Dr Muhammad Asif Naeem

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Senior Lecturer

Email: muhammad.asif.naeem@aut.ac.nz

ORCID: ORCID logo https://orcid.org/0000-0001-5112-3711

Links to relevant web pages:

Research outputs:

Books

Journal articles

  • Naeem, M. A., Mehmood, E., Abbas, M. G., & Jamil, N. (2020). Optimizing semi-stream CACHEJOIN for near-real-time data warehousing. Journal of Database Management, 31(1), 20-37. doi:10.4018/JDM.2020010102

  • Denham, B., Pears, R., & Naeem, M. A. (2020). HDSM: A distributed data mining approach to classifying vertically distributed data streams. Knowledge-Based Systems, 189. doi:10.1016/j.knosys.2019.105114

  • Denham, B., Pears, R., & Naeem, M. A. (2020). Enhancing random projection with independent and cumulative additive noise for privacy-preserving data stream mining. Expert Systems with Applications, 152. doi:10.1016/j.eswa.2020.113380

  • Kamran Ul haq, A., Khattak, A., Jamil, N., Naeem, M. A., & Mirza, F. (2020). Data Analytics in Mental Healthcare. Scientific Programming, 2020, 1-9. doi:10.1155/2020/2024160

  • Feng, Y., Naeem, M. A., Mirza, F., & Tahir, A. (2020). Reply using past replies—a deep learning-based e-mail client. Electronics (Switzerland), 9(9), 1-20. doi:10.3390/electronics9091353

  • Naeem, M., Khan, H. U., Aslam, S., & Jamil, N. (2020). Parallelisation of a cache-based stream-relation join for a near-real-time data warehouse. Electronics (Switzerland), 9(8). doi:10.3390/electronics9081299

  • Mansoor, A., Usman, M. W., Jamil, N., & Naeem, M. A. (2020). Deep learning algorithm for brain-computer interface. Scientific Progamming, 2020. doi:10.1155/2020/5762149

  • Naeem, M., Mirza, F., Khan, H. U., Sundaram, D., Jamil, N., & Weber, G. (2020). Big data velocity management - from stream to warehouse via high performance memory optimised index join. IEEE Access. doi:10.1109/access.2020.3033464

  • Nguyen, H. V., Naeem, M. A., Wichitaksorn, N., & Pears, R. (2019). A smart system for short-term price prediction using time series models. Computers and Electrical Engineering, 76, 339-352. doi:10.1016/j.compeleceng.2019.04.013

  • Naeem, M. A. (2019). Optimization and extension of stream-relation joins. International Journal of Information Technology and Decision Making, 18(4), 1289-1315. doi:10.1142/S0219622019500214

  • Khan, Z., Naeem, M., Khalil, U., Khan, D. M., Aldahmani, S., & Hamraz, M. (2019). Feature selection for binary classification within functional genomics experiments via interquartile range and clustering. IEEE Access, 7, 78159-78169. doi:10.1109/ACCESS.2019.2922432

  • Darliansyah, A., Naeem, M. A., Mirza, F., & Pears, R. (2019). SENTIPEDE: A smart system for sentiment-based personality detection from short texts. Journal of Universal Computer Science, 25(10), 1323-1352.

  • Nguyen, H., Mirza, F., Naeem, M. A., & Baig, M. M. (2018). Falls management framework for supporting an independent lifestyle for older adults: a systematic review. Aging Clinical and Experimental Research, 30(11). doi:10.1007/s40520-018-1026-6

  • Kithulgoda, C. I., Pears, R., & Naeem, M. A. (2018). The incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streams. Expert Systems with Applications, 97. doi:10.1016/j.eswa.2017.12.023

  • Jamil, N., Mirza, F., Naeem, M. A., & Baghaei, N. (2018). A refinement of an iterative orthogonal projection method. Journal of Computational and Applied Mathematics, 341. doi:10.1016/j.cam.2018.02.025

  • Naeem, M. A., Weber, G., & Lutteroth, C. (2018). A memory-optimal many-to-many semi-stream join. Distributed and Parallel Databases, 37(4), 623-649. doi:10.1007/s10619-018-7247-z

  • Wandabwa, H., Naeem, M. A., Pears, R., & Mirza, F. (2018). A Metamodel Enabled Approach for Discovery of Coherent Topics in Short Text Microblogs. IEEE Access, 6. doi:10.1109/ACCESS.2018.2878441

  • Naeem, M. A., Linggawa, I. W. S., Mughal, A. A., Lutteroth, C., & Weber, G. (2018). A Smart Email Client Prototype for Effective Reuse of Past Replies. IEEE Access, 6, 69453-69741. doi:10.1109/ACCESS.2018.2878523

  • Munir, M. S., Bajwa, I. S., Naeem, M. A., & Ramzan, B. (2018). Design and implementation of an IoT system for smart energy consumption and smart irrigation in tunnel farming. Energies, 11(12). doi:10.3390/en11123427

  • Ullah, I., Khusro, S., Ullah, A., & Naeem, M. (2018). An overview of the current state of linked and open data in cataloging. Information Technology and Libraries, 37(4), 47-80. doi:10.6017/ital.v37i4.10432

  • Bajwa, I. S., Asghar, M. N., & Naeem, M. A. (2017). Learning-based improved seeded region growing algorithm for brain tumor identification. Proceedings of the Pakistan Academy of Sciences: Part A, 54(2), 127-133.

  • Perera, R., Nand, P., & Naeem, A. (2017). Utilizing typed dependency subtree patterns for answer sentence generation in question answering systems. Progress in Artificial Intelligence, 6(2), 105-119. doi:10.1007/s13748-017-0113-9

  • Jamil, N., Müller, J., Naeem, M. A., Lutteroth, C., & Weber, G. (2016). Extending linear relaxation for non-square matrices and soft constraints. Journal of Computational and Applied Mathematics, 308. doi:10.1016/j.cam.2016.05.006

  • Naeem, M. A., Bajwa, I. S., & Jamil, N. (2016). A cached-based stream-relation join operator for semi-stream data processing. International Journal of Data Warehousing and Mining, 12(3). doi:10.4018/IJDWM.2016070102

  • Naeem, M. A., Dobbie, G., Lutteroth, C., & Weber, G. (2016). Skewed distributions in semi-stream joins: How much can caching help?. Information Systems, 64. doi:10.1016/j.is.2016.09.007

  • Bajwa, I. S., Sarwar, N., & Naeem, M. A. (2016). Generating express data models from SBVR. Proceedings of the Pakistan Academy of Sciences: Part A, 53(4A), 381-389.

  • Naeem, M. (2015). An empirical analysis and performance evaluation of feature selection techniques for belief network classification system. International Journal of Control and Automation, 8(3), 375-386. doi:10.14257/ijca.2015.8.3.37

  • Naeem, M., & Asghar, S. (2014). A parameter free BBN discriminant function for optimum model complexity versus goodness of data fitting. Journal of Applied Research and Technology, 12(4), 734-749. doi:10.1016/S1665-6423(14)70090-2

  • Naeem, M., & Jamil, N. (2014). An Efficient Stream-based Join to Process End User Transactions in Real-Time Data Warehousing. Journal of Digital Information Management, 12(3). Retrieved from http://dline.info/fpaper/jdim/v12i3/5.pdf

  • Naeem, M. A., Dobbie, G., & Weber, G. (2014). Efficient processing of streaming updates with archived master data in near-real-time data warehousing. Knowledge and Information Systems, 40(3). doi:10.1007/s10115-013-0653-7

  • Naeem, M., & Asghar, S. (2014). Structure learning via non-parametric factorized joint likelihood function. Journal of Intelligent and Fuzzy Systems, 27(3), 1589-1599. doi:10.3233/IFS-141125

  • Naeem, M., & Asghar, S. (2014). An information theoretic scoring function in belief network. International Arab Journal of Information Technology, 11(5).

  • Naeem, M., & Asghar, S. (2014). Scientific study of religion in vexillology. European Journal of Science and Theology, 10(1), 55-65.

  • Naeem, M. (2014). Etiological evaluation of seminal traits using bayesian belief network. International Journal of Bio-Science and Bio-Technology, 6(6), 79-86. doi:10.14257/ijbsbt.2014.6.6.08

  • Naeem, M., & Asghar, S. (2013). Parameter free and non penalized scoring metric for bayesian belief network. Control Engineering and Applied Informatics, 15(4), 117-126.

  • Naeem, M., & Asghar, S. (2013). A hybrid model for pattern discovery in HCV. International Journal of Bio-Science and Bio-Technology, 5(4), 129-137.

  • Naeem, M., Naeem, M., & Asghar, S. (2013). Knowledge discovery in metabolic pathways. International Journal of Bio-Science and Bio-Technology, 5(3), 11-28.

  • Jamil, N., & Naeem, M. (2013). Speeding Up SOR and Kaczmarz for Constraint-based GUIs with a Warm-Start
    Strategy. Journal of Multimedia Processing and Technologies (, 4(3). Retrieved from http://www.dline.info/jmpt/publishers.php

  • Naeem, M., & Asghar, S. (2013). A novel mutual dependence measure in structure learning. Journal of the National Science Foundation of Sri Lanka, 41(3), 203-208. doi:10.4038/jnsfsr.v41i3.6054

  • Naeem, M. A., Dobbie, G., & Weber, G. (2011). HYBRIDJOIN for Near-Real-Time Data Warehousing. International Journal of Data Warehousing and Mining, 7(4). doi:10.4018/jdwm.2011100102

  • Bajwa, I. S., Ali, A., & Naeem, M. A. (2011). Processing Large Data Sets using a Cluster Computing Framework. Australian Journal of Basic and Applied Sciences, 5(6). Retrieved from http://www.insipub.com/ajbas/2011/june-2011/1614-1618.pdf

  • Naeem, M. A., & Jamil, N. (2011). A Web Smart Space Framework for Intelligent Search Engines. International Journal of Emerging Sciences, 1(1). Retrieved from http://ijes.info/1/1/4254111.pdf

  • Naeem, M. A., & Bajwa, I. S. (2008). Knowledge Retrieval from Dynamically Generated Web Pages. Unknown Journal, 2(1). Retrieved from http://www.researchgate.net/

Book chapters

  • Chanane, N., Mirza, F., Naeem, M. A., & Mirza, A. (2017). Acceptance of technology-driven interventions for improving medication adherence. In Unknown Book (Vol. 759, pp. 188-198). doi:10.1007/978-3-319-65548-2_15

  • Naeem, M., Moalla, N., Ouzrout, Y., & Bouras, A. (2016). Weaving trending, costing and recommendations using big data analytic: An enterprise capability evaluator. In Proceedings of the I-ESA Conferences (Vol. 8, pp. 163-173). doi:10.1007/978-3-319-30957-6_13

  • Asif Naeem, M., & Jamil, N. (2015). Online processing of end-user data in real-time data warehousing. In Improving Knowledge Discovery through the Integration of Data Mining Techniques (pp. 13-31). IGI Global. doi:10.4018/978-1-4666-8513-0.ch002

  • Asif Naeem, M., Dobbie, G., & Weber, G. (2013). Big data management in the context of real- time data warehousing. In Big Data Management, Technologies, and Applications (pp. 150-176). doi:10.4018/978-1-4666-4699-5.ch007

  • Naeem, M. A., Dobbie, G., & Weber, G. (2013). Efficient processing of stream data over persistent data. In Big Data Computing (pp. 315-342). CRC Press.

  • Naeem, M. A., Dobbie, G., & Weber, G. (2013). Processing of Stream Data in a Real-Time Data Warehouse. In W. C. Hu, & N. Kaabouch (Eds.), Big Data Management (pp. 150-176). Information Science Reference.

  • Naeem, M. A., Bajwa, I. S., & Choudhary, M. A. (2007). Hidden Web Data Processing for Knowledge Management. In K. V. Kale (Ed.), Advances in Computer Vision and Information Technology. I. K. International Publishing House 1. Retrieved from http://books.google.co.uk/

Conference contributions

  • Darliansyah, A., Wandabwa, H. M., Naeem, M. A., Mirza, F., & Pears, R. (2019). Long-Term Trends in Public Sentiment in Indian Demonetisation Policy. In Communications in Computer and Information Science Vol. 932 (pp. 65-75). Bahawalpur. doi:10.1007/978-981-13-6052-7_6

  • Nguyen, H., Zhou, F., Mirza, F., & Naeem, M. A. (2019). Fall Detection Using Smartphones to Enhance Safety and Security of Older Adults at Home. In 2018 11th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2018. Auckland. doi:10.23919/ICMU.2018.8653613

  • Darliansyah, A., Wandabwa, H., Naeem, M. A., Mirza, F., & Pears, R. (2019). Long-Term Trends in Public Sentiment in Indian Demonetisation Policy. In I. S. Bajwa, F. Kamareddine, & A. Costa (Eds.), Intelligent Technologies and Applications, Conference Proceedings INTAP 2018 Vol. 932 (pp. 65-75). Bahawalpur: Springer. doi:10.1007/978-981-13-6052-7

  • Nayak, S., Hossain, M. A., Mirza, F., Naeem, M. A., & Jamil, N. (2019). E-BRACE: A Secure Electronic Health Record Access Method in Medical Emergency. In Intelligent Technologies and Applications Vol. 932 (pp. 16-27). Bahawalpur. doi:10.1007/978-981-13-6052-7_2

  • Rapson, C. J., Seet, B. C., Naeem, M. A., Eun Lee, J., & Klette, R. (2019). A performance comparison of deep learning methods for real-time localisation of vehicle lights in video frames. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 567-572). Auckland. doi:10.1109/ITSC.2019.8917087

  • Rapson, C., Seet, B. -C., Naeem, M., Lee, J., Al-Sarayreh, M., & Klette, R. (2018). Reducing the Pain: A Novel Tool for Efficient Ground-Truth Labelling in Images. In Accepted Papers - IVCNZ 2018 (pp. 6 pages). Auckland. Retrieved from http://ivcnz2018.massey.ac.nz/accepted-papers/

  • Karamchandani, H., Naeem, M. A., Mirza, F., & Baig, M. M. (2018). Improving post-hospital discharge management by implementing the discharge summary on a mobile application. In 2018 11th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2018. Auckland. doi:10.23919/ICMU.2018.8653261

  • Chanane, N., Mirza, F., & Naeem, M. (2018). Technology-driven medication management: A vision through the lens of New Zealand experts. In 2018 HiNZ Conference Proceedings. Wellington. Retrieved from https://www.hinz.org.nz/page/proceedings

  • Nguyen, H., Mirza, F., & Naeem, M. (2018). IoT-based monitoring in healthcare: Case study of falls detection and management. In 2018 HiNZ Conference Proceedings. Wellington. Retrieved from https://www.hinz.org.nz/page/proceedings

  • Alam, I., Khusro, S., & Naeem, M. (2018). A review of smart TV: Past, present, and future. In ICOSST 2017 - 2017 International Conference on Open Source Systems and Technologies, Proceedings Vol. 2018-January (pp. 35-41). Lahore. doi:10.1109/ICOSST.2017.8279002

  • Nguyen, H., Mirza, F., Naeem, M. A., & Baig, M. M. (2017). Detecting falls using a wearable accelerometer motion sensor. In ACM International Conference Proceeding Series (pp. 422-431). doi:10.1145/3144457.3144484

  • Chanane, N., Mirza, F., Naeem, M., & Mirza, A. (2017). Acceptance of technology-driven interventions for improving medication adherence. In Third International Conference, FNSS 2017. Florida: Springer. doi:10.1007/978-3-319-65548-2

  • Mehmood, E., & Naeem, M. A. (2017). Optimization of cache-based semi-stream joins. In 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017 (pp. 76-81). doi:10.1109/ICCCBDA.2017.7951887

  • Wandabwa, H., Naeem, M., & Mirza, F. (2017). Aspect of blame in tweets: A deep recurrent neural network approach. In WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1423-1424). Perth: ACM. doi:10.1145/3041021.3051157

  • Nguyen, H. H., Mirza, F., Naeem, M. A., & Nguyen, M. (2017). A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback. In Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 257-262). Wellington: IEEE. Retrieved from http://cscwd17.sim.vuw.ac.nz/

  • Wandabwa, H., Naeem., & Mirza, F. (2017). Document level semantic comprehension of noisy text streams via convolutional neural networks. In Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 475-479). Wellington: IEEE. doi:10.1109/CSCWD.2017.8066740

  • Naeem, M. A., Nguyen, K. T., & Weber, G. (2017). A multi-way semi-stream join for a near-real-time data warehouse. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10538 LNCS (pp. 59-70). . doi:10.1007/978-3-319-68155-9_5

  • Hossain, A., Mirza, F., Naeem, M. A., Gutierrez, J., & IEEE. (2017). A Crowd Sourced Framework for Neighbour Assisted Medical Emergency System. In 2017 27TH INTERNATIONAL TELECOMMUNICATION NETWORKS AND APPLICATIONS CONFERENCE (ITNAC) (pp. 480-485). . doi:10.1109/ATNAC.2017.8215436

  • Ghazala., Naeem., Mirza, F., & Jamil, N. (2017). Uncovering useful patterns in shopping cart data. In Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 349-354). Wellington: IEEE. doi:10.1109/CSCWD.2017.8066719

  • Naeem, M. A., Bajwa, I. S., & Jamil, N. (2016). A Cached-based approach to enrich Stream data with master data. In The 10th International Conference on Digital Information Management, ICDIM 2015 (pp. 57-62). : Institute of Electrical and Electronics Engineers Inc.. doi:10.1109/ICDIM.2015.7381874

  • Naeem, M. A., Lutteroth, C., & Weber, G. (2016). Optimising queue-based semi-stream joins by introducing a queue of frequent pages. In Databases Theory and Applications Vol. LNCS 9877 (pp. 407-418). Sydney: Springer International Publishing. doi:10.1007/978-3-319-46922-5_32

  • Naeem, M., Moalla, N., Ouzrout, Y., & Bouras, A. (2016). A business collaborative decision making system for network of SMEs. In IFIP Advances in Information and Communication Technology Vol. 492 (pp. 99-107). doi:10.1007/978-3-319-54660-5_10

  • Gao, H., Naeem, M. A., Lutteroth, C., & Weber, G. (2015). S3J: A parallel semi-stream similarity join. In Proceedings of the ACM International Workshop on Data Warehousing and OLAP Vol. 23-Oct-2015 (pp. 49-57). Melbourne: Association for Computing Machinery. doi:10.1145/2811222.2811226

  • Naeem, M. A., Sarwar Bajwa, I., & Jamil, N. (2015). A cache-based semi-stream join to deal with unmatched stream data. In Database Theory and Applications Vol. LNCS 9093 (pp. 54-65). Melbourne: Springer Verlag. doi:10.1007/978-3-319-19548-3_5

  • Naeem, M. A., Bajwa, I. S., Jamil, N., & IEEE. (2015). A Cached-based Approach to Enrich Stream Data with Master Data. In 2015 TENTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT (ICDIM) (pp. 140-145). Retrieved from http://gateway.webofknowledge.com/

  • Naeem, M., Moalla, N., Ouzrout, Y., & Bouras, A. (2015). Opportunity analysis for enterprise collaboration between networks of SMEs. In CEUR Workshop Proceedings Vol. 1414.

  • Naeem, M., Fahad, M., Moalla, N., Ouzrout, Y., & Bouras, A. (2015). Big data perspective with otological modeling for long term traceability of cultural heritage. In IFIP Advances in Information and Communication Technology Vol. 467 (pp. 562-571). doi:10.1007/978-3-319-33111-9_51

  • Mahmood, A., Qazi, K., Bajwa, I. S., & Naeem, M. A. (2014). Natural language processing based interpretation of skewed graphs. In Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014 (pp. 2700-2704). : Institute of Electrical and Electronics Engineers Inc.. doi:10.1109/ICACCI.2014.6968323

  • Naeem, M., Weber, G., Lutteroth, C., & Dobbie, G. (2014). Optimizing queue-based semi-stream joins with indexed master data. In Data Warehousing and Knowledge Discovery (pp. 171-182). Munich: Springer International Publishing. doi:10.1007/978-3-319-10160-6_16

  • Naeem, M. A. (2014). A caching approach to process stream data in data warehouse. In 9th IEEE International Conference on Digital Information Management (ICDIM) (pp. 162-167). . doi:10.1109/ICDIM.2014.6991406

  • Ramzan, S., Bajwa, I. S., Ul Haq, I., & Naeem, M. A. (2014). A model transformation from NL to SBVR. In 9th IEEE International Conference on Digital Information Management (ICDIM) (pp. 220-225). : IEEE. doi:10.1109/ICDIM.2014.6991430

  • Naeem, M., Moalla, N., Ouzrout, Y., & Bouaras, A. (2014). An ontology based digital preservation system for enterprise collaboration. In Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA Vol. 2014 (pp. 691-698). doi:10.1109/AICCSA.2014.7073267

  • Naeem, M. A. (2013). Tuned X-HYBRIDJOIN for near-real-time data warehousing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 7808 LNCS (pp. 494-505). Sydney: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-642-37401-2_49

  • Naeem, M. A., Weber, G., Dobbie, G., & Lutteroth, C. (2013). SSCJ: A semi-stream cache join using a front-stage cache module. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8057 (pp. 236-247). Prague: Springer Berlin Heidelberg. doi:10.1007/978-3-642-40131-2_20

  • Naeem, M. A., Weber, G., Dobbie, G., & Lutteroth, C. (2013). A generic front-stage for semi-stream processing. In CIKM '13 Proceedings of the 22nd ACM international conference on Information & Knowledge Management (pp. 769-774). San Francisco, CA: ACM. doi:10.1145/2505515.2505734

  • Naeem, M. A. (2013). Efficient processing of semi-stream data. In 8th International Conference on Digital Information Management, ICDIM 2013 (pp. 7-10). : IEEE Computer Society. doi:10.1109/ICDIM.2013.6694035

  • Naeem, M. A. (2013). A robust join operator to process streaming data in real-time data warehousing. In 8th International Conference on Digital Information Management, ICDIM 2013 (pp. 119-124). : IEEE Computer Society. doi:10.1109/ICDIM.2013.6693964

  • Naz, S., Naeem, M., Afzal, M. T., & Qayyum, A. (2012). Expertise identification and visualization. In Proceedings - 2012 8th International Conference on Computing and Networking Technology (INC, ICCIS and ICMIC), ICCNT 2012 (pp. 16-20).

  • Gillani, S., Naz, S., Naeem, M., Afzal, M. T., & Qayyum, A. (2012). ERRAGMap: Visualization tool. In Proceedings - ICIDT 2012, 8th International Conference on Information Science and Digital Content Technology Vol. 3 (pp. 736-740).

  • Naeem, M., Dobbie, G., & Weber, G. (2012). Optimised X-HYBRIDJOIN for near-real-time data warehousing. In Proceedings of the Twnty-Third Australasian Database Conference (ADC 2012), Melbourne, Australia Vol. 124 (pp. 21-30). Australia: Australian Computer Society, Inc. Darlinghurst, Australia. Retrieved from http://dl.acm.org/citation.cfm?id=2483739.2483744

  • Naeem, M. A., Dobbie, G., Weber, G., & Bajwa, I. S. (2012). Efficient usage of memory resources in near-real-time data warehousing. In Communications in Computer and Information Science Vol. 281 CCIS (pp. 326-337). Jamshoro: Springer Berlin Heidelberg. doi:10.1007/978-3-642-28962-0_32

  • Naeem, M. A., Dobbie, G., Weber, G., & Bajwa, I. S. (2012). A parametric analysis of stream based joins. In Communications in Computer and Information Science Vol. 281 CCIS (pp. 314-325). Pakistan: Springer Berlin Heidelberg. doi:10.1007/978-3-642-28962-0_31

  • Hameed, K., Bajwa, I. S., & Naeem, M. A. (2012). A novel approach for automatic generation of UML class diagrams from XMI. In Communications in Computer and Information Science Vol. 281 CCIS (pp. 164-175). Pakistan: Springer Berlin Heidelberg. doi:10.1007/978-3-642-28962-0_17

  • Naeem, M. A., Ullah, S., & Bajwa, I. S. (2012). Interacting with data warehouse by using a natural language interface. In Natural language processing and information systems Vol. LNCS 7337 (pp. 372-377). Groningen. doi:10.1007/978-3-642-31178-9_50

  • Naeem, M. A., & Bajwa, I. S. (2012). Generating OLAP queries from natural language specification. In ACM International Conference Proceeding Series (pp. 768-773). Chennai: ACM. doi:10.1145/2345396.2345522

  • Naeem, M. A., Dobbie, G., Weber, G., & Bajwa, I. S. (2012). Resource optimization for processing of stream data in data warehouse environment. In ACM International Conference Proceeding Series (pp. 62-68). Chennai: ACM. doi:10.1145/2345396.2345407

  • Naeem, M. A., Dobbie, G., & Weber, G. (2012). A lightweight stream-based join with limited resource consumption. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 7448 (pp. 431-442). Vienna: Springer-Verlag. doi:10.1007/978-3-642-32584-7_35

  • Naeem, M. A., Dobbie, G., & Weber, G. (2011). X-HYBRIDJOIN for Near-real-time Data Warehousing. In 28th British National Conference on Databases (BNCOD 2011). UK: Springer-Verlag.

  • Bajwa, I. S., & Naeem, M. A. (2011). On specifying requirements using a semantically controlled representation. In Proceedings of the 16th international conference on Natural language processing and information systems (pp. 217-220). Alicante: Springer-Verlag. Retrieved from http://dl.acm.org/citation.cfm?id=2026037

  • Umber, A., Bajwa, I. S., & Naeem, M. A. (2011). NL-Based Automated Software Requirements Elicitation and Specification. In Advances in Computing and Communications (pp. 30-39). India: Springer Berlin Heidelberg. Retrieved from http://www.springerlink.com/content/t805232277530t23/

  • Bajwa, I. S., Naeem, M. A., Chaudhri, A. A., & Ali, S. (2011). A Controlled Natural Language Interface to Class Models. In Artificial Intelligence and Decision Support Systems (pp. 102-110). China: SciTePress. Retrieved from http://iub-pk.academia.edu/

  • Naeem, M., Asghar, S., Irfan, S. R., & Fong, S. (2010). Multilevel classification scheme for AGV perception. In Proc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications (pp. 485-489).

  • Naeem, M., Asghar, S., & Fong, S. (2010). Hiding sensitive association rules using central tendency. In Proc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications (pp. 478-484).

  • Naeem, M. A., Dobbie, G., Weber, G., & Alam, S. (2010). R-MESHJOIN for Near-Real-Time Data Warehousing. In Proceedings of the ACM 13th international workshop on Data warehousing and OLAP (pp. 53-60). Canada: ACM.

  • Alam, S., Dobbie, G., Riddle, P., & Naeem, M. A. (2010). Particle Swarm Optimization Based Hierarchical Agglomerative Clustering. In WI-IAT '10 (pp. 64-68). : IEEE Computer Society. Retrieved from http://ieeexplore.ieee.org/

  • Alam, S., Gillian, D., Riddle, P., & Naeem, M. A. (2010). A Swarm Intelligence Based Clustering Approach for Outlier Detection. In 2010 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-7). Spain: IEEE. Retrieved from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5586152

  • Naeem, M. A., Dobbie, G., & Weber, G. (2009). Comparing Global Optimization and Default Settings of Stream-based Joins. In BIRTE (pp. 155-170). France: Springer. Retrieved from http://www.springerlink.com/content/qn813171148k631r/?MUD=MP

  • Naeem, M. A., Dobbie, G., & Weber, G. (2008). An Event-Based Near Real-Time Data Integration Architecture. In EDOCW '08 Proceedings of the 2008 12th Enterprise Distributed Object Computing Conference Workshops (pp. 401-404). Germany: IEEE Computer Society. Retrieved from http://dl.acm.org/citation.cfm?id=1545749

  • Bajwa, I. S., Naeem, M. A., & Nawaz, M. (2006). Web Information Mining Framework using XML Based Knowledge Representation Engine. In International Conference on Software Engineering (ISE’06) (pp. 18-23). Pakistan. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.7235

  • Bajwa, I. S., & Naeem, M. A. (2006). Speech Language Processing Interface for Object-Oriented Application Design using a Rulebased Framework. In 4TH INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS 2006 (pp. 323-329). : Ministry of Science and Technology Myanmar. Retrieved from http://iub-pk.academia.edu/

  • Naeem, M., Bajwa, I. S., Amin, R. -U., & Choudhary, M. A. (2006). A Web Smart Space Framework for Information Mining:
    A base for Intelligent Search Engines. In International Conference on Software Engineering, ISE 2006. Pakistan: University of Lahore. Retrieved from http://www.cs.bham.ac.uk/~isb855/papers/Web%20Smart%20Space%20-%20ISE%202006.pdf

Theses

  • Naeem, M. A. (2011). Efficient Joins to Process Stream Data. (The University of Auckland, Auckland). Retrieved from http://hdl.handle.net/2292/16925

  • Naeem, M. A. (2006). Web Structure Mining of Dynamic Pages. (Balochistan University of Information Technology and Management Sciences, Pakistan).

Working paper/discussions

  • Bunker, R. P., Naeem, M. A., & Zhang, W. (2016). Improving a credit scoring model by incorporating bank statement derived features. arXiv.org. Retrieved from http://arxiv.org/abs/1611.00252v2

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