Materials Modelling for Nanocomposite Optimization – Nanografi Nano Technology
Nanografi Nanotechnology has been producing and supplying nano and micro particles such as graphene, fullerene, carbon nanotubes as well as 3D printer materials. The company has been developing new nanomaterial applications for challenging engineering works beside supplying them to over 80 countries. Numerous experiments are conducted for the optimized production of polymer-graphene composites by the company. This case study aims to reduce the cost and duration of the experiments through the material simulations using the National e-Infrastructure TRUBA.
Text Processing of Social Media Messages – Somera
Somera’s in-house developed systems process millions of social media posts and web pages for its clients and business partners. Their solutions require big data analysis and live streaming of the analysis results through dashboards. For the complete analytics workflow, Somera uses in-house servers as well as cloud resources. Within the scope of this case study, Somera will utilize National e-Infrastructure TRUBA to train and deploy AI-based models, porting components of its analytics workflow.
Public Transport Analysis Platform – Parabol Software
Parabol has been carrying out R&D activities in the intelligent transportation systems sector. The company has also been developing a “Public Transport Analysis Platform” called Cermoni. This platform aims to analyze the passengers’ boarding data obtained through smart cards and the GPS location data of the vehicles, such as buses and trams, collected from the public transportation system of a city. Within the scope of this case study, Parabol will run some modules of the Cermoni on National e-infrastructure TRUBA’s HPC clusters. The company also aims to port an optimized, high-performance version of the Origin-Destination matrix computation module onto the HPC environment.
Image-Based Content Moderation Project – MachineTutors
Machinetutors offers machine learning consultancy and custom AI services. This case study will try to solve real-time image-based content moderation using a custom image dataset crawled from the web with proper licenses and annotated by in-house tools. This training dataset comprises 600.000 images. Using the national HPC infrastructure TRUBA, the company will develop deep learning models to achieve highly accurate image categorization and minimize the adverse effects experienced by web users.
Fraud Detection with Graph Processing – Yapı Kredi Technology
Yapı Kredi Technology aims to use machine learning (ML) to detect fraudulent bank transactions. The goal is to provide the business unit with better predictions, reduce human effort, and increase the detection rate of accounts participating in fraudulent transactions. High-performance computing (HPC) capability is critical for effective experimentation and to build models based on graphs. NCC Turkey provides ML expertise and also resources from the national HPC infrastructure TRUBA for this case study.