Solving the 8-puzzle problem with DFS (depth-first-search) -BFS (breadth-first-search) -IDS (Itterative-deepening-search) -UCS (uniform-cost-search) -A* (GitHub) Problem Components: Heuristic Function: Algorithm Comparison: A* Performance: Other Algorithms:
I had a great opportunity to presenting about AI’s latest’s developments in economic department of our university. This presentation holed in E-commerce class (Prof. Nouri)
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Exploring reinforcement learning concepts and solve a maze problem. Discusses model state determination, techniques for state reduction, impact of learning rate (α), and impact of
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Multilayer Perceptron, Neural Network & Polynomial Regression from scratch. (Github) In this project we weren’t allowed to use ML libraries. Classification Perceptron Analysis Description: Explore
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This project aims to classify five classes of grapevine leaves using pre-trained models and transfer learning techniques. By leveraging the power of autoencoders, we explore
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Classification using Decision Tree, KNN, SVM, & Random Forest with PCA for Maternal Health Risk Dataset and Parkinson’s Disease. Clustering using Lloyd’sk-means, FuzzyC-means, and DBSCAN
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In the Bio-Inspired Algorithms class, I explored the application of three algorithms—Genetic Algorithm, Simulated Annealing, and Tabu Search—to solve the cutting stock problem (CSP). The
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Genetic Algorithm to solve TSP. Memetic Algorithm to solve N queens problem. Ant colony optimization for Assignment Problem. Simulated Annealing for cutting stock problem. Particle
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