Introduction The Author Classifier project aims to classify authors based on the text they have written using advanced natural language processing techniques. Leveraging concepts from the Information Retrieval course, we built a dataset comprising a minimum of 35 books for each of
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These projects cover diverse data analysis, machine learning, and practical applications, including blood donation analysis, maternal health risk prediction, speech disorder classification, facial recognition, and clustering analysis. You can see the project code along with the work report on GitHub. Assignment1 Project
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Homeworks of meta-heuristic algorithm course This collection includes implementations of several algorithms created from scratch, including Ant Colony Optimization (ACO), Genetic Algorithm (GA) for Traveling Salesman Problem (TSP), Memetic Algorithm, Multi-Layer Perceptron (MLP), Neural Network, Particle Swarm Optimization (PSO), Simulated Annealing, and
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This project focuses on classifying grapevine leaves into five distinct classes using advanced techniques like transfer learning and autoencoders. By harnessing pre-trained models, we aim to enhance classification accuracy while exploring the effects of dimensionality reduction and data augmentation through image generation.
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This project is presented in Jupyter Notebook format, providing visibility into the implementation of class definitions and algorithm training. You can observe the program’s execution by using the rl.play(True, True) command within the pygame environment. This command executes the program, irrespective of
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