My Projects

Project List

• Book Store Management Application
  Developed a Python-based bookstore search interface using tkinter, with separate tabs for Author, Category, and Publisher searches.
  Implemented a Data Access Object (DAO) to manage MySQL database connections and ensure separation between the GUI and database.
  Designed interface classes to support search functionalities and dynamically update search results based on user selections.
• Mini Linux Shell
  Developed a Custom Linux Shell in C: Created a shell program that supports command parsing, directory navigation, process management with fork/execvp, and background task handling.
  Implemented Input/Output Redirection and Command History: Enhanced the shell with redirection capabilities and integrated a history feature for efficient recall and execution of past commands, handling up to 100 recent commands.
  
  

  Here's a basic Linux shell video and how it would work:

• Forest Fire Simulation and Probability Analysis
  Designed and implemented a Python program to simulate forest fires using different search algorithms and calculate the probability of fire spreading based on forest density.
  Conducted Monte Carlo simulations on forests with different densities to estimate fire spread probabilities using Python's plotting library.
  Developed a sophisticated simulation program to replicate real-world forest fire behavior, laying the foundation for future research aimed at optimizing firefighting strategies.
• Optimization of Bitstring and Strategy Evolution using Genetic Algorithms
  

  Optimization Type	Details
  Optimized Genetic Algorithm	Implemented a Genetic Algorithm to maximize ones in a 20-bit genome using a population of 100 genomes, achieving an average best fitness of 19 across 50 generations with a 70% crossover rate and 0.1% mutation rate.
  Evolved Robot Strategy	Developed a Genetic Algorithm for Robby’s World with a 243-character genome, achieving a peak fitness score by evolving strategies across 300 generations with a 100-genome population and a 1.0 crossover rate.
  Data-Driven GA Analysis	Conducted 50 trials, systematically varying mutation and crossover rates, demonstrating a 20% decrease in optimal solution time when using a crossover rate of 70% and mutation rate of 0.1%. Documented results with detailed performance graphs.