(73 ratings) 9147 views
A dress-up game live on Google Play and the App Store with 14 models, 1,000+ outfits, accessories, hairstyles and makeup options, and four pet packs. It uses a Flutter client and server-side purchase verification on Firebase Cloud Functions; layout defects were closed by measurement rather than guesswork. Started in April 2026; PEGI 3, built under the family-policy rules.
(56 ratings) 7238 views
A secure and scalable HSM-backed digital signature infrastructure was developed for KamuSM-compliant XML (XAdES) and PDF (PAdES) signing/verification processes. With REST APIs, PKCS#11 integration, asynchronous queue architecture, and Docker-based deployment, the solution delivered high performance, regulatory compliance, and strong operational observability.
(22 ratings) 3872 views
More than 10 mission-critical VB.NET services, including domain activation, hosting provisioning, payment processing, customer management, and renewal workflows, were modernized and migrated to a more sustainable, observable, and high-performance .NET 7 architecture for one of Turkey’s high-traffic hosting platforms. This transformation reduced production error rates, improved API response times, shortened incident detection time, and significantly increased operational reliability in production environments.
(21 ratings) 4294 views
Contributed to the development of a real-time video-based collaboration platform designed for remote interviews, field operations, digital channel management, and enterprise communication scenarios. With a Jitsi-based communication infrastructure, a high-concurrency service architecture, performance optimizations, and operational modules, the platform evolved into a reliable, scalable, and enterprise-ready solution.
(36 ratings) 5117 views
A deep learning-based classification system was developed to detect Parkinson’s disease through voice signal analysis. In the project, multiple phonation recordings of the vowel “A” were analyzed to predict the presence of the disease. An end-to-end machine learning pipeline covering data preprocessing, feature extraction, model training, and evaluation was established, achieving approximately 92% classification accuracy.
