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Artificial Intelligence and Intelligent Systems

Foundational and applied research in Artificial Intelligence and Machine Learning for Intelligent Systems for both industry, medical and business applications. The research focuses on methods and techniques enabling learning, reasoning, experience reuse, and experience sharing. We work with both autonomous AI applications as well as decision support systems.



Peter Funk




Shahina Begum



To create intelligent behaviour in systems and services we use artificial intelligence including machine learning and reasoning, deep learning, data analysis, knowledge discovery, ontologies, domain knowledge, instance-based learning, deep learning, multi agent systems (MAS) to mention some of the methodologies and techniques. AI is today an essential "core" technology in many projects which is reflected in our broad collaboration with other groups, projects and universitys both national and international.

Research Focus:

  • Research on Machine Learning and Reasoning for a wide area of application in industry and health care for monitoring, classification, diagnostics, prediction and decision support.
  • Research on Data analysis, feature extraction and selection, data mining, and knowledge discovery
  • Research on Intelligent sensor, data fusion and sensor signal abstraction
  • Research on Big data to Smart data and Predictive analytics
  • Research on Distributed Artificial Intelligence and Machine Learning for Big data
  • Research on Deep learning for Image Processing and Computer Vision

Ongoing research projects

BRAINSAFEDRIVE will develop a tool as attentional detectors that detect drivers’ mental state in terms of stress, cognitive load, sleepiness in real time during simulated and/or natural driving situations.

Project manager at MDH: Mobyen Uddin Ahmed

Main financing: Vetenskapsrådet

In DIGICOGS, cutting-edge solutions will be achieved through data-driven analytics, real-time monitoring and intelligent adaptive prediction based on combination of information i.e, sensor data, domain and context.

Project manager at MDH: Mobyen Uddin Ahmed

Main financing: Vinnova, PiiA

The ESS-H+ profile will aim for an increased specialization, a significant progression, and raised scientific ambitions, as compared to ESS-H, through deepened co-production with our partner companies within one integrated Focus Area: Sensor systems for health monitoring/monitoring of humans.

Project manager at MDH: Maria Lindén

Main financing: KK-stiftelsen

The purpose of the project "Machine Learning for the prevention of occupational accidents in the construction industry" is to use the best-suited ML algorithm and ensure that the data collected can be used to prevent workplace accidents in the construction industry.

Project manager at MDH: Shahina Begum

Main financing: Vinnova

The purpose of the SIMUSAFE is to improve road safety by understanding the individual and collective behaviour of road users involved (drivers, two wheelers, pedestrians), their interaction between themselves and safety-related systems and services e.g. assess risk perception and decision making .

Project manager at MDH: Mobyen Uddin Ahmed

Main financing: H2020, Vinnova

Underhålls- och supportsystem behöva förmågan att samverka med andra system i nätverk samt funktioner samt inkluderar smarta sensorer, big-data analys, diagnostik, prognostik och resonerande system. Projektet kommer att utforska kombinationen av maskininlärning och big-data-teknik, för att matcha taktiska behov mot underhållsresurser.

Main financing: Vinnova

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