PATENTS
1. Shaheen, M., Khan, S., 2020., Wisdom Mining., Filed in: Pakistan Patent Office on 20th March 2020., Filing No. 196/ 2020. [LINK]
2. Shaheen, M., Shahbaz, M., 2009., Association Rule Mining to predict co-varying software metrics., Filed in: US Patent Office on 6th Sep 2009. No. 20110061040. (Abandoned) [LINK]
JOURNAL
1. Khan, S., Shaheen, M., 2022. WisRule: First Cognitive Algorithm of Wise Association Rule Mining. SAGE Journal of Information Science. Doi:10.1177/01655515211030872. [LINK]
2. Manzoor, A., Rehman ZU., Shaheen, M., Khan, MZ., 2022. Data mining of IoT-Based Sentiments to Classify Political Opinions. Journal of Experimental & Theoretical Artificial Intelligence. Vol. 34. [LINK]
3. Shaheen, M., Naheed, N., Ahsan, A., 2023. Relevance-Diversity Algorithm for Feature Selection and Modified Bayes for Prediction. Elsevier Alexandria Engineering Journal (Accepted, In Press). [LINK]
4. Imran-Daud M., Shaheen, M., Ahmed, A., 2022. Multivariate Micro aggregation of Set-Valued Data. Information Technology & Control. Vol. 51, Issue. 1. [LINK]
5. Shaheen, M., 2023. A Novel Algorithm of Data Mining to Predict Future Scenario of Covid-19 Pandemic. Journal of Emergency Management. Accepted. [LINK]
6. Khan, S., Shaheen, M., 2023. Wisdom Mining: Future of Data Mining. Recent Patents on Engineering. Vol. 17, Issue. 1. Article ID: e280322202673. [LINK]
7. Shaheen, M., Abdullah., 2022., A Method of Data Mining for Detecting Outliers From K-Means Clusters., Journal of Engineering & Applied Sciences, Vol. 40, Issue. 2. [LINK]
8. Khan, S., Shaheen, M., 2021. From Data Mining to Wisdom Mining. SAGE Journal of Information Science. Vol. 47. [LINK]
9. Nisar, K., Shaheen, M., 2021. Determining Context of Association Rules by using Machine Learning. Taylor & Francis Journal of Experimental & Theoretical Artificial Intelligence. Vol. 33. [LINK]
10. Abdullah, U., Shaheen, M., Ujager, FS. 2021. Implementing Rule-based Healthcare Edits. KSII Transactions on Internet and Information System. Vol. 16, No.1. [LINK]
11. Shaheen, M., 2021. The Concept of Originality in Academic Research of Engineering. Education Research International. Vol. 2021. [LINK]
12. Shaheen, M., Rehman, S.U., Ghaffar, F., 2021. Correlation and Congruence modulo Based Clustering Technique and Its Application in Energy Classification. Elsevier Sustainable Computing: Informatics and Systems. Vol. 30. [LINK]
13. Shaheen, M., 2021. Decision Tree for PLOs of Undergraduate Computing Program Based on CLO of Computer Programming. Taylor & Francis Interactive Learning Environments. Vol. 29. [LINK]
14. Shaheen, M., Abdullah, U. 2021. CARM: Context Based Association Rule Mining for Conventional Data. Tech Science Computers, Materials & Continua. Vol. 68. [LINK]
15. Shaheen, M., Ahsan, A., Iqbal, S., 2021. Data Mining of Scientometrics for Classifying Science Journals., Techscience Intelligent Automation & Soft Computing. Vol. 28. [LINK]
16. Shaheen, M., 2021., Research in Engineering., Foundation University Journal of Engineering & Applied Sciences, Vol.2, Issue.1. [LINK]
17. Tabish, M., Rehman, Z., Shaheen, M., 2020. Activity Recognition Framework in Sports Videos., Springer Multimedia Tools and Applications. [LINK]
18. Mansoor, M., Rehman, Z., Shaheen, M., Khan, MA., Habib, M. 2020. Deep Learning based Semantic Similarity Detection using Text Data. Information Technology and Control. Vol. 49. [LINK]
19. Naheed, N., Shaheen, M., Khan, S.A., Alawairdhi, M., Khan, M.A., 2020., Importance of Features Selection, Attribute Selection, Challenges and Future Directions for Medical Imaging Data. Tech Science Computer Modelling in Engineering & Sciences. Vol. 125, Issue. 1. [LINK]
20. Mehmood, A., Attique, M., Sharif, M., Khan, SA., Shaheen, M., Saba, T., Riaz, N., 2020., Prosperous Human Gait Recognition: An End-to-End System based on Pre-trained CNN Features., Springer Multimedia Tools and Applications.[LINK]
21. Shaheen, M., 2020., Mining Patterns from Complex Energy Datasets., Journal of Engineering & Applied Sciences, Vol. 39, Issue. 1. [LINK]
22. Shaheen, M., Zafar, T., Khan, S.A., 2019., Decision Tree Classification: Ranking Journals/Conferences using IGIDI., SAGE Journal of Information Science, Vol. 45, Issue. 2. [LINK]
23. Shaheen, M., 2019., Application of Labelled K Means Clustering for GIS Contract Automation., Journal of Engineering & Applied Sciences, Vol. 38, Issue. 1. [LINK]
24. Shaheen, M., Khan, S.A., 2018., Face Recognition under Varying Expressions and Illumination using Particle Swarm Optimization., Elsevier Journal of Computational Science, Vol. 28. [LINK]
25. Shaheen, M., Shahbaz, M., 2017., An Algorithm of Association Rule Mining for Microbial Prospection., Nature Scientific Reports, Vol. 7, Article: 46108. [LINK]
26. Shaheen, M., Khan, S.A., Akram, R., 2017., Brief Review of Facial Expression Recognition Techniques., International Journal of Advanced and Applied Sciences. [LINK]
27. Shaheen, M., Khan, Z., 2016., A Method of Data Mining for Selection of Site for Wind Turbines., Elsevier Renewable and Sustainable Energy Reviews. [LINK]
28. Shaheen, M., Rehman, Z., Shahbaz, M., Guergachi, A., 2015., FPS-Tree Algorithm to Find Top-K Closed Itemsets in Data Streams., Springer Arabian Journal for Science & Engineering.[LINK]
29. Shaheen, M., Shahbaz, M., Guergachi, A., Rehman, Z., 2010., Data Mining Applications in Hydrocarbon Exploration., Springer Artificial Intelligence Review, Vol. 35, Number.1, pp. 1-18.[LINK]
30. Shaheen, M., Shahbaz, M., Guergachi, A., Rehman, Z., 2010., Mining Sustainability Indicators to Classify Hydrocarbon Development., Elsevier Knowledge-based Systems. Vol. 24, No.8, pp. 1159 – 1168. [LINK]
31. Shaheen, M., Shahbaz, M., Guergachi, A., 2013., Context-based positive and negative spatio temporal association rule mining., Elsevier Knowledge-based Systems. Vol. 37, No.8, pp. 261-273. [LINK]
32. Shaheen, M., 2011., An Intelligent Mechanism for GIS Contract Automation., Vol. 39, Lecture Notes in Engineering & Computer Science. [LINK]
33. Masood, S.A., Shahbaz, M., Shaheen, M., 2012., Transformational Leadership and Attribution Theory: The Situational Strength Perspective., Marsland L. Sci journal., Vol. 9, No.1, pp. 317-329. [LINK]
34. Shaheen, M., Shahbaz, M., Ahsan, S.M., Masood, S.A., 2012., Integrated Data Mining Framework for Natural Resource Exploration., Marsland L. Sci journal., Vol. 9, No.1, pp. 314-316. [LINK]
35. Shahbaz, M., Shaheen, M., Masood, S.A., 2012., Cancer Diagnosis Using Data Mining Technology., Marsland L. Sci journal., Vol. 9, No.1, pp. 318-313. [LINK]
36. Shaheen,M., Shahbaz, M., Rehman, Z., Aurangzeb, S., 2010., Issues in Interacting with GIS for Hydrocarbon Exploration., Journal of American Science, Vol. 6(7)., pp. 262-271. [LINK]
37. Rehman, Z., Shahbaz, M., Shaheen, M., Mehmood, S., Masood, S.A., 2012., Anomalous Pattern Detection Using Context Aware Ubiquitous Data Mining., Marsland L. Sci journal., Vol. 9, No.3, pp. 6-12. [LINK]
38. Shahbaz, M., Saleem, W., Syed, A., Aslam, M., Shaheen, M., 2012., Evaluating the Factors Responsible for Slow rate of Technology Diffusion in Livestock Sector of South Asia and Developing a Framework to Accelerate this Process: A Case Study using Data Analysis for Pakistan’s Livestock Sector., Marsland L. Sci journal, Vol. 9, No. 3, pp. 23 – 30. [LINK]
39. Shaheen, M., Shahbaz, M., Rehman, Z., Mehmood, S., 2010., A Method of Data Mining for Sustainable Hydrocarbon Exploration., Journal of Engineering & Applied Sciences, Vol. 29(2), pp. 18-25.[LINK]
40. Shaheen, M., Aslam, M., Shahbaz, M., Martinaquez, A., Rehman, Z., 2010., An Intelligent Mechanism for GIS Contract Automation., Journal of Research in Computing Mexico, Vol. 47. [LINK]
41. Rehman, Z., Shahbaz, M., Shaheen, M., Mehmood, S., 2010., Context Aware Ubiquitous Data Mining Framework to predict Malicious Activities., Journal of American Science, Vol. 6 (8), pp. 161-171. [LINK]
42. Shahbaz, M., Shaheen, M., Aslam, M., Masood, S.A., Khan, A., 2012., Data Mining Methodology in Perspective of Manufacturing Databases., Marsland L. Sci journal, Vol. 9, No. 3, pp. 13 – 22. [LINK]
43. Nusrat K,R., Khalil, M.S., Masood, S.A., Shaheen,M., 2010., Design and Manufacturing of Parabolic Trough Solar Collector System for a developing country Pakistan., Journal of American Science, Vol. 7 (1), pp. 365-372.[LINK]
44. Shahbaz, M., Ahsan, S., Shaheen, M., 2011., Automatic Generation of Extended ERD using NLP., Journal of American Science, Vol. 7(8)., pp. 1-10. [LINK]
45. Shaheen, M., Shahbaz, M., Ahsan, S., Hussain, K., 2011., CAD and Spatial Data Mining Framework for Petro-Chemical Industry., Journal of Pakistan Institute of Chemical Engineers., Vol. 39. [LINK]
CONFERENCE
1. Shaheen, M., Mehmood, M., 2019., Knowledge Discovery From Scientometrics Database., Proceedings of World Congress on Engineering, International Conference on Data Mining and Knowledge Engineering 2012., Imperial College London. [LINK]
2. Shaheen, M., Abdullah., 2015., An algorithm of Outlier Detection., Proceedings of International Conference on Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan. [LINK]
3. Shaheen, M., CBPNARM: An algorithm of association rule mining for spatial data., Proceedings of International Conference on Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan. [LINK]
4. Shaheen, M., Rehman, Zia ur.,2014., Geographic Information System of Gas Network in Pakistan., Proceedings of 14thInSITE Conference, Informing Science Institute USA. Wollongong, Australia, July 2014. [LINK]
5. Shaheen, M., Basit, F., Rehman, Zia ur.,2014., Design Issues in Human-GIS Interaction ., Proceedings of 14th InSITE Conference, Informing Science Institute USA. Wollongong, Australia, July 2014. [LINK]
6. Iqbal, S., Shaheen, M., Basit, F., 2013., A Machine Learning Based Method for Optimal Journal Classification., Proceedings of 8th International Conference on Internet and Secured Transaction., London, UK. [LINK]
7. Shaheen, M., 2013., Labeled clustering: A Unique Method to Label Unsupervised Classes., Proceedings of 8th International Conference on Internet and Secured Transaction., London, UK. [LINK]
8. Shaheen, M., Shahbaz, M., Jadoon, K., 2012., Data Mining for Wind Energy Site Selection., Proceedings of World Congress on Engineering, International Conference on Data Mining and Knowledge Engineering 2012., Imperial College London. [LINK]
9. Shahbaz, M., Shaheen, M., Noureen, A., 2012., Classification by Object Recognition in Satellite Images., Proceedings of World Congress on Engineering, International Conference on Data Mining and Knowledge Engineering 2012., Imperial College London. [LINK] (WonBest Paper Award by WCE UK)
10. Shaheen, M., Aslam, M., Shahbaz, M., Khan, J., Shaheen, N., 2011., An Intelligent method for GIS Contracts., Proceedings of World Congress on Engineering and Computer Science, International Conference on Data Mining and Knowledge Engineering 2011., USA. [LINK]
11. Shaheen, M., Shahbaz, M., Rehman, Z., Guergachi, A., 2010., Mining Sustainability Indicators to Predict Optimal Hydrocarbon Exploration Rate., IASTED International Conference on Artificial intelligence and Applications., Innsbruck, Austria, pp.394-400. [LINK]
12. Shaheen, M., Rehman, Z., 2008., Critical Skills for Computer Academicians., Proceedings of Informing Science & IT Education Conference (InSITE) 2008, Bulgaria, pp. 405-421. [LINK]
13. Rehman, Z., Shahbaz, M., Shaheen, M., Guergachi, A., 2009., Situation Awareness and Sensor Stream Mining for Sustainable Society., International Conference on Soft Computing and Pattern Recognition Malaysia.[LINK]
14. Rehman, Z., Imran, M., Aslam, M., Shaheen, M., 2009., An Intelligent Mathematical Production Assistant., International Conference on Frontiers of Information Technology (FITS), Pakistan. [LINK]
BOOKS
1. Shaheen, M. – POETRY BOOK “Meri Zindagi Ka Maqsad”.
2. Shahbaz, M., Guergachi, A., Noreen, A., Shaheen, M., 2013., A Data Mining Approach to Recognize Objects in Satellite Images to Predict Natural Resources., Book Chapter in Springer IAENG Transactions on Engineering Technologies., Vol. 229, pp. 215-230. [LINK]
PHD/ MASTERS THESES
1. Shaheen, M., 2011., Spatio – Temporal Data Mining Framework For Natural Resource Exploration. [LINK]
2. Shaheen, M., 2006., Mining Metrics To Predict The Success Of Object-Oriented Software Systems.