Mobyen Uddin Ahmed, Professor


Mobyen Uddin Ahmed is a 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. His current research in trustworthy AI with several ongoing project. Mobyen has 150+ scientific publication and more than 3080+ citations.

Mobyen on ResearchGate

Mobyen on IVA list twice

He is involved in research and development since 2005 after completing his M.Sc. in Computer Engineering (Specialization in Intelligent Systems, thesis) from Dalarna University, Sweden. He received his PhD (thesis) in Artificial Intelligence/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 projects ‘SimuSafe’; Artimation; TRUSTY and FitDrive; Also, ‘BrainSafeDrive’, a bilateral project between Italy and Sweden funded by VR and Several national projects i.e., Digicogs, adapt2030, CPMXai and ‘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 Machine Learning, Machine Learning Concepts, 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. Also, he is involved in the development of learning materials for AI and deep learning in the project Into DeeP.


Available theses

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Journal Editors:

Sustainability Journal, IF:3.88, "Interpretable and Explainable AI Applications"

Sensors Journal, IF: 3.84, "Deep Learning in Biomedical Informatics and Healthcare"

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 different application domains such as healthcare, road safety, air safety, industry 4.0, energy, and software application domains.  He is using adaptive methods, algorithms and techniques to develop safe, and robust AI systems for increasing trustworthiness in AI systems that includes generative AI, AI Unification, XAI, human-centric AI, bias and fairness.

His current research also includes XAI, 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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Latest publications:

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)

Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields (Apr 2024)
Marcus Bengtsson, Mobyen Uddin Ahmed, Ricky Stanley D Cruze , Peter Funk, Tomohiko Sakao , Rickard Sohlberg Dr H.C.
THE 11th SWEDISH PRODUCTION SYMPOSIUM (SPS24)

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 (ICAART24)

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 (ICAART24)

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 (ICAART24)

Neurophysiological mental fatigue assessment for developing usercentred Artificial Intelligence as a solution for autonomous driving (Nov 2023)
Andrea Giorgi , Vincenzo Ronca , Alessia Vozzi , Pietro Aricò , Gianluca Borghini , Rossella Capotorto , Luca Tamborra , Ilaria Simonetti , Simone Sportiello , Marco Petrelli , Carlo Polidori , Rodrigo Varga , Marteyn van Gasteren , Arnab Barua, Mobyen Uddin Ahmed, Fabio Babiloni , Gianluca Di Flumeri
Frontiers in Neurorobotics (Front Neurorobot)

Project TitleStatus
xApp: Explainable AI for Industrial Applications active
ADAPT2030, Adaptive lifecycle design by applying digitalization and AI techniques to production - Adapt 2030 active
ARTIMATION :TRANSPARENT ARTIFICIAL INTELLIGENCE AND AUTOMATION TO AIR TRAFFIC MANAGEMENT SYSTEMS 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
ecare@home finished
EKEN-Efficient knowledge and experience reuse within the business world finished
Embedded Sensor Systems for Health Plus finished
ESS-H - Embedded Sensor Systems for Health Research Profile finished
ExAct, Intelligent experience sharing for industrial applications finished
FitDrive: Monitoring devices for overall FITness of DRIVErs active
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
Mätning av näringsstatus och förebyggande av undernäring genom integrering av hälsoteknik i äldrevården finished
MONITOR: A Data-driven Intelligent MONITORring System to Improve Quality of Working Life active
NovaMedTech finished
Pain Out, WP decision support for pain relief finished
PDF: Personalized, Dynamic and Flexible Educational Model for Industrial Professionals active
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 finished
Third Eye: An Intelligent Assisting Aid for Older Individuals with a Recently Acquired Visual Impairment finished
TRUSTY: TRUSTWORTHY INTELLIGENT SYSTEM FOR REMOTE DIGITAL TOWER active
PhD students supervised as main supervisor:

Arnab Barua
Hamidur Rahman (former)
Mir Riyanul Islam
Sharmin Sultana Sheuly

PhD students supervised as assistant supervisor:

Hadi Banaee (former)
Md Rakibul Islam
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