Artificial Intelligence and Intelligent Systems


Foundational and applied research in Artificial Intelligence and Machine Learning for Intelligent Systems for both industrial, medical and business applications. The research focuses on methods and techniques enabling learning, reasoning, Explainable AI, 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, multimodal and lifelong machine learning, knowledge discovery, ontologies, domain knowledge, instance-based learning, deep learning, Explainable AI,  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 universities 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 sensors, data fusion and sensor signal abstraction
  • Research on Multimodal and lifelong machine learning,
  • 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
  • Research on Trustworthy AI including technical robustness, explainability/transparency, algorithmic fairness and auditability


See a Video about the Group


Awards and Achievements  

The Artificial Intelligence and Intelligent Systems group at Mälardalen University is one of the 6 most productive and successful AI groups in Sweden according to Sweden's innovation agency (Vinnova’s governmental report of April 2018). 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, internationally, at companies (ABB, Siemens, Hägglunds, etc) and by government organisations (e.g. VR, Vinnova) and conferences with an audience of up to 1000 participants. Both Peter Funk and Shaina Begum were nominated and are listed on IVAs (the Royal Swedish Academy of Engineering Sciences) 100-list of influential researchers in digitalization (based on a set of criteria including innovation potential, productivity, application in society, and scientific excellence)

Teaching and Bachelor/Masters Thesis

The AI group has courses on all levels from bachelor level, a 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's and Master's programs are confronted with solving real problems for health care and industry and in their master's thesis, they are strongly linked to ongoing research projects. Many 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 systemsMachine Learning With Big Data (a distance course for industrial professionals), Deep learning for industrial imaging, Predictive analytics etc.

Funding and Grants

The AI group collaboration with industry and projects are in healthcare, road safety, air transport management, and industry 4.0. T industry and business sector with an interest and need for a Trustworthy 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 in 2001 by Peter Funk and is well integrated into 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 of 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 of 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, lecturers 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. 


Project TitleStatus
xApp: Explainable AI for Industrial Applications active
ADAPT2030, Adaptive lifecycle design by applying digitalization and AI techniques to production - Adapt 2030 active
ARCUS, Autonomous reconnaissance capability for unmanned aerial systems active
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road active
CPMXai:Cognitive Predictive Maintenance and Quality Assurance using Explainable Ai and Machine Learning active
CTEDS, Cooperative Perimeter Protection with Heterogeneous Drone Swarms active
DIGICOGS:DIGital Twins for Industrial COGnitive Systems through Industry 4.0 and Artificial Intelligence active
FitDrive: Monitoring devices for overall FITness of DRIVErs active
HeatTrack: Enhanced Reliability, Monitoring and Diagnostics of Complex Cooling Systems through Advanced Thermal Management active
MALPA:Machine Learning for the prevention of occupational accidents in the construction industry active
MONITOR: A Data-driven Intelligent MONITORring System to Improve Quality of Working Life active
NFFP7-DYMA:System for dynamic matching of aviation maintenance capabilities and tactical needs using machine learning and big data active
PDF: Personalized, Dynamic and Flexible Educational Model for Industrial Professionals active
PICO - Philosophy of Information and Computing active
PREST:Predictive Strategy using Machine Learning for Smart Test Case Selection active
AIM, Artificial Intelligence in Medical Applications finished
AproC, Automated Process Control finished
Artificiell Intelligens för att förvandla kvalitetsregister till individanpassat beslutstöd i vården, 2017-01555 finished
AUTOMAD:AUTOnomous Decision Making in Industry 4.0 using MAchine Learning and Data Analytics finished
Computational Intelligence in Process Modelling and Prediction finished
CREATE ITEA2 finished
E-MOTIONS finished
EKEN-Efficient knowledge and experience reuse within the business world finished
El-hybrid hjullastare, Utveckling och analys med avseende på energieffektivitet, säkerhet och körbarhet finished
Embedded Sensor Systems for Health Plus finished
EMOPAC - Evolutionary Multi-Objective Optimization and Its Applications in Analog Circuit Design finished
ESS-H - Embedded Sensor Systems for Health Research Profile finished
ExAct, Intelligent experience sharing for industrial applications finished
Food4Health: A Personalized System for Adaptive Mealtime Situations for Elderly finished
FuturE finished
Genetic Algorithm Theory finished
HR R-peak detection quality index analysis finished
IMod - Intelligent Concentration Monitoring and Warning System for Professional Drivers finished
InMaint - Intelligent Monitoring and Maintenance in Production Industry finished
Into DeeP finished
INVIP: Indoor Navigation for Visual Impairment Persons using Computer Vision and Machine learning finished
Pain Out, WP decision support for pain relief finished
PROEK, Ökad Produktivitet och Livskvalitet finished
SafeDriver: A Real Time Driver's State Monitoring and Prediction System finished
SIMMILAR: Systems-of-Systems for Intelligent Manufacturing Maintenance using Industry 4.0, Lean, AI Reasoning finished
SimuSafe : Simulator of Behavioural Aspects for Safer Transport finished
Third Eye: An Intelligent Assisting Aid for Older Individuals with a Recently Acquired Visual Impairment finished
V-trustEE finished
VDM - Vehicle Driver Monitoring finished

[Show all publications]

Research Issues and Challenges in the Computational Development of Trustworthy AI (Aug 2024)
Shahina Begum, Mobyen Uddin Ahmed, Shaibal Barua, Md Alamgir Kabir , Abu Naser Masud
IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET 2024)

Balancing Fairness: Unveiling the Potential of SMOTE-Driven Oversampling in AI Model Enhancement (May 2024)
Md Alamgir Kabir , Mobyen Uddin Ahmed, Shahina Begum, Shaibal Barua, Md Rakibul Islam
International Conference on Machine Learning Technologies (ICMLT)

Enhancing Speech Emotion Recognition Using Deep Convolutional Neural Networks (May 2024)
M M Manjurul Islam , Md Alamgir Kabir , Alamin Sheikh , Muhammad Saiduzzaman , Abdelakram Hafid, Saad Abdullah
International Conference on Machine Learning Technologies (ICMLT)

Deep-IDS: A Real-time Intrusion Detector for IoT Nodes using Deep Learning (May 2024)
Sandeepkumar Racherla , PRATHYUSHA SRIPATHI , Nuruzzaman Faruqui , Md Alamgir Kabir
Journal of IEEE Access (IEEE-Access)

Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields (Apr 2024)
Marcus Bengtsson, Mobyen Uddin Ahmed, Ricky Stanley D Cruze , Peter Funk, Tomohiko Sakao , Rickard Sohlberg Dr H.C.

Examining Decision-Making in Air Traffic Control: Enhancing Transparency and Decision Support Through Machine Learning, Explanation, and Visualization: A Case Study (Mar 2024)
Christophe Hurter , Augustin Degas , Arnaud Guibert , Maelan Poyer , Nicolas Durand , Alexandre Veyrie , Ana Ferreira , Stefano Bonelli , Mobyen Uddin Ahmed, Waleed Reafee Sbu Jmoona, Shaibal Barua, Shahina Begum, Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Fabio Babiloni , Pietro Aricò
16th International Conference Agents and Artificial Intelligence (ICAART2024)

Mobyen Uddin Ahmed, Professor

Room: U1-089
Phone: +46-021-107369

Peter Funk, Professor

Room: U1-126
Phone: +46-21-103153

Shahina Begum, Professor

Room: U1-089
Phone: +46-21-107370