Artificial Intelligence and Intelligent Systems
Focus:
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.
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.
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
Awards and Achievements
The Artificial Intelligence and Intelligent Systems group at Mälardalen University is one of the most productive and successful AI groups in Sweden according to Sweden's innovation agency. (Vinnova’s governmental report of April 2018, ranked 6th amongst universities and institutes in Sweden). Members of the group are frequently notified by Research gate to be the most read authors of scientific papers in the department. Members of the group are frequently invited to give speeches, both internationally, at companies (ABB, Siemens, Hägglunds, etc) and by government organisations (e.g. VR, Vinnova) and conferences with an audience up to 1000 participants.
Teaching and Bachelor/Masters Thesis
The AI group has courses on all levels from bachelor level, master level, PhD courses and for companies where staff wish to extend their knowledge in Artificial Intelligence. The courses are consistently getting high ratings from students in course evaluation. Students in our Bachelors and Masters programs are confronted with solving real problems for health-care and industry and in their master thesis they are strongly linked to ongoing research projects. A large number of master’s projects and research projects have been performed with both SME companies and large companies, e.g. SAAB Group, Volvo CE, ABB Automation, GKN (former Volvo Aero), Volvo Cars, SKF, Ericsson and Siemens.
To mention some courses those we involved in teaching: Applied Artificial Intelligence, Project in intelligent embedded systems, Machine Learning With Big Data (a distance course for industrial professionals), Deep learning for industrial imaging, Predictive analytics etc.
Funding and Grants
The AI group has much collaboration with industry and projects in healthcare, industry and business sector with an interest and need for AI and ML competence. We are always looking for new companies and organisations to collaborate with, much of the funding we receive requires co-funding from companies and we are always looking for partners for interesting and applied AI projects, don’t hesitate to contact us if you have an idea or challenge we may help with, we are both collaborating with large multinational, organisations and Small medium sized companies.
History of AI group at MDH
The group was founded 2001 by Peter Funk and is well integrated in the global AI community and the Swedish network for artificial intelligence and machine learning. Peter was the chairman of the Swedish AI society (2006-2009), invited as a conference chair/organiser (ECAI Spain 2004, SAIS Västerås 2005, SCAI 2008 at IVA, ICCBR 2018 hosted by the group, located at Stockholmsmässan), Members in the group are also frequently asked to be part if examination boards, promotion issues, invited guest editors and reviewers for high ranked journals. Strong track record in applied research.
The Artificial Intelligence group is part in one of 4 key research groups within Mälardalen University’s Embedded Systems research profile (one of Mälardalen University's six prioritized research and education profiles with +200 researchers, lecturer and PhD students) and the Artificial Intelligence and Intelligent Systems group is also an active partner in Innovation and Product Realisation profile with joint research projects and applications.
A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS (Jan 2019) Mobyen Uddin Ahmed, Staffan Brickman , Alexander Dengg , Niklas Fasth , Marko Mihajlovic , Jacob Norman International Conference on Modern Intelligent Systems Concepts (MISC'18)
A Vision based Indoor Navigation System for Individual with Visual Impairment (Jan 2019) Mobyen Uddin Ahmed, Mohammed Ghaith Altarabichi, Shahina Begum, Fredrik Ginsberg , Robert Glaes , Magnus Östgren , Hamidur Rahman, Magnus Sörensen International Conference on Modern Intelligent Systems Concepts (MISC'18)
Automatic Driver Sleepiness Detection using EEG, EOG and Contextual Information (Jan 2019) Shaibal Barua, Mobyen Uddin Ahmed, Christer Ahlström , Shahina Begum Expert Systems with Applications (ESWA)
Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy - A Machine Learning Approach (Aug 2018) Mobyen Uddin Ahmed, Peter Andersson , Tim Andersson , Elena Tomas Aparicio , Hampus Baaz , Shaibal Barua, Albert Bergström , Daniel Bengtsson , Jan Skvaril , Jesús Zambrano 10th International Conference on Applied Energy (ICAE2018)
Cognition as Embodied Morphological Computation (Aug 2018) Gordana Dodig-Crnkovic Philosophy and Theory of Artificial Intelligence 2017 (PT-AI 2017)
Towards Distributed k-NN similarity for Scalable Case Retrieval (Jul 2018) Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed The Third Workshop on Synergies between CBR and Machine Learning ( CBRML 2018)
