Abdulhamit Subasi

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Professor

University of Turku

Finland

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Professor Abdulhamit Subasi is a faculty member at the University of Turku, Finland. His research interests encompass medical imaging, artificial intelligence applications in healthcare, and novel deep learning methodologies, as reflected in his recent publications. These include investigations into Alzheimer’s Disease detection using MRI and automated classification of COVID-19 scans, as well as advancements in credit scoring and mental health diagnostics.

Recent Grants

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Cloud Computing-Based Parallel Real-time EEG Signal Analysis using Ensemble Machine Learning Techniques

Open Date: 2015-09-01

Close Date:

Grant: Close

Secure User Authentication and Intrusion Detection for Mobile Phones

Open Date: 2013-09-01

Close Date: 2015-01-01

Grant: Close

Comparison of Different Feature Extraction and Machine Learning Techniques in EEG-Based Wireless BCI System in a Cloud

Open Date: 2013-01-01

Close Date: 2015-01-01

Grant: Close

Comparison of Machine Learning Methods for Biomedical Signal Analysis

Open Date: 2011-09-01

Close Date: 2013-12-01

Grant: Close

Comparison of Data Mining Techniques for Robust Intrusion Detection

Open Date: 2011-01-01

Close Date: 2013-01-01

Abdulhamit Subasi

Research Interests

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About

Recent Grants

Articles (21)

FiboNeXt: Investigations for Alzheimer’s Disease detection using MRI

EEG-based schizophrenia classification using penalized sequential dictionary learning in the context of mobile healthcare

An Ensemble Voting Approach With Innovative Multi-Domain Feature Fusion for Neonatal Sleep Stratification

Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation

Automated facial expression recognition using novel textural transformation

An IoT-Based Noncontact ECG System: Sole of the Feet/Hands Palm

Promoting accuracy in low-magnification histopathology grading: With augmentation and multi-dilation model

Risk prediction model for cannabis use with artificial intelligence approach

OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans

Intelligent credit scoring using deep learning methods

Automated and accurate focal EEG signal detection method based on the cube pattern

EEG-based emotion recognition using dual tree complex wavelet transform and random subspace ensemble classifier

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

EEG-Based Driver Fatigue Detection Using FAWT and Multiboosting Approaches

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Tourism demand forecasting using stacking ensemble model with adaptive fuzzy combiner

Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator

Novel finger movement classification method based on multi-centered binary pattern using surface electromyogram signals

The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection

EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier

Collaborators (1)

Dr. Saadullah Farooq Abbasi

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