Publications

Development of explainable AI-based predictive models for bubbling fluidised bed gasification process

Published in Fuel, 2023

[Fuel`23] The study focused on developing seven regression models to predict gas composition and gas yield, employing an Explainable AI (XAI) method for interpretability. The gradient boosting algorithm outperformed other regression-based models. The application of SHAP (Shapley additive explanations) values was utilized to elucidate the impact of input variables on the target. The results suggest that XAI, particularly when coupled with the gradient boosting algorithm, serves as a valuable tool for enhancing decision-making processes in fluidized bed gasifiers, providing transparency and interpretability to the predictive models. Code are available: GitHub

Recommended citation: Pandey, D.S., Raza, H. and Bhattacharyya, S., 2023. Development of explainable AI-based predictive models for bubbling fluidised bed gasification process. Fuel, 351, p.128971.

Decoding numeracy and literacy in the human brain: insights from MEG and MVPA

Published in Scientific Reports, 2023

[23SciRep] This study investigates the temporal dynamics of number and letter processing using magnetoencephalography (MEG) data from two experiments with 25 participants each. The results reveal an early dissociation (~100 ms) between numbers and letters compared to false fonts. Number processing remains consistent when presented as isolated items or in strings, while letter processing exhibits distinct classification accuracy for single items versus strings. The findings suggest that early visual processing is differentially influenced by experiences with numbers and letters, with a stronger dissociation observed for strings. This research was funded by Economic and Social Research Council (ESRC) funded Business and Local Government Data Research Centre under Grant ES/S007156/1.

Recommended citation: Nara, S., Raza, H., Carreiras, M. and Molinaro, N., 2023. Decoding numeracy and literacy in the human brain: insights from MEG and MVPA. Scientific Reports, 13(1), p.10979.

CLEFT: Contextualised Unified Learning of User Engagement in Video Lectures With Feedback

Published in IEEE Access, 2023

[23IEEEAcc] The paper introduces CLEFT, a unified model for predicting engagement in online teaching videos and offering constructive feedback to creators. Utilizing multi-modal features, it reliably detects engagement and provides valuable insights for content enhancement.

Recommended citation: Roy, S., Gaur, V., Raza, H. and Jameel, S., 2023. CLEFT: Contextualised Unified Learning of User Engagement in Video Lectures With Feedback. IEEE Access, 11, pp.17707-17720.

Event Classification and Intensity Discrimination for Forest Fire Inference With IoT

Published in IEEE Sensors Journal, 2022

[22IEEESen] This paper focused on Event Classification and Intensity Discrimination for Forest Fire Inference With IoT and funded by Essex GCRF.

Recommended citation: Singh, V.K., Singh, C. and Raza, H., 2022. Event classification and intensity discrimination for forest fire inference with IoT. IEEE Sensors Journal, 22(9), pp.8869-8880.

Emojional: Emoji Embeddings

Published in UK Workshop on Computational Intelligence, 2021

[2021UKCI] Emojis serve as visual language elements, transcending linguistic barriers but evolving in meaning based on context and emotions. Online, they play a role in cancel culture, with emojis like the clown or snake conveying subtle aggression. Novel emoji embeddings, emphasizing emotional content, outperform existing embeddings in sentiment analysis, capturing nuanced expressions effectively.

Recommended citation: Barry, E., Jameel, S. and Raza, H., 2021, September. Emojional: Emoji Embeddings. In UK Workshop on Computational Intelligence (pp. 312-324). Cham: Springer International Publishing.

A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

Published in Scientific Data, 2021

[2021SciData] The dataset will be available for all AI and ML researchers around the globe to test/explore their MachineLearning and DataAnalytics algorithms to the first publicly available 306 channel four class MEG BCI data.

Recommended citation: Rathee, D., Raza, H., Roy, S. and Prasad, G., 2021. A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface. Scientific Data, 8(1), p.120.

Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients’ for Neuro-Rehabilitation Application

Published in IEEE-IJCNN, Glasgow, Scotland, 2020

Recommended citation: Raza, H., Chowdhury, A. and Bhattacharyya, S., 2020, July. Deep learning based prediction of EEG motor imagery of stroke patients’ for neuro-rehabilitation application. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Published in IEEE-IJCNN, Glasgow, Scotland, 2020

Recommended citation: Raza, H., Chowdhury, A., Bhattacharyya, S. and Samothrakis, S., 2020, July. Single-trial EEG classification with EEGNet and neural structured learning for improving BCI performance. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

Bagging Adversarial Neural Networks for Domain Adaptation in Non-Stationary EEG

Published in IEEE-IJCNN, 2019

Recommended citation: Raza, H. and Samothrakis, S., 2019, July. Bagging adversarial neural networks for domain adaptation in non-stationary eeg. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.

Predictors of Objectively Measured Physical Activity in 12 month-Old Infants: A Study of Linked Birth Cohort Data with Electronic Health Records

Published in Paediatric Obesity, 2019

Recommended citation: Raza, H., Zhou, S., Todd, S., Christian, D., Merchant, E., Morgan, K., Khanom, A., Hill, R., Lynos, R., and Brophy, S. Predictors of objectively measured physical activity in 12‐month‐old infants: A study of linked birth cohort data with electronic health records. Pediatric obesity, p.e12512..

An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation

Published in Journal of neuroscience methods, 2019

Recommended citation: Chowdhury, A., Raza, H., Meena, Y.K., Dutta, A. and Prasad, G., 2019. An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation. Journal of neuroscience methods, 312, pp.1-11.

Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability

Published in IEEE Journal of Biomedical and Health Informatics, 2018

Recommended citation: Chowdhury, Anirban, Yogesh Kumar Meena, Haider Raza, Braj Bhushan, Ashwani Kumar Uttam, Nirmal Pandey, Adnan Ariz Hashmi, Alok Bajpai, Ashish Dutta, and Girijesh Prasad. "Active physical practice followed by mental practice using BCI-driven hand exoskeleton: a pilot trial for clinical effectiveness and usability." IEEE journal of biomedical and health informatics 22, no. 6 (2018): 1786-1795.

Published in , 2024

Adaptive learning for modelling non-stationarity in EEG-based brain-computer interfacing.

Published in University of Ulster, 2015, 2016

Recommended citation: Raza, H. (2016). "Adaptive learning for modelling non-stationarity in EEG-based brain-computer interfacing." PhD Thesis, 2016.

Cortico-Muscular-Coupling and Covariate Shift Adaptation based BCI for Personalized NeuroRehabilitation of Stroke Patients.

Published in Proceedings of the 6th International Brain-Computer Interface Meeting, 2016, 2016

Recommended citation: Chowdhury, A., Raza, H., Dutta, A. and Prasad, G., 2016, June. Cortico-muscularcoupling and covariate shift adaptation based bci for personalized neurorehabilitation of stroke patients. In Proc. of BCI Meeting (p. 136).

Published in , 2024

A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation.

Published in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, 2015

Recommended citation: Chwodhury, A., Raza, H., Dutta, A., Nishad, S.S., Saxena, A. and Prasad, G., 2015, August. A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4610-4614). IEEE.

Published in , 2024

GBG Approach for Connectivity and Coverage Control in Wireless Sensor Network

Published in International Journal of Computer Applications, 2011

Recommended citation: Singh, B. and Raza, H., 2011. GBG Approach for Connectivity and Coverage Control in Wireless Sensor Network. International Journal of Computer Applications, 16(3), pp.13-18.

Selection of cluster-head using PSO in CGSR protocol

Published in 2010 IEEE International Conference on Methods and Models in Computer Science (ICM2CS-2010), 2010

Article publicly available here

Recommended citation: Raza, H., Nandal, P. and Makker, S., 2010, December. Selection of cluster-head using PSO in CGSR protocol. In 2010 International Conference on Methods and Models in Computer Science (ICM2CS-2010) (pp. 91-94). IEEE.