Chandan Karmakar

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Senior Lecturer

Deakin University

Australia

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About

Chandan Karmakar is a Senior Lecturer at Deakin University in Australia. His research focuses on medical technology, particularly automatic ECG analysis, Internet of Medical Things (IoMT), and machine learning applications in healthcare. Recent articles by Dr. Karmakar examine robust mechanisms for adversarial defense in medical image analysis, patient survival prediction, and diabetes classification across various environments.

Recent Grants

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New entropy measures of short term signals for smart wearable devices

Open Date: 2019-01-01

Close Date: 2021-12-31

Articles (27)

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Collaborators (7)

Ahsan Habib

Lecturer

Deakin University

AUSTRALIA

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Fiona Brownfoot

Senior lecturer

University of Melbourne

AUSTRALIA

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Lei Pan

Deakin University

AUSTRALIA

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Zahra Jadidi

Griffith University

AUSTRALIA

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Emerson Keenan

University of Melbourne

AUSTRALIA

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Robin Ram Mohan Doss

Professor of Information Technology

Deakin University

AUSTRALIA

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Marimuthu Swami Palaniswami

Professor

University of Melbourne

AUSTRALIA

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Chandan Karmakar

Research Interests

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About

Recent Grants

Articles (27)

Robust Cyber Threat Intelligence Sharing Using Federated Learning for Smart Grids

Attack-data independent defence mechanism against adversarial attacks on ECG signal

RAD-IoMT: Robust adversarial defence mechanisms for IoMT medical image analysis

Impact and mitigation of blind filtering in automatic ECG analysis for wearable sensors

A key-controlled watermarking framework for tamper localization in physiological signals

Delay-aware partial task offloading using multicriteria decision model in IoT–fog–cloud networks

A robust and generalized framework in diabetes classification across heterogeneous environments

Photoplethysmography as a noninvasive surrogate for microneurography in measuring stress-induced sympathetic nervous activation — A machine learning approach

The Impact of Simultaneous Adversarial Attacks on Robustness of Medical Image Analysis

Double-layer data-hiding mechanism for ECG signals

Robustness of Deep Learning models in electrocardiogram noise detection and classification

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal

SYN-GAN: A robust intrusion detection system using GAN-based synthetic data for IoT security

A secure and light‐weight patient survival prediction in Internet of Medical Things framework

Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives

Analysing Performances of DL-Based ECG Noise Classification Models Deployed in Memory-Constraint IoT-Enabled Devices

Exploration of an intrinsically explainable self-attention based model for prototype generation on single-channel EEG sleep stage classification

Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network

Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages

Interpretability and Optimisation of Convolutional Neural Networks Based on Sinc-Convolution

Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG

Multi-stage sleep classification using photoplethysmographic sensor

Use of Ensemble Learning to Detect Buffer Overflow Exploitation

A Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices

Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling

Robustness of electrocardiogram signal quality indices

Novel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors

Collaborators (7)

Ahsan Habib

Fiona Brownfoot

Lei Pan

Zahra Jadidi

Emerson Keenan

Robin Ram Mohan Doss

Marimuthu Swami Palaniswami

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