Krydus Artificial Intelligence

Machine Learning, Computer Vision, Deep Learning, Data Mining, Cleaning, and Analysis

Krydus Artificial Intelligence

Krydus Artificial Intelligence automates time-consuming manual tasks, extract hidden insights, and gain unrealized efficiencies through State of the Art proprietary algorithms. Krydus provides automated and tailored machine learning solutions across all disciplines.

Machine Learning

A method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Computer Vision

A field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”

Deep Learning

Concepts of distributed computing impact networking from the application layer to the physical layer, from cloud servers in the core network to sensors in wireless edge networks. Krydus works on foundational aspects of distributed computing, with particular focus on fault-tolerance, security, and timeliness properties. Formal methods are used to specify and verify designs, often with component-based reasoning. Self-stabilization and other self-* designs are studied for dynamic and/or mobile networks. And scalability of distributed algorithms, both in terms of limits and feasibility is addressed.

Deep Learning Model

Data Mining, Cleaning, & Analysis

A process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.

Features

  • Autonomous UAS Search and Rescue
  • SLAM, Kalman Filter, Object Detection
  • Autonomous Construction Robotics
  • ROS, Control Systems
  • Autonomous Data Cleaning / Labeling
  • Reinforcement Learning
  • Active Learning
  • Bayesian Meta-Learning