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.
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 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 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.
|First Name||Last Name||Title|
|Atiq Ur||Rehman||Post Doc|
|Md Alamgir||Kabir||Post Doc|
|Md Rakibul||Islam||Doctoral student|
|Mir Riyanul||Islam||Doctoral student|
|Rickard||Sohlberg Dr H.C.||Senior Project Manager|
|Ricky Stanley||D Cruze|
|Sharmin Sultana||Sheuly||Doctoral student|
|Waleed Reafee Sbu||Jmoona|
|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|
|ARTIMATION :TRANSPARENT ARTIFICIAL INTELLIGENCE AND AUTOMATION TO AIR TRAFFIC MANAGEMENT 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|
|MALPA:Machine Learning for the prevention of occupational accidents in the construction industry||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|
|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|
|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|
|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|
|VDM - Vehicle Driver Monitoring||finished|
A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions (Mar 2023) Arnab Barua, Mobyen Uddin Ahmed, Shahina Begum Journal of IEEE Access (IEEE-Access)
Quantitative Performance Analysis of Machine Learning Model from Discrete Perspective: A CaseStudy of Chip Detection in Turning Process (Mar 2023) Sharmin Sultana Sheuly, Mobyen Uddin Ahmed, Shahina Begum 15th International Conference on Agents and Artificial Intelligence (ICAART2023)
Interpretable Machine Learning for Modelling and Explaining Car Drivers' Behaviour: An Exploratory Analysis on Heterogeneous Data (Feb 2023) Mir Riyanul Islam, Mobyen Uddin Ahmed, Shahina Begum 15th International Conference on Agents and Artificial Intelligence (ICAART2023)
Artificial Intelligence-based Life Cycle Engineering in Industrial Production: A Systematic Literature Review (Jan 2023) Hamidur Rahman, Ricky Stanley D Cruze , Mobyen Uddin Ahmed, Rickard Sohlberg Dr H.C., Tomohiko Sakao , Peter Funk Journal of IEEE ACCESS (IEEE ACCESS)
Cognitive Architectures Based on Natural Info-Computation (Dec 2022) Gordana Dodig-Crnkovic Philosophy and Theory of Artificial Intelligence 2021 (PTAI-2021)