List of Publications

Refereed Journal Articles: 62
Refereed Book Chapters: 11
Textbooks: 3
Edited Books: 2
Edited Volumes: 9
Submitted to Journals for Publication: 1
Refereed Conference Proceedings: 119
Non-Refereed Conference Proceedings: 5
Other Non-Refereed Publications: 6
Total 218

Invited talks 45

Refereed Journal articles (book chapters listed separately): 62

  1. Ammar, A., Elouedi, Z, and Lingras, P. 2016. Meta-clustering of possibilistically segmented retail datasets, Fuzzy Sets and Systems, Vol 286, pp. 173-196. http://dx.doi.org/10.1016/j.fss.2015.07.019
  2. Hilliard, T, Swan, L.G,, Kavgic, M., Qin, Z., Lingras, P. 2016. Development of a whole building model predictive control strategy for a LEED silver community college Energy and Buildings, Energy and Buildings, Vol 111, pp. 224-232.
  3. Lingras, P. and Haider, F. 2015. Partially ordered rough ensemble clustering for multigranular representations, Intelligent Data Analysis, Vol. 19, No. S1, pp. S103-S116. http://dx.doi.org/10.3233/IDA-150772
  4. Lingras, P., Haider, F., and Triff, M. 2016. Granular meta-clustering based on hierarchical, network, and temporal connections, Granular Computing, Vol 1, Issue 1, pp. 1-22. http://dx.doi.org/10.1007/s41066-015-0007-9.
  5. Ammar, A., Elouedi, Z, and Lingras, P. 2015. Segmented Clustering Based on Possibilistic and Rough Set Theories, International Journal of Intelligent Systems, Vol 30, pp. 676-706. http://dx.doi.org/10.1002/int.21723
  6. Lingras P. and Triff, M. 2015. Fuzzy and Crisp Recursive Profiling of Online Reviewers and Businesses, the IEEE Transactions on Fuzzy Systems, Vol. 23, No. 4, pp. 1242-1258, http://dx.doi.org/10.1109/TFUZZ.2014.2349532
  7. Lingras, P., Elagamy, A., Ammar, A., and Elouedi, Z. 2014. Iterative meta-clustering through granular hierarchy of supermarket customers and products, Information Sciences, Vol. 257, pp. 14-31, http://dx.doi.org/10.1016/j.ins.2013.09.018.
  8. Lingras, P., Chen, M., and Miao, D. 2013. Qualitative and Quantitative Combinations of Crisp and Rough Clustering Schemes using Dominance Relations, International Journal of Approximate Reasoning, Vol. 55, pp. 238-258, http://dx.doi.org/10.1016/j.ijar.2013.05.007.
  9. Lingras, P., Peters, G., Crespo, F., and Weber, R., February, 2013. Soft Clustering - Fuzzy and Rough Approaches and Their Extensions and Derivatives, International Journal of Approximate Reasoning, Vol. 54, No. 2, pp. 307-322, http://dx.doi.org/10.1016/j.ijar.2012.10.003.
  10. Joshi, M., Lingras, P., Rao, C.R. 2012. Correlating Rough and Fuzzy Clustering, Fundamenta Informaticae, Vol. 115, Issue 2-3, pp. 233-246.
  11. Lingras, P. and Butz, C.J., 30 September, 2011. Conservative and Aggressive Rough SVR Modeling, Theoretical Computer Science, Vol. 412, pp. 5885-5901.
  12. Lingras, P. and Peters, G. 2011. Rough Clustering, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Vol. 1, No. 1, pp. 64-72, DOI: 10.1002/widm.16
  13. Trabelsi, S., Elouedi, Z, and Lingras, P. 2011. Classification Systems based on Rough Sets under the Belief Function Framework, International Journal of Approximate Reasoning, Vol. 52, pp. 1409-1432, doi:10.1016/j.ijar.2011.08.002.
  14. Butz, C.J., Chen, J., Konkel, K., Lingras, P. 2011. Join Tree Propagation Utilizing Both Arc Reversal and Variable Elimination, International Journal of Approximate Reasoning, Vol. 52, No. 7, pp. 948-959, doi:10.1016/j.ijar.2010.11.006.
  15. Trabelsi, S., Elouedi, Z, and Lingras, P. 2011. Classification systems based on dynamic reduct under the belief function framework, Transactions on Rough Sets XIV, pp. 202-233, DOI: 10.1007/978-3-642-21563-6_11
  16. Lingras, P., Joshi, M. 2011. Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering, International Journal of Computational Intelligence Systems (IJCIS), Vol. 4, No. 1, pp. 12-28, http://dx.doi.org/10.2991/ijcis.2011.4.1.2
  17. Zhang, P., Joshi, M. and Lingras, P. 2011. Use of Stability and Seasonality Analysis for Optimal Inventory Prediction Models, Journal of Intelligent Systems, Vol. 20, No. 2, pp. 147-166, DOI 10.1515 / JISYS.2011.009.
  18. Lingras, P. and Butz, C.J. 2010. Rough Support Vector Regression, European Journal of Operational Research, Vol. 206, pp. 445-455.
  19. Lingras, P., Chen, M., and Miao, D. 2009. Rough Cluster Quality Index Based on Decision Theory, IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 7, pp. 1014-1026.
  20. Lingras, P., Chen, M., and Miao, D. 2009. Semi-supervised Rough Cost/Benefit Decisions, Fundamenta Informaticae, Vol. 94, No. 2, pp. 233-244.
  21. Butz, C.J., Chen, J., Konkel, K., Lingras, P. 2009. A Formal Comparison of Variable Elimination and Arc Reversal in Bayesian Network Inference, Intelligent Decision Technologies Journal, Vol. 177, pp. 3782-3798.
  22. Lingras, P. and Butz, C.J. 2007. Rough Set based 1-v-1 and 1-v-r Approaches to Support Vector Machine Multi-classification, Information Sciences, Vol. 177, pp. 3782-3798.
  23. Lingras, P. 2007. Applications of Rough Set Based K-Means,Kohonen, GA Clustering, Transactions on Rough Sets, VII, pp. 120-139.
  24. Zhong, M., Sharma, S.C. and Lingras, P. 2007. Rationalizing Reliable Imputation Durations of Genetically Designed Time Delay Neural Network and Locally Weighted Regression Models, Transportation Planning and Technology, December 2007 Vol. 30, No. 6, pp. 609-626.
  25. Zhong, M., Sharma, S.C. and Lingras, P. 2006. Genetically-Designed Time Delay Neural Networks for Multiple-interval Urban Freeway Traffic Flow Forecasting, Neural Information Processing - Letters and Reviews, Vol. 10, Nos. 8-9, pp. 201-209.
  26. Zhong, M., Sharma, S.C. and Lingras, P. 2006. Matching Patterns for Updating Missing Values of Traffic Counts, , Transportation Planning & Technology, Vol 29, No. 2, pp. 141-156.
  27. Lingras, P., Hogo, M., Snorek, M., and West, C. 2005. Temporal Analysis of Clusters of Supermarket Customers: Conventional versus Interval Set Approach, Information Sciences, Vol. 172, pp. 215-240.
  28. Lingras, P. and Huang, X. 2005. Statistical, Evolutionary, and Neurocomputing Clustering Techniques: cluster-based versus object-based approaches, AI Review, Vol. 23, Number 1, pp. 3-29.
  29. Zhong, M., Sharma, S.C. and Lingras, P. 2005. Refining Genetically Designed Models for Improved Traffic Prediction on Rural Roads, Journal of Transportation Planning and Technology, Vol. 28, No. 3.
  30. West, C., MacDonald, S., Lingras, P., and Adams, G. 2004. Relationship between Product Based Loyalty and Clustering based on Supermarket Visit and Spending Patterns, International Journal of Computer Science and Applications, Vol. 2, No. 2, pp. 85-100.
  31. Zhong, M. Sharma S.C. and Lingras, P. 2005. Short-term Traffic Prediction on Different Types of Roads with Genetically Designed Regression and Time Delay Neural Network Models, Journal for Computing in Civil Engineering, American Society of Civil Engineers, Vol. 19, No. 1, pp. 94-103.
  32. Zhong, M. Sharma S.C. and Lingras, P. 2005. Genetically Designed Models for Accurate Imputations of Missing Traffic Counts, Transportation Research Record 1879, The Journal of Transportation Research Board National Research Council, Washington DC, pp. 71-79.
  33. Zhong M., Lingras P., and Sharma S.C. 2005. Effect of Missing Values Estimations on Traffic Parameters, Journal of Transportation Planning & Technology, Vol. 27, No. 2, pp. 119-144.
  34. Lingras, P. and West, C. 2004. Interval Set Clustering of Web Users with Rough K-means, Journal of Intelligent Information System, Vol. 23, No. 1, pp. 5-16.
  35. Lingras, P., Hogo, M., Snorek, M., 2004. Interval Set Clustering of Web Users using Modified Kohonen Self-Organizing Maps based on the Properties of Rough Sets, Web Intelligence and Agent Systems: An International Journal, Vol. 2, No. 3, pp. 217-230.
  36. Lingras, P., Yan, R., and Hogo, M. 2004. Evolutionary, Neural, and Statistical Approaches to Interval Clustering for Web Mining Journal of Intelligent Systems, Vol. 13, No. 4, pp. 329-350.
  37. Lingras, P., Yan, R., and Jain A. 2004. Unsupervised Characterization of Web users: Conventional and Fuzzy Approaches, Journal of Intelligent Systems, Vol. 13, No. 4, pp. 311-327.
  38. Zhong M., Lingras P., and Sharma S.C. 2004. Estimation of Missing Traffic Counts: Factor, Genetic, Neural, and Regression Techniques, Transportation Research, Part C, Vol. 12, pp. 139-166.
  39. Davies, C. and Lingras, P. 2003. Genetic Algorithms for Rerouting Shortest Paths in Dynamic and Stochastic Networks, European Journal of Operational Research, Vol. 144, pp.27-38.
  40. Lingras, P., Zhong, M. and Sharma, S.C. 2002. Prediction of Recreational Travel using Genetically Designed Regression and Time Delay Neural Network Models, Transportation Research Record 1805, The Journal of Transportation Research Board, National Research Council, Washington, D.C, pp. 16-24.
  41. Lingras, P. 2001. Unsupervised Rough Set Classification using GAs, Journal of Intelligent Information Systems, Vol. 16, No. 3, pp. 215-228.
  42. Lingras, P. and Davies, C. 2001. Applications of Rough Genetic Algorithms, Computational Intelligence: An International Journal, Vol. 17, No. 3, pp. 435-445.
  43. Lingras, P. 2001. Fuzzy-Rough and Rough-Fuzzy Serial Combinations in Neurocomputing, Neurocomputing Journal, Vol. 36, pp. 29-44.
  44. Lingras, P. 2001. Statistical and Genetic Algorithms Classification of Highways, Journal of Transportation Engineering, American Society of Civil Engineering, Vol. 127, No. 3, pp. 237-243.
  45. Sharma, S.C., Lingras, P., Xu, F., and Kilburn, P. 2001. Application of Neural Networks to Estimate AADT on Low-Volume Roads, Journal of Transportation Engineering, American Society of Civil Engineers, Vol. 127, No. 5, pp. 426-432.
  46. Sharma, S.C., Lingras, P., Liu, G., and Xu, F. 2000. Estimation of Annual Average Daily Traffic on Low-volume Roads: The Factor Approach versus Neural Networks, Transportation Research Record – 1719, The Journal of Transportation Research Board, National Research Council, Washington, D.C., No. 1719, pp. 103-111.
  47. Lingras, P. Sharma, S.C., Osborne, P., and Kalyar, I. 2000. Traffic Volume Time Series Analysis According to the Type of Road Use: Neurocomputing Versus Statistical Approach, Computer-Aided Civil and Infrastructure Engineering Journal, Vol. 15, pp. 365-473.
  48. Sharma, S.C., Lingras P., Xu, F., and Liu, G.X. 1999. Neural Networks as an Alternative to the Traditional Factor Approach of AADT Estimation from Traffic Counts, Transportation Research Record: Journal of the Transportation Research Board, National Research Council, Washington, D.C, TRR 1660, pp. 24-31.
  49. Lingras P. and Yao, Y.Y. 1998. Data Mining Using Extension of Rough Set Model, Journal of American Society of Information Science, Vol. 49, No. 5, pp. 415-422.
  50. Lingras, P. 1998. Traffic Parameter Estimation and Highway Classification: Rough Patterns Using a Neural Networks Approach., Journal of Transportation Planning & Technology, Vol. 21, pp. 155-179.
  51. Yao, Y.Y. and Lingras, P.1998. Interpretations of belief functions in the theory of rough sets, Information Sciences: an International Journal, Vol.104, No. 1-2, pp. 81-106.
  52. Lingras, P. 1998. Comparison of neofuzzy and rough neural networks, Information Sciences: an International Journal, Vol. 110, pp. 207-215. (A shorter version appeared in the Proceedings of The fifth international workshop on rough sets and soft computing RSSC'97.)
  53. Lingras, P. and Adamo, M. 1996. Average and Peak Traffic Volumes: Neural Nets, Regression, Factor Approaches, Journal of Computing in Civil Engineering, American Society of Civil Engineers, Vol. 10, No. 4, 300-306.
  54. Lingras, P. 1996. Evidential Comparisons Using Belief Functions, Rough Sets and Nonmonotonic Preferences, Intelligent Automation and Soft Computing: An International Journal, Vol. 2, No. 2, pp. 203-209.
  55. Lingras, P. 1996. Traffic Engg. Recurrent Spatial Knowledge Base: Design and Implementation, Journal of Computing in Civil Engineering, American Society of Civil Engineers, Vol 10, No. 1, 50-59.
  56. Lingras, P. 1995. Classifying Highways: Hierarchical Grouping Vs Kohonen Neural Networks, Journal of Transportation Engineering, American Society of Civil Engineers, Vol. 121, No. 4, 364-368.
  57. Wong, S. K. M., Lingras, P. 1994. Representation of Qualitative User Preference by Quantitative Belief Functions, IEEE Transactions on Knowledge and Data Engineering, Vol. 6, No. 1, pp. 72-78.
  58. Wong, S. K. M., Yao, Y. Y. and Lingras, P. 1994. Comparative Beliefs and Their Measurements, International Journal of General Systems, Vol. 22, No. 1, pp. 69-90.
  59. Lingras, P., Wong, S. K. M., 1990. Two Perspectives of the Dempster-Shafer Theory of Belief Functions, International Journal of Man-machine Studies, Vol. 33, pp. 467-487.
  60. Sharma, S. C., Lingras, P., 1988. Cost of Driving Comfort and Convenience for Two-Lane Highways, Transportation Engineering Journal , American Society of Civil Engineers, Vol. 115, No. 3, pp. 283-297.
  61. Sharma, S. C., Lingras, P., and Werner, Al., 1987. Highway Economic Analysis Experiences with the 1985 Highway Capacity Manual, Transportation Research Record 1112, Transportation Research Board, National Research Council, Washington, D.C., pp. 1-9.
  62. Sharma, S.C., Lingras, P., Hassan, M.U. and Murthy, A.S., 1986. Road Classification According to Driver Population, Transportation Research Record 1090, Transportation Research Board, National Research Council, Washington, D.C. pp. 61-69.

Refereed Book Chapters: 11

  1. Joshi, M., Lingras, P., Wani, G. and Zhang, P. 2014. Clustering based Stability and Seasonality Analysis for Optimal Inventory Prediction, in Tripathy, B., & Acharjya, D. (Eds.), Global Trends in Intelligent Computing Research and Development, IGI-Global, doi:10.4018/978-1-4666-4936-1, pp. 1-20.
  2. Lingras, P., Bhalchandra, P., Butz, C., and Asharaf, S. 2012. Rough Support Vectors: Classification, Regression, Clustering, Rough Sets and Intelligent Systems, Intelligent Systems Reference Library (ISRL), 42, Springer-Verlag Berlin Heidelberg, pp. 491–515.
  3. Lingras, P. and Peters, G. 2011. Applying Rough Set Concepts to Clustering, Selected Methods and Applications of Rough Sets to Management and Engineering, Springer, U.K., pp. 23-37.
  4. Lingras, P., Butz, C. and Bhalchandra, P. 2011. Financial Series Forecasting using Dual Rough Support Vector Regression, Selected Methods and Applications of Rough Sets to Management and Engineering, Springer, U.K., pp. 115-127.
  5. Lingras, P., S. Asharaf, and Butz, C.J. 2008. Rough Clustering, to appear in the Handbook of Granular Computing, John Wiley & Sons.
  6. Lingras, P. and Lingras, R. 2008. Hyperlink Structure Inspired by Web Usage, Handbook of Research on Text and Web Mining Technologies, Idea group, pp. 386-400.
  7. Lingras, P., Zhong, M., Sharma, S.C. 2008. Evolutionary Regression and Neural Imputations of Missing Values, Soft Computing Applications in Industry, Studies in Fuzziness and Soft Computing Series, Vol. 226, Springer, pp. 151-163.
  8. Lingras, P., Yan, R., and Jain A. 2004. Web Usage Mining: Comparison of Conventional, Fuzzy, and Rough Set Clustering, Computational Web Intelligence: Intelligent Technology for Web Applications (Eds. Y. Zhang and Y. Yao), chapter 7, pp. 133-148.
  9. Lingras, P., Yan, R., Hogo, M. and West C. 2004. Interval Set Representations of Clusters Fuzzy, and Rough Set Clustering, to appear in Encyclopedia of Data Warehousing and Mining (Ed. J. Wang).
  10. Lingras, P. 1998. Applications of Rough Patterns, chapter in the book: Rough Sets in Data Mining and Knowledge Discovery 2 , L. Polkowski and A. Skowron (editors), series Soft Computing , Physica Verlag (Springer), pp. 369-384.
  11. Wong, S. K. M., Yao, Y. Y. and Lingras, P. 1992. Comparative Beliefs, Advances in the Dempster-Shafer Theory of Evidence, (Ed. M. Fedrizzi, J. Kacprzyk, and R. Yager), pp. 115-132

Textbooks: 3

  1. Lingras, P., Triff, M., Lingras, R. 2014. Building Cross-Platform Mobile and Web Apps for Engineers and Scientists: An Active Learning Approach, Cengage Learning, Inc.
  2. Lingras, P., Scobey, P., 2011. Web Programming and Internet Technologies: An e-commerce approach, Jones and Bartlett.
  3. Lingras, P. Akerkar, R., 2007. Building an Intelligent Web: Theory and Practice, Jones and Bartlett.

Edited Books: 2

  1. Lingras, P., Peters, G., Slezak, D., and Yao, Y., 2011. Selected Methods and Applications of Rough Sets to Management and Engineering, Springer, U.K.
  2. Lingras, P., Hassanien, A., Suraj, Z., and Slezak, D., 2008. Rough Computing: Theories, Technologies and Applications, IGI Global (Idea Group).

Edited Volumes: 9

  1. Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (2013) Multi-disciplinary Trends in Artificial Intelligence 7th International Workshop, MIWAI 2013, Krabi, Thailand, December 9-11, 2013. Proceedings Series: Lecture Notes in Computer Science, Springer, Vol. 8271.
  2. Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (2013) Rough Sets and Knowledge Technology 8th International Conference, RSKT 2013, Halifax, NS, Canada, October 11-14, 2013, Proceedings Series: Lecture Notes in Computer Science, Subseries: Lecture Notes in Artificial Intelligence, Springer, Vol. 8171.
  3. Li, T., Lingras, P., Li, F., Herbert, J. 2011. Special Issue on Computational Intelligence in Decision Making, International Journal of Computational Intelligence Systems, Volume 4, Issue 1, 2011.
  4. Lingras, P., and Butz, C., May 25-27, 2011. 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John's, Canada, Proceedings, Series: Lecture Notes in Computer Science, Subseries: Lecture Notes in Artificial Intelligence, Springer, Vol. 6657.
  5. Lingras, P., An, A., Petty, S., and Huang, R., August 28-30, 2010. Active Media Technology: 6th International Conference, AMT 2010, Toronto, Canada, Proceedings, Lecture Notes in Computer Science, Springer, Vol. 6335.
  6. Lingras, P., Yu, J., Greco, S., Wang, G., and Skowron, A., October 15-17, 2010. Rough Sets and Knowledge Technology, 5th International Conference, RSKT 2010 Beijing, China, October 2010 Proceedings, Lecture Notes in Artificial Intelligence, Subseries of Lecture Notes in Computer Science, Springer, Vol. 6401.
  7. Lingras, P., Slowinski, R., Miao, D., Tsumoto, S., Peters, J.F., and Skowron, A., 2010. Rough Set Structuring of Knowledge, A special issue of the Transaction on Rough Sets, Volume X, Springer Verlag, Vol. 6190.
  8. Lingras, P., Yao, J., Wu, W-Z., Szczuka, M., Cercone, N., and Slezak, D., May 14-16, 2007. Rough Sets and Knowledge Technology, Second International Conference, RSKT 2007 Toronto, Canada, Proceedings, Lecture Notes in Artificial Intelligence 4481, Subseries of Lecture Notes in Computer Science, Springer.
  9. Lingras, P., Prasad, B., and Ram, A., December 16-18, 2009. Proceedings of the 4th Indian International Conference on Artificial Intelligence Tumkur, India.

Submitted to Journals For Publication: 3

  1. Lingras, P., Haider, F., Triff, M. 2015. Fuzzy Temporal Meta-clustering of Financial Trading Volatility Patterns. IEEE Transactions on Fuzzy Systems, submitted.
  2. Lingras, P., Haider, F. 2015. Recursive Temporal Meta-clustering. Applied Soft Computing, submitted.
  3. Lingras, P., Haider, F., Triff, M. 2015. Granular Meta-clustering based on Hierarchical, Network. Granular Computing, submitted.

Refereed Conference Proceedings: 119

  1. Triff, M., Pavlovski, I. Liu, Z., Morgan, L., Lingras, P. 2017. Clustering ensemble for prioritized sampling based on average and rough patterns, Proceedings of 25th International Symposium on Methodologies for Intelligent Systems (ISMIS 2017), Warsaw, Poland, June 2017, LNAI 10352, Springer-Verlag Berlin Heidelberg, pp. TBA.
  2. Triff, M., Wiechert,G., Lingras, P. 2016. Nonlinear Classification, Linear Clustering, Evolutionary Semi-Supervised Three-Way Decisions: A Comparison, Proceedings of 2017 IEEE International Conference on Fuzzy Systems, Naples, Italy, July 2017, pp. TBA.
  3. Wiechert,G., Triff, M., Liu, Z., Yin, Z., Zhao, S., Zhong, Z., Lingras, P. 2016. Identifying Users and Activities from Brain Wave Signals Recorded from a Wearable Headband, Proceedings of 15th IEEE International Conference on Cognitive Informatics and Cognitive Computing, Stanford University, USA, August 22-23, pp. 129-136.
  4. Wiechert,G., Triff, M., Liu, Z., Yin, Z., Zhao, S., Zhong, Z., Lingras, P. 2016. Evolutionary Semi-supervised Rough Categorization of Brain Signals from a Wearable Headband, Proceedings of 2016 IEEE World Congress on Computational Intelligence, Vancouver, Canada, July 24-19.
  5. Lingras, P., Triff, M. 2016. Advances in Rough and Soft Clustering: Meta-Clustering, Dynamic Clustering, Data-Stream Clustering, Proceedings of International Joint Conference on Rough Sets, V. Flores et al. (Eds.): IJCRS 2016, LNAI 9920, pp. 1-20. DOI: 10.1007/978-3-319-47160-0 1
  6. Rhinelander, J., Kallada, M. and Lingras, P. 2015. Visual Predictions of Traffic Conditions, 28th Canadian Conference on Artificial Intelligence, Canadian AI 2015 Halifax, Nova Scotia, Canada, June 2-5, 2015, pp. 122-129.
  7. Lingras, P., Haider, F. 2015. Combining Rough Clustering Schemes as a Rough Ensemble International Joint Conference on Rough Sets (IJCRS 2015), Tianjin, China, November 20-23, 2015, RSKT 2015, LNAI 9436, pp. 1-12. http://dx.doi.org/10.1007/978-3-319-25754-9 34.
  8. Ammar, A., Elouedi, Z, and Lingras, P. 2014. Rough Possibilistic Meta-Clustering of Retail Datasets, 2014 International Conference on Data Science and Advanced Analytics (DSAA'2014), Shanghai, October 30-November 1, 2014.
  9. Peters, G. and Lingras, P. 2014. Analysis of User-Weighted π Rough k-Means, 9th International Conference on Rough Sets and Knowledge Technology (RSKT2014), Shanghai, October 24-26, 2014, pp. 547-556.
  10. Ammar, A., Elouedi, Z, and Lingras, P. 2014. Meta-Clustering Approach using Possibilistic Membership: Application to Retail Datasets, 2nd World conference on Complex Systems (WCCS14), Agadir-Morocco, November 10-11, 2014.
  11. Ammar, A., Elouedi, Z, and Lingras, P. 2014. Semantically Enhanced Clustering in Retail using Possibilistic K-Modes, 9th International Conference on Rough Sets and Knowledge Technology(RSKT2014), Shanghai, October 24-26, 2014, pp. 753-764.
  12. Ammar, A., Elouedi, Z, and Lingras, P. 2014. Decremental Rough Possibilistic K-Modes, The 2014 International Conference on Adaptive and Intelligent Systems, September 8-10, 2014, Bournemouth - UK.
  13. Ammar, A., Elouedi, Z, and Lingras, P. 2013. Decremental Possibilistic K-Modes, SCAI 2013 : Twelfth Scandinavian Conference on Artificial Intelligence November 20-22, 2013, Aalborg, Denmark.
  14. Ammar, A., Elouedi, Z, and Lingras, P. 2013. Incremental Rough Possibilistic K-Modes, Proceedings of The 7th Multi-Disciplinary International Workshop on Artificial Intelligence December 9-11, 2013 at Krabi, Thailand, Springer, LNAI 8271.
  15. Triff, M. and Lingras P. 2013. Recursive Profiles of Businesses and Reviewers on Yelp.com, Proceedings of 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, LNCS 8170, pp. 325-335.
  16. Joshi, M. and Lingras P. 2013. Enhancing Rough Clustering with Outlier Detection Based on Evidential Clustering, Proceedings of 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, LNCS 8170, pp. 127-137.
  17. Ammar, A., Elouedi, Z, and Lingras, P. 2013. Incremental Possibilistic K-Modes, Proceedings of 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, LNCS 8170, pp. 293-303.
  18. Trabelsi, S., Elouedi, Z, and Lingras, P. 2013. Belief Discernibility Matrix and Function for Incremental or Large Data, Proceedings of 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, LNCS 8170, pp. 67-76.
  19. Rathinavel, K. and Lingras, P. 2013. A Granular Recursive Fuzzy Meta-clustering Algorithm for Social Networks, Proceedings of 2013 IFSA World Congress NAFIPS Annual Meeting Edmonton, Canada June 24-28, 2013, IEEE press, pp. 567-572.
  20. Ammar, A., Elouedi, Z, and Lingras, P. 2012. The K-Modes Method using Possibility and Rough Set Theories, Proceedings of 2013 IFSA World Congress NAFIPS Annual Meeting Edmonton, Canada June 24-28, 2013, IEEE press, pp. 1297-1302.
  21. Ammar, A., Elouedi, Z, and Lingras, P. 2012. The K-Modes Method under Possibilistic Framework, Proceedings of Canadian AI 2013, LNAI 7884, pp. 211-217.
  22. Trabelsi, S., Elouedi, Z, and Lingras, P. 2012. Exhaustive Search with Belief Discernibility Matrix and Function, Proceedings of Canadian AI 2013, LNAI 7884, pp. 162-173.
  23. Nagrecha, S., Lingras, P., and Chawla, N. 2013. Comparison of Gene Co-expression Networks and Bayesian Networks, Proceedings of the 5th Asian Conference On Intelligent Information and Database Systems (ACIIDS 2013), Part I, LNAI 7802, Springer-Verlag Berlin Heidelberg, pp. 507-516.
  24. Ammar, A., Elouedi, Z, and Lingras, P. 2012. RPKM: The Rough Possibilistic K-Modes, Proceedings of 20th International Symposium on Methodologies for Intelligent Systems (ISMIS 2012), LNAI 7661, Springer-Verlag Berlin Heidelberg, pp. 81-86.
  25. Ammar, A., Elouedi, Z, and Lingras, P. 2012. K-Modes Clustering Using Possibilistic Membership, Proceedings of 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, (IPMU'2012), Part III, Catania, Italy, July 9-13, 2012, pp. 596-605.
  26. Trabelsi, S., Elouedi, Z, and Lingras, P. 2012. Heuristic for attribute selection using belief discernibility matrix Proceedings of 2012 Joint Rough Set Symposium (JRS2012),Chengdu, China, August 17-20, 2012, pp. 129-138.
  27. Joshi, M. and Lingras P. 2012. Evidential Clustering or Rough Clustering: The Choice is yours, Proceedings of 2012 Joint Rough Set Symposium (JRS2012),Chengdu, China, August 17-20, 2012.
  28. Lingras, P., Nimse, S., Darkunde, N. and Muley, A. 2011. Soft Clustering from Crisp Clustering using Granulation for Mobile Call Mining, Proceedings of 2011 IEEE International Conference on Granular Computing, November 8-10, National University of Kaohsiung, Garden Villa, Kaohsiung, Taiwan, pp. 410-416.
  29. Zhang, P., Joshi, M., and Lingras, P. 2011. Clustering of Products to Identify Optimal Inventory Prediction Models, Proceedings of Fifth Indian International Conference on Artificial Intelligence, IICAI'11, Tumkur, India, December 16-18, pp. 71-88.
  30. Lingras, P., Bhalchandra, P., Mekewad, S., Rathod, R. and Khamitkar, S. 2011. Comparing Clustering Schemes at Two Levels of Granularity for Mobile Call Mining, Rough Sets and Knowledge Technology, Proceedings of 6th International Conference, RSKT 2011 Banff, Canada, Lecture Notes in Artificial Intelligence, Subseries of Lecture Notes in Computer Science, LNCS 6954, Springer-Verlag, pp. 696-705.
  31. Joshi, M., and Lingras, P., Wani, G., Zhang, P. 2011. Clustering based Stability despite of Temporal Variations, Proceedings of the International Conference on Information Technology, Systems and Management (ITSM 2011), Indian Institute of Management Kozhikode, Dec 17-18, 2011, pp. 306-311.
  32. Joshi, M., Lingras, P., Yao, Y., Bhavsar, V. 2010. Rough, Fuzzy, Interval Clustering for Web Usage Mining, 2010 International Conference on Intelligent Systems Design and Applications (ISDA 2010) Proceedings.
  33. Trabelsi, S., Elouedi, Z, and Lingras, P. 2010. Belief Rough Set Classification for Web Mining based on Dynamic Core, 2010 International Conference on Intelligent Systems Design and Applications (ISDA 2010) Proceedings.
  34. Joshi, M., Lingras, P., Rao, C.R., 2010. Analysis of Rough and Fuzzy Clustering, Rough Sets and Knowledge Technology, 5th International Conference, RSKT 2010 Beijing, China, October 2010 Proceedings, Lecture Notes in Artificial Intelligence 6401, Subseries of Lecture Notes in Computer Science, Springer, pp. 653-660.
  35. Trabelsi, S., Elouedi, Z, and Lingras, P. 2010. A Comparison of Dynamic and Static Belief Rough Set Classifier, Proceedings of Seventh International Conference on Rough Sets and Current Trends in Computing, RSCTC 2010, Warsaw, Poland, June 28-30, M. Szczuka et al. (Eds.): RSCTC 2010, LNAI 6086, Springer-Verlag, pp. 366-375.
  36. Trabelsi, S., Elouedi, Z, and Lingras, P. 2010. Rule Discovery Process Based on Rough Sets under the Belief Function Framework, Proceedings of International Conference on Management of Uncertainty in Knowledge-Based Systems, IPMU 2010, E. Hullermeier, R. Kruse, and F. Hoffmann (Eds.): IPMU 2010, LNAI 6178, Springer-Verlag, pp. 726-736.
  37. Joshi, M. and Lingras, P. 2009. Evolutionary and Iterative Crisp and Rough Clustering (Parts: I&II), Proceedings of Third International Conference on Pattern Recognition & Machine Intelligence (PReMI'09) (Dec. 16-20, 2009, IIT Delhi, India). S. Chaudhury et al. (Eds.): PReMI 2009, LNCS 5909, Springer-Verlag, pp. 615-627.
  38. Trabelsi, S., Elouedi, Z, and Lingras, P. 2009. Dynamic reduct from partially uncertain data using rough sets, Proceedings of Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC'09, New Delhi, December 16-18. H. Sakai et al. (Eds.): RSFDGrC 2009, LNAI 5908, Springer-Verlag, pp. 160-167.
  39. Yao, Y.Y., Lingras, P., Wang, R., Miao, D. 2009. Interval Set Cluster Analysis, Proceedings of Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC'09, New Delhi, December 16-18, Springer-Verlag, pp. 398-405.
  40. Joshi, M., Bhavsar, V., and Lingras, P. 2009. An Algorithm for the Estimation of a Time Period of2-Sequences, Proceedings of Fourth Indian International Conference on Artificial Intelligence, IICAI'09, Tumkur, India, December 16-18, pp. 71-88.
  41. Trabelsi, S., Elouedi, Z, and Lingras, P. 2009. Belief Rough Set Classifier, Proceedings of Canadian AI 2009, Kelowna, British Columbia, Canada, Lecture Notes in Artificial Intelligence 5549, Springer Verlag, pp. 257-261.
  42. Lingras, P. 2009. Rough K-Medoid Clustering using GAs, Proceedings of ICCI 2009, Hong Kong.
  43. Lingras, P. 2009. Evolutionary Rough K-Means Clustering, Proceedings of Rough Set and Knowledge Technologies 2009, Lecture Notes in Computer Science 5589, Springer Verlag, pp. 68-75.
  44. Butz, C.J., Konkel, K. and Lingras, P. 2009. Join Tree Propagation utilizing both Arc Reversal and Variable Elimination, Twenty Second International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp. 523-528.
  45. Butz, C.J., Chen, J., Konkel, K. and Lingras, P. 2009. A Comparative Study of Variable Elimination and Arc Reversal in Bayesian Network Inference, Twenty Second International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp. 571-572.
  46. Lingras, P., Chen, M., and Miao, D. 2008. Precision of Rough Set Clustering, Proceedings of Sixth International Conference on Rough Sets and Current Trends in Computing Akron, Ohio, USA, October 23 - 25, 2008.
  47. Lingras, P., Chen, M., and Miao, D. 2008. Rough Multi-category Decision Theoretic Framework, Proceedings of 3rd International Conference on Rough Sets and Knowledge Technology, pp. 676-683.
  48. Butz, C.J., Yan, W., Lingras, P., Konkel, K. and Yao, Y. 2008. On Variable Elimination in Discrete Bayesian Network Inference, 9th World Meeting of the International Society for Bayesian Analysis (ISBA08), pp. 96-97.
  49. Butz, C.J., Lingras, P. and Konkel, K. 2008 A Web-based Interface for Hiding Bayesian Network Inference, Proceedings of 17th International Symposium on Methodologies for Intelligent Systems (ISMIS08), pp. 612-617.
  50. Lingras, P. and Butz, C.J. 2007. Precision and Recall based Implementation of Binary Rough Support Vector Machine, Proceedings of 2007 IEEE International Conference on Granular Computing, pp. 654-658.
  51. Lingras, P. and Lingras, R. 2007. Adaptive hyperlinks using page access sequences and minimum spanning trees, Proceedings of 2007 IEEE International Conference on Fuzzy Systems, pp. 529-534.
  52. Lingras, P. and Jensen, R. 2007. Survey of Rough and Fuzzy Hybridization, Proceedings of 2007 IEEE International Conference on Fuzzy Systems, pp. 125-130.
  53. Bain, K., Hines, J., Lingras, P., Yumei, Q. 2007. Using Speech Recognition and Intelligent Search Tools to Enhance Information Accessibility, Proceedings of HCI International 2007, Lecture Notes in Computer Science Series (LNCS), 2007.
  54. Butz, C.J. and Lingras, P. 2005. On the Practical Irrelevance of Diverging Implication between Probabilistic Conditional Independence and Embedded Multivalued Dependency, 2nd Indian International Conference on Artificial Intelligence (IICAI-05), pp. 2464-2475.
  55. Lingras, P. and Butz, C.J. 2005. Interval Set Representations of 1-v-r Support Vector Machine Multi-classifiers, the IEEE International Conference on Granular Computing, pp. 193-198.
  56. Lingras, P. and Butz, C.J. 2005. Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers, 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC05), vol. 2, pp. 166-173.
  57. Lingras, P. Hogo, M., and Snorek, M. 2004. Temporal Cluster Migration Matrices for Web Usage Mining, Proceedings of 2004 IEEE/WIC International Conference on Web Intelligence, WI2004, Beijing, China, pp. 441-444.
  58. Lingras, P. and Yan, R. 2004. Interval Clustering Using Fuzzy and Rough Set Theory, Proceedings of 2004 conference of the North American Fuzzy Information Processing Society, Banff, Alberta, June 27-30, 2004, pp. 780-784.
  59. Lingras, P. and Butz, C. 2004. Interval Set Classifiers using Support Vector Machines, Proceedings of 2004 conference of the North American Fuzzy Information Processing Society, Banff, Alberta, June 27-30, 2004, pp. 707-710.
  60. Butz, C., and Lingras, P. 2004. Granular jointree probability propagation, Proceedings of 2004 conference of the North American Fuzzy Information Processing Society, Banff, Alberta, June 27-30, 2004, pp. 69-72.
  61. Zhong, M., Sharma, S.C. and Lingras, P. 2004. Analyzing The Performance Of Genetically Designed Short-term Traffic Prediction Models Based On Road Types and Functional Classes, Proceedings of the Sixteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Ottawa, Canada, Lecture Notes in Computer Science, 3029, Springer-Verlag, pp. 1133-1145.
  62. Zhong, M., Sharma, S.C. and Lingras, P. 2004. Multiple-interval Urban Freeway Traffic Forecasting: Factor Approaches, Time Series Analysis versus Genetically Designed Regression and Time Delay Neural Networks, Proceedings of Annual Conference of the Canadian Society of Civil Engineering, Saskatoon, Saskatchewan, Canada, June 2-5, 2004.
  63. Zhong, M., Sharma, S.C. and Lingras, P. 2004. Matching Patterns for Updating Missing Values of Traffic Counts, Proceedings of Annual Conference of the Canadian Society of Civil Engineering, Saskatoon, Saskatchewan, Canada, June 2-5, 2004, extended version appeared in Transportation Planning and Technology.
  64. West, C., Jain A., Lingras, P. and Leonard, B. 2003. Supermarket Customer Attrition Analysis based on Cluster Membership Patterns, Proceedings of the First Indian International Conference on Artificial Intelligence, pp. 1132-1140.
  65. Lingras, P., Yan, R., and Jain A. 2003. Clustering of Web Users: K-Means vs. Fuzzy C-Means, Proceedings of the First Indian International Conference on Artificial Intelligence, pp. 1062-1073.
  66. Lingras, P., Yan, R., and Hogo, M. 2003. Rough Set Based Clustering: Evolutionary, Neural, and Statistical Approaches, Proceedings of the First Indian International Conference on Artificial Intelligence, pp. 1074-1087.

  67. Lingras, P. and Yan, R., and West, C. 2003. Fuzzy C-Means Clustering of Web Users for Educational Sites, Proceedings of Sixteenth Conference of the Canadian Society of Computational Studies of Intelligence, AI 2003, Halifax, Canada, June 11-13, 2003, Advances in Artificial Intelligence Series 2671, Springer, Toronto, pp. 557-562.
  68. Lingras, P. and Yan, R., and West, C. 2003. Comparison of Conventional and Rough K-Means Clustering, Proceedings of the 9th. International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Chongqing, P. R. China, Lecture Notes in Artificial Intelligence Series, 2639, Springer, pp.130-137.
  69. Lingras, P., Hogo, M., Snorek, M., and Leonard, B. 2003. Clustering Supermarket Customers using Rough Set Based Kohonen Networks, Proceedings of Fourteenth International Symposium on Methodologies for Intelligent Systems, Maebashi City, Japan, October 28-31, 2003, Lecture Notes in Artificial Intelligence Series, 2871, Springer, pp.169-173.
  70. Hogo, M., Snorek, M., and Lingras, P. 2003. Temporal Web Usage Mining, Proceedings of 2003 IEEE/WIC International Conference on Web Intelligence, WI2003, Halifax, Canada, pp. 450-453.
  71. Zhong, M. Lingras, P. and Liu Z. 2003. Differentiating True Zero Hourly Volumes with Missing Values and their Distributions on Different Types of Roads, Proceedings of Annual Conference of the Canadian Society of Civil Engineering, Moncton, New Brunswick, Canada, June 4-7, 2003.
  72. Zhong, M., Sharma, S.C. and Lingras, P. 2003. Accuracy of Genetically Designed Models for Predicting Short-term Traffic on Rural Roads, Proceedings of Annual Conference of the Canadian Society of Civil Engineering, Moncton, New Brunswick, Canada, June 4-7, 2003.
  73. Lingras, P. and Yao, Y.Y., 2002. Time complexity of Rough Clustering: GAs Versus K-Means, Proceedings of Third International Conference on Rough Sets and Current Trends in Computing (RSCTC 2002), Malvern, PA, USA.
  74. Lingras, P. 2002. Rough Set Clustering for Web Mining, Proceedings of 2002 IEEE International Conference on Fuzzy Systems, Honolulu, Hawaii, May 12-17, 2002.
  75. Lingras P. and Young, L., 2001. Multi-criteria Time-Series based Clustering of Supermarket Customers using Kohonen Networks. Proceedings of the 2001, International Conference on Artificial Intelligence (IC-AI'2001): June 25-28, 2001, Las Vegas, Nevada, USA, Vol. I, pp. 158-164.
  76. Lingras, P. and Mountford, P. 2001.Time Delay Neural Networks Designed Using Genetic Algorithms for Short Term Inter-City Traffic Forecasting Engineering of Intelligent Systems: the proceedings of The Fourteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Budapest, Hungary, L. Monostori, J. Vancza & M. Ali (Eds.), Lecture notes in AI 2070, Springer, pp. 290-299.
  77. Xiao, Y., Lingras, P., and Gao, Q. 2000. Asthma Data Mining: An Experimental Comparison of Statistical Method and Neural Networks, Proceedings of the IASTED International Conference on Neural Networks (NN'2000), May 15-17, 2000, Pittsburgh, pp. 20-23.
  78. Lingras, P. 2000. Evolutionary Information Retrieval, Proceedings of the Fifth Joint Conference on Information Sciences, Vol. I, Atlantic City, Feb.27-March 3, 2000, pp.166-169.
  79. Lingras, P. and Davies, C. 1999. Rough Genetic Algorithms, New directions in Rough Sets, and Granular-Soft Computing, (Ed. N. Zhong, A. Skowron, S. Ohsuga), Proceedings of 7th International Workshop RSFDGrC'99, Yamaguchi, Japan, Lecture Notes in Artificial Intelligence 1171, Springer, pp. 38-46. An extended version of this paper was published in Computational Intelligence.
  80. Lingras, P. and Yao, Y.Y. 1999. Neural Networks As Queries For Linear And Non-Linear Retrieval Models, Proceedings of the 5th International Conference of the Decision Sciences Institute, Athens, Greece, pp. 1394-1396.
  81. Lingras, P. and Mannella, R. 1999. Effect Of Weather On Prediction Of Electricity Demand, Proceedings of the 5th International Conference of the Decision Sciences Institute, Athens, Greece, pp. 574-576.
  82. Lingras, P. and Sharma S. 1999. Short-Term Traffic Volume Forecasts: Existing And Future Research, Proceedings of the Annual Conference of Canadian Society of Civil Engineers, Regina, Saskatchewan, IV, pp. 429-438.
  83. Lingras, P., Sharma S., Osborne, P. and Kalyar, I. 1998. Prediction of Next Day Traffic Volume Using Artificial Neural Networks, Proceedings of the First International Conference on New Information Technologies for Decision Making in Civil Engineering, Montreal, pp. 1439-1450.
  84. Lingras, P., Sharma S. and Kalyar, I. 1998. Prediction of Recreational Traffic on Sunday Afternoons Using Neural Network Techniques, Proceedings of the Conference of Canadian Society of Civil Engineers, Halifax, Nova Scotia, June, pp. 97-107.
  85. Sharma S., Lingras, P. and Xu, F. 1998. Estimation of Annual Average Daily Traffic Using Neural Networks, Proceedings of the Conference of Canadian Society of Civil Engineers, Halifax, Nova Scotia, June, pp. 109-119.
  86. Thompson, D. J. and Lingras, P. 1998. Input Models of Neural Networks for the Prediction of Traffic Volume Time Series, Proceedings of the Conference of Canadian Society of Civil Engineers, Halifax, Nova Scotia, June, pp. 75-86.
  87. Lingras, P.J and Mannella R. J. 1997. Prediction of Electricity Demand Utilizing the Previous Day's Summative Historical Data, Proceedings of the Fifth International Conference on Computing in Urban Planning and Urban Management (CUPUM'97), Mumbai(Bombay), India, pp. 60-69.
  88. Lingras, P.J, Osborne P. and Thompson, D. 1997. Internal Structure of Neural Networks for the Prediction of Traffic Volume Time Series, Proceedings of the Fifth International Conference on Computing in Urban Planning and Urban Management (CUPUM'97), Mumbai(Bombay), India, pp. 124-133.
  89. Lingras, P.J and Sharma, S.C.1997. Determination of Traffic Volume Parameters Using Neural Networks, Proceedings of the Fifth International Conference on Computing in Urban Planning and Urban Management (CUPUM'97), Mumbai(Bombay), India, pp. 226-234.
  90. Lingras, P.J and Osborne P. 1997. Effect of Noise on Regression and Neural Network Predictions, Proceedings of the Conference of Canadian Society of Civil Engineers, Sherbrooke, Quebec, June, pp. 331-339.
  91. Lingras, P.J and Osborne P. 1997. Linearity in Daily Traffic Volume Time Series, Proceedings of the Conference of Canadian Society of Civil Engineers, Sherbrooke, Quebec, June, pp. 341-350.
  92. Lingras, P. 1997. Comparison of neofuzzy and rough neural networks, Proceedings of the fifth international workshop on rough sets and soft computing (RSSC'97) in the Third Joint Conference on Information Sciences, Durham, North Carolina, March, pp. 259-262.
  93. Lingras, P. 1996. Belief and Probability Based Database Mining, Proceedings of the Ninth Florida Artificial Intelligence Symposium (FLAIRS'96) , Key West (Florida), pp. 316-320.
  94. Lingras, P. 1996. Unsupervised Learning Using Rough Kohonen Neural Network Classifiers, Proceedings of Symposium on Modelling, Analysis and Simulation , CESA'96 IMACS Multiconference, Lille (France), pp. 753-757.
  95. Lingras, P. 1996. Rough Neural Networks, Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'96) , Granada (Spain), pp. 1445-1450.
  96. Lingras, P. 1996. Comparison of Statistical Methods and Neural Networks for Traffic Engineering Analysis, Proceedings of the First Canadian Society of Civil Engineers Transportation Specialty Conference, Edmonton, Alberta, May 29 - June 1, Vol. IIIa, pp. 467-479.
  97. Lingras, P. 1996. Choice of Classification Parameters on Traffic Estimation Using Neural Computing, Proceedings of the First Canadian Society of Civil Engineers Transportation Specialty Conference, Edmonton, Alberta, May 29 - June 1, Vol. IIIa, pp. 503-510.
  98. Lingras, P., and Yao, Y.Y. 1995. Belief functions in rough set models. Proceedings of the 2nd Annual Joint Conference on Information Sciences, P.P. Wang (Ed.), Wrightsville Beach, North Carolina, USA, pp. 190-193.
  99. Lingras, P. and Adamo, M. 1995. Estimation of AADT Volume Using Neural Networks, Computing in Civil and Building Engineering, Pahl & Werner (Eds.), pp. 1355-1362.
  100. Lingras, P. 1995. Plausibilistic Rules Extraction from Incomplete Databases Using Non-Transitive Rough Set Model, Proceedings of 23rd Annual Computer Science Conference Workshop on Rough Set and Database Mining, ACM, March 2, pp. 1-12.
  101. Lingras, P. 1994. Rough Set Theoretic Operators And Nonmonotonic Preference Structures, Proceedings of the Third International Workshop on Rough Sets and Soft Computing, Nov. 10-13, San Jose, California, pp. 134-141.
  102. Lingras, P. and Sharma S.C. 1994. A Knowledge-based System for Traffic Engineering Analysis, Proceedings of Canadian Society of Civil Engineers, Winnipeg, June 1-4, pp. 599-608.
  103. Lingras, P. 1994. Scientific Analysis Language for Retrieval and Analysis of Knowledge, Intelligent Systems: Third Golden West International Conference (Ed. E.A. Yfantis), Kluwer Academic Publishers, pp. 85-94.
  104. Chau, Lingras, P. and Wong, S. K. M., 1993. Upper and Lower Entropies of Belief Functions, Methodologies of Intelligent Systems 8 (Ed. Z.W.Ras and J. Komorowski), North Holland.
  105. Lingras, P. 1993. Combination of Evidence in Rough Set Theory, Computing and Information: ICCI'93, Fifth International Conference (Ed. O. Abou-Rabia, C.K. Chang, W.W. Koczkodaj), IEEE Computer Society Press, pp. 289-293.
  106. Lingras, P. 1993. Nonmonotonic Comparative Probability Structures, Proceedings of the Sixth Annual Florida AI Research Symposium, Fort Lauderdale, April 18-21, pp. 147-151.
  107. Wong, S.K.M., Lingras, P. and Yao, Y.Y., 1991. Propagation of Preference Relations in Qualitative Inference Networks, The Proceeding of 12th International Joint Conference on Artificial Intelligence, Vol. 2., (Ed. J. Mylopoulos and R. Reiter), August 24-30, Sydney, Australia, pp. 1204-1209.
  108. Wong, S.K.M., Yao, Y.Y. and Lingras, P., 1991. Compatibility of Quantitative and Qualitative Representations of Belief, Uncertainty in AI: Proceedings of the Seventh Conference, (Ed. D'Ambrosio, B., Smets, P. & Bonissone, P.), July 13-15, Los Angeles, Morgan Kaufmann Publishers, Inc., pp. 418-424.
  109. Wong, S.K.M., Lingras, P. and Yao, Y.Y., 1991. Towards Implementing Valuation Based Systems with Relational Databases, Methodologies for Intelligent Systems 6, (Ed. Z.W. Ras,), North Holland, New York, pp. 172-182.
  110. Yao, Y., Wong, S. K. M. and Lingras, P., 1990. A Decision-Theoretic Rough Set Model, Methodologies for Intelligent Systems 5, (Ed. Z.W. Ras, M. Zemankova, M. Emrich), North Holland, New York, pp. 17-24.
  111. Wong, S. K. M., Lingras, P., 1990. Generation of Belief Functions from Qualitative Preference Relations, The Proceedings of the Third International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, Paris, France, July 2-6, pp. 450-459.
  112. Wong, S. K. M., Lingras, P., 1990. Combination of Evidence Using the Principle of Minimum Information Gain, The Proceedings of the Sixth Conference on Uncertainty in AI, Cambridge, Mass., July 27-29, pp. 49-51.
  113. Lingras, P., Wong S.K.M. and Yao, Y.Y. 1990. Probability Estimation in Intelligent Information Systems, Proceedings of IASTED International Symposium on Expert Systems Theory & Applications, Los Angeles, December 12-15.
  114. Lingras, P., Sharma, S. C., 1987. Cost of Comfort and Convenience: A Proposed Model for Use with the 1985 Highway Capacity Manual, in Proceedings of the Road and Transportation Association of Canada Conference, Saskatoon, Canada, September 14-17.
  115. Wong, S. K. M., Lingras, P., 1989. The Compatibility View of Shafer-Dempster Theory Using the Concept of Rough Set, Methodologies for Intelligent Systems 4, (Ed. Z.W. Ras), North Holland, New York, pp. 33-42.
  116. Lingras, P., Wong, S. K. M., 1988. An Optimistic Rule for Accumulation of Evidence, Methodologies for Intelligent Systems III (Eds. Z.W. Ras, L. Saitta), North Holland, New York, pp. 60-69.
  117. Sharma, S. C., Lingras, P., and Werner, Al., 1986. Some Highway Economic Analysis Experiences with the 1985 U.S. Highway Capacity Manual, presented at the Transportation Research Board Annual Conference, Washington D.C., January 12-16.
  118. Sharma, S.C., Lingras, P., 1986. A Comparative Economic Analysis for Highway Reconstruction Projects: New Vs. Old HCM, In the Institute of Transportation Engineers Proceedings, 56th Annual Meeting, Indianapolis, U.S.A., September 7-11.
  119. Sharma, S.C., Lingras, P. and Hassan, M.U., 1986. A Note on the Driver Population Factor in the New U.S. Highway Capacity Manual, in the proceeding of the Canadian Society of Civil Engineers Annual Conference, Toronto, May 12-16.

Non-Refereed Conference Proceedings: 5

  1. Lingras, P., Trabelsi, S., and Elouedi, Z., 2011. Belief Classification Approach based on Dynamic Core for Web Mining database, International workshop of Rough set Theory.
  2. Lingras, P., Joshi, M., and Rao, C.R., 2011. Fuzzy Rough Correlation Factor, International workshop of Rough set Theory.
  3. Lingras, P., Butz, C.J., Yan, W., Konkel, K., and Yao, Y., 2008. On Variable Elimination in Discrete Bayesian Network Inference, 9th World Meeting of the International Society for Bayesian Analysis (ISBA08), pp. 96-97.
  4. Lingras, P. 2007. Neighbourhood Sets based on Web Usage Mining, Proceedings of 2007 North American Fuzzy Information Processing Society Conference, pp. 659-664.
  5. Lingras, P. and Butz, C.J. 2006. Evaluation and Simplification of rules created by 1-v-r Rough SVM multiclassification, Proceedings of 2006 North American Fuzzy Information Processing Society Conference.

Other Non-Refereed Publications: 6

  1. Lingras, P., 2000. Professional Development for Faculty: A Time for Reflection, Teaching and Learning at Saint Mary's, Vol. 11, No. 1.
  2. Lingras, P., 1999. Survey of Computer Educational Institutions and Industry in India, Proceedings of the South Asia Council Conference, Canadian Asian Studies Association, Montreal.
  3. Lingras, P., 1991. Qualitative and Quantitative Reasoning in Intelligent Information Systems, unpublished Ph.D. thesis, Department of Computer Science, University of Regina, Regina, Sask.
  4. Lingras, P., and Sharma, S.C., 1989. Weigh-in-motion System Evaluation, project report prepared for Saskatchewan Highways and Transportation.
  5. Lingras, P., 1988. Different Perspectives of Probability for Information Science, unpublished M. Sc. thesis, Department of Computer Science, University of Regina, Regina, Sask.
  6. Lingras, P., 1986. The New Highway Capacity Manual and Economic Analysis for Rural Roads, unpublished M. Sc. thesis, Faculty of Engineering, University of Regina, Regina, Sask.

Invited talks: 45
(Based on papers listed earlier. The objective is diseemination of research hence some of the topics tend to repeat)

  1. Lingras P (2017). Rough and Soft Clustering, GLS (Gujrat Law Society) university, Ahmedabad, Gujrat, August 17, 2017.
  2. Lingras P (2016). Practical Applications of Data Mining at National Institute of Technology, Goa, India Dec 5-9, 2016
  3. Lingras P (2016). Advances in Rough and Soft Clustering: meta-clustering, dynamic clustering, data-stream clustering, International Joint Conference on Rough Sets October 7-9, 2016, Santiago, Chile.
  4. Lingras P (2015). Recursive and iterative clustering in granular hierarchical, network, and temporal datasets, 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN 2015) to be held during November 20-22, 2015
  5. Lingras P (2015). Clustering in Temporal Environment: Retail and Financial applications 4th International Conference on "Frontiers in Intelligent Computing, Theory and Application" (FICTA 2015), National Institute of Technology, Durgapur, India, November 16-18, 2015.
  6. Lingras P (2014). Big Data Analytics, International Conference on Information and Communication Technology for Competitive Strategies (ICTCS - 2014), Udaipur, India, 14-16 November, 2014.
  7. Lingras P (2014). Big Data Analytics, First International Conference on Rough Sets and Knowledge Technologies (ICRSKT), Jawaharlal Nehru University of Technology, Hyderabad, 9-11 November, 2014.
  8. Lingras P (2014). Rough Cluster Quality Index Based on Decision Theory, International Conference on Computational Intelligence (ICCI’14) University of Mumbai, India, March 21-22 2014.
  9. Lingras P (2013). Recursive and iterative clustering in granular hierarchical, network, and temporal datasets, delivered at the Workshop on Big Data: A Soft Computing Perspective held in conjunction with PREMI 2013 at Indian Statistical Institute (ISI), Kolkata on December 14, 2013.
  10. Lingras P (2013). Recursive and iterative clustering in granular hierarchical, network, and temporal datasets, delivered online at the Workshop on Rough Set and Knowledge Technologies, Jawaharlal Nehru Technical University, Hyderbad, India, November 10, 2013.
  11. Lingras P (2013). Recursive and iterative clustering in granular hierarchical, network, and temporal datasets, Jodrey School of Computer Science, Acadia University, Wolfeville, Nova Scotia, September 27, 2013.
  12. Lingras P (2013). Recursive and iterative clustering in granular hierarchical, network, and temporal datasets, AICTE SPONSORED FACULTY DEVELOPMENT PROGRAM ON MACHINE LEARNING AND DATA MINING APPROACHES : A RESEARCH PERSPECTIVE, March 30, 2013. College of Engineering Pune, Pune, India.
  13. Lingras P (2013). Recursive and iterative clustering in granular hierarchical, network, and temporal datasets, Refresher Course on the Recent Trends in ICT, January 5, 2013, University of Mumbai, Mumbai, Maharashtra.
  14. Lingras P (2012). Delivered a two day Research Workshop on "Data Mining using Soft Computing: Applications in Business, Finance, Engineering, Web, and Mobile phones", February 3-4, Kongu Engineering College, Perundurai, Tamilnadu
  15. Lingras P (2012). Rough Cluster Quality Index Based on Decision Theory, International Conference on Perspectives of Computer Confluence with Sciences, December 10-12, Nowrosjee Wadia College, Pune.
  16. Lingras P (2012). Rough Cluster Quality Index Based on Decision Theory, Workshop on Fuzzy Logic and Related Topics, March 29-30 2012, Modern College, Pune, Maharashtra
  17. Lingras P (2012). Rough clustering and its applications, Workshop on Fuzzy Logic and Related Topics, March 29-30 2012, Modern College, Pune, Maharashtra
  18. Lingras P (2012). Rough Cluster Quality Index Based on Decision Theory, International Conference on Pattern Recognition, Informatics, and Medical Engineering, Periyar University, Salem, Tamilnadu
  19. Lingras P (2012). Research in Soft Computing, April 27, University of Pune, Pune, Maharashtra.
  20. Lingras P (2012). Research in Soft Computing, May 6, University of Mumbai, Mumbai, Maharashtra.
  21. Lingras P (2012). Rough clustering and its applications, March 9, 2012, Jawaharlal Nehru University, New Delhi.
  22. Lingras P (2012). Rough Cluster Quality Index Based on Decision Theory, March 7, 2012, Jawaharlal Nehru University, New Delhi.
  23. Lingras P (2011). Rough clustering and its applications, International Workshop on 'Fuzzy sets, Rough Sets: Theory and Applications', November 21-25, 2011. National Institute of Technology, Durgapur, West Bengal.
  24. Lingras P (2011). Rough clustering and its applications,Workshop on Soft Computing and Knowledge Mining March 29-30 2011, Department of Computer Applications & Mathematics, Maulana Azad National Institute of Technology , Bhopal.
  25. Lingras P (2011). Dual rough support vector regression, Workshop on Soft Computing and Knowledge Mining March 29-30 2011, Department of Computer Applications & Mathematics, Maulana Azad National Institute of Technology , Bhopal.
  26. Lingras P (2011). Rough clustering and its applications, Workshop series on Soft Computing, March 31, Post Graduate Department of Computer Science & Engineering, NRI Institute of Information Science & Technology, Bhopal-MP-INDIA
  27. Lingras P (2011). Research in data mining, Refresher Course in Computer Science, Department of Computer Science, University of Pune, March 28.
  28. Lingras P (2011). Soft clustering, Refresher Course in Computer Science, Department of Computer Science, University of Pune, March 28.
  29. Lingras P (2011). Classification using R, Refresher Course in Computer Science, Department of Computer Science, University of Pune, March 27th.
  30. Lingras P (2011). Clustering using R, Refresher Course in Computer Science, Department of Computer Science, University of Pune, March 27th.
  31. Lingras P (2011). Rough clustering and its applications, Workshop on Research Areas in Soft Computing RASCOMP. 2011, March 18, 2011 organized by Department Of Computer Technology, Kavikulguru Institute Of Technology & Science, Ramtek, Nagpur.
  32. Lingras P (2011). Dual rough support vector regression, Workshop on Research Areas in Soft Computing RASCOMP. 2011, March 18, 2011 organized by Department Of Computer Technology, Kavikulguru Institute Of Technology & Science, Ramtek, Nagpur.
  33. Lingras P (2011). Rough clustering and its applications, Indian Statistical Institute, Kolkata on March 2.
  34. Lingras P (2011). Rough sets, Indian Statistical Institute, Kolkata on Feb 28.
  35. Lingras P (2011). Dual rough support vector regression, National Conference on .Emerging Trends in Soft Computing. (NCETSC 11) in Pune from Feb 1-3.
  36. Lingras P (2011). Rough support vector machines, Pune University Computing Science department on February 5.
  37. Lingras P (2011). Rough sets, National Conference on .Emerging Trends in Soft Computing. (NCETSC 11) in Pune from Feb 1-3.
  38. Lingras P (2011). Rough clustering and its applications, International Conference on Knowledge Engineering in Aurangabad from Jan 29-31.
  39. Lingras P (2011). Dual rough support vector regression, International Conference on Knowledge Engineering in Aurangabad from Jan 29-31.
  40. Lingras P (2010). Dual rough support vector regression, Colloquium on Computational Intelligence (Rough Set based approach) (in Connection to DCIS Foundation Day Celebrations), Central University of Hyderabad, March 23-24, 2010
  41. Lingras P (2010). Conservative and Aggressive Dual Rough SVR Modeling, Jodrey School of Computer Science, Acadia University, Wolfville, NS, Feb 12, 2010.
  42. Lingras P (2009). Introduction to Rough and Fuzzy sets, International Symposium on Softcomputing on December, 1-2, 2009
  43. Lingras P (2009). Introduction to Rough and Fuzzy sets, International Workshop on Rough Set, Fuzzy Set and Soft Computing, July 7-8, 2009
  44. Lingras P (2007). Conventional, Fuzzy, and Interval Set Based Temporal Cluster Migration Matrices, Jodrey School of Computer Science, Acadia University, Wolfville, NS, September 13, 2007.
  45. Lingras P (2004). Conventional, Fuzzy, and Interval Set Based Temporal Cluster Migration Matrices, Department of Computer Science, Hong Kong Baptist University, January, 2004.