Yakup Özdemir

Yakup Özdemir is a seasoned computer engineer with expertise in AI. He holds leadership roles, including Vice President of the Gazi AI Society. Yakup has led projects in medical AI, including cancer detection and remote sensing object segmentation. Proficient in C, C#, Java, and Python, he utilizes frameworks like Spring Boot and React. He excels in teamwork as evidenced by his roles as Team Captain and Head of Societies. Yakup is dedicated to advancing AI and delivering impactful solutions.

Artificial Intelligence in Medicine Category - Teknofest Project [ 11/2022 – 05/2023 ]

• Participated as Team Captain in the Teknofest Project, focusing on the Artificial Intelligence in Medicine category.
• Led a team named Gazi Mergen 3, comprising five dedicated members, in a competition involving BI-RADS Category, Quadrant Information, and Breast Composition Estimation in Computer Vision.
• Utilized Python along with relevant libraries, such as Tensorflow, to implement EfficientNet models for analyzing mammograms.
• Developed an anomaly detection system for identifying anomalies in breast tissue, emphasizing intricate data preprocessing techniques to enhance accuracy.
• Achieved a notable success rate of 75% in the competition, demonstrating the efficacy of the team's approach in advancing medical imaging and diagnosis.

Paper about IoT and Machine Learning [ 12/2022 – Current ]

• Spearheaded a groundbreaking research project titled "Optimizing Limited Bandwidth in Military Operations through IoT and Machine Learning."
• Investigated the potential synergies between Internet of Things (IoT) technologies and machine learning algorithms to address challenges within constrained bandwidth environments in military contexts.
• Leveraged expertise in both IoT and machine learning to develop innovative solutions aimed at enhancing communication efficiency, data processing, and decision-making capabilities.
• Strategically integrated IoT and machine learning technologies to provide the military with effective tools for maximizing the utility of limited bandwidth resources.
• Demonstrated multidisciplinary proficiency and commitment to developing practical applications at the intersection of IoT and machine learning for critical military operations.

Article - Remote Sensing Object Segmentation [ 12/2022 – Current ]

• Directed an innovative project titled "AI-Enhanced Remote Sensing Object Segmentation," focusing on remote sensing applications using Python.
• Employed advanced models and techniques to address the complexities of segmenting objects in remote sensing imagery with meticulous attention to detail.
• Utilized diverse datasets, including satellite imagery, to train cutting-edge segmentation models for accurately delineating and identifying objects of interest.
• Demonstrated proficiency in AI, image analysis, and remote sensing, showcasing the ability to contribute impactful solutions in geospatial applications.

Article - AI-Driven Analysis for BI-RADS Category, Quadrant Information, and Breast Composition Estimation from Mammograms [ 03/2023 – Current ]

• Led a comprehensive project titled "Article for BI-RADS Category, Quadrant Information, and Breast Composition Estimation from Mammograms," utilizing Python.
• Leveraged Kaggle datasets and additional data from the Ministry of Health to enhance breast cancer detection.
• Implemented state-of-the-art models such as EfficientNet and ImageNet to improve accuracy.
• Employed advanced preprocessing techniques and integrated confusion matrix analysis to optimize model performance for BI-RADS category classification, quadrant information extraction, and breast composition estimation from mammograms.
• Demonstrated proficiency in data science, image processing, and healthcare analytics, showcasing the ability to deliver impactful solutions in a complex domain.

End to End Ticket Reservation System [ 16/07/2023 – 28/07/2023 ]

• Designed an End-to-End ticket system using Figma for project design.
• Implemented Java with Spring Boot Framework for the backend.
• Utilized HTML, CSS, and JS for the frontend, with plans to integrate the VUE framework.
• Connected PostgreSQL as the database backend.
• Developed three main pages: main, payment, and ticket.
• Integrated functionality to view flight details and choose a flight from the database on the main page.
• Implemented a process to redirect users to the payment page after selecting a flight.
• Enabled users to enter necessary information for payment, saved this data into the database, and generated a ticket.
• Displayed the ticket number, seat number, and additional flight information on the ticket page.

GitHub Repositories:

• Backend: https://github.com/yakupcan21/TKTestKnowledgeBE
• Frontend: https://github.com/yakupcan21/TKTestKnowledgeFE

AI-supported Lung Cancer Detection and Report platform [ 11/2023 – 01/2024 ]

• Designed an end-to-end system for an AI-supported Lung Cancer Detection and Report platform, utilizing Adobe for the design phase.
• Developed the backend using Java with the Spring Boot Framework, with Python employed for the AI component.
• Utilized React for the frontend, complemented by Adobe for design elements.
• Implemented a PostgreSQL database backend.
• Created three main pages: Patient Information, AI Analysis, and Report.
• Initiated the application with the Patient Information page for inputting relevant data received from patients.
• Leveraged Python-based algorithms on the AI Analysis page for processing information and detecting potential lung cancer cases.
• Enabled doctors to generate detailed reports for each patient on the Report page.
• Emphasized teamwork by involving multiple collaborators, managing backend and frontend components through GitHub repositories for seamless coordination.

GitHub Repositories: 

• Backend: https://github.com/yakupcan21/medicBE
• Frontend: https://github.com/yakupcan21/medicfrontend

Computer Vision-based Mammogram Analysis and Entity Extraction from Health Reports using Natural Language Processing - Teknofest Project[ 01/2024 – Current ]

• Led Team Mergen Aya in the Teknofest artificial intelligence in medicine category.
• Developed a project focused on chest qualification, mass detection, and BI-RADS score estimation.
• Implemented boundary box marking for identified abnormalities.
• Integrated natural language processing models to extract entities from health reports, aiming to simplify the doctor's reading process.
• Utilized Python programming language and various deep learning and machine learning algorithms.
• Employed intensive data preprocessing techniques to enhance the quality of data fed into the models.

• Veri Bilimi Zirvesi, BTK, Ankara
• WEB3 Teknolojileri Zirvesi, BTK, Ankara
• Siber Güvenlik Haftası, Congresium, Ankara

• Leadership
• Speaker
• Team-Worker

• AM
• B1
• B

• Date of Birth: 09/08/2003
• Gender: Male
• Nationality: Turkish

• Student Of The Month, American School of Kosova, 02/2021
• Student Of The Year in Physics, American School of Kosova, 06/2021
• TEDx Youth, ASKosova, 02/2021

• Head of Socities
• Current Chairman of Gazi AI Society.
• Previous Vice President (Responsible of Foreign Affairs) and Board Member of the Gazi AI Society and Board Member of Gazi BMT.
• Team Captain in Teknofest Projects (Gazi Mergen 3 and Mergen Aya)