Browsing by Author "Shayer, Mirza Ahmad"

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A comparative study of car image generation quality using DCGAN and VSGAN
(BRAC University, 2022-05) Shayer, Mirza Ahmad; Anjum, Nafisha; Mim, Sushana Islam; Chowdhury, Md. Abu Sajid; Preoshi, Noshin Nanjiba Islam; Mostakim, Moin
In today’s modern society, image generation (synthesis) has a great number of uses in various tasks. Image generation is used in crime forensics, improving image quality and generating better images. In 2014, a scientific breakthrough occurred in the machine learning community when Ian Goodfellow and his colleagues introduced the GAN (Generative Adversarial Network). Ever since then, GANs have become a more popular concept in the scientific community. Even today, GANs are being used, utilized and upgraded. This thesis is a comparative study of two GANs used for generating images of cars- DC-GAN (Deep Convolution) and VS-GAN (Vehicle Synthesis). The study will determine which of the two is better suited to generate high quality images of cars. We will train both GANs using the same dataset. The dataset consists of about 16185 Google images of random cars, 8144 for training and another 8041 for testing. The dataset is already preprocessed and split. We will compare the GANs training times, losses, accuracies and pictures generated, showing how well they perform. We will run all the GANs for 40 epochs in both training and testing. We will compare the CGAN, DCGAN, VSGAN, WGAN and WGAN-GP, to see which performs the best. We have used K-Nearest Neighbors, Regression and Random Forest Classifier to calculate the accuracies of all the GANs. We have displayed the results in tabular and graphical formats. We believe this will improve GAN research by providing an excellent comparison between the GANs and determine which is better suited for the given task. We also hope to improve the models further in the future and make an even more in depth comparison between the GAN architectures.
AI thesis helper
(BRAC University, 2024-12) Shayer, Mirza Ahmad; Mostakim, Moin
In 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.