● Contributed to the "Development of a Real-Time Fully Automatic Quality Control System to Detect Surface Defects in Steel Bars Progressing at High Speed," specializing in advanced image processing and deep learning techniques.
● Applied and enhanced state-of-the-art algorithms such as Mask-RCNN and YOLO, achieving superior performance in defect detection and real-time processing.
● Collaborated with a multidisciplinary team of engineers and researchers, fostering a collaborative environment that led to innovative problem-solving and project success.
● Gained advanced skills in data management, statistical analysis, and computer vision, significantly contributing to the project's real-time quality control achievements.
Projects:
1. Applying Deep Learning Techniques to Predict Stock Prices (Feb 2023 - May 2023).
● Developed and compared deep learning models to predict stock prices, achieving high predictive accuracy with LSTM and GRU networks.
● Demonstrated the effectiveness of deep learning in financial forecasting and risk management applications.
Technologies: Python, LSTM, GRU, Linear Regression.
2. E-commerce Application for Selling Computer Parts (Oct 2022 - Dec 2022).
● Built a Flutter-based e-commerce app with Dart, featuring product filtering, search, and secure payment integration.
● Implemented an inventory management system using Firebase for real-time product updates.
Technologies: Dart, Flutter, Firebase.
3. Deep Learning with PyTorch: Image Segmentation (Aug 2022 - Sep 2022).
● Created custom datasets and augmented images for segmentation tasks using PyTorch.
● Trained and evaluated a pre-trained segmentation model (UNet) to achieve accurate image segmentation results.
Technologies: Python, PyTorch.
4. Library Management System Development (Oct 2021 - Dec 2021).
● Developed an automation system for library management using C# and SQL Server, enabling efficient database operations.
● Designed interfaces for book, member, and student management with capabilities for record insertion, deletion, and modification.
Technologies: C#, Microsoft SQL Server.
5. CT Brain Imaging Stroke Detection (Mar 2021 – Sep 2021).
● Developed an AI model using EfficientNet and U-Net for stroke detection and segmentation in CT brain images.
● Participated in Teknofest 2021 AI in Healthcare Competition.
Technologies: Python, Fast.ai, EfficientNet, U-Net.
