More Cases

Our AI team
with
deep AI expertise

VRLabs participated at the COST Action IC1406 High-Performance Modeling and Simulation for Big Data Applications and the I-Genoa project of the Municipality of Genoa -Italy
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VRLabs has also organized training courses and conferences on artificial intelligence and its applications.

TeamResourceManagement

VRLabs AI based solution to team selection problem: ranked list of
possible teams for each objective considered and a global list.

VRLabs AI based solution to team turnover problem: ranked list of
the most suitable candidates to replace the outgoing team member
.

SmartCity

I-Genoa project is one of the VRLabs AI based solution about smart city. It means a more connected city than ever before. Our team have worked on traffic monitoring and prediction, city security and also Intelligent Lamp & audio sensor installation under the structure of “Sopraelevata” road of Genoa.

SmartPredictiveMaintenance

Exploit sensor data from each component of the system to obtain a reliable estimate of the breakage likelihood of a sub-system in a given future time-frame.
Machine learning approach able to take a final decision with high confidence exploiting a minimal number of observations

AI Implementation

AI implementation involves integrating AI technologies into systems, processes, or applications to enhance their functionality, efficiency or capabilities.the specific form of implementation varies based on the use case, industry and goals.
Here’s an overview of the steps involved in AI implementation:

  • Define Goals: Clearly outline what you want to achieve with AI.
  • Data Preparation: Gather and clean data for AI algorithms.
  • Choose Techniques: Select suitable AI algorithms like machine learning or NLP.
  • Model Development: Train AI models using prepared data.
  • Integration: Incorporate AI models into your systems or processes.
  • Testing and Deployment: Test extensively and deploy the AI solution.
  • Maintenance: Maintain and update.
  • User Training and Support: Train users and provide ongoing support.
  • Continuous Improvement: Monitor and improve AI performance over time.
ONE OF OUR EXPERIENCE

TRL 7

AI on the edge:
Deep NN – Yolo v.5

Jetson Nano NVIDIA

Design stages

Sub-images labeling —-> DNN model training —-> Object classification on video streams