Umm e Hani's Projects
This project implements a neural network architecture using separate processes and threads on a multi-core processor. It utilizes inter-process communication through pipes for exchanging weights and biases between processes. Implemented in C++, it leverages multi-core processing for efficient training. Designed for Linux environments.
This project creates a live product recommender system using the Amazon dataset. It involves data processing with Apache Spark, MongoDB, and exploratory analysis. A recommendation model (ALS) is trained and integrated into a Flask web application, with real-time updates via Apache Kafka.
A comprehensive C++ project implementing image processing operations such as statistics computation, connected component extraction, run-length coding, quad tree representation, and shape recognition. Utilizing data structures like arrays, queues, stacks, and linked lists.
This project implements a real-time data processing pipeline using Apache Kafka, Python, and MongoDB. It processes Microsoft and Apple stock data, with Kafka consumers analyzing price differences, risk, and percentage changes, then storing results in MongoDB.
Implementation of Locality-Sensitive Hashing (LSH) with Mel-Frequency Cepstral Coefficients (MFCC) features for audio duplicate detection and explores its application using a Flask web application.
Web application for meme sentiment analysis using machine learning classifiers (text & image) with Flask deployment.
This is an implementation of a multiplayer game where players navigate a two-dimensional grid to collect items. Players can move concurrently on the game board and collect items to earn points. The game is implemented using threads to handle player movement and item collection, ensuring a seamless and engaging gameplay experience.
This project implements a scalable algorithm to rank nodes in a network based on their proximity to user-specified source nodes. It leverages Hadoop's distributed processing power to handle large networks efficiently. The core algorithm, implemented in Python using MapReduce, personalizes traditional PageRank.
The repository provides solutions to two concurrency challenges in C/C++. The first involves implementing a lock and unlock mechanism using the Bakery algorithm for mutual exclusion. The second addresses traffic management on a bridge, ensuring limited capacity is maintained using mutex synchronization.
This repository contains solutions to operating system assignments covering process management, inter-process communication, and system administration tasks using shell scripting. Explore for practical implementations and explanations.
sentriX is a web application that leverages Flask and React to perform sentiment analysis on text. Utilizing the TextBlob library, the application analyzes user-input text to determine whether it is positive, negative, or neutral. The application features four main pages: Home, Tool, About, and Contact.
Config files for my GitHub profile.