Shahina Begum, Professor


Dr. Shahina Begum, Professor, and deputy leader of the Artificial Intelligence and Intelligent Systems group at MDU. Shahina’s research focuses on developing intelligent systems in medical and industrial applications. Shahina Begum received her PhD in Artificial Intelligence in 2011, from Mälardalen University. Her research areas are Artificial Intelligence, Multimodal Machine Learning and reasoning, Explainable AI (XAI), Data Analytics, Decision Support Systems, Knowledge-based Systems, and Intelligent Monitoring and Prediction Systems. 

Shahina has been the principal applicant and project manager for a number of research projects at MDU. She 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 sustainable AI algorithm development by the Royal Swedish Academy of Engineering Sciences 2020. 

Shahina has been involved (as the course main responsible/designer/teacher/examiner) of total 21 distance and campus-based courses/learning modules mainly in Artificial Intelligence and Machine learning at MDU both for regular students and industrial professionals. She is the co-applicant and main responsible for the Artificial Intelligence content for the proposal “Bachelor program in Applied AI” at MDU. Shahina has been involved in several initiatives for lifelong learning at MDU for example,

  • IntoDeep: Developed AI and Deep Learning materials for process industries, serving as the project leader.
  • KIT: Led the Work Package ‘AI and Big Data for production industries’, responsible for courses ‘Introduction to Machine Learning’ and ‘Machine Learning for Industry 4.0’.
  • PROMPT: Professional Master’s program in Software Engineering, course responsible for 'Machine Learning with Big Data', attracting over 500 applicants every year during 2018 – 2024.
  • MOOC Course: Designed and facilitated the ‘Basic Knowledge on ML’ MOOC course and AIClass ( MOOC course.
  • PDF: Implemented a Personalized, Dynamic, and Flexible Educational Model for Industrial Professionals.
  • DECREASE: Responsible for the 'Trustworthy AI course' funded by KK-Stiftelsen.
  • CyberSäk: AI Security course within the Master’s program in Cybersecurity, funded by KK-Stiftelsen (Swedish Knowledge Foundation).
  • FuturE’: courses on 'Predictive Data Analytics' and 'Deep Learning for Industrial Imaging' as part of the NU 17 initiative funded by the Swedish Knowledge Foundation. 

Shahina Begum has extensive involvement in 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 grading committee member, evaluator of promotions, evaluator of funding applications, advisory board member, steering committee member, program chair, co-chair and organizer of international conferences and workshops.

Popular Science Activities 

  • Press release on 'Kognitivt prediktivt underhåll, en ny trend?' (Cognitive predictive maintenance, a new trend?), Nordiske Medier, 16 Jan, 2024
  • Press release published an article in Air Traffic Technology International Magazine based on the interview, 2023
  • Interview on P4 Västmanland Radio, ‘ett AI-system som ska minska flygförseningar på flygplatser’, 2023
  • Interview and press release on Industry-leading magazine Aviation Today (covering NextGen, connectivity and technical aviation intelligence) 'New AI System Offers Potential Air Traffic Management Solutions', Avionics International, 24 Mar, 2023
  • Press release on 'MDU forskare hjalper flygtrafiken', VLT, 28 Feb, 2023
  • Interviewed by Framgång (a local Swedish newspaper VLT's Business Magazine) and a press release is published 'Want to increase trust in artificial intelligence', 26 Oct, 2022
  • Press release 'Projekt ska förutse behov av underhåll på industrier' (Project will predict the need for maintenance at industries), 25 Mar, 2022
  • Interviewed and press release 'Explanation towards Trustworthy AI' published by new papers Dagens Industri, is a financial newspaper, Jun 7, 2022
  • Press release on 'MDH-forskning ska förebygga arbetsplatsolyckor på byggen' (MDH research aims to prevent workplace accidents on construction sites), BIg Norden, 1st Jan, 2020
  • Press release on 'Mer kunskap om maskininlärning ska förebygga arbetsplatsolyckor' (More knowledge about machine learning should prevent workplace accidents), Hållbart Byggande, 15 Jan, 2020
  • Forskning om 'hur folk mar och beter sig i trafiken ska minska olycksriskerna', VLT, Jan, 2020
  • Press release 'Free AI-education to improve production in Swedish process industry', MDU news, 05th Nov, 2019
  • Press release on 'MDH develops digital twin to strengthen Swedish industry' MDU news, 8th Oct, 2020
  • Press release ’Forskning om hur folk mår och beter sig i trafiken ska minska olycksriskerna’ (Research into how people feel and behave in traffic should reduce the risk of
  • accidents). Interview published in the local newspaper. VLT 3rd July 2017
  • Press release 'With a new system AI vehicles get even smarter- MDH News', Jun 19, 2017
  • Press release 'Mälardalens högskola med i stort projekt nytt system ska göra självkörande bilar säkrare' (Mälardalen University with a major new system project to make self-driving cars safer). VLT, 20 Juni, 2017
  • Press release on ‘Saving lives with sensors’ article to present my research directions published in 'Pan European Networks Science and Technology', June 2014, issue 11.
  • Interviewed and press release ‘Vakna, du håller på att somna vid ratten!’ interview published in the local newspaper. Vestmanlands Läns Tidning, VLT 7th May,2011
  • Press release 'Unik forskning förhindrar trafikolycker för yrkesförare' ( Unique research prevents traffic accidents for professional drivers) article published in the "Swedish Newspaper for Research", nr 3/2011.
  • Speaker and organiser of the ‘Trustworthy AI Seminar'. Lecture on ‘From Explainability towards trustworthy AI’ and discuss the foundational challenges associated with ensuring the trustworthiness of current AI systems. 30th Jan, 2024
  • Invited talk on 'AI overview for Companies' organized by AI Sweden and CGI, 6th Dec, 2023, Kopparbergsvägen, Sweden
  • Invited talk on 'ChatGPT: Where the AI bot is moving towards?' arranged by deans of school MDU. 10th Mar 2023
  • Invited speaker on 'AI for health care' Seminar on Al in Health Care - research, development, and applications. Region Västmanland, Västerås hospital, 29th Sep 2023
  • Seminar on Artificial Intelligence for 25 retired persons (aged between 60 and 80), 14th Mar, 2018, MDH, Västerås.
  • Presentation and discussion on Artificial Intelligence and machine learning for year 5 and year 6 students (aged between 10 and 12) during their career day, 6th Dec, 2018, International English School, Västerås
  • Invited talk on ''A New Era Of Artificial Intelligence' Kunskapfest (Knowledge Festival), for students (aged between 16-20), 29th Oct 2022, Esklistuna, Sweden
  • Speaker on 'Trustworthy AI' for 'Management and IT' conference, 19th Apr 2023, Västerås, Sweden arranged by the national research school with 12 universities cooperating in the field of business administration and information system.
Biography, 26 Oct, 2022

My research activities in the field of AI are centred around research on AI that investigates the underlying principles, theories, and core concepts of AI algorithms, methods, and techniques as well as the demonstration of that in real complex application domains. 

As an AI researcher, my main goal is to enhance our understanding of AI systems—their theoretical foundations and capabilities to make the best use of them and promote
the deployment of AI in society and industry.

Shahina has been listed amongst the 100 most relevant researchers in sustainable AI algorithm development by the Royal Swedish Academy of Engineering Sciences 2020. 


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Latest 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)

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)

iXGB: Improving the Interpretability of XGBoost using Decision Rules and Counterfactuals (Mar 2024)
Mir Riyanul Islam, Mobyen Uddin Ahmed, Shahina Begum
16th International Conference Agents and Artificial Intelligence (ICAART2024)

Second-Order Learning with Grounding Alignment: A Multimodal Reasoning Approach to Handle Unlabelled Data (Feb 2024)
Arnab Barua, Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Andrea Giorgi
16th International Conference Agents and Artificial Intelligence (ICAART2024)

Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification (Nov 2023)
Arnab Barua, Mobyen Uddin Ahmed, Shahina Begum
IEEE International Conference on System Engineering and Technology (ICSET2023)

Project TitleStatus
xApp: Explainable AI for Industrial Applications active
AUTOMAD:AUTOnomous Decision Making in Industry 4.0 using MAchine Learning and Data Analytics finished
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
DIGICOGS:DIGital Twins for Industrial COGnitive Systems through Industry 4.0 and Artificial Intelligence active
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
ESS-H - Embedded Sensor Systems for Health Research Profile finished
FitDrive: Monitoring devices for overall FITness of DRIVErs active
Food4Health: A Personalized System for Adaptive Mealtime Situations for Elderly finished
FutureE finished
HeatTrack: Enhanced Reliability, Monitoring and Diagnostics of Complex Cooling Systems through Advanced Thermal Management active
HR R-peak detection quality index analysis finished
IMod - Intelligent Concentration Monitoring and Warning System for Professional Drivers finished
Into DeeP finished
INVIP: Indoor Navigation for Visual Impairment Persons using Computer Vision and Machine learning finished
IPOS, Integrated Personal Health Optimizing System finished
KIT - Competence needs and courses for professionals in IT and competitive production finished
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
NovaMedTech finished
PDF: Personalized, Dynamic and Flexible Educational Model for Industrial Professionals active
PROMPT - Professional Master’s in Software Engineering (step II, phase B&C) active
SafeDriver: A Real Time Driver's State Monitoring and Prediction System 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
PhD students supervised as main supervisor:

Md Rakibul Islam
Mohammed Ghaith Altarabichi (former)
Shaibal Barua (former)
Tahira Salwa Rabbi Nishat

PhD students supervised as assistant supervisor:

Arnab Barua
Hamidur Rahman (former)
Mir Riyanul Islam (former)
Sara Abbaspour (former)
Sharmin Sultana Sheuly (former)
Taha Kahn (former)

MSc theses supervised (or examined):
Thesis TitleStatus
Feature Selection through Artificial Intelligence for EEG Signal Classification available
A Decision Support System for medical diagnosis using Data Mining and Machine Learning available
A systematic review on theoretical aspects of k-nearest neighbour algorithm available
Activity monitoring in daily life using Shimmer sensing available
Correlation analysis among EEG, EOG and EMG signals for identification of ocular and muscle activities available
Data-driven actors modelling for road transportation available
Data-driven cognitive load classification system using machine-learning algorithm available
Data-driven Modelling on Powered Two Wheelers using Machine Learning available
Deep Learning based Eye Tracking and Head Movement Detection available
Deep learning to classify driving events using GPS data available
Detect drug abuse by AI processed eye movement data from a smart phone film available
GameAlyzer - a wearables and AI based system to monitor gambling and gaming available
Non-Contact Intelligent System to monitor driver’s alcoholic state using Biological Signals available
Artifact handling or filtering noise from the biological sensor signals EEG and ECG selected
Distributed case retrieval for big data using Spark platform and Case-Based Reasoning selected
A case study on Heart Rate Variability and Finger Temperature to use it in a stress diagnosis system finished
Using AI and Statistics on Structured Electronic Patient Records for Clinical Decision Support Systems finished
A decision support system for stress diagnosis using ECG signal. finished
An Intelligent Portable Sensor System in Diagnosing Stress finished
An optimized case matching algorithm in diagnosing the stress patients finished
Case representation methodology for a scalable Case-Based Reasoning finished
Decision Support System for Lung Diseases (DSS) finished
Decision support system: Knowledge capture and sharing for Telecom network management finished
Develop an Automated System for EEG Artifacts Identification finished
Evaluation of jCOLIBRI finished
Feature Extraction From Sensor Data To Represent And Matching Cases For Patient Health Care finished
Individual Stress Diagnosis Using Skin Conductance Sensor Signals finished
Intelligent System for Monitoring Physiological Parameters Using Camera finished
Investigation of Feature Optimization Algorithms for EEG Signal Analysis For Monitoring the Drivers finished
Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal finished
Multi-Sensor Information Fusion for Classification of Driver's Physiological Sensor Data finished