Application of Computational Intelligence and Machine Learning to Conventional Operational Research Methods

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dc.contributor.authorAli, Atif
dc.contributor.authorSaid, Raed A.
dc.contributor.authorRizwan, Hafiz Muhammad Amir
dc.contributor.authorShehzad, Khurram
dc.contributor.authorNaz, Imran
dc.date.accessioned2022-05-22T08:39:36Z
dc.date.available2022-05-22T08:39:36Z
dc.date.copyright© 2022
dc.date.issued2022
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2022 International Conference on Business Analytics for Technology and Security (ICBATS) (2022), available online at: https://doi.org/10.1109/ICBATS54253.2022.9759033
dc.description.abstractMachine learning and computational intelligence are two methods for achieving this (CI); traditional operational research methods are combined with machine learning-based computational techniques (OR). Students can handle complex decision-making problems thanks to the synergy between those methods and techniques. This research's primary goal is to present and demonstrate potential connections amid the two computational arenas. Using applications, we show how machine learning techniques like fuzzy logic, neural networks and reinforcement learning can be combined to provide a simpler solution to more complex problems than traditional OR methods., which is a research contribution in and of itself. © 2022 IEEE.
dc.identifier.citationAli, A., Said, R. A., Rizwan, H. M. A., Shehzad, K., & Naz, I. (2022). Application of computational intelligence and machine learning to conventional operational research methods. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). https://doi.org/10.1109/ICBATS54253.2022.9759033
dc.identifier.isbn978-166540920-9
dc.identifier.urihttps://doi.org/10.1109/ICBATS54253.2022.9759033
dc.identifier.urihttp://hdl.handle.net/20.500.12519/648
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationAuthors Affiliations : Ali, A., Pmas Arid Agriculture University, School of Computer Science, Rawalpindi, Pakistan; Said, R.A., Canadian University, Dubai, United Arab Emirates; Rizwan, H.M.A., Federal Urdu University of Arts, Science and Technology, School of Computer Science, Islamabad, Pakistan; Shehzad, K., Comsats University, Wah Campus, School of Computer Sciencce, Islamabad, Pakistan; Naz, I., Shah Abdul Latif University Khairpur, School of Computer Science, Khairpur, Pakistan
dc.relation.ispartofseries2022 International Conference on Business Analytics for Technology and Security (ICBATS)
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectHybrid Models
dc.subjectMachine Learning
dc.subjectOperational Research
dc.subjectOptimization
dc.titleApplication of Computational Intelligence and Machine Learning to Conventional Operational Research Methods
dc.typeConference Paper

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