Profiles

Shuxiang Xu

UTAS Home Dr Shuxiang Xu

Shuxiang Xu

Lecturer
Information & Communication Technology

Room V173 , Newnham Campus

+61 3 6324 3416 (phone)

Shuxiang.Xu@utas.edu.au

Dr. Shuxiang Xu is a lecturer within the School of Information & Communication Technology, College of Science and Engineering, University of Tasmania, Tasmania, Australia. His research interests are Artificial Intelligence, Machine Learning, and Data Mining. Much of his work is focused on developing new Machine Learning algorithms and using them to solve problems in various application fields. He has taught in the fields of Artificial Intelligence, ICT Architecture and Operating Systems, Computer Networks, and Programming and Software Development.

Biography

Before joining the University of Tasmania as a faculty member in Information & Communication Technology, Shuxiang received an Overseas Postgraduate Research Scholarship from the Australian government to research a PhD in Computing degree with University of Western Sydney, Sydney, Australia. Prior to this, He worked as a Lecturer with Hubei University of Technology, Wuhan, China, following completions of his MSc in Applied Mathematics and BSc in Applied Mathematics degrees with University of Electronic Science and Technology of China, Chengdu, China.

Career summary

Qualifications

Degree

Thesis title

 

University

Country

Date of award

PhD

Neuron-adaptive Neural Networks Models and Applications

 

University of Western Sydney

Australia

1/10/2000

Languages (other than English)

Mandarin Chinese

Memberships

Professional practice

Member of Australian Computer Society (ACS)

Teaching

Machine Learning, Data Mining, Artificial Intelligence, Neural Networks

Teaching expertise

Artificial Intelligence, Machine Learning, Computer Networks, Programming, Operating Systems

Teaching responsibility

KIT213 Operating Systems

KIT107 Programming

KXO151 Programming and Problem Solving

View more on Dr Shuxiang Xu in WARP

Expertise

Shuxiang’s research involves collecting, building, and analysing application datasets to build new machine learning models to improve community, environmental and economic outcomes, providing a better foundation for policy and business practice. His research expertise includes developing new machine learning algorithms or improving existing machine learning algorithms and applying them in solving problems in the general categories of classification, regression, clustering, and pattern recognition.

Research Themes

Shuxiang’s research aligns to the University’s research theme of Data, Knowledge and Decisions. His research mainly involves collecting, building, and analysing datasets to build new machine learning models to improve community, environmental and economic outcomes, providing a better foundation for policy and business practice. These new machine learning approaches are significant not only for Tasmania, but across international jurisdictions. His research projects mainly involve developing new machine learning algorithms or improving existing machine learning algorithms and applying them in solving problems in the general categories of classification, regression, clustering, and pattern recognition.

Collaboration

Shuxiang has cross school research collaborations (within UTAS) with Australian Maritime College, and IMAS Centre for Oceans & Cryosphere. Shuxiang has international research collaboration with China Agricultural University, China.

Fields of Research

  • Neural networks (461104)
  • Knowledge representation and reasoning (460206)
  • Information modelling, management and ontologies (460903)
  • Human information interaction and retrieval (461003)
  • Networking and communications (460609)
  • Computer vision (460304)
  • Computational complexity and computability (461302)
  • Decision support and group support systems (460902)
  • Image processing (460306)
  • Pattern recognition (460308)
  • Electrical energy generation (incl. renewables, excl. photovoltaics) (400803)
  • Operating systems (460610)
  • Business information systems (350303)
  • Modelling and simulation (460207)
  • Economic models and forecasting (380203)
  • Data management and data science (460599)

Research Objectives

  • Information systems, technologies and services (220499)
  • Other information and communication services (229999)
  • Application software packages (220401)
  • Electronic information storage and retrieval services (220302)
  • Communication technologies, systems and services (220199)
  • Teaching and instruction technologies (160304)
  • Wind energy (170808)
  • Information services (220399)
  • Machinery and equipment (241299)
  • Network systems and services (220105)
  • Taxation (150210)

Publications

Total publications

54

Highlighted publications

(10 outputs)
YearTypeCitationAltmetrics
2017Journal ArticleKabir MMJ, Xu S, Kang BH, Zhao Z, 'A new multiple seeds based genetic algorithm for discovering a set of interesting Boolean association rules', Expert Systems With Applications, 74 pp. 55-69. ISSN 0957-4174 (2017) [Contribution to Refereed Journal]

DOI: 10.1016/j.eswa.2017.01.001 [eCite] [Details]

Citations: Scopus - 18Web of Science - 15

Co-authors: Kabir MMJ; Kang BH; Zhao Z

Tweet

2015Journal ArticleZhao Z, Xu S, Kang BH, Kabir MMJ, Liu Y, et al., 'Investigation and improvement of multi-layer perceptron neural networks for credit scoring', Expert Systems With Applications, 42, (7) pp. 3508-3516. ISSN 0957-4174 (2015) [Refereed Article]

DOI: 10.1016/j.eswa.2014.12.006 [eCite] [Details]

Citations: Scopus - 84Web of Science - 63

Co-authors: Zhao Z; Kang BH; Kabir MMJ; Wasinger R

Tweet

2014Journal ArticleXu S, Liu Y, 'Neural networks for business decision making', International Journal of Advancements in Computing Technology, 6, (2) pp. 49-58. ISSN 2005-8039 (2014) [Refereed Article]

[eCite] [Details]

2012Chapter in BookXu S, 'HONNs with Extreme learning Machine to Handle Incomplete Datasets', Artificial Higher Order Neural Networks for Modeling and Simulation, Information Science Reference, J Gamon (ed), United States of America, pp. 276-292. ISBN 978-1-4666-2175-6 (2012) [Research Book Chapter]

DOI: 10.4018/978-1-4666-2175-6.ch013 [eCite] [Details]

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2010Chapter in BookXu S, 'Adaptive Higher Order Neural Network Models for Data Mining', Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, Information Science Reference, Ming Zhang (ed), Hershey, United States, pp. 86-98. ISBN 978-1-61520-711-4 (2010) [Research Book Chapter]

DOI: 10.4018/978-1-61520-711-4.ch004 [eCite] [Details]

Citations: Scopus - 2

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2010Journal ArticleXu S, 'Data mining using higher order neural network models with adaptive neuron activation functions', International Journal of Advancements in Computing Technology , 2, (4) pp. 168-177. ISSN 2005-8039 (2010) [Refereed Article]

DOI: 10.4156/ijact.vol2.issue4.18 [eCite] [Details]

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2009Chapter in BookXu S, 'Adaptive higher order neural network models and their applications in business', Artificial Higher Order Neural Networks for Economics and Business, Information Science Reference, Ming Zhang (ed), Hershey, PA, pp. 314-329. ISBN 978-1-59904-897-0 (2009) [Research Book Chapter]

DOI: 10.4018/978-1-59904-897-0.ch014 [eCite] [Details]

Citations: Scopus - 18

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2008Conference PublicationXu S, Chen L, 'A novel approach for determining the optimal number of hidden layer neurons for FNN's and its application in data mining', Proceedings The 5th International Conference on Information Technology and Applications, 23-26 June 2008, Carins, Qld, pp. 683-686. ISBN 978-0-9803267-2-7 (2008) [Refereed Conference Paper]

[eCite] [Details]

2007Journal ArticleZhang M, Xu S, Fulcher JA, 'ANSER: Adaptive Neuron Artificial Neural Network System for Estimating rainfall', International Journal of Computers & Applications, 29, (3) pp. 215-222. ISSN 1206-212X (2007) [Refereed Article]

DOI: 10.2316/Journal.202.2007.3.202-1585 [eCite] [Details]

Citations: Scopus - 10

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2002Journal ArticleZhang M, Xu S, Fulcher J, 'Neuron-Adaptive Higher Order Neural-Network Models for Automated Financial Data Modeling', IEEE Transactions on Neural Networks, 13, (1) pp. 188 - 204. ISSN 1045-9227 (2002) [Refereed Article]

DOI: 10.1109/72.977302 [eCite] [Details]

Citations: Scopus - 92Web of Science - 56

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Journal Article

(21 outputs)
YearCitationAltmetrics
2020Forouzandeh S, Aghdam AR, Xu S, Forouzandeh S, 'Addressing the cold-start problem using data mining techniques and improving recommender systems by Cuckoo algorithm: a case study of Facebook', Computing in Science and Engineering, 22, (4) pp. 62-73. ISSN 1521-9615 (2020) [Refereed Article]

DOI: 10.1109/MCSE.2018.2875321 [eCite] [Details]

Citations: Scopus - 1Web of Science - 1

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2020Wei Y, Tran S, Xu S, Kang B, Springer M, 'Deep learning for retail product recognition: challenges and techniques', Computational Intelligence and Neuroscience, 2020 Article ID 8875910. ISSN 1687-5265 (2020) [Refereed Article]

DOI: 10.1155/2020/8875910 [eCite] [Details]

Co-authors: Wei Y; Tran S; Kang B; Springer M

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2019Arafat MY, Hoque S, Xu S, Farid DM, 'Machine learning for mining imbalanced data', IAENG International Journal of Computer Science, 46, (2) pp. 332-348. ISSN 1819-656X (2019) [Refereed Article]

[eCite] [Details]

Citations: Scopus - 4

2019Badhon B, Kabir MMJ, Xu S, Kabir A, 'A survey on association rule mining based on evolutionary algorithms', International Journal of Computers and Applications pp. 1-11. ISSN 1206-212X (2019) [Refereed Article]

DOI: 10.1080/1206212X.2019.1612993 [eCite] [Details]

Citations: Scopus - 2

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2019Kabir M, Kabir MMJ, Xu S, Badhon B, 'An empirical research on sentiment analysis using machine learning approaches', International Journal of Computers and Applications pp. 1-9. ISSN 1206-212X (2019) [Refereed Article]

DOI: 10.1080/1206212X.2019.1643584 [eCite] [Details]

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2019Yang W, Fan S, Xu S, King P, Kang B, et al., 'Autonomous underwater vehicle navigation using sonar image matching based on convolutional neural network', IFAC PapersOnLine, 52, (21) pp. 156-162. ISSN 2405-8963 (2019) [Refereed Article]

DOI: 10.1016/j.ifacol.2019.12.300 [eCite] [Details]

Citations: Scopus - 3Web of Science - 2

Co-authors: Yang W; King P; Kang B; Kim E

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2018Forouzandeh S, Sheikhahmadi A, Rezaei Aghdam A, Xu S, 'New centrality measure for nodes based on user social status and behavior on Facebook', International Journal of Web Information Systems, 14, (2) pp. 158-176. ISSN 1744-0084 (2018) [Refereed Article]

DOI: 10.1108/IJWIS-07-2017-0053 [eCite] [Details]

Citations: Scopus - 6Web of Science - 5

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2018Iskandar I, Willett R, Xu S, 'The development of a government cash forecasting model', Journal of Public Budgeting, Accounting & Financial Management, 30, (4) pp. 368-383. ISSN 1096-3367 (2018) [Refereed Article]

DOI: 10.1108/JPBAFM-04-2018-0036 [eCite] [Details]

Citations: Scopus - 2

Co-authors: Iskandar I

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2017Aghdam AR, Xu S, Kaveie A, Fahimi SA, Khani EG, et al., 'Performance assessment of payment gateways in banking services in Tehran, Iran', International Journal on Computer Science and Engineering, 9, (8) pp. 496-505. ISSN 0975-3397 (2017) [Refereed Article]

[eCite] [Details]

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2017Bin Shafaat A, Xu S, 'A comparative study of technologies developed in perspective of distributed operating systems', Advances in Modelling and Analysis B, 60, (3) pp. 613-629. ISSN 1240-4543 (2017) [Refereed Article]

DOI: 10.18280/ama_b.600307 [eCite] [Details]

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2017Kabir MMJ, Xu S, Kang BH, Zhao Z, 'A new multiple seeds based genetic algorithm for discovering a set of interesting Boolean association rules', Expert Systems With Applications, 74 pp. 55-69. ISSN 0957-4174 (2017) [Contribution to Refereed Journal]

DOI: 10.1016/j.eswa.2017.01.001 [eCite] [Details]

Citations: Scopus - 18Web of Science - 15

Co-authors: Kabir MMJ; Kang BH; Zhao Z

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2017Kampalpour M, Aghdam AR, Xu S, Khani EG, Baghi A, 'Uncovering hotel guests preferences through data mining techniques', International Journal of Computer Science and Network Security, 17, (8) pp. 1-10. ISSN 1738-7906 (2017) [Refereed Article]

[eCite] [Details]

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2016Hasan MAM, Xu S, Kabir MMJ, Ahmad S, 'Performance evaluation of different kernels for support vector machine used in intrusion detection system', International Journal of Computer Networks and Communications, 8, (6) pp. 39-54. ISSN 0974-9322 (2016) [Refereed Article]

DOI: 10.5121/ijcnc.2016.8604 [eCite] [Details]

Citations: Scopus - 2

Co-authors: Kabir MMJ

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2015Zhao Z, Xu S, Kang BH, Kabir MMJ, Liu Y, et al., 'Investigation and improvement of multi-layer perceptron neural networks for credit scoring', Expert Systems With Applications, 42, (7) pp. 3508-3516. ISSN 0957-4174 (2015) [Refereed Article]

DOI: 10.1016/j.eswa.2014.12.006 [eCite] [Details]

Citations: Scopus - 84Web of Science - 63

Co-authors: Zhao Z; Kang BH; Kabir MMJ; Wasinger R

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2014Kabir MMJ, Xu S, Kang BH, Zhao Z, 'Association rule mining for both frequent and infrequent items using particle swarm optimization algorithm', International Journal on Computer Science and Engineering, 6, (7) pp. 221-231. ISSN 0975-3397 (2014) [Refereed Article]

[eCite] [Details]

Co-authors: Kabir MMJ; Kang BH; Zhao Z

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2014Kabir MMJ, Xu S, Kang BH, Zhao Z, 'A hybrid GeneticMax algorithm for improving the traditional genetic based approach for mining maximal frequent item sets', International Journal of Computer Science and Network Security, 14, (10) pp. 27-35. ISSN 1738-7906 (2014) [Refereed Article]

[eCite] [Details]

Co-authors: Kabir MMJ; Kang BH; Zhao Z

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2014Xu S, Liu Y, 'Neural networks for business decision making', International Journal of Advancements in Computing Technology, 6, (2) pp. 49-58. ISSN 2005-8039 (2014) [Refereed Article]

[eCite] [Details]

Tweet

2014Zhao Z, Xu S, Kang BH, Kabir MMJ, Liu Y, 'Investigation of multilayer perceptron and class imbalance problems for credit rating', International Journal of Computer and Information Technology, 3, (4) pp. 805-812. ISSN 2279-0764 (2014) [Refereed Article]

[eCite] [Details]

Co-authors: Zhao Z; Kang BH; Kabir MMJ

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2010Xu S, 'Data mining using higher order neural network models with adaptive neuron activation functions', International Journal of Advancements in Computing Technology , 2, (4) pp. 168-177. ISSN 2005-8039 (2010) [Refereed Article]

DOI: 10.4156/ijact.vol2.issue4.18 [eCite] [Details]

Tweet

2007Zhang M, Xu S, Fulcher JA, 'ANSER: Adaptive Neuron Artificial Neural Network System for Estimating rainfall', International Journal of Computers & Applications, 29, (3) pp. 215-222. ISSN 1206-212X (2007) [Refereed Article]

DOI: 10.2316/Journal.202.2007.3.202-1585 [eCite] [Details]

Citations: Scopus - 10

Tweet

2002Zhang M, Xu S, Fulcher J, 'Neuron-Adaptive Higher Order Neural-Network Models for Automated Financial Data Modeling', IEEE Transactions on Neural Networks, 13, (1) pp. 188 - 204. ISSN 1045-9227 (2002) [Refereed Article]

DOI: 10.1109/72.977302 [eCite] [Details]

Citations: Scopus - 92Web of Science - 56

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Chapter in Book

(6 outputs)
YearCitationAltmetrics
2016Xu S, Liu Y, 'A theoretical framework for parallel implementation of deep higher order neural networks', Applied Artificial Higher Order Neural Networks for Control and Recognition, Information Science Reference, M Zhang (ed), Hershey PA, USA, pp. 351-361. ISBN 9781522500636 (2016) [Research Book Chapter]

DOI: 10.4018/978-1-5225-0063-6.ch013 [eCite] [Details]

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2016Zhao Z, Xu S, Kang BH, Kabir MMJ, Liu Y, et al., 'Utilizing Feature Selection on Higher Order Neural Networks', Applied Artificial Higher Order Neural Networks for Control and Recognition, Information Science Reference, M Zhang (ed), Hershey PA, USA, pp. 375-390. ISBN 9781522500636 (2016) [Research Book Chapter]

DOI: 10.4018/978-1-5225-0063-6.ch015 [eCite] [Details]

Co-authors: Zhao Z; Kang BH; Kabir MMJ; Wasinger R

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2012Xu S, 'HONNs with Extreme learning Machine to Handle Incomplete Datasets', Artificial Higher Order Neural Networks for Modeling and Simulation, Information Science Reference, J Gamon (ed), United States of America, pp. 276-292. ISBN 978-1-4666-2175-6 (2012) [Research Book Chapter]

DOI: 10.4018/978-1-4666-2175-6.ch013 [eCite] [Details]

Tweet

2010Xu S, 'Adaptive Higher Order Neural Network Models for Data Mining', Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, Information Science Reference, Ming Zhang (ed), Hershey, United States, pp. 86-98. ISBN 978-1-61520-711-4 (2010) [Research Book Chapter]

DOI: 10.4018/978-1-61520-711-4.ch004 [eCite] [Details]

Citations: Scopus - 2

Tweet

2009Xu S, 'Adaptive higher order neural network models and their applications in business', Artificial Higher Order Neural Networks for Economics and Business, Information Science Reference, Ming Zhang (ed), Hershey, PA, pp. 314-329. ISBN 978-1-59904-897-0 (2009) [Research Book Chapter]

DOI: 10.4018/978-1-59904-897-0.ch014 [eCite] [Details]

Citations: Scopus - 18

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2006Fulcher JD, Zhang M, Xu S, 'Application of Higher-Order Neural Networks to Financial Time-Series Prediction', Artificial Neural Networks in Finance and Manufacturing, Idea Group Publishing, J Kamruzzaman, RK Begg & RA Sarker (ed), Hershey, United States, pp. 80-108. ISBN 1-59140-671-4 (2006) [Research Book Chapter]

DOI: 10.4018/978-1-59140-670-9.ch005 [eCite] [Details]

Citations: Scopus - 38

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Conference Publication

(27 outputs)
YearCitationAltmetrics
2019Arafat MY, Hoque S, Xu S, Farid DM, 'An under-sampling method with support vectors in multi-class imbalanced data classification', Proceedings of the 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2019), 26-28 August 2019, Ukulhas, Maldives, pp. 1-6. ISBN 978-1-7281-2741-5 (2019) [Refereed Conference Paper]

DOI: 10.1109/SKIMA47702.2019.8982391 [eCite] [Details]

Citations: Scopus - 2

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2018Liu Y, Xu Z, Li N, Xu S, Gang Y, 'A path planning algorithm for plant protection UAV for avoiding multiple obstruction areas', Proceedings of the 6th IFAC Conference on Bio-Robotics (BIOROBOTICS 2018), 12-16 July 2018, Beijing, China, pp. 483-488. ISSN 2405-8963 (2018) [Refereed Conference Paper]

DOI: 10.1016/j.ifacol.2018.08.163 [eCite] [Details]

Citations: Scopus - 3

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2016Kabir MMJ, Xu S, Kang BH, Zhao Z, 'Multiple seeds based evolutionary algorithm for mining Boolean association rules', Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining Workshops (PAKDD 2016), 19 April 2016, Auckland, New Zealand, pp. 61-72. ISBN 9783319429953 (2016) [Refereed Conference Paper]

DOI: 10.1007/978-3-319-42996-0_6 [eCite] [Details]

Co-authors: Kabir MMJ; Kang BH; Zhao Z

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2016Kaminski B, Wasinger R, Norris K, Zehntner C, Xu S, et al., 'Learning through shared note-taking visualisations in the classroom', Proceedings of the 28th Australian Computer-Human Interaction Conference (OzCHI 2016), 29 November - 2 December 2016, Launceston, Tasmania, pp. 576-580. ISBN 978-1-4503-4618-4 (2016) [Refereed Conference Paper]

DOI: 10.1145/3010915.3010970 [eCite] [Details]

Co-authors: Wasinger R; Norris K; Zehntner C; Chinthammit W; Duh B

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2016Lunt AJ, Xu S, 'An empirically-sourced heuristic for predetermining the size of the hidden layer of a multi-layer perceptron for large datasets', Lecture Notes in Computer Science 9992: Proceedings of the 29th Australasian Joint Conference on Artificial Intelligence (AI 2016): Advances in Artificial Intelligence), 5-8 December 2016, Hobart, Tasmania, pp. 542-547. ISBN 978-3-319-50126-0 (2016) [Refereed Conference Paper]

DOI: 10.1007/978-3-319-50127-7_47 [eCite] [Details]

Citations: Scopus - 1

Co-authors: Lunt AJ

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2015Kabir MMJ, Xu S, Kang BH, Zhao Z, 'Comparative analysis of genetic based approach and apriori algorithm for mining maximal frequent item sets', Proceedings of the 2015 IEEE Congress on Evolutionary Computation, 25-28 May 2015, Sendai, Japan, pp. 39-45. ISBN 978-1-4799-7492-4 (2015) [Refereed Conference Paper]

DOI: 10.1109/CEC.2015.7256872 [eCite] [Details]

Citations: Scopus - 7

Co-authors: Kabir MMJ; Kang BH; Zhao Z

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2015Kabir MMJ, Xu S, Kang BH, Zhao Z, 'Discovery of interesting association rules using genetic algorithm with adaptive mutation', Lecture Notes in Computer Science: 22nd International Conference, ICONIP 2015 - Neural Information Processing, 09-12 November 2015, Istanbul, Turkey, pp. 96-105. ISBN 978-3-319-26534-6 (2015) [Refereed Conference Paper]

DOI: 10.1007/978-3-319-26535-3_12 [eCite] [Details]

Citations: Scopus - 3Web of Science - 1

Co-authors: Kabir MMJ; Kang BH; Zhao Z

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2015Kabir MMJ, Xu S, Kang BH, Zhao Z, 'A new evolutionary algorithm for extracting a reduced set of interesting association rules', Lecture Notes in Computer Science: 22nd International Conference, ICONIP 2015 - Neural Information Processing, 09-12 November 2015, Istanbul, Turkey, pp. 133-142. ISBN 978-3-319-26534-6 (2015) [Refereed Conference Paper]

DOI: 10.1007/978-3-319-26535-3_16 [eCite] [Details]

Citations: Scopus - 12Web of Science - 9

Co-authors: Kabir MMJ; Kang BH; Zhao Z

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2014Kabir M, Xu S, Kang BH, Zhao Z, 'A novel approach to mining maximal frequent itemsets based on genetic algorithm', Proceedings of the 9th International Conference on Information Technology and Applications, 1-4 July 2014, Sydney, Australia, pp. 1-6. ISBN 978-0-9803267-6-5 (2014) [Refereed Conference Paper]

[eCite] [Details]

Co-authors: Kabir M; Kang BH; Zhao Z

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2014Wasinger R, Adam A, Chinthammit W, Montgomery J, Stannus S, et al., 'Towards the effective use of multiple displays in teaching and learning environments', Workshop on HCI Education in Asia Pacific at OzCHI, 2 December 2014, Sydney, Australia, pp. 21-24. ISBN 978-1-4503-0653-9 (2014) [Refereed Conference Paper]

[eCite] [Details]

Co-authors: Wasinger R; Adam A; Chinthammit W; Montgomery J; Stannus S

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2014Zhao Z, Xu S, Kang BH, Kabir M, Liu Y, 'Instance selection and optimization of neural networks', Proceedings of the 9th International Conference on Information Technology and Applications, 1-4 July 2014, Sydney, Australia, pp. 1-6. ISBN 978-0-9803267-6-5 (2014) [Refereed Conference Paper]

[eCite] [Details]

Co-authors: Zhao Z; Kang BH; Kabir M

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2013Xu S, Liu Y, Kang B-H, Gao W, 'A machine learning approach for modeling and its applications', Proceedings of the European Modeling and Simulation Symposium, 25-27 September, 2013, Athens, Greece, pp. 659-663. ISBN 978-88-97999-16-4 (2013) [Refereed Conference Paper]

[eCite] [Details]

Co-authors: Kang B-H

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2012Xu S, Liu Y, 'HONNs with ELM algorithm for medical applications', Proceedings of the 12th International Conference on Control, Automation, Robotics and Vision, 5-7 December 2012, Guangzhou, China, pp. 1215-1219. ISBN 978-1-4673-1872-3 (2012) [Refereed Conference Paper]

DOI: 10.1109/ICARCV.2012.6485360 [eCite] [Details]

Citations: Scopus - 1

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2011Xu S, 'An Extreme Learning Machine Algorithm for Higher Order Neural Network', Proceedings of the 23rd European Modeling & Simulation Symposium, 12-14 September 2011, Rome, Italy, pp. 418-422. ISBN 978-88-903724-4-5 (2011) [Refereed Conference Paper]

[eCite] [Details]

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2010Xu S, 'Features of higher order neural network with adaptive neurons', Proceeding of the 2nd International Conference on Software Engineering and Data Mining (SEDM2010), 23-25 June 2010, Chengdu, China, pp. 484-488. ISBN 978-1-4244-7324-3 (2010) [Refereed Conference Paper]

[eCite] [Details]

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2010Xu S, 'A novel higher order artificial neural networks', Proceedings of the Second International Symposium on Computational Mechanics and the 12th International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science, 30 November - 3 December 2009, Hong Kong, Macau, pp. 1507-1511. ISBN 978-0-7354-0778-7 (2010) [Refereed Conference Paper]

DOI: 10.1063/1.3452131 [eCite] [Details]

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2010Xu S, 'Data Mining Using an Adaptive HONN Model with Hyperbolic Tangent Neurons', Knowledge Management and Acquisition for Smart Systems and Services , 20 Aug - 3 Sept 2010, Daegu, Korea, pp. 73-81. ISBN 978-3-642-15036-4 (2010) [Refereed Conference Paper]

DOI: 10.1007/978-3-642-15037-1_7 [eCite] [Details]

Citations: Scopus - 1

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2009Xu S, Chen L, 'Adaptive Higher Order Neural Networks ', Proceedings of the 2009 WRI Global Congress on Intelligent Systems, 19-21 May 2009, Xiamen, China, pp. 26-30. ISBN 978-0-7695-3571-5 (2009) [Refereed Conference Paper]

DOI: 10.1109/GCIS.2009.296 [eCite] [Details]

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2009Xu S, Chen L, 'Adaptive Higher Order Neural networks for Effective data Mining', Sixth International Symposium on Neural Networks (ISNN 2009), 26-29 May 2009, Wuhan, China, pp. 165-173. ISBN 978-3-642-01216-7 (2009) [Refereed Conference Paper]

DOI: 10.1007/978-3-642-01216-7_18 [eCite] [Details]

Citations: Scopus - 2

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2008Xu S, Chen L, 'Application of new adaptive higher order neural networks in data mining', Proceedings International Conference on Computer Science and Software Engineering CSSE 2008, 12-14 December 2008, Wuhan, China, pp. 115-118. ISBN 978-0-7695-3336-0 (2008) [Refereed Conference Paper]

DOI: 10.1109/CSSE.2008.897 [eCite] [Details]

Citations: Scopus - 5

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2008Xu S, Chen L, 'A novel approach for determining the optimal number of hidden layer neurons for FNN's and its application in data mining', Proceedings The 5th International Conference on Information Technology and Applications, 23-26 June 2008, Carins, Qld, pp. 683-686. ISBN 978-0-9803267-2-7 (2008) [Refereed Conference Paper]

[eCite] [Details]

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2007Xu S, Zhang M, 'A New Adaptive Neural Network Model for Financial Data Mining', Proceedings part 1, 4th International Symposium on Neural Networks, 3-7 June 2007, Nanjing, China, pp. 1265-1273. ISBN 3-540-72382-X (2007) [Refereed Conference Paper]

DOI: 10.1007/978-3-540-72383-7_147 [eCite] [Details]

Citations: Scopus - 1

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2005Xu S, Zhang M, 'Data Mining - An Adaptive Neural Network Model for Financial Analysis', Proceedings Third International Conference on Information Technology and Application, 4-7 July 2005, Sydney, Australia, pp. 336-340. ISBN 0-7695-2316-1 (2005) [Refereed Conference Paper]

DOI: 10.1109/ICITA.2005.109 [eCite] [Details]

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2002Xu S, Zhang M, 'An Adaptive Activation Function for Higher Order Neural Networks', Proceedings / AI 2002: Advances in Artificial Intelligence, 2-6 December 2002, Canberra, Australia, pp. 356-362. ISBN 3-540-00197-2 (2002) [Refereed Conference Paper]

DOI: 10.1007/3-540-36187-1_31 [eCite] [Details]

Citations: Scopus - 4

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2001Hulme D, Xu S, 'Application of Genetic Algorithms to the Optimisation of Neural Network Configuration for Stock Market Forecasting', AI 2001: Advances in Artificial Intelligence, December 10-14, 2001, Adelaide, pp. 285-296. ISBN 3-540-42960-3 (2001) [Refereed Conference Paper]

DOI: 10.1007/3-540-45656-2_25 [eCite] [Details]

Citations: Scopus - 1

Co-authors: Hulme D

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2001Xu S, Zhang M, 'A Novel Adaptive Activation Function', International Joint Conference on Neural Networks, July 15-19, 2001, Washington, DC, pp. 2779- 2782. ISBN 0-7803-7046-5 (2001) [Non Refereed Conference Paper]

DOI: 10.1109/IJCNN.2001.938813 [eCite] [Details]

Citations: Web of Science - 7

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2000Xu S, Zhang M, 'Justification of a Neuron-Adaptive Activation Function', IJCNN Proceedings, 24 - 27 July 2000, Como, Italy, pp. 456-470. ISBN 0-7695-0619-4 (2000) [Non Refereed Conference Paper]

DOI: 10.1109/IJCNN.2000.861351 [eCite] [Details]

Citations: Web of Science - 4

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Grants & Funding

Funding Summary

Number of grants

5

Total funding

$28,007

Projects

A Machine Learning Approach for Predicting the Locations of Tasmanian Threatened Plant Species (2018)$9,007
Description
This project develops Machine Learning models for predicting the locations of Tasmanian threatened plant species.Biodiversity (the variety of life at community, species and genotype scales) functions within ecosystems that supply oxygen, clean air and water, pollination of plants, pest control, and many other ecosystem services. It merits conservation in itself, as recognised in international treaties. Mapping of the areas that are most important to maintain the variety of life is therefore critical in land use planning and land management. Such mapping rests on distributional knowledge of the elements of biodiversity.
Funding
University of Tasmania ($9,007)
Scheme
null
Administered By
University of Tasmania
Research Team
Xu S; Garg SK; Kirkpatrick JB; Carter O
Year
2018
Helping An Autonomous Underwater Vehicle Navigating (2018)$7,000
Funding
University of Tasmania ($7,000)
Scheme
null
Administered By
University of Tasmania
Research Team
Xu S; King PD; Kang BH
Year
2018
Helping An Autonomous Underwater Vehicle Navigate in Deep Ocean Using Sonar Image Recognition and Matching Techniques (2018)$7,000
Description
On 18 August 2017, a new world class $5 million autonomous underwater vehicle (AUV) capable of diving into the deep ocean (5000 meters) to gather data on Antarctic research missions was unveiled at the University of Tasmanias Australian Maritime College (AMC). This project aims to develop intelligent methods to allow the AUV to navigate long distances in environments (such as Antarctic and Southern Ocean) where underwater/under-ice absolute navigation aid (GPS) is not available.Geophysical referencing allows an AUV to position itself using sensory feedback. An effective geophysical approach is teach-and-repeat (TR) path following, which does not require the estimation of position in the global reference frame, but only with respect to previously collected data. TR enables an autonomous vehicle to re-follow a path by relating its current sensory input to a stored sequence of sensory input from a previous traversal. This approach is beneficial to both AUV homing operation and area coverage mission. The objectives of the project are to develop an effective TR path following algorithm for AUV by online sonar image processing (including image recognition and matching), and to design a reliable path following control algorithm which will compensate for the ocean current disturbance on AUV motion.
Funding
University of Tasmania ($7,000)
Scheme
Grant- Research Enhancement Program
Administered By
University of Tasmania
Research Team
Xu S; King PD; Kang BH
Year
2018
ICITA 2008 (5th International Conference on Information Technology and Applications) (2008)$1,000
Funding
University of Tasmania ($1,000)
Scheme
Grant-Conference Support Scheme
Administered By
University of Tasmania
Research Team
Xu S
Year
2008
An Artificial Neural network System for Forecasting Government Tax Revenues (2002)$4,000
Funding
University of Tasmania ($4,000)
Scheme
Grant-Institutional Research Scheme
Administered By
University of Tasmania
Research Team
Xu S
Year
2002

Research Supervision

Current

5

Completed

3

Current

DegreeTitleCommenced
PhDDeep Learning for Multiple Products Image Recognition2018
PhDMachine Learning Approach for Sentiment Analysis (or Opinion Mining)2019
PhDDeep Learning-based Absolute Pose Estimation of On-road Vehicles for Autonomous Driving2019
PhDA Machine Learning Approach for Sentiment Analysis (or Opinion Mining)2019
PhDA Machine Learning Approach for Developing Recommender Systems (or Recommendation System)2020

Completed

DegreeTitleCompleted
PhDThe Development of a Government Cash Forecasting Model: The case for the Indonesian Government
Candidate: Iskandar Iskandar
2019
PhDNovel Feature Selection for Algorithms for Improving Neural Network Performance
Candidate: Zongyuan Zhao
2016
PhDNew Evolutionary Algorithms for Mining Interesting Association Rules
Candidate: Mir Md. Jahangir Kabir
2016