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The Classification of EEG-based Wink Signals: A CWT-Transfer Learning Pipeline
Jothi Letchumy Mahendra Kumar,Mamunur Rashid,Rabiu Muazu Musa,Mohd Azraai Mohd Razman,Norizam Sulaiman,Rozita Jailani,Anwar P.P. Abdul Majeed 한국통신학회 2021 ICT Express Vol.7 No.4
Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research investigates the efficacy of Transfer Learning models namely ResNet50 V2, ResNet101 V2, and ResNet152 V2 in extracting features from CWT converted wink-based EEG signals, prior to its classification via a fine-tuned Support Vector Machine (SVM) classifier. It was shown that ResNet152 V2-SVM pipeline could achieve an excellent accuracy on all train, test and validation datasets.
The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images
Amiir Haamzah Mohamed Ismail,Mohd Azraai Mohd Razman,Ismail Mohd Khairuddin,Rabiu Muazu Musa,Anwar P.P. Abdul Majeed 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Radiography is used in medical treatment as a method to diagnose the internal organs of the human body from diseases. However, the advancement in machine learning technologies have paved way to new possibilities of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. VGG19 learning model created by the Visual Geometry Group is used for extraction of features from the patient’s chest X-ray images. To evaluate the combination of various pipelines, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19.
Human activity recognition based on wrist PPG via the ensemble method
Omair Rashed Abdulwareth Almanifi,Ismail Mohd Khairuddin,Mohd Azraai Mohd Razman,Rabiu Muazu Musa,Anwar P.P. Abdul Majeed 한국통신학회 2022 ICT Express Vol.8 No.4
Human activity recognition via Electrocardiography (ECG) and Photoplethysmography (PPG) is extensively researched. While ECG requires less filtering and is less prone to disturbance and artifacts, nonetheless, PPG is cheaper and widely available in smart devices, making it a desired alternative. In this study, we explore the employment of the ensemble method with several pre-trained machine learning models namely Resnet50V2, MobileNetV2, and Xception for the classification of wrist PPG data of human activity, in comparison to its ECG counterpart. The study produced promising results with a test classification accuracy of 88.91% and 94.28% for PPG and ECG, respectively.