Kazi Mahathir Rahman

Detecting Small Vessels or Boats with Satellite Imagray using DDPM and YOLOv7

In our project, we tackled the challenge of detecting ships from Synthetic Aperture Radar (SAR) images using the YOLOv7 model. SAR images are often plagued by significant noise. To mitigate this, we applied noise reduction techniques such as Denoising Diffusion Probabilistic Models (DDPM) and anisotropic diffusion, which significantly enhanced image quality. Our model was trained on SSDD, DS SDD, and FUSAR datasets and demonstrated high accuracy in ship detection.

Content Recommendation System based on Facial Expression Recognition using Swin transformer and Haar Cascade

This project investigates facial expression recognition using Swin Transformer models (Swin-S, Swin-M, Swin-B). The system initially employs Haar Cascade for face detection, followed by Swin Transformer-based classification on datasets like RAF-DB, CK+, and FER2013.

FIFA22 Position Prediction

This project aims to predict player positions in FIFA 23 by employing various data preprocessing techniques such as normalization, scaling, feature engineering, and handling missing values on a Kaggle dataset. Several machine learning models including Random Forest, Support Vector Machines, and Neural Networks were compared to optimize player position prediction accuracy.

BRACU Helper ChatBot

BRACU Helper is a university chatbot built to assist BRAC University students by answering their common queries. Developed using Django for the backend and React for the frontend, the chatbot utilizes a fine-tuned Sentence Transformer model to generate accurate and relevant responses. The project includes features like user authentication, a responsive chat interface, and data storage for chat logs, making it a useful tool for enhancing the student experience.

EssenceAI

EssenceAI is a text summarizer website developed using Django and React, designed to help users generate concise summaries of large bodies of text. The site leverages a custom-trained T5 model, fine-tuned with the Gigaword dataset, ensuring high-quality and contextually relevant summaries. The interface is built using the Bootstrap CSS framework, offering a modern and responsive design.

Speller Monkey

Islamic Assistance BOT

This project is an Islamic Knowledge Assistance Chatbot built using Django, HTML, and CSS, designed to provide accurate answers to questions related to Islam. The chatbot employs a retrieval-based approach utilizing TF-IDF vectorization and cosine similarity, along with natural language processing techniques like tokenization and stopword removal, to match user queries with the most relevant answers from a custom dataset of Islamic questions and answers. The project demonstrates the integration of machine learning and NLP into a web-based chatbot interface, offering users a helpful and interactive way to gain knowledge about Islam.

Pandemic Tweet Sentiment Prediction

This project focuses on sentiment analysis of a dataset using both machine learning and deep learning models, including Random Forest, RNN, LSTM, and GRU. The goal was to classify the sentiment of textual data, with word embeddings as inputs. Despite employing techniques like dropout, regularization, and hyperparameter tuning, all models exhibited overfitting, suggesting that the primary challenge lies in the dataset quality. Improvements in data preprocessing and acquiring a more diverse dataset are necessary to enhance the models' performance and generalization to unseen data.

Trip Advisor Hotel Review Data Analysis

This project aimed to investigate the relationship between TripAdvisor reviews and corresponding ratings. By employing a combination of Natural Language Processing (NLP) techniques and deep learning models, the study sought to understand the extent to which review text could predict or explain rating variations.

Daraz Data Web Scraper

This project is a web scraping bot designed to extract product details from the Daraz online shopping platform. By providing a search keyword and specifying the page number, the bot uses Selenium to navigate Daraz's website, collect data such as product name, brand, price, and seller information, and then saves this data into a CSV file. The file is named according to the search keyword, making it easy to organize and access the collected data. The project is flexible and can be adapted to scrape information for any search term across multiple pages.

Bikroy.com Mobile Data Web Scraping

This project involved web scraping mobile phone data from Bikroy.com using Python. Key information such as mobile name, model, price, reviews, features, and other details were extracted from the first five pages of the website due to hardware constraints. The Python libraries Beautiful Soup 4, requests and selenium were primarily used for this task.

Mr. Silly Snake

Mr. Silly Snake is a classic snake game developed using OpenGL and Pygame. The game employs the midpoint line algorithm to construct the game border, snake body, and interactive buttons. Randomly generated gems, visualized using the circle drawing algorithm, serve as rewards for the player. Incorporating AABB collision detection for obstacles and the snake body enhances gameplay. Players navigate the snake using arrow keys and interact with game elements through mouse clicks, while the score is displayed on the terminal for real-time tracking.

ResNet-34 Object Detection (Build From Scratch)

This project explores the development of object detection models using ResNet-34, ApexNet, InceptionNet, and GoogleNet architectures build from scratch on a specified image dataset utilizing TensorFlow. The objective is to evaluate the performance of these models in detecting objects within images without relying on pre-trained weights.

Breast Cancer Survival Analysis and Prediction

This project investigated the potential of machine learning for breast cancer analysis and prediction. By exploring various models like SVM, Logistic Regression, KNN, and Random Forest on a preprocessed dataset, the project aimed to identify the most effective approach. While all models achieved decent accuracy, SVM stood out with a superior F1 score, indicating its better ability to balance precision and recall. The analysis highlights the potential of machine learning in breast cancer diagnosis, with further research focusing on refining model performance and incorporating additional data sources.

Protein Sequence Classification Project

This research investigates the prediction and classification of protein sequences using a dataset encompassing diverse protein information. A comprehensive approach was employed, incorporating various preprocessing techniques to prepare the data effectively. Multiple machine learning models were then applied and compared to determine the most suitable method for accurately predicting and categorizing protein sequences based on their underlying characteristics.

Stock Prediction Project

This project explores the potential of machine learning for stock price prediction using the S&P 300 index as a dataset. Random Forest, Logistic Regression, and Support Vector Machine models were implemented and compared to forecast future stock prices. The project encompasses data collection, preprocessing, feature engineering, model training, evaluation, and visualization. While offering a foundational approach to stock prediction, it serves as a starting point for more complex models and strategies.

Fitness Tracking Website

This fitness tracking website empowers users to take control of their health and wellness by providing a comprehensive platform for monitoring physical activity, nutrition, and overall progress. With features including personalized workout plans, social interaction, and integration with wearable devices, this application aims to motivate and support individuals in achieving their fitness goals. The website is made with PHP, MySQL (XAMPP), HTML and CSS

Household Service Platform

This platform offers a comprehensive solution for managing household chores and maintenance. Users can easily book a variety of services, from cleaning and repairs to gardening and pest control, through detailed service provider profiles and a user-friendly booking system. Built with PHP, MySQL (XAMPP), HTML, and CSS, the platform ensures a seamless experience for both service seekers and providers.

Github Portfolio Website

I've constructed a dynamic online portfolio showcasing my web development abilities. Leveraging HTML, CSS, JavaScript, and PHP, I've crafted a responsive and interactive platform hosted on GitHub. This digital showcase effectively highlights my skills and projects, providing a comprehensive overview of my professional capabilities.