Dr. Shahina Begum, Professor, deputy leader of the Artificial Intelligence and Intelligent Systems group at MDH. Shahina’s research focuses on developing intelligent systems in medical and industrial applications. Shahina Begum received her PhD in Computer Science/Artificial Intelligence in 2011, Mälardalen University. Her research areas are Decision Support Systems, Knowledge-based Systems, Machine Learning, Big Data Analytics, and Intelligent Monitoring Systems.
Shahina has been the principal applicant and project manager for a number of research projects at MDH. She has received a Swedish Knowledge Foundation’s Prospect individual grant for prominent young researchers in 2011 and is today leading several research projects in the area of intelligent -monitoring and prediction systems in collaboration with industrial partners. Shahina has been listed amongst the 100 most relevant researchers in digitalization by the Royal Swedish Academy of Engineering Sciences 2020.
Shahina has been involved (as course main responsible/designer/teacher/examiner) of total 17 distant and campus-based courses/learning modules mainly in Artificial Intelligence and Machine learning at MDH both for regular students and industrial professionals. She is the co-applicant and main responsible for the Artificial Intelligence contents for the proposal “Bachelor program in Applied AI” at MDH. Shahina has been involved in several initiatives for lifelong learning at MDH for example,
Shahina Begum has an extensive involvement of both research and teaching activities driven by industry needs and collaborative initiatives with both the public and private sectors. Shahina is active in the research community and has served as a steering committee member, program chair, co-chair and organizer of international conferences and workshops.
Press:
http://www.byggnorden.se/projekt/mdh-forskning-ska-fa-rebygga-arbetsplatsolyckor-pa-byggen
https://www.fagersta-posten.se/logga-in/manga-olyckor-pa-byggen-nu-ska-mdh-gora-arbetet-sakrare
https://hallbartbyggande.com/mer-kunskap-om-maskininlarning-ska-forebygga-arbetsplatsolyckor/
https://maskinentreprenoren.se/ai-ska-hindra-dodliga-olyckor/
http://anlaggningsvarlden.se/ny-forskning-ska-forebygga-arbetsplatsolyckor-pa-byggen/
https://www.entreprenad.com/article/view/695175/ai_ska_forebygga_olyckor_pa_byggen
http://www.orebronyheter.com/mdh-forskning-ska-forebygga-arbetsplatsolyckor-pa-byggen/
https://twitter.com/NCC_AB/status/1219531303093252096
https://www.instagram.com/p/B2hK5bEjR-w/?igshid=10lp16ebrn2gm&fbclid=IwAR1RNz-hBEF-qmqgImb7PeDFUcsocVj0URzIhEkrExXFlcsVxmIKgAx0JC4
https://www.vlt.se/logga-in/forskning-om-hur-folk-mar-och-beter-sig-i-trafiken-ska-minska-olycksriskerna
https://www.mdh.se/en/malardalen-university/articles/free-ai-education-to-improve-production-in-swedish-process-industry
Convolutional Neural Network for Driving Manoeuvre Identification based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) (Sep 2020) Mobyen Uddin Ahmed, Shahina Begum Frontiers in Sustainable Cities-Governance and Cities (Advances in Road Safety Planning) (Frontiers)
Machine Learning for Cognitive Load Classification – a Case Study on Contact-free Approach (Aug 2020) Mobyen Uddin Ahmed, Shahina Begum, Rikard Gestlöf , Hamidur Rahman, Johannes Sörman 16th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2020)
Artificial Intelligence, Machine learning and Reasoning in Health Informatics – Case Studies (Aug 2020) Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum Signal Processing Techniques for Computational Health Informatics (SPT)
Artificial Intelligence, Machine learning and Reasoning in Health Informatics – An Overview (Aug 2020) Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum Signal Processing Techniques for Computational Health Informatics (SPT)
Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification (Aug 2020) Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum Brain Sciences (Special Issue: Brain Plasticity, Cognitive Training and Mental States Assessment) (Brain Sci)
A Novel Mutual Information based Feature Set for Drivers’ Mental Workload Evaluation using Machine Learning (Aug 2020) Mir Riyanul Islam, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum, Pietro Aricò , Gianluca Borghini , Gianluca Di Flumeri Brain Sciences (Special Issue: Brain Plasticity, Cognitive Training and Mental States Assessment) (Brain Sci)
Mohammed Ghaith Altarabichi (former)
Shaibal Barua (former)
Hamidur Rahman
Mir Riyanul Islam
Sara Abbaspour (former)
Sharmin Sultana Sheuly (former)
Taha Kahn (former)