This repository contains three Java projects related to Artificial Intelligence. These projects were built from scratch without the use of any machine learning libraries. Each project focuses on a specific AI concept and aims to demonstrate the implementation of these algorithms in Java.
Project 1: Perceptron
The Perceptron is one of the fundamental building blocks of neural networks and machine learning. It is a simple algorithm used for binary classification problems. The Perceptron takes input features, applies weights to them, and passes them through an activation function to make predictions. During the training process, the algorithm adjusts the weights based on the input data to improve the accuracy of predictions.
Features:
- Implementation of the Perceptron algorithm in Java.
- Training the Perceptron on a custom dataset for binary classification tasks.
- Evaluating the performance of the trained Perceptron.
Project 2: Depth-First Search (DFS)
Depth-First Search is an essential graph traversal algorithm used to explore all the vertices in a graph. It starts from a specific node and explores as far as possible along each branch before backtracking. DFS can be applied to find paths, connected components, and cycles in a graph.
Features:
- Implementation of Depth-First Search algorithm in Java.
- Representation of the graph data structure using adjacency lists or matrices.
- Exploration of various applications like finding paths or connected components using DFS.
Project 3: Genetic Algorithm
The Genetic Algorithm is a search heuristic inspired by the process of natural selection. It mimics the process of evolution to find the best solution to a problem. The algorithm starts with a population of potential solutions and uses genetic operators such as selection, crossover, and mutation to evolve the population over generations, gradually improving the solutions.
Features:
- Implementation of the Genetic Algorithm in Java.
- Representation of the problem domain and its solutions.
- Applying selection, crossover, and mutation operators to create the next generation of solutions.
- Analyzing the convergence of the algorithm and discussing its performance.
Each project contains its own directory with the source code, instructions, and sample data where applicable. You can explore each project separately by navigating to its directory and following the instructions provided in the respective README.md files.
- Java Development Kit (JDK) 8 or higher installed on your system.
- IDE (Eclipse, IntelliJ, etc.) for ease of development (optional but recommended).
Feel free to contribute to any of these projects by submitting pull requests. Bug fixes, feature enhancements, and new AI-related projects are welcome!
These projects are under the MIT License, allowing you to use, modify, and distribute the code freely.
Special thanks to all contributors and open-source projects that provided inspiration and valuable insights into AI algorithms and Java programming.
Let's explore the fascinating world of AI and Java together! Happy coding! 🚀