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Developing an intelligent virtual assistant for extended reality environments: a multi-modal approach for understanding user intent and context using computer vision, natural language processing, and large language model

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorMukta, Jannatun Noor
dc.contributor.authorRayan, Nafis
dc.contributor.authorJoy, Srejon Ahamed
dc.contributor.authorKhan, MD. Ehtashamul Islam
dc.contributor.authorShipar, Abtahi
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2025-09-16T05:51:19Z
dc.date.available2025-09-16T05:51:19Z
dc.date.copyright2025
dc.date.issued2025-06
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 73-81).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.en_US
dc.description.abstractVirtual reality (VR) and Mixed reality (MR) technologies have the capacity to enhance and revolutionize a variety of industry sectors, providing individuals with greater immersive and engaging interactions. Nevertheless, modern virtual reality technologies lack a virtual intellectual assistant that recognises and responds to users’ visual and auditory inputs. Thus hindering users from executing tasks, accessing information, and interacting with the overall virtual system environment. The goal of this research is to develop an immersive and intelligent virtual reality assistant to work with individual users’ contextual intent. The proposed Virtual Reality Intelligent Assistant (VRIA) will be able to understand and respond to users’ auditory and visual inputs and help users’ daily endeavours involving task automation, information retrieval, navigation, object recognition, entertainment, personalisation, education, home automation and communication to enhance overall productivity. VRIA offers the ability to leverage natural language processing, computer vision and large language models for understanding visual and auditory inputs. The virtual ecosystem enables user-friendly, tailored, personalised and highly adaptable VR environments to accomplish real-world endeavours. In addition with the incorporation of powerful UI, IoT devices, Mixed Reality attributes, and Cloud Computing infrastructure, the Virtual Reality Intelligent Assistant (VRIA) will meet the requirement for real virtual world interactions and be scalable and flexible across multiple VR/MR platforms and applications.en_US
dc.description.degreeBachelor of Science in Computer Science and Engineering
dc.description.statementofresponsibilityNafis Rayan
dc.description.statementofresponsibilitySrejon Ahamed Joy
dc.description.statementofresponsibilityMD. Ehtashamul Islam Khan
dc.description.statementofresponsibilityAbtahi Shipar
dc.format.extent93 pages
dc.identifier.otherID 21301559
dc.identifier.otherID 21301354
dc.identifier.otherID 21101087
dc.identifier.otherID 21301233
dc.identifier.urihttp://hdl.handle.net/10361/26756
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectVirtual realityen_US
dc.subjectMixed realityen_US
dc.subjectInteractive experienceen_US
dc.subjectVirtual assistanten_US
dc.subjectLarge language modelsen_US
dc.subjectHuman-computer interactionen_US
dc.subjectIoTen_US
dc.subjectComputer visionen_US
dc.subject.lcshNatural language processing (Computer science).
dc.subject.lcshComputer simulation.
dc.subject.lcshIntelligent personal assistants (Computer software).
dc.subject.lcshExpert systems (Computer science).
dc.subject.lcshUser interfaces (Computer systems).
dc.subject.lcshInternet of things.
dc.subject.lcshVirtual reality.
dc.titleDeveloping an intelligent virtual assistant for extended reality environments: a multi-modal approach for understanding user intent and context using computer vision, natural language processing, and large language modelen_US
dc.typeThesisen_US

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