Professor Ajit Narayanan

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Professor

Email: ajit.narayanan@aut.ac.nz

ORCID: ORCID logo https://orcid.org/0000-0003-3075-7672

Links to relevant web pages:

Academic appointments:

  • Professor, Auckland University of Technology (2007 - ongoing)
  • Head of School of Computing and Mathematical Sciences, Auckland University of Technology (2007 - 2013)
  • Professor and Head of School, University of Portsmouth (2005 - 2007)
  • Lecturer, Professor, Dean, University of Exeter (1980 - 2005)

Qualifications:

  • BSc (Hons), University of Aston in Birmingham
  • PhD, University of Exeter

Overview:

Professor of Computer Science and Artificial Intelligence.

Research interests:

Research interests in artificial intelligence, nature inspired computing, machine learning, computational statistics and machine ethics.

Teaching summary:

Artificial Intelligence, Nature Inspired Compouting (undergraduate); Health Analytics, Nature Inspired Computing (postgraduate).

Research outputs:

Journal articles

  • Jarden, R. J., Narayanan, A., Sandham, M., Siegert, R. J., & Koziol-Mclain, J. (2019). Bibliometric mapping of intensive care nurses' wellbeing: Development and application of the new iAnalysis model. BMC Nursing, 18(1). doi:10.1186/s12912-019-0343-1

  • Naidu, V., Whalley, J., & Narayanan, A. (2018). Generating Rule-Based Signatures for Detecting Polymorphic Variants Using Data Mining and Sequence Alignment Approaches. Journal of Information Security, 9. doi:10.4236/jis.2018.94019

  • Tirumala, S. S., & Narayanan, A. (2018). Classification and diagnostic prediction of prostate cancer using gene expression and artificial neural networks. Neural Computing and Applications. doi:10.1007/s00521-018-3589-8

  • Ahmad, W., & Narayanan, A. (2018). Time series data analysis using Artificial Immune System. Intelligent Decision Technologies, 12(2). doi:10.3233/IDT-170315

  • Narayanan, A., Farmer, E. A., & Greco, M. J. (2018). Multisource feedback as part of the Medical Board of Australia's Professional Performance Framework: Outcomes from a preliminary study. BMC Medical Education, 18(1), 1-11. doi:10.1186/s12909-018-1432-7

  • Liu, W., Mirza, F., Narayanan, A., & Souligna, S. (2018). Is it possible to cure Internet addiction with the Internet?. AI and Society. doi:10.1007/s00146-018-0858-0

  • Wedyan, A., Whalley, J., & Narayanan, A. (2018). Solving the Traveling Salesman Problem Using Hydrological Cycle Algorithm. American Journal of Operations Research, 8(3), 34 pages. doi:10.4236/ajor.2018.83010

  • Paterson, J., Medvedev, O. N., Sumich, A., Tautolo, E. S., Krageloh, C. U., Sisk, R., . . . Siegert, R. J. (2018). Distinguishing transient versus stable aspects of depression in New Zealand
    Pacific Island children using Generalizability Theory. Journal of Affective Disorders, 227, 698-704. doi:10.1016/j.jad.2017.11.075

  • Wedyan, A., Whalley, J., & Narayanan, A. (2017). Hydrological cycle algorithm for continuous optimization problems. Journal of Optimization, 2017(2017). doi:10.1155/2017/3828420

  • Naidu, V., Whalley, J., & Narayanan, A. (2017). Exploring the effects of gap-penalties in sequence-alignment approach to polymorphic virus detection. Journal of Information Security, 8(4). doi:10.4236/jis.2017.84020

  • Medvedev, O. N., Krageloh, C. U., Narayanan, A., & Siegert, R. J. (2017). Measuring mindfulness: Applying generalizability theory to distinguish between state and trait. Mindfulness, 8(4). doi:10.1007/s12671-017-0679-0

  • Vaidyanathan, V., Naidu, V., Kao, C. H. J., Karunasinghe, N., Bishop, K. S., Wang, A., . . . Ferguson, L. R. (2017). Environmental factors and risk of aggressive prostate cancer among a population of New Zealand men-a genotypic approach. Molecular BioSystems, 13(4). doi:10.1039/c6mb00873a

  • Narayanan, A., & Greco, M. (2016). Patient experience of Australian general practices. Big Data, 4(1). doi:10.1089/big.2016.0010

  • Usman, A. B., Liu, W., Bai, Q., & Narayanan, A. (2015). Trust of the same: Rethinking trust and reputation management from a structural homophily perspective. International Journal of Information Security and Privacy, 9(2). doi:10.4018/IJISP.2015040102

  • Narayanan, A., & Greco, M. (2014). The Dental Practice Questionnaire: a patient feedback tool for improving the quality of dental practices. Australian dental journal, 59(3). doi:10.1111/adj.12200

  • Narayanan, A., Greco, M., Reeves, P., Matthews, A., & Bergin, J. (2014). Community pharmacy performance evaluation: Reliability and validity of the pharmacy patient questionnaire. International Journal of Healthcare Management, 7(2). doi:10.1179/2047971913Y.0000000067

  • Narayanan, A., & Greco, M. (2014). The dental practice questionnaire: A patient feedback tool for improving the quality of dental practices. Australian Dental Journal. doi:10.1111/adj.12200

  • Narayanan, A., Greco, M., Powell, H., & Coleman, L. (2013). The reliability of big 'patient satisfaction' data. Big Data, 1(3). doi:10.1089/big.2013.0021

  • Narayanan, A., Chen, Y., Pang, S., & Tao, B. (2013). The effects of different representations on static structure analysis of computer malware signatures.. ScientificWorldJournal, 2013. doi:10.1155/2013/671096

  • Keedwell, E., & Narayanan, A. (2013). Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid. International Journal of Data Mining and Bioinformatics, 7(4).

  • Narayanan, A. (2013). Society under threat... but not from AI. AI and Society, 28(1). doi:10.1007/s00146-012-0401-7

  • Narayanan, A. (2013). Society under threat... but not from AI. AI and Society, 28(1). doi:10.1007/s00146-012-0401-7

  • Ahmad, W., & Narayanan, A. (2012). Outlier detection using humoral-mediated clustering (HAIS). International Journal of Computational Intelligence and Applications, 11(1). doi:10.1142/S1469026812500034

  • Ahmad, W., & Narayanan, A. (2012). Humoral artificial immune system (HAIS) for supervised learning.. International Journal of Computational Intelligence and Applications, 11(1). doi:10.1142/S1469026812500046

  • Glaysher, S., Gabriel, F. G., Johnson, P., Polak, M., Knight, L. A., Parker, K., . . . Cree, I. A. (2010). Molecular basis of chemosensitivity of platinum pre-treated ovarian cancer to chemotherapy. British Journal of Cancer, 103(5). doi:10.1038/sj.bjc.6605817

  • Parker, K. A., Glaysher, S., Polak, M., Gabriel, F. G., Johnson, P., Knight, L. A., . . . Hurren, J. (2010). The molecular basis of the chemosensitivity of metastatic cutaneous melanoma to chemotherapy. Journal of Clinical Pathology, 63. doi:10.1136/jcp.2010.080119

  • Narayanan, A., Campbell, J. A., & Greco, M. J. (2010). Generalisability in unbalanced, uncrossed and fully nested studies. Medical Education, 44(4). doi:10.1111/j.1365-2923.2009.03606.x

  • Glaysher, S., Yiannakis, D., Gabriel, F. G., Johnson, P., Polak, M. E., Knight, L. A., . . . Cree, I. (2009). Resistance gene expression determines the in vitro chemosensitivity of non-small cell lung cancer (NSCLC. BMC Cancer, 9:300. doi:10.1186/1471-2407-9-300

  • Campbell, J. A., Greco, M. J., Johnson, J., Richards, S., Dickens, A., & Narayanan, A. (2008). Assessing the professional performance of UK doctors: an evaluation of the utility of the General Medical Council patient and colleague questionnaires. Quality and Safety in Health Care, 17(3). doi:10.1136/qshc.2007.024679

  • Narayanan, A., & Greco, M. (2007). What distinguishes general practitioners from consultants, according to colleagues?. Journal of Management and Marketing in Healthcare, 1(1). Retrieved from http://www.ingentaconnect.com/content/maney/mmh/2007/00000001/00000001/art00011

Book chapters

  • Shanmuganathan, S., Narayanan, A., & Medagoda, N. (2015). Temporal data analysis and mining methods for modelling the climate change effects on Malaysia’s oil palm yield at different regional scales. In S. Dinda (Ed.), Handbook of Research on Climate Change Impact on Health
    > and Environmental Sustainability
    .

  • Ahmad, W., & Narayanan, A. (2015). The role of hypermutation and affinity maturation in AIS approaches to clustering. In M. Usman (Ed.), Improving Knowledge Discovery through the Integration of Data Mining Techniques (pp. 124-144). IGI Global. doi:10.4018/978-1-4666-8513-0.ch007

  • Narayanan, A., Sallis, P., & Shanmuganathan, S. (2012). Climate change and grape wine quality: A GIS approach to analyzing New Zeland wine regions. In N. Chhetri (Ed.), Human and social dimensions of climate change. InTech. doi:10.5772/51252

  • Ahmad, W., & Narayanan, A. (2011). Principles and Methods of Artificial Immune System Vaccination of Learning Systems. In P. Lio, G. Nicosia, & T. Stibor (Eds.), Artificial Immune Systems: 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011. Proceedings (pp. 268-281). Berlin Heidelberg: Springer Verlag. doi:10.1007/978-3-642-22371-6_24

  • Ahmad, W., & Narayanan, A. (2011). Population-based artificial immune system clustering algorithm. In P. Lio, G. Nicosia, & T. Stibor (Eds.), Artificial Immune Systems: 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011. Proceedings (pp. 348-360). Berlin Heidelberg: Springer Verlag. doi:10.1007/978-3-642-22371-6_30

  • Baker, L., Greco, M., & Narayanan, A. (2009). Doctors using patient feedback to establish professional learning goals: Results from a communication skill development program. In W. Pease, M. Cooper, & R. Garurajan (Eds.), Biomedical Knowledge Management: Infrastructures and Processes for eHealth Systems. IGI Global.

Conference contributions

  • Naidu, V., Narayanan, A., & Mohanty, M. (2019). Using Amino Acids of Images for Identifying Pornographic Images. In Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 (pp. 9-14). San Jose. doi:10.1109/MIPR.2019.00010

  • Singh, A., Yan, W. -Q., & narayanan, A. (2017). Image phylogeny for simulating multiple print-scan. In International Conference Image and Vision Computing New Zealand. University of Canterbury: IEEE. Retrieved from http://ivcnz2017.canterbury.ac.nz/

  • Zhang, Y., Yan, W. -Q., & Narayanan, A. (2017). A virtual keyboard implementation based on finger recognition. In International Conference Image and Vision Computing New Zealand. University of Canterbury: IEEE. Retrieved from http://ivcnz2017.canterbury.ac.nz/program.html

  • Tirumala, S. S., & Narayanan, A. (2017). Transpositional neurocryptography using deep learning. In International Conference on Information Technologty. Singapore.

  • Naidu, V., & Narayanan, A. (2016). Needleman-Wunsch and Smith-Waterman algorithms for identifying viral polymorphic malware variants. In Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016 (pp. 326-333). : Institute of Electrical and Electronics Engineers Inc.. doi:10.1109/DASC-PICom-DataCom-CyberSciTec.2016.73

  • Yang, M. L., Narayanan, A., Parry, D., & Wang, X. (2016). A Lightweight Authentication Scheme for Transport System Farecards. In International Conference on RFID Technology and Applications, 2016 (pp. 167-172). Shunde.

  • Usman, A., Liu, W., Bai, Q., & Narayanan, A. (2016). Exploring the Role of Structural Similarity in Securing Smart Metering Infrastructure. In 2015 IEEE IEEE International Conference on Data Science and Data Intensive Systems (pp. 343-349). Australia: IEEE DSDIS Proceedings. doi:10.1109/DSDIS.2015.95

  • Yang, M. L., Narayanan, A., Parry, D., & Wang, X. (2016). A lightweight authentication scheme for transport system farecards. In 2016 IEEE International Conference on RFID Technology and Applications (RFID-TA) (pp. 150-155). Marriott Hotel: IEEE. doi:10.1109/RFID-TA.2016.7750746

  • Tirumala, S. S., & Narayanan, A. (2016). Attribute selection and classification of prostate cancer gene expression data using artificial neural networks. In Trends and Applications in Knowledge Discovery and Data Mining Vol. 9794 (pp. 26-34). Auckland: Springer. doi:10.1007/978-3-319-42996-0_3

  • Naidu, V., & Narayanan, A. (2016). A syntactic approach for detecting viral polymorphic malware variants. In Intelligence and Security Informatics Vol. 9650 (pp. 146-165). : Springer Verlag. doi:10.1007/978-3-319-31863-9_11

  • Usman, A., Liu, W., Bai, Q., & Narayanan, A. (2015). Revealing the role of topological transitivity in efficient trust and reputation system in smart metering network. In 2015 IEEE International Conference on Data Science and Data Intensive Systems (pp. 337-342). : IEEE. doi:10.1109/DSDIS.2015.114

  • Tirimula, S. S., & Narayanan, A. (2015). Hierarchical data classification using deep neural networks. In Neural Information Processing Vol. 9489 (pp. 492-500). Istanbul: Springer. doi:10.1007/978-3-319-26532-1

  • Ahmad, W., & Narayanan, A. (2015). Artificial immune system: An effective way to reduce model overfitting. In Computational Collective Intelligence 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I (pp. 316-327). Madrid: Springer. doi:10.1007/978-3-319-24069-5

  • Wedyan, A. (2014). Solving capacitated vehicle routing problem using intelligent water drops algorithm. In Natural Computation (ICNC), 2014 10th International Conference on (pp. 469-474). : IEEE. doi:10.1109/ICNC.2014.6975880

  • Naidu, V., & Narayanan, A. (2014). Further experiments in biocomputational structural analysis of malware. In Proceedings of the IEEE 2014 10th International Conference on Natural Computation (ICNC 2014) (pp. 605-610). China: IEEE. doi:10.1109/ICNC.2014.6975904

  • Naidu, V., & Narayanan, A. (2014). Further Experiments in Biocomputational Structural
    Analysis of Malware. In Proceedings of the IEEE 2014 10th International Conference on Natural Computation (pp. 610-615). Xiamen: IEEE.

  • Narayanan, A., & Wedyan, A. (2014). Solving capacitated vehicle routing problem using Intelligent Water Drops algorithm.. In Proceedings of the IEEE 2014 10th International Conference on Natural Computation (pp. 471-476). Xiamen: IEEE.

  • Narayanan, A., & Keedwell, E. (2014). An evolutionary computational approach to phase and synchronization in biological circuits. In Natural Computation (ICNC), 2014 10th International Conference on National Computation (pp. 419-424). Xiamen: IEEE. doi:10.1109/ICNC.2014.6975872

  • Shanmuganathan, S., Narayanan, A., Mohamed, M., Ibrahim, R., & Khalid, H. (2014). A hybrid approach to modelling the climate change effects on Malaysia’s oil palm yield at the regional scale. In Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing Vol. 287 (pp. 335-346). Malaysia: Springer Verlag. doi:10.1007/978-3-319-07692-8_32

  • Liu, F., & Narayanan, A. (2014). Intelligent collision avoidance between autonomous agents using adaptive local views. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8861 (pp. 190-205). Gold Coast, Queensland: Springer Verlag. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-13191-7_16

  • Narayanan, A., & Liu, F. (2013). A Human-Inspired Collision Avoidance Method for Multi-robot and Mobile Autonomous Robots. In PRIMA 2013: Principles and Practice of Multi-Agent Systems Vol. 8291 (pp. 181-196). Dunedin: Springer-Verlag. Retrieved from http://link.springer.com/content/pdf/10.1007/978-3-642-44927-7_13.pdf

  • Liu, F., & Narayanan, A. (2013). Roundabout collision avoidance for multiple robots based on minimum enclosing rectangle. In 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 Vol. 2 (pp. 1375-1376). Saint Paul, MN.

  • Narayanan, A., Apenteng, O., & Klymchuk, S. (2013). The Use of Cellular Automata for Modelling (SIR)N Diseases with Migratory Reinfection. In Proceedings of 2012 3rd International Conference on Computer and Computational Intelligence (ICCCI 2012) (pp. 5 pages). Bali: ASME Press. doi:10.1115/1.860182_ch10

  • Shanmuganathan, S., & Narayanan, A. (2012). Modelling the climate change effects on Malaysia’s
    oil palm yield. In IEEE Xplore Digital Library indexed by EI Compendex. Malaysia: IEEE, Malaysia Chapter.

  • Liu, F., Narayanan, A., & Bai, Q. (2012). Effective methods for generating collision free paths for multiple robots based on collision type. In The 11th international conference on autonomous agents and multiagent systems (pp. 1459-1460). .

  • Apenteng, O., Narayanan, A., & Klymchuk, S. (2012). The use of cellular automata for modelling (SIR)N diseases with migratory reinfection. In Program and Abstracts of the 3rd International Conference on Computer and Computational Intelligence (ICCCI 2012) (pp. 23). Bali.

  • Narayanan, A., Chen, Y., Shaoning, P., & Ban, T. (2012). The Effects of Different Representations on Malware Motif Identification. In Proceedings of the 8th International Conference on Computational Intelligence and Security (CIS2012) (pp. 86-90). Guangzhou: IEEE/CPS. doi:10.1109/CIS.2012.27

  • Ahmad, W., & Narayanan, A. (2011). Quantum jump clustering. In Proceedings of 7th International Conference on Natural Computation (ICNC 2011) Vol. 3 (pp. 1352-1357). : IEEE. doi:10.1109/ICNC.2011.6022341

  • Ahmad, W., & Narayanan, A. (2011). Principles and Methods of Artificial Immune System Vaccination of Learning Systems. In Artificial Immune Systems: 10th International Conference, ICARIS 2011, Proceedings Vol. 6825 (pp. 268-281). United Kingdom: Springer. doi:10.1007/978-3-642-22371-6

  • Ahmad, W., & Narayanan, A. (2011). Population-Based Artificial Immune System Clustering Algorithm. In Artificial Immune Systems: 10th International Conference, ICARIS 2011, Proceedings Vol. 6825 (pp. 348-360). : Springer. doi:10.1007/978-3-642-22371-6

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2011). Data mining and χ2 test based hybrid approach to modelling climate effects on grape crop in varieties of Kumeu, New Zealand. In IEEE Proceedings of the 3rd International Conference on Machine Learning and Computing (ICMLC 2011) Vol. 1 (pp. 355-359). : IEEE.

  • Shanmuganathan, S., Kuroki, P., Narayanan, A., & Sallis, P. (2011). Modelling the seasonal climate variability and its effects on vintage wines from Marlborough, NZ. In Proceedings of 8th IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'11) (pp. 2128-2133). Shanghai: IEEE. Retrieved from http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001615

  • Shanmuganathan, S., Narayanan, A., & Robinson, N. (2011). A cellular automaton framework for within-field vineyard variance and grape production simulation.. In 2011 Seventh IEEE International Conference on Natural Computation (pp. 1430-1435). : IEEE. doi:10.1109/ICNC.2011.6022364

  • Gruenwald, N., Sauerbier, G., Narayanan, A., Klymchuk, S., & Zverkova, T. (2011). Mathematical modelling of infectious disease with biomathematics: Implications for teaching and research. In Proceedings of the 14 th International Conference on the Teaching of Mathematical Modelling and Applications (ICTMA-14. Hamburg. doi:10.1007/978-94-007-0910-2

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Modelling the effects of daily extreme weather on grapevine and wine quality. In Fifth Biennial Meeting International Environmental Modelling and Software Society (iEMSs) 2010 International Congress on Environmental Modelling and Software Modelling for Environment’s Sake (pp. 2327-2335). Modelling for Environment’s Sake, Fifth Biennial Meeting, Ottawa. Retrieved from http://www.iemss.org/iemss2010/Volume3.pdf

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Modelling the effects of daily extreme weather on grapevine and wine quality. In Fifth Biennial Meeting International Environmental Modelling and Software Society (iEMSs) 2010 International Congress on Environmental Modelling and Software Modelling for Environment's Sake. Ottawa.

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Modelling the seasonal climate effects on grapevine yield at different spatial and unconventional temporal scales. In 2010 International Congress on Environmental Modelling and Software Modelling for Environment's Sake. Ottawa.

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Data Mining Techniques for Modelling Seasonal Climate Effects on Grapevine Yield and Wine Quality. In 2010 Second International Conference on Computational Intelligence, Communication Systems and Networks (pp. 84-89). Liverpool. doi:10.1109/CICSyN.2010.16

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Data Mining Techniques for Modelling the Influence of Daily Extreme Weather Conditions on Grapevine, Wine Quality and Perennial Crop Yield. In Communication Systems and Networks (CICSyN), 2010 Second International Conference on Computational Intelligence (pp. 90-95). Liverpool.

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Modelling the seasonal climate effects on grapevine yield at different spatial and unconventional temporal scales. In Proceedings of International Congress on Environmental Modelling and Software (iEMSs. Ottawa.

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Modelling the effects of daily extreme weather on grapevine and wine quality. In Proceedings of International Congress on Environmental Modelling and Software (iEMSs. Ottawa.

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Data mining techniques for modelling the influence of daily extreme weather conditions on grapevine, wine quality and perennial crop yield. In Proceedings of the IEEE Second International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN (pp. 90-95). Liverpool. doi:10.1109/CICSyN.2010.15

  • Shanmuganathan, S., Sallis, P., & Narayanan, A. (2010). Data mining techniques for modelling seasonal climate effects on grapevine yield and wine quality. In Proceedings of the IEEE Second International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN. Liverpool.

  • Sauerbier, G., Narayanan, A., Gruenwald, N., Klymchuk, S., & Zverkova, T. (2010). Teaching Non-Traditional Applications to Engineering Students. In Proceedings of the International Conference on Educational Interface between Mathematics and Industry (ICMI-20 Study (pp. 437-447). Lisbon.

  • Sallis, P., Shanmuganathan, S., & Narayanan, A. (2010). Micro-climate variations related to vineyard crop quality. In Proceedings of WAC2010 (World Automation Congress) on Soft Computing for Industry ISSCI 2010. Kobe.

  • Gruenwald, N., Sauerbier, G., Narayanan, A., Klymchuk, S., & Zverkova, T. (2010). Teaching Unusual Applications in Engineering Mathematics: Students' Attitudes. In Proceedings of the 15th conference of the Mathematics Working Group (MWG) of the European Society for Engineering Education (SEFI. Wismar.

  • Ahmad, W., & Narayanan, A. (2010). Outlier detection using humoral-mediated clustering (HAIS). In Proceedings of NaBIC2010 (IEEE Second World Congress on Nature and Biologically Inspired Computing (pp. 45-52). Kitakyushu. doi:10.1109/NABIC.2010.5716298

  • Ahmad, W., & Narayanan, A. (2010). Humoral mediated clustering. In Proceedings of the IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2010 (pp. 1471-1481). Liverpool. doi:10.1109/BICTA.2010.5645279

  • Ahmad, W., & Narayanan, A. (2010). Humoral artificial immune system (HAIS) for supervised learning. In Proceedings of NaBIC2010 (IEEE World Congress on Nature and Biologically Inspired Computing). Kitakyushs. doi:10.1109/NABIC.2010.5716297

  • Narayanan, A. (2008). Will quantum algorithms prove that NP=P?. In Algorithms Conference, New Zealand Institute of Mathematics and its Applications. Napier.

Reports

  • Campbell, J., Hill, J., Hobart, J., Narayanan, A., Norman, G., Richards, S., . . . Wright, C. (2010). GMC Multi-Source Feedback Study:
    Scientific report of the Main Survey (2008-10)
    : GMC Multi-Source Feedback Study
    Scientific report of the Main Survey (2008-10)
    . Exeter, UK: Peninsula Medical School, Universities of Exeter and Plymouth.

  • Campbell, J. L., Richards, S. H., Dickens, A., Greco, M., Narayanan, A., & Brearley, S. (2007). Assessing the professional performance of UK doctors: An evaluation of the utility of the General Medical Council patient and colleague questionnaires. UK.

  • Campbell, J., Richards, S., Dickens, A., Narayanan, A., Greco, M., Jolliffe, J., . . . Gay, T. (2007). The General Medical Council patient and colleague questionnaire study: Report of the main study. UK.

Working paper/discussions

  • Narayanan, A. (2019). When is it right and good for an intelligent autonomous vehicle to take
    over control (and hand it back)?
    . Taylor & Francis. Retrieved from https://arxiv.org/abs/1901.08221v1

  • Narayanan, A., & Chen, Y. (2013). Bio-inspired data mining: Treating malware signatures as biosequences. Retrieved from http://arxiv.org/abs/1302.3668v1

Other outputs

  • Vaidyanathan, V., Karunasinghe, N., Krishnamurthy, V., Kao, C. H. J., Naidu, V., Pallati, R., . . . Ferguson, L. R. (2017). Aggressive prostate cancer incidence in New Zealand— “united we fall, divided we stand”. In New Zealand Medical Journal (Iss. 1466). Retrieved from https://www.nzma.org.nz/