M.Phil / MS

Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36

This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.

Browse

Author: search.filters.author.Dr. Hasan Jamal

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    QUBIT
    (Library Information Services, CUI Lahore, 2024) Shafeen Noor; FA20-BCS-090; Dr. Hasan Jamal
    QUBIT (Quality Unit-Based Intelligent Testing) utilizes the use of Large Language Models (LLMs) to boost-up the potential of not only developers but all coding enthusiasts. Its primary focus revolves around Code Review, an integral step in software development. This project comprises of two distinct parts. The first component is an extension specifically designed for Visual Code Studio. It seamlessly integrates with the user's development environment, offering real-time Code Review, Code Rating, Bug detection, and Code Refactoring. Moreover, it also generates test cases for robust development along with Code Review. It allows the selection of multiple files for the sake of better efficiency. The second component of QUBIT manifests as a website, also utilizing the prowess of LLMs, specifically Gemini, to conduct features involving, Code Review Code Rating, Bug detection, Code Refactoring. The website not just mirrors the capabilities of the VS Code extension like a chatbot acting as a virtual assistant which offers dynamic suggestions, it also introduces additional features. It includes a user-friendly dashboard; QUBIT community and customizable profiles enhance the overall user experience. Prompt engineering serves as the backbone, facilitating seamless interactions with the LLMS. Gemini is utilized for all the functionalities other than test case generation, which harnesses the use of Llama2. QUBIT not only increases the efficiency of developers but also explores the frontier of AI-driven coding assistance. Both components of this project aspire to redefine the landscape of coding tools.
  • No Thumbnail Available
    Item
    Fake News Detection for Urdu language using Machine Learning Techniques
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Ahmad Raza Khan; FA18-RCS-005; LHR TP 7587; Dr. Hasan Jamal
    The main target of this research is to detect fake news in Urdu language. Although many studies have already been conducted on fake news detection for different languages but not much work has been done for Urdu language and there is a research gap for fake news detection in Urdu language. To compare, evaluate and develop a fake news detection system for Urdu language, standardized and high-quality benchmark resources are needed but unfortunately corpora for Urdu language are not available, even though Urdu is a morphologically rich language and has more than 300 million speakers all over the world. To compensate for this gap, we develop a novel high quality, and a large-sized benchmark corpus for Urdu fake news detection. Only one benchmark dataset is previously available for fake news detection tasks in the Urdu language that contains a total of 900 news articles in which 500 are real news and 400 are fake news articles. Our novel corpus contains 1800 news articles out of which 1000 are real news articles and 800 are fake news articles. Our dataset contains five domain data (Business, Health, Showbiz, Sports, and Technology). These news articles are tagged manually into real and fake news tags. These news articles are collected from different authentic news sources. After corpus creation, feature extraction is performed using different combinations of word n-gram, character n-gram, and function n-gram. The range of this n-gram varies from 1 to 6 for our experiments. We use different weighting schemes such as Term Frequency-Inverse Document Frequency (TF-IDF), Log entropy, Binary weighting schemes, etc. After extracting features from the text, state-of-the-art machine learning techniques are applied to the corpus to evaluate the fake news detection system. In machine learning techniques, Multinomial Naïve Bayes (MNB), Linear Support Vector Classifier, Logistic regression, Random Forest, Decision Tree, Bernoulli Naïve Bayes (BNB) and Ada-Boost are used. Each classifier is trained and tested separately. Standard evaluation measures are used to check the working of the fake news detection system i.e., accuracy, precision, recall, F1-score. We compare our results with previous research results and Linear SVC and Logistic Regression outperforms other classifiers.
2025 @ COMSATS University Islamabad, Lahore Campus. All rights reserved.