mareca strepera wintering in the Lea Valley, UK. Ringing and Migration. 37 (1-2), pp. 13-25. https://doi.org/10.1080/03078698.2023.2242050Article
Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks
Ivanescu, T., Yetgin, H., Merrett, G.V. and El-Hajjar, M. 2022. Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks. IEEE Access. 10, pp. 119956-119969. https://doi.org/10.1109/access.2022.3221446Article
A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data
Li, J., Wu, Y., Fong, S., Tallon-Ballesteros, A., Yang, X., Mohammed, S. and Wu, F. 2022. A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data. Journal of Supercomputing. 78, p. 7428–7463. https://doi.org/10.1007/s11227-021-04177-6Article
AMAZONIA CAMTRAP: A dataset of mammal, bird, and reptile species recorded with camera traps in the Amazon forest
Antunes, A., Montanarin, A., Gräbin, D., Monteiro, E., de Pinho, F., Alvarenga, G., Ahumada, J., Wallace, R., Ramalho, E., Barnett, A., Bager, A., Lopes, A., Keuroghlian, A., Giroux, A., Herrera, A., Correa, A., Meiga, A., Jácomo, A., Barban, A., Antunes, A., Coelho, A., Camilo, A., Nunes, A., Gomes, A., Zanzini, A., Castro, A., Desbiez, A., Figueiredo, A., de Thoisy, B., Gauzens, B., Oliveira, D., de Lima, C., Peres, C., Durigan, C., Brocardo, C., da Rosa, C., Zárate-Castañeda, C., Monteza-Moreno, C., Carnicer, C., Trinca, C., Polli, D., Ferraz, D., Lane, D., da Rocha, D., Barcelos, D., Auz, D., Rosa, D., Silva, D., Silvério, D., Eaton, D., Nakano-Oliveira, E., Venticinque, E., Junior, E., Mendonça, E., Vieira, E., Isasi-Catalá, E., Fischer, E., Castro, E., Oliveira, E., de Melo, F., Muniz, F., Rohe, F., Baccaro, F., Michalski, F., Paim, F., Santos, F., Anaguano, F., Palmeira, F., Reis, F., Aguiar-Silva, F., Batista, G., Zapata-Ríos, G., Forero-Medina, G., Neto, G., Alves, G., Ayala, G., Pedersoli, G., Bizri, H., do Prado, H., Mozerle, H., Costa, H., Lima, I., Palacios, J., Assis, J., Boubli, J., Metzger, J., Teixeira, J., Miranda, J., Polisar, J., Salvador, J., Borges-Almeida, K., Didier, K., Pereira, K., Torralvo, K., Gajapersad, K., Silveira, L., Maioli, L., Maracahipes-Santos, L., Valenzuela, L., Benavalli, L., Fletcher, L., Paolucci, L., Zanzini, L., da Silva, L., Rodrigues, L., Benchimol, M., Oliveira, M., Lima, M., da Silva, M., Junior, M., Viscarra, M., Cohn-Haft, M., Abrahams, M., Benedetti, M., Marmontel, M., Hirt, M., Tôrres, N., Junior, O., Alvarez-Loayza, P., Jansen, P., Prist, P., Brando, P., Perônico, P., Leite, R., Rabelo, R., Sollmann, R., Beltrão-Mendes, R., Ferreira, R., Coutinho, R., Oliveira, R., Ilha, R., Hilário, R., Pires, R., Sampaio, R., Moreira, R., Botero-Arias, R., Martinez, R., Nóbrega, R., Fadini, R., Morato, R., Carneiro, R., Almeida, R., Ramos, R., Schaub, R., Dornas, R., Cueva, R., Rolim, S., Laurindo, S., Espinosa, S., Fernandes, T., Sanaiotti, T., Alvim, T., Dornas, T., Piña, T., Andrade, V., Santiago, W., Magnusson, W., Campos, Z. and Ribeiro, M. 2022. AMAZONIA CAMTRAP: A dataset of mammal, bird, and reptile species recorded with camera traps in the Amazon forest. Ecology. 103 (9). https://doi.org/10.1002/ecy.3738Article
Combining geospatial abundance and ecological niche models to identify high-priority areas for conservation: The neglected role of broadscale interspecific competition
Cavalcante, T., Weber, M.M. and Barnett, A. 2022. Combining geospatial abundance and ecological niche models to identify high-priority areas for conservation: The neglected role of broadscale interspecific competition. Frontiers in Ecology and Evolution . 10, pp. 1-20. https://doi.org/10.3389/fevo.2022.915325Article
Pulp Fiction: why some populations of ripe-fruit specialists Ateles chamek and A. marginatus prefer insect-infested foods
dos Santos-Barnett, T., Cavalcante, T., Boyle, S., Matte, A., Bezerra, B., de Oliveira, T. and Barnett, A. 2022. Pulp Fiction: why some populations of ripe-fruit specialists Ateles chamek and A. marginatus prefer insect-infested foods. International Journal of Primatology. 43 (3), pp. 384-408. https://doi.org/10.1007/s10764-022-00284-0Article
Biotic indicators for ecological state change in Amazonian floodplains
Correa, S.B., van der Sleen, P., Siddiqui, S.F., Bogotá-Gregory, J.D., Arantes, C.C., Barnett, A., Couto, T.B.A., Goulding, M. and Anderson, E.P. 2022. Biotic indicators for ecological state change in Amazonian floodplains. BioScience. 72 (8), pp. 753-768. https://doi.org/10.1093/biosci/biac038Article
Factors influencing terrestriality in primates of the Americas and Madagascar
Eppley, T.M., Hoeks, S., Chapman, C.A., Ganzhorn, J.U., Hall, K., Owen, M.A., Adams, D.B., Allgas, N., Amato, K.R., Andriamahaihavana, M., Aristizabal, J.F., Baden, A.L., Balestri, M., Barnett, A., Bicca-Marques, J.C., Bowler, M., Boyle, S.A., Brown, M., Caillaud, D., Calegaro-Marques, C., Campbell, C.J., Campera, M., Campos, F. A., Cardoso, T.S., Carretero-Pinzón, X., Champion, J., Chaves, Ó.M., Chen-Kraus, C., Colquhoun, I.C., Dean, B., Dubrueil, C., Ellis, K.M., Erhart, E.M., Evans, K.J.E., Fedigan, L.M., Felton, A.M., Ferreira, R.G., Fichtel, C., Fonseca, M.L., Fontes, I.P., Fortes, V.B., Fumian, I., Gibson, D., Guzzo, G.B., Hartwell, K.S., Heymann, E.W., Hilário, R.R., Holmes, S.M., Irwin, M.T., Johnson, S.E., Kappeler, P.M., Kelley, E.A., King, T., Knogge, C., Koch, F., Kowalewski, M.M., Lange, L.R., Lauterbur, M.E., Louis, E.E., Lutz, M.C, Martínez, J., Melin, A.D., de Melo, F.R., Mihaminekena, T.H., Mogilewsky, M.S., Moreira, L.S., Moura, L.A., Muhle, C.B., Nagy-Reis, M.B., Norconk, M.A., Notman, H., O’Mara, M. T., Ostner, J., Patel, E.R., Pavelka, M.S.M., Pinacho-Guendulain, B., Porter, L.M., Pozo-Montuy, G., Raboy, B.E., Rahalinarivo, V., Raharinoro, N.A., Rakotomalala, Z., Ramos-Fernández, G., Rasamisoa, D.C., Ratsimbazafy, J., Ravaloharimanitra, M., Razafindramanana, J., Razanaparany, T.P., Righini, N., Robson, N.M., Gonçalves, J.R., Sanamo, J., Santacruz, N., Sato, H., Sauther, M.L., Scarry, C.J., Serio-Silva, J.C., Shanee, S., Lins, P., Smith, A.C., Smith Aguilar, S.E., Souza-Alves, J.P., Stavis, V.K., Steffens, K.J.E., Stone, A.I., Strier, K.B., Suarez, S.A., Talebi, M., Tecot, S.R., Tujague, M.P., Valenta, K., Van Belle, S., Vasey, N., Wallace, R.B., Welch, G., Wright, P. C., Donati, G. and Santini, L. 2022. Factors influencing terrestriality in primates of the Americas and Madagascar. Proceedings of the National Academy of Sciences. 119 (42), pp. 1-10. https://doi.org/10.1073/pnas.2121105119Article
Artificial Intelligence to improve learning outcomes through online collaborative activities
Nalli, G., Amendola, D. and Smith, S. 2022. Artificial Intelligence to improve learning outcomes through online collaborative activities. Fotaris, P. and Blake, A. (ed.) 21st European Conference on e-Learning. Brighton, UK 27 - 28 Oct 2022 Academic Conferences International Limited (ACI). pp. 475-479 https://doi.org/10.34190/ecel.21.1.661Conference paper
SuperPoint features in endoscopy
Leon Barbed, O., Chadebecq, F., Morlana, J., Montiel, J. and Murillo, A. 2022. SuperPoint features in endoscopy. Manfredi, L., Ahmadi, S., Bronstein, M., Kazi, A., Lomanto, D., Mathew, A., Magerand, L., Mullakaeva, K., Papiez, B., Taylor, R. and Trucco, E. (ed.) 1st MICCAI Workshop on Imaging Systems for GI Endoscopy. Singapore 18 - 18 Sep 2022 Cham Springer. https://doi.org/10.1007/978-3-031-21083-9_5Conference paper
Evaluating the use of alternative normalization approaches on SARS-CoV-2 concentrations in wastewater: experiences from two catchments in northern Sweden
Isaksson, F., Lundy, L., Hedstrom, A., Szekely, A. and Mohamed, N. 2022. Evaluating the use of alternative normalization approaches on SARS-CoV-2 concentrations in wastewater: experiences from two catchments in northern Sweden. Environments. 9 (3). https://doi.org/10.3390/environments9030039Article
xURLLC in 6G with meshed RAN
Khoshkholghi, A., Mahmoodi, T., Pal, S., Chopra, S., Tendulkar, M. and Sarka, S. 2022. xURLLC in 6G with meshed RAN. ITU Journal on Future and Evolving Technologies. 3 (3), pp. 612-622. https://doi.org/10.52953/JTPE9471Article
Blue-green infrastructure for all seasons: the need for multicolored thinking
Sagrelius, P., Lundy, L., Blecken, G., Rizzo, A. and Viklander, M. 2022. Blue-green infrastructure for all seasons: the need for multicolored thinking. Journal of Sustainable Water in the Built Environment. 8 (4). https://doi.org/10.1061/JSWBAY.0000997Article
Edge intelligence for service function chain deployment in NFV-enabled networks
Khoshkholghi, A. and Mahmoodi, T. 2022. Edge intelligence for service function chain deployment in NFV-enabled networks. Computer Networks. 219. https://doi.org/10.1016/j.comnet.2022.109451Article
IntOpt: in-band network telemetry optimization framework to monitor network slices using P4
Bhamare, D., Kassler, A., Vestin, J., Khoshkholghi, A., Taheri, J., Mahmoodi, T., Ohlen, P. and Curescu, C. 2022. IntOpt: in-band network telemetry optimization framework to monitor network slices using P4. Computer Networks. 216. https://doi.org/10.1016/j.comnet.2022.109214Article
Translational researchers' training and development needs, preferences, and barriers: a survey in a National Institute for Health Research Biomedical Research Centre in the United Kingdom
Bell, K., Shah, S., Henderson, L. and Kiparoglou, V. 2022. Translational researchers' training and development needs, preferences, and barriers: a survey in a National Institute for Health Research Biomedical Research Centre in the United Kingdom. Clinical and Translational Science. 15 (7), pp. 1737-1752. https://doi.org/10.1111/cts.13289Article
Perceptions of gender equity and markers of achievement in a National Institute for Health Research Biomedical Research Centre: a qualitative study
Henderson, L., Dam, R., Shah, S., Ovseiko, P. and Kiparoglou, V. 2022. Perceptions of gender equity and markers of achievement in a National Institute for Health Research Biomedical Research Centre: a qualitative study. Health Research Policy and Systems. 20 (1). https://doi.org/10.1186/s12961-022-00904-4Article
Digital poverty in the UK: analysis of secondary data
Dafoulas, G., Ueno, A. and Dennis, C. 2022. Digital poverty in the UK: analysis of secondary data. London, UK British Academy.Project report
The Online Behaviour Taxonomy: a conceptual framework to understand behaviour in computer-mediated communication
Kaye, L., Rousaki, A., Joyner, L., Barrett, L. and Orchard, L. 2022. The Online Behaviour Taxonomy: a conceptual framework to understand behaviour in computer-mediated communication. Computers in Human Behavior. 137. https://doi.org/10.1016/j.chb.2022.107443Article
Towards resolving challenges associated with climate change modelling in Africa
Oluwagbemi, O., Hamutoko, J.T., Fotso-Nguemo, T.C., Lokonon, B.O.K., Emebo, O. and Kirsten, K.L. 2022. Towards resolving challenges associated with climate change modelling in Africa. Applied Sciences. 12 (14). https://doi.org/10.3390/app12147107Article
Molecular dynamic simulation reveals structure differences in APOL1 variants and implication on pathogenesis of chronic kidney disease
Mayanja, R., Kintu, C., Diabate, O., Soremekun, O., Oluwagbemi, O., Wele, M., Kalyesubula, R., Jjingo, D., Chikowore, T. and Fatumo, S. 2022. Molecular dynamic simulation reveals structure differences in APOL1 variants and implication on pathogenesis of chronic kidney disease. Genes. 13 (8). https://doi.org/10.3390/genes13081460Article
Bioinformatics, computational informatics, and modeling approaches to the design of mRNA COVID-19 vaccine candidates
Oluwagbemi, O., Oladipo, E.K., Kolawole, O.M., Oloke, J.K., Adelusi, T.I., Irewolede, B.A., Dairo, E.O, Ayeni, A.E., Kolapo, K.T., Akindiya, O.E., Oluwasegun, J.A., Oluwadara, B.F. and Fatumo, S. 2022. Bioinformatics, computational informatics, and modeling approaches to the design of mRNA COVID-19 vaccine candidates. Computation. 10 (7). https://doi.org/10.3390/computation10070117Article
Ensemble machine learning for Monkeypox transmission time series forecasting
Dada, E.G., Oyewola, D.O., Joseph, S.B., Emebo, O. and Oluwagbemi, O. 2022. Ensemble machine learning for Monkeypox transmission time series forecasting. Applied Sciences. 12 (23). https://doi.org/10.3390/app122312128Article
Application of deep learning techniques and Bayesian optimization with tree parzen estimator in the classification of supply chain pricing datasets of health medications
Oyewola, D., Dada, E., Omotehinwa, T., Emebo, O. and Oluwagbemi, O. 2022. Application of deep learning techniques and Bayesian optimization with tree parzen estimator in the classification of supply chain pricing datasets of health medications. Applied Sciences. 12 (19). https://doi.org/10.3390/app121910166Article
Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains
Oluwagbemi, O., Oladipo, E., Dairo, E., Ayeni, A., Irewolede, B., Jimah, E., Oyewole, M., Olawale, B., Adegoke, H. and Ogunleye, A. 2022. Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains. Informatics in Medicine Unlocked . 28. https://doi.org/10.1016/j.imu.2022.100845Article
Using deep 1D convolutional grated recurrent unit neural network to optimize quantum molecular properties and predict intramolecular coupling constants of molecules of potential health medications and other generic molecules
Oyewola, D.O., Dada, E.G., Emebo, O. and Oluwagbemi, O. 2022. Using deep 1D convolutional grated recurrent unit neural network to optimize quantum molecular properties and predict intramolecular coupling constants of molecules of potential health medications and other generic molecules. Applied Sciences. 12 (14). https://doi.org/10.3390/app12147228Article
Insights into the impacts of and responses to COVID-19 pandemic: The South African food retail supply chains perspective
Omoruyi, O., Dakora, E.A. and Oluwagbemi, O. 2022. Insights into the impacts of and responses to COVID-19 pandemic: The South African food retail supply chains perspective. Journal of Transport and Supply Chain. 16. https://doi.org/10.4102/jtscm.v16i0.739Article
Fencing
Bottoms, L., Cawdron, R., Kemp, S. and Oates, L. 2022. Fencing. in: Davison, R., Smith, P., Hopker, J., Price, M., Hettinga, F., Tew, G. and Bottoms, L. (ed.) Sport and Exercise Physiology Testing Guidelines: Volume I - Sport Testing. The British Association of Sport and Exercise Sciences Guide Taylor & Francis (Routledge). pp. 325-330Book chapter
New generation psychological treatments in chronic pain
McCracken, L., Yu, L. and Vowles, K. 2022. New generation psychological treatments in chronic pain. British Medical Journal. 376. https://doi.org/10.1136/bmj-2021-057212Article
Generation of anatomically inspired human airway tree using electrical impedance tomography: A method to estimate regional lung filling characteristics
Zamani, M., Kallio, M., Bayford, R. and Demosthenous, A. 2022. Generation of anatomically inspired human airway tree using electrical impedance tomography: A method to estimate regional lung filling characteristics. IEEE Transactions on Medical Imaging. 41 (5), pp. 1125-1137. https://doi.org/10.1109/TMI.2021.3136434Article
A framework for privacy and security requirements analysis and conflict resolution for supporting GDPR compliance through privacy-by-design
Alkubaisy, D., Piras, L., Al-Obeidallah, M., Cox, K. and Mouratidis, H. 2022. A framework for privacy and security requirements analysis and conflict resolution for supporting GDPR compliance through privacy-by-design. Ali, R., Kaindl, H. and Maciaszek, L. (ed.) 16th International Conference on Evaluation of Novel Approaches to Software Engineering. Virtual 26 - 27 Apr 2021 Cham Springer. pp. 67-87 https://doi.org/10.1007/978-3-030-96648-5_4Conference paper
Developing secured Android applications by mitigating code vulnerabilities with machine learning
Senanayake, J., Kalutarage, H., Al-Kadri, M., Petrovski, A. and Piras, L. 2022. Developing secured Android applications by mitigating code vulnerabilities with machine learning. ACM Asia Conference on Computer and Communications Security (ASIA CCS '22). Nagasaki, Japan 30 May - 03 Jun 2022 Association for Computing Machinery (ACM). pp. 1255–1257 https://doi.org/10.1145/3488932.3527290Conference poster
Advances in electrical impedance tomography and bioimpedance including applications in COVID-19 diagnosis and treatment
Bayford, R., Rosalind, S. and Frerichs, I. 2022. Advances in electrical impedance tomography and bioimpedance including applications in COVID-19 diagnosis and treatment. Physiological Measurement. 43 (2). https://doi.org/10.1088/1361-6579/ac4e6cArticle
A low-power recursive I/Q signal generator and current driver for bioimpedance applications
Hanzaee, F., Neshatvar, N., Rahal, M., Jiang, D., Bayford, R. and Demosthenous, A. 2022. A low-power recursive I/Q signal generator and current driver for bioimpedance applications. IEEE Transactions on Circuits and Systems II: Express Briefs. 69 (10), pp. 4108-4112. https://doi.org/10.1109/TCSII.2022.3187076Article
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.2212.11826Pre-print
Managing risk in nurseries
Ball, D. 2022. Managing risk in nurseries. Mark Allen Group.Magazine
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