Research

Research 1

Optimizing Reinforcement Learning Agents in Games Using Curriculum Learning and Reward Shaping

Reinforcement learning (RL) is increasingly used in gaming to develop intelligent agents capable of autonomous decision-making. However, traditional RL methods often face challenges such as slow convergence and poor performance in complex environments. This research integrates curriculum learning and reward shaping to address these issues, providing a structured and efficient learning process for agents.

Objectives

The objective is to design and implement a framework that combines curriculum learning with reward shaping to accelerate the training of RL agents and enhance their adaptability to increasingly complex game environments.

Results

The proposed framework demonstrated improved learning efficiency and performance. Agents trained using this method converged faster and outperformed those trained with conventional RL approaches in strategic and complex game tasks.

Research 2

Alzheimer’s Disease Detection: A Novel Deep Convolutional Neural Network Approach

Alzheimer’s disease, a progressive neurodegenerative disorder, requires early detection for effective management. Advances in deep learning, particularly convolutional neural networks (CNNs), have shown promise in medical imaging. This study proposes a novel CNN architecture tailored for accurate and early detection of Alzheimer’s disease using brain imaging data.

Objectives

The study aims to design a novel CNN model capable of detecting Alzheimer’s disease with improved accuracy, focusing on identifying early-stage markers from brain imaging datasets.

Results

The model achieved state-of-the-art results in detecting Alzheimer’s disease, outperforming existing techniques in terms of classification accuracy and robustness. The approach holds significant potential for aiding clinicians in early diagnosis.

Research 3

An Innovative Framework for Improving Diabetic Retinopathy Detection and Classification

Diabetic retinopathy is a leading cause of blindness globally. Early detection and accurate classification of its stages are critical for effective treatment. This bachelor thesis introduces an advanced deep learning framework to improve the accuracy of diabetic retinopathy detection and classification.

Objectives

The research aims to develop a deep learning-based framework that enhances the detection and classification of diabetic retinopathy stages, providing a reliable tool to assist healthcare professionals.

Results

The proposed framework achieved superior precision and recall rates in detecting and classifying diabetic retinopathy, outperforming existing methods. The results indicate its potential for integration into clinical workflows for early and accurate diagnosis.

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