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Amitabha Chakrabarty on Muck Rack

Amitabha Chakrabarty

Bangladesh
As seen in: PLOS ONE, MDPI, Canadian Journal of Infectious Diseases & Medical Microbiology
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Articles

Optimizing network bandwidth slicing identification: NADAM-enhanced CNN and VAE data preprocessing for enhanced interpretability

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By Shahriar Hossain, Nafees Mansoor, Amitabha Chakrabarty
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Citation: Islam MFU, Hossain S, Alam MGR, Mansoor N, Chakrabarty A (2025) Optimizing network bandwidth slicing identification: NADAM-enhanced CNN and VAE data preprocessing for enhanced interpretability. PLoS One 20(10): e0333286.
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Aggregating Different Scales of Attention on Feature Variants for Tomato Leaf Disease Diagnosis from Image Data: A Transformer Driven Study

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By Shahriar Hossain, Amitabha Chakrabarty, Yong Ju Jung, Tanzim Reza
| MDPI
Open AccessArticle by Shahriar Hossain 1, Md Tanzim Reza 1, Amitabha Chakrabarty 1 and Yong Ju Jung 2,* 1 Department of Computer Science and Engineering, BRAC University, Dhaka 1212, Bangladesh 2 School of Computing, Gachon University, Seongnam 13120, Republic of Korea * Author to whom correspondence should be addressed.
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Emotional State Classification from MUSIC-Based Features of Multichannel EEG Signals

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By Sakib Hossain, Asadur Rahman, Amitabha Chakrabarty, Mohd Rashid Verified
| MDPI
Abstract : Electroencephalogram (EEG)-based emotion recognition is a computationally challenging issue in the field of medical data science that has interesting applications in cognitive state disclosure. Generally, EEG signals are classified from frequency-based features that are often extracted using non-parametric models such as Welch’s power spectral density (PSD). These non-parametric methods are not computationally sound due to having complexity and extended run time.
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