Ahsan Habib

Lecturer

Deakin University

Australia

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About

Dr. Ahsan Habib is a Lecturer at Deakin University in Australia. His research focuses on machine learning applications in energy demand forecasting, medical signal analysis, and health monitoring technologies. Recent publications include studies on ECG signals, EEG sleep stage classification, and COVID-19 prediction from chest X-ray images.

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2020

Collaborators (5)

Chandan Karmakar

Senior Lecturer

Deakin University

AUSTRALIA

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Farnad Nasirzadeh

Deakin University - Geelong Waterfront Campus

AUSTRALIA

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Lei Pan

Deakin University

AUSTRALIA

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Zahra Jadidi

Griffith University

AUSTRALIA

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Marimuthu Swami Palaniswami

Professor

University of Melbourne

AUSTRALIA

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Ahsan Habib

Research Interests

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About

Articles (19)

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

Forecasting Tetouan energy demand employing shift approach in machine-learning: complementing econometric insights

Enhanced power demand forecasting for Bangladesh: using feature engineering associated with environmental and economic impact

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

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

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

A light-weight and generalizable deep learning model for the prediction of COVID-19 from chest X-ray images

Continuous monitoring of body temperature for objective detection of health and safety risks in construction sites: An analysis of the accuracy and comfort of off-the-shelf wearable sensors

Continuous monitoring of body temperature for objective detection of health and safety risks in construction sites: An analysis of the accuracy and comfort of off-the-shelf wearable sensors

Cesarean Section Classification Using Machine Learning With Feature Selection, Data Balancing, and Explainability

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

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

Impact of synthetic noise signature and physiologic ECG signal on designing ML-based ECG noise detection framework

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

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

Collaborators (5)

Chandan Karmakar

Farnad Nasirzadeh

Lei Pan

Zahra Jadidi

Marimuthu Swami Palaniswami

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