Dr Ali Anaissi

PhD
Associate Lecturer in Data Science
School of Computer Science

J12 - The School of Information Technologies
The University of Sydney


Website School of Computer Science

Research interests

The ongoing safety of built structures such as buildings and bridges relies on the early identification of any damage. But most approaches to maintenance are time-based, scheduling inspections only at predetermined intervals of time. Dr Ali Anaissi’s research aims to convert this procedural convention to a condition-based approach, whereby continuous monitoring of a structure by networked sensors would detect early signs of damage and trigger a responsive inspection.

“The condition-based approach that my research explores is known as structural health monitoring (SHM). It is a continuous automated process that aims to detect damage in a structure using data gathered from several networked sensors attached to it. In a structure such as a building or bridge, such early detection of damage is critical to avoid further risks to life, safety and economic loss.

“The main idea of this data-driven approach is for machine learning algorithms to ‘learn’ a model from the data sensed, in order to construct a baseline. This learned model is then applied to the new continuous real-time measurements being taken by the sensors, in order to generate real-time health scores for components of the structure by comparing the new measured responses to the established baseline.

“In this way my team and I have equipped the Sydney Harbour Bridge with a large number of networked sensors to provide information about the safety of the structure.

“We have also equipped a vehicle with sensors to collect vibration data during its journey on the roads, and then applied machine learning algorithms to assess the condition of those roads and detect any damage.

“I started working in the field of machine learning and data mining when I began my PhD in 2009, and later brought my research to the University of Sydney.”

Teaching and supervision

  • COMP5310 - Principles of Data Science
  • COMP5318 - Machine Learning and Data Mining
  • COMP9103 - Software Development in Java
  • INFO3406 - Introduction to data Analytics
  • DATA1002 - Informatics: Data and Programming

Selected publications

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Book Chapters

  • Lu Dang Khoa, N., Alamdari, M., Rakotoarivelo, T., Anaissi, A., Wang, Y. (2018). Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features. In Jianlong Zhou & Fang Chen (Eds.), Human and Machine Learning, (pp. 409-435). Cham: Springer International Publishing. [More Information]
  • Anaissi, A., Lu Dang Khoa, N., Mustapha, S., Alamdari, M., Braytee, A., Wang, Y., Chen, F. (2017). Adaptive one-class support vector machine for damage detection in structural health monitoring. In Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin and Yang-Sae Moon (Eds.), Lecture Notes in Artificial Intelligence: Advances in Knowledge Discovery and Data Mining, (pp. 42-57). Cham: Springer. [More Information]

Journals

  • Anaissi, A., Makki Alamdari, M., Rakotoarivelo, T., Khoa, N. (2018). A tensor-based structural damage identification and severity assessment. Sensors, 18(1). [More Information]
  • Anaissi, A., Goyal, M., Catchpoole, D., Braytee, A., Kennedy, P. (2016). Ensemble Feature Learning of Genomic Data Using Support Vector Machine. PloS One, 11(6), 1-17. [More Information]
  • Anaissi, A., Goyal, M., Catchpoole, D., Braytee, A., Kennedy, P. (2015). Case-Based Retrieval Framework for Gene Expression Data. Cancer Informatics, 14, 21-31. [More Information]
  • Anaissi, A., Kennedy, P., Goyal, M., Catchpoole, D. (2013). A balanced iterative random forest for gene selection from microarray data. BMC Bioinformatics, 14(1), 1-10. [More Information]
  • Anaissi, A., Kennedy, P., Goyal, M. (2011). Dimension reduction of microarray data based on local principal component. World Academy of Science, Engineering and Technology. Proceedings, 53, 1176-1181.

Conferences

  • Anaissi, A., Braytee, A., Naji, M. (2018). Gaussian Kernel Parameter Optimization in One-Class Support Vector Machines. 2018 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Anaissi, A., Lu Dang Khoa, N., Rakotoarivelo, T., Alamdari, M., Wang, Y. (2017). Self-advised incremental one-class support vector machines: An application in structural health monitoring. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part I), Cham: Springer. [More Information]
  • Lu Dang Khoa, N., Anaissi, A., Wang, Y. (2017). Smart Infrastructure Maintenance Using Incremental Tensor Analysis. 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, New York, NY: Association for Computing Machinery (ACM).
  • Braytee, A., Hussain, F., Anaissi, A., Kennedy, P. (2016). ABC-sampling for balancing imbalanced datasets based on artificial bee colony algorithm. IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Piscataway, New Jersey, US: Institute of Electrical and Electronics Engineers Inc. [More Information]
  • Anaissi, A., Goyal, M. (2016). SVM-based association rules for knowledge discovery and classification. 2nd Asia-Pacific World Congress on Computer Science and Engineering, Piscataway, New Jersey, US: Institute of Electrical and Electronics Engineers Inc. [More Information]
  • Anaissi, A., Kennedy, P., Goyal, M. (2011). Feature selection of imbalanced gene expression microarray data. 2011 12th ACIS International Conference on Software Engineering, Artificial Intelligence Networking and Parallel Distributed Computing, SNPD 2011, Mt. Pleasant, MI, 48858, U.S.A: International Association for Computer and Information Science. [More Information]
  • Anaissi, A., Kennedy, P., Goyal, M. (2010). A framework for high dimensional data reduction in the microarray domain. 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010, United States: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2018

  • Anaissi, A., Makki Alamdari, M., Rakotoarivelo, T., Khoa, N. (2018). A tensor-based structural damage identification and severity assessment. Sensors, 18(1). [More Information]
  • Anaissi, A., Braytee, A., Naji, M. (2018). Gaussian Kernel Parameter Optimization in One-Class Support Vector Machines. 2018 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Lu Dang Khoa, N., Alamdari, M., Rakotoarivelo, T., Anaissi, A., Wang, Y. (2018). Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features. In Jianlong Zhou & Fang Chen (Eds.), Human and Machine Learning, (pp. 409-435). Cham: Springer International Publishing. [More Information]

2017

  • Anaissi, A., Lu Dang Khoa, N., Mustapha, S., Alamdari, M., Braytee, A., Wang, Y., Chen, F. (2017). Adaptive one-class support vector machine for damage detection in structural health monitoring. In Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin and Yang-Sae Moon (Eds.), Lecture Notes in Artificial Intelligence: Advances in Knowledge Discovery and Data Mining, (pp. 42-57). Cham: Springer. [More Information]
  • Anaissi, A., Lu Dang Khoa, N., Rakotoarivelo, T., Alamdari, M., Wang, Y. (2017). Self-advised incremental one-class support vector machines: An application in structural health monitoring. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part I), Cham: Springer. [More Information]
  • Lu Dang Khoa, N., Anaissi, A., Wang, Y. (2017). Smart Infrastructure Maintenance Using Incremental Tensor Analysis. 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, New York, NY: Association for Computing Machinery (ACM).

2016

  • Braytee, A., Hussain, F., Anaissi, A., Kennedy, P. (2016). ABC-sampling for balancing imbalanced datasets based on artificial bee colony algorithm. IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Piscataway, New Jersey, US: Institute of Electrical and Electronics Engineers Inc. [More Information]
  • Anaissi, A., Goyal, M., Catchpoole, D., Braytee, A., Kennedy, P. (2016). Ensemble Feature Learning of Genomic Data Using Support Vector Machine. PloS One, 11(6), 1-17. [More Information]
  • Anaissi, A., Goyal, M. (2016). SVM-based association rules for knowledge discovery and classification. 2nd Asia-Pacific World Congress on Computer Science and Engineering, Piscataway, New Jersey, US: Institute of Electrical and Electronics Engineers Inc. [More Information]

2015

  • Anaissi, A., Goyal, M., Catchpoole, D., Braytee, A., Kennedy, P. (2015). Case-Based Retrieval Framework for Gene Expression Data. Cancer Informatics, 14, 21-31. [More Information]

2013

  • Anaissi, A., Kennedy, P., Goyal, M., Catchpoole, D. (2013). A balanced iterative random forest for gene selection from microarray data. BMC Bioinformatics, 14(1), 1-10. [More Information]

2011

  • Anaissi, A., Kennedy, P., Goyal, M. (2011). Dimension reduction of microarray data based on local principal component. World Academy of Science, Engineering and Technology. Proceedings, 53, 1176-1181.
  • Anaissi, A., Kennedy, P., Goyal, M. (2011). Feature selection of imbalanced gene expression microarray data. 2011 12th ACIS International Conference on Software Engineering, Artificial Intelligence Networking and Parallel Distributed Computing, SNPD 2011, Mt. Pleasant, MI, 48858, U.S.A: International Association for Computer and Information Science. [More Information]

2010

  • Anaissi, A., Kennedy, P., Goyal, M. (2010). A framework for high dimensional data reduction in the microarray domain. 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010, United States: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

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