Shaibal Barua is a Senior Lecturer at Mälardalen University in Computer Science and working with the Artificial Intelligence and Intelligent Systems group. He received his Ph.D. in computer science in 2019 from Mälardalen University. In 2013 he received his MSc in Computer Science and in 2015 he received licentiate degree from Mälardalen University.

Research Interest:

Applied Artificial Intelligence & Machine Learning
Data Analytics
Statistical Learning Theory
Signal Processing and Multi-sensor Data Fusion
Distributed AI and ML for big data

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Latest publications:

Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction (Jun 2023)
Waleed Reafee Sbu Jmoona, Mobyen Uddin Ahmed, Mir Riyanul Islam, Shaibal Barua, Shahina Begum, Ana Ferreira , Nicola Cavagnetto
19th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

Usage of more transparent and explainable conflict resolution algorithm: air traffic controller feedback (Oct 2022)
Christophe Hurter , Augustin Degas , Arnaud Guibert , Nicolas Durand , Mir Riyanul Islam, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum, Stefano Bonelli , Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Pietro Aricò , Fabio Babiloni
Conference of the European Association for Aviation Psychology (EAAP22)

When a CBR in Hand is Better than Twins in the Bush (Sep 2022)
Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Mir Riyanul Islam, Rosina O Weber
Fourth Workshop on XCBR: Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR)

Transparent Artificial Intelligence and Automation to Air Traffic Management Systems: Conflict Detection and Resolution (Aug 2022)
Christophe Hurter , Augustin Degas , Mir Riyanul Islam, Shaibal Barua, Hamidur Rahman, Minesh Poudel , Daniele Ruscio , Mobyen Uddin Ahmed, Shahina Begum, Md Aquif Rahman, Stefano Bonelli , Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Pietro Aricò , Fabio Babiloni
International Conference on Cognitive Aircraft Systems ICCAS (ICCAS2022)

A Systematic Review of Explainable Artificial Intelligence in terms of Different Application Domains and Tasks (Jan 2022)
Mir Riyanul Islam, Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum
Applied Sciences (ApplSci)

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory (Jan 2022)
Augustin Degas , Mir Riyanul Islam, Christophe Hurter , Shaibal Barua, Hamidur Rahman, Minesh Poudel , Daniele Ruscio , Mobyen Uddin Ahmed, Shahina Begum, Md Aquif Rahman, Stefano Bonelli , Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Fabio Babiloni , Pietro Aricò
Applied Sciences (ApplSci)

Project Title	Status
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
DIGICOGS:DIGital Twins for Industrial COGnitive Systems through Industry 4.0 and Artificial Intelligence	active
Embedded Sensor Systems for Health Plus	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
PREST:Predictive Strategy using Machine Learning for Smart Test Case Selection	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
Software Center	active
TRUSTY: TRUSTWORTHY INTELLIGENT SYSTEM FOR REMOTE DIGITAL TOWER	active
VDM - Vehicle Driver Monitoring	finished

MSc theses supervised (or examined):

Thesis Title	Status
	available
A systematic review on theoretical aspects of k-nearest neighbour algorithm	available
An intelligent system for driver cognitive load detection using eye tracking data	available
Data-driven cognitive load classification system using machine-learning algorithm	available
Distributed case retrieval for big data using Spark platform and Case-Based Reasoning	selected
Applying artificial intelligence to identify drivers cognitive load based on correlation between EEG signals and driving behaviour signals	in progress
Machine Learning applied on embedded system recordings	in progress
Case representation methodology for a scalable Case-Based Reasoning	finished
Feature selection of EEG-signal data for cognitive load	finished
Stress Management for Pilots	finished

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