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AI thesis helper

dc.contributor.advisorMostakim, Moin
dc.contributor.authorShayer, Mirza Ahmad
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2025-04-30T10:01:15Z
dc.date.available2025-04-30T10:01:15Z
dc.date.copyright2024
dc.date.issued2024-12
dc.descriptionCataloged from the PDF version of the project.
dc.descriptionIncludes bibliographical references (pages 47-49).
dc.descriptionThis project is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024.en_US
dc.description.abstractIn the modern world, Artificial Intelligence (AI) has become popular. Machine Learning (ML), is another popular term used on the internet, it is when AI is applied to a system which allows it to learn. Deep Learning (DL) is another popular term that became very common in recent times, it is the application of ML that utilizes algorithms and trains models. In 2020, Generative Pre-trained Transformer (GPT), came to the mainstream and had become highly successful. In just another year it became a game changing tool in the scientific landscape. Using GPT, various chatbots are designed. Chat-GPT in particular is by far the most used and most useful model of GPT. The model is useful in generating replies to various questions with high accuracy and usefulness. Can be used to help users with almost practically any question. In a few years this model will develop to help people even further. As such, this project - AI Thesis Helper, is one such implementation of further helping students to aid their thesis writing. Many students struggle to write their thesis, as such this chat-bot can help students with various thesis tasks like idea generation. This project aims to assist students in their writing. Project challenges and objectives are elaborated. Prior works are looked into along with reasons for choosing GPT over other models. Requirements, pros, cons and feasibility - both economic and technical along with model diagrams are dealt with. Details on the dataset are discussed. Fine-tuning of the model is elaborated with all details like imports, hyperparameters and training loop etc. Front-end, integration and backend are discussed along with UI/UX components. Implementation shows the project workings and how well it performs. To evaluate the performance, the following metrics are utilized - model loss during training, precision, recall, F1 score, perplexity and spelling, grammar with overall writing quality. Along with a comparison of generated text, made with two other AI chatbots - ChatGPT and Perplexity AI for the same text input. Future updates are also described.en_US
dc.description.degreeM.Sc. in Computer Science and Engineering
dc.description.statementofresponsibilityMirza Ahmad Shayer
dc.format.extent49 pages
dc.identifier.otherID 22273008
dc.identifier.urihttp://hdl.handle.net/10361/25852
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University projects 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.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.subjectGenerative pre-trained transformeren_US
dc.subjectArtificial intelligenceen_US
dc.subjectChat-GPTen_US
dc.subject.lcshData mining.
dc.subject.lcshDeep learning.
dc.subject.lcshArtificial intelligence.
dc.titleAI thesis helperen_US
dc.typeProject Reporten_US

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