Shahina Begum, Professor

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 2011Mälardalen 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,

  • IntoDeep  (Project leader for MDH, AI and Deep learning materials for Process industries),
  • KIT (WP: AI and Big Data for Production Industries, leader for MDH ),
  • Prompt (Main responsible for the courses, 'Machine Learning with Big Data course' with more than 500 applicants in HT 2018, MOOC course: Basic Knowledge on ML),
  • FutureE (Main responsible for the course: Predictive Data Analytics,  Developer and teacher: Deep Learning for Industrial Imaging)


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.














[Show all publications]

Latest publications:

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)

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)

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)

Project TitleStatus
AUTOMAD:AUTOnomous Decision Making in Industry 4.0 using MAchine Learning and Data Analytics active
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road 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
Food4Health: A Personalized System for Adaptive Mealtime Situations for Elderly finished
FutureE 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
Machine Learning for the prevention of occupational accidents in the construction industry active
NovaMedTech finished
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 active
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:

Mir Riyanul Islam
Mohammed Ghaith Altarabichi (former)
Shaibal Barua (former)

PhD students supervised as assistant supervisor:

Hamidur Rahman
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