Mobyen Uddin Ahmed, Associate Professor

E-mail:
mobyen.ahmed@mdh.se
Room:
U1-089
Phone:
+46-021-107369
Phone (alt):
0736620804
Division:
Division of Computer Science and Software Engineering
Research groups:
Artificial Intelligence and Intelligent Systems
Biomedical Engineering
Web:
Official university homepage



Mobyen Uddin Ahmed is an Associate Professor in Artificial Intelligence/Computer Science at  Artificial Intelligence and Intelligent Systems group and a member of ESS-H - Embedded Sensor Systems for Health Research Profile. Mobyen has 125+ scientific publication and more than 1773+ citations.

Mobyen on ResearchGate

He is involved in research and development since 2005 after completing his M.Sc. in Computer Engineering (thesis) from Dalarna University, Sweden. He received his PhD (thesis) in computer science in 2011 from Mälardalen University. He has completed his postdoctoral study between the years 2012 and 2014 in Computer Science and Engineering (Center for Applied Autonomous Sensor Systems) at School of Science and Technology, Örebro University, Sweden

Mobyen is the co-applicant and project leader for MDH for the H2020 project ‘SimuSafe’; ‘BrainSafeDrive’, a bilateral project between Italy and Sweden funded by VR and a national project ‘InVIP’. He has been also involved in many other national and international projects, such as ecare@home, ESS-H, SafeDriver, PainOut, VDM, Prompt, FutureE, etc. He is one of the Principle Investigator of the research profile Embedded Sensor Systems for Health (ESS-H) at MDH.

 Ongoing conference, Advanced Artificial Intelligence & Robotics, ASPAI' 2020. Mobyen has been selected twice (i.e. HealthyIoT2016, HealthyIoT2017) to be the general chair of an international conference ‘International Conference on IoT Technologies for HealthCare’. He has organized several other international conferences namely ICCBR2018, pHealth2015, ESS-HIoT2015.

Mobyen is involved in teaching and is responsible for courses Applied Artificial Intelligence, Project in intelligent embedded systems, and Databases. He is also involved in the development and teaching for the course Machine Learning With Big Data (a distance course for industrial professionals), Deep learning for industrial imaging, Predictive Data Analytics, MooC course Ground Knowledge on Machine Learning (coming). Also, he is involved in the development of learning materials for AI and deep learning in the project Into DeeP.


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Mobyen is involved in AI and Machine Learning related research and development since 2005. He is currently involved with several national and H2020 projects within AI and ML related area.

His research focuses on developing intelligent systems in medical and industrial applications using multimodal machine learning and reasoning. He is trying to invent adaptive methods, techniques to develop intelligent systems for IOT- based data analytic environment.

His current research interest includes deep learning, case-based reasoning, data mining, fuzzy logic and other machine learning and machine intelligence approaches for analytics especially in Big data.

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[Google Scholar author page]

Latest publications:

Analysis of Breakdown Reports using Natural Language Processing and Machine Learning (Aug 2021)
Mobyen Uddin Ahmed, Marcus Bengtsson, Antti Salonen, Peter Funk
International Congress and Workshop on Industrial AI (IAI2021)

AI-LCE: Adaptive and Intelligent Life Cycle Engineering by applying digitalization and AI methods – An emerging paradigm shift in Life Cycle Engineering (May 2021)
Tomohiko Sakao , Peter Funk, Johannes Matschewsky , Marcus Bengtsson, Mobyen Uddin Ahmed
28th CIRP Conference on Life Cycle Engineering (CIRP LCE 2021)

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)

Project TitleStatus
Adaptive lifecycle design by applying digitalization and AI techniques to production - Adapt 2030 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
DIGICOGS:DIGital Twins for Industrial COGnitive Systems through Industry 4.0 and Artificial Intelligence active
ecare@home finished
EKEN-Efficient knowledge and experience reuse within the business world finished
Embedded Sensor Systems for Health Plus active
ESS-H - Embedded Sensor Systems for Health Research Profile finished
ExAct, Intelligent experience sharing for industrial applications 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
Mätning av näringsstatus och förebyggande av undernäring genom integrering av hälsoteknik i äldrevården finished
Monitoring devices for overall FITness of DRIVErs active
NovaMedTech finished
Pain Out, WP decision support for pain relief finished
Process Industrial Big Data Analytics finished
PROEK, Ökad Produktivitet och Livskvalitet 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
SIMMILAR: Systems-of-Systems for Intelligent Manufacturing Maintenance using Industry 4.0, Lean, AI Reasoning 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
TRANSPARENT ARTIFICIAL INTELLIGENCE AND AUTOMATION TO AIR TRAFFIC MANAGEMENT SYSTEMS active
PhD students supervised as main supervisor:

Hamidur Rahman
Mir Riyanul Islam
Sharmin Sultana Sheuly

PhD students supervised as assistant supervisor:

Hadi Banaee (former)
Mohammed Ghaith Altarabichi (former)
Shaibal Barua (former)

MSc theses supervised (or examined):
Thesis TitleStatus
available
Feature Selection through Artificial Intelligence for EEG Signal Classification available
A Decision Support System for medical diagnosis using Data Mining and Machine Learning available
Activity monitoring in daily life using Shimmer sensing available
An intelligent system for driver cognitive load detection using eye tracking data 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 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
Remote monitoring of physiological parameters using facial images available
Smart Mirror to monitor Health Status using Biological Signals available
Applying artificial intelligence to identify drivers’ cognitive load based on correlation between EEG signals and driving behaviour signals in progress
Human Emotion Detection using Deep Learning in progress
Machine Learning applied on embedded system recordings in progress
A decision support system for stress diagnosis using ECG signal. finished
A Generic System-level framework for Intelligent Sensor Data Management finished
Artificial Intelligence Search Algorithms In Travel Planning finished
Artificial Intelligence Search Algorithms In Travel Planning finished
Business intelligent systems for small and medium enterprises. finished
Case-based reasoning in postoperative pain treatment finished
Clinical Decision Support System for Post operative Pain relief finished
Decision Support System for Lung Diseases (DSS) finished
Develop an Automated System for EEG Artifacts Identification finished
Develop Experience Reusing System by Combining Vector Space Model and Nearest Neighbour. finished
Efficient Remote Instruction Procedures Using Case Base Reasoning finished
Experience Sharing Over the InternetCase study e-learning finished
Feature Extraction From Sensor Data To Represent And Matching Cases For Patient Health Care finished
Feature selection of EEG-signal data for cognitive load finished
Intelligent System for Monitoring Physiological Parameters Using Camera finished
Investigation of Feature Optimization Algorithms for EEG Signal Analysis For Monitoring the Drivers finished
MACHINE LEARNING BASED PEDESTRIAN EVENT MONITORING USING IMU AND GPS finished
Multi-Sensor Information Fusion for Monitoring Driver’s Level of Performance finished
Online fuzzy case-based individual stress diagnosing system finished
Test Oracle Automation with Machine Learning: A Feasibility Study finished
Textual CBR system using domain specific ontology finished
Using NLP and context for improved search result in specialized search engines finished