Seguir
Shaibal Barua
Shaibal Barua
Mälardalen University
Dirección de correo verificada de mdu.se - Página principal
Título
Citado por
Citado por
Año
A systematic review of explainable artificial intelligence in terms of different application domains and tasks
MR Islam, MU Ahmed, S Barua, S Begum
Applied Sciences 12 (3), 1353, 2022
1912022
Artificial Intelligence, Machine Learning and Reasoning in Health Informatics—Case Studies
MU Ahmed, S Barua, S Begum
Signal Processing Techniques for Computational Health Informatics, 261-291, 2021
1652021
Automatic driver sleepiness detection using EEG, EOG and contextual information
S Barua, MU Ahmed, C Ahlström, S Begum
Expert systems with applications 115, 121-135, 2019
1292019
Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning
S Begum, S Barua, MU Ahmed
Sensors 14 (7), 11770-11785, 2014
682014
A survey on artificial intelligence (ai) and explainable ai in air traffic management: Current trends and development with future research trajectory
A Degas, MR Islam, C Hurter, S Barua, H Rahman, M Poudel, D Ruscio, ...
Applied Sciences 12 (3), 1295, 2022
672022
A review on machine learning algorithms in handling EEG artifacts
S Barua, S Begum
The Swedish AI Society (SAIS) Workshop SAIS, 14, 22-23 May 2014, Stockholm …, 2014
412014
Classification of physiological signals for wheel loader operators using multi-scale entropy analysis and case-based reasoning
S Begum, S Barua, R Filla, MU Ahmed
Expert systems with applications 41 (2), 295-305, 2014
402014
A novel mutual information based feature set for drivers’ mental workload evaluation using machine learning
MR Islam, S Barua, MU Ahmed, S Begum, P Aricò, G Borghini, ...
Brain Sciences 10 (8), 551, 2020
342020
Non-contact-based driver’s cognitive load classification using physiological and vehicular parameters
H Rahman, MU Ahmed, S Barua, S Begum
Biomedical Signal Processing and Control 55, 101634, 2020
312020
Towards intelligent data analytics: A case study in driver cognitive load classification
S Barua, MU Ahmed, S Begum
Brain sciences 10 (8), 526, 2020
292020
Classifying Drivers' Cognitive Load Using EEG Signals.
S Barua, MU Ahmed, S Begum
pHealth, 99-106, 2017
282017
Automated EEG artifact handling with application in driver monitoring
S Barua, MU Ahmed, C Ahlstrom, S Begum, P Funk
IEEE journal of biomedical and health informatics 22 (5), 1350-1361, 2017
262017
Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.
S Barua, S Begum, MU Ahmed
pHealth, 241-248, 2015
242015
A machine learning approach for biomass characterization
MU Ahmed, P Andersson, T Andersson, ET Aparicio, H Baaz, S Barua, ...
Energy Procedia 158, 1279-1287, 2019
212019
Intelligent driver monitoring based on physiological sensor signals: Application using camera
H Rahman, S Barua, B Shahina
2015 IEEE 18th International Conference on Intelligent Transportation …, 2015
202015
Vehicle Driver Monitoring: sleepiness and cognitive load
E Nilsson, C Ahlström, S Barua, C Fors, P Lindén, B Svanberg, S Begum, ...
Statens väg-och transportforskningsinstitut, 2017
162017
Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning
H Rahman, MU Ahmed, S Barua, P Funk, S Begum
Sensors 21 (23), 8019, 2021
142021
Deep learning for automatic EEG feature extraction: an application in drivers’ mental workload classification
MR Islam, S Barua, MU Ahmed, S Begum, G Di Flumeri
Human Mental Workload: Models and Applications: Third International …, 2019
132019
Data analytics using statistical methods and machine learning: a case study of power transfer units
SS Sheuly, S Barua, S Begum, MU Ahmed, E Güclü, M Osbakk
The International Journal of Advanced Manufacturing Technology 114, 1859-1870, 2021
112021
Classification of ocular artifacts in EEG signals using hierarchical clustering and case-based reasoning
S Barua, S Begum, MU Ahmed, P Funk
Workshop on Synergies between CBR and Data Mining at 22nd International …, 2014
92014
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20