Bahauddin Taha's Projects
Python little program for solving systems of linear equations with Cramer's rule
This project aims to address the unique challenges of automated traffic detection in the context of Dhaka, Bangladesh. The project utilizes the YOLOv8 model to detect and recognize various types of vehicles on the roads of Dhaka.
This project focuses on upscaling low-resolution images using ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks). ESRGAN is a deep learning-based technique that leverages generative adversarial networks to enhance image resolution and quality.
This project aims to detect whether a person is wearing a face mask or not using computer vision techniques and a convolutional neural network (CNN) model. If the person is wearing a face mask, the system will automatically open the door to allow entry else, the system will deny access and activate an alarm.
The Face Recognition Based Attendance System is a desktop application that leverages facial recognition technology to automate attendance tracking. This system allows users to capture their facial images using a webcam, train the system with their information, and then use the face detection feature to mark their attendance.
Fake-News-Detector is a machine learning project that utilizes the TfidfVectorizer and PassiveAggressiveClassifier algorithms to classify news articles as either fake or real. The project includes a web interface built with Flask and HTML, allowing users to input news articles and receive the classification results.
This project aims to classify pneumonia using the transfer learning model VGG19. The dataset used for training the model consists of 5,856 images of chest X-rays. The trained model can classify whether an input chest X-ray image is positive or negative for pneumonia.
The project involves working with an Excel file and performing some data manipulation and analysis tasks using Excel and then usning Python to convert the Excel sheet into a csv file then using KMeans clustering to perform clustering on the dataset based on the feature called Division.
This project focuses on sentiment analysis of movie reviews using the IMDb dataset. The dataset consists of 50,000 movie reviews labeled as positive or negative. The main goal of this project is to develop models that can accurately classify the sentiment of movie reviews.
Config files for my GitHub profile.
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