Mohammad A. B. S. Akhonda

Orcid: 0000-0003-0826-453X

Affiliations:
  • University of Maryland, Baltimore, MD, USA


According to our database1, Mohammad A. B. S. Akhonda authored at least 20 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
An Efficient Analytic Solution for Joint Blind Source Separation.
IEEE Trans. Signal Process., 2024

Identifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data.
IEEE Access, 2024

Fusion of Novel FMRI Features Using Independent Vector Analysis for a Multifaceted Characterization of Schizophrenia.
Proceedings of the 32nd European Signal Processing Conference, 2024

2023
A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis.
Sensors, 2023

Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data.
Proceedings of the IEEE International Conference on Acoustics, 2023

Fusion of Multi-Modal Neuroimaging Data and Association With Cognitive Data.
Proceedings of the IEEE International Conference on Acoustics, 2023

Independent Vector Analysis with Multivariate Gaussian Model: a Scalable Method by Multilinear Regression.
Proceedings of the IEEE International Conference on Acoustics, 2023

Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness.
IEEE Signal Process. Mag., 2022

Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data.
Sensors, 2022

Independent Vector Analysis Based Subgroup Identification from Multisubject fMRI Data.
Proceedings of the IEEE International Conference on Acoustics, 2022

Multi-Task fMRI Data Fusion Using IVA and PARAFAC2.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
ICA with Orthogonality Constraint: Identifiability And A New Efficient Algorithm.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Dictionary Learning-Based fMRI Data Analysis for Capturing Common and Individual Neural Activation Maps.
IEEE J. Sel. Top. Signal Process., 2020

Joint-IVA for identification of discriminating features in EEG: Application to a driving study.
Biomed. Signal Process. Control., 2020

Identification of Subgroup Differences Using IVA: Application to fMRI Data Fusion.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

2019
C-ICT for Discovery of Multiple Associations in Multimodal Imaging Data: Application to Fusion of fMRI and DTI Data.
Proceedings of the 53rd Annual Conference on Information Sciences and Systems, 2019

Disjoint Subspaces for Common and Distinct Component Analysis: Application to Task FMRI Data.
Proceedings of the 53rd Annual Conference on Information Sciences and Systems, 2019

2018
Consecutive Independence and Correlation Transform for Multimodal Fusion: Application to Eeg and Fmri Data.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Capturing Common and Individual Components in fMRI Data by Discriminative Dictionary Learning.
Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018


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