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Majeed, Anwar P.P. Abdul,Taha, Zahari,Abdullah, Muhammad Amirul,Azmi, Kamil Zakwan Mohd,Zakaria, Muhammad Aizzat Techno-Press 2018 Advances in robotics research Vol.2 No.3
This study evaluates the efficacy of a class robust control scheme namely active force control in performing a joint based trajectory tracking of an upper limb exoskeleton in rehabilitating the elbow joint. The plant of the exoskeleton system is obtained via system identification method whilst the PD gains were tuned heuristically. The estimated inertial parameter that enables the AFC disturbance rejection effect is attained by means of a non-nature based metaheuristic optimisation technique known as simulated Kalman filter (SKF). It was demonstrated from the present investigation that the proposed PDAFC scheme outperformed the classical PD algorithm in tracking the prescribed trajectory both in the presence and without the presence of disturbance attributed by the mannequin limb weights (1 kg and 1.5 kg) that mimics the weight of actual human limb weight. Therefore, it is apparent from the results obtained from the present study that the proposed control scheme, i.e., PDAFC is suitable for the application of exoskeleton for stroke rehabilitation.
The Diagnosis of Diabetic Retinopathy: A Transfer Learning Approach
Farhan Nabil Mohd Noor,Anwar P.P. Abdul Majeed,Mohd Azraai Mod Razman,Ismail Mohd Khairuddin,Wan Hasbullah Mohd Isa 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Diabetic Retinopathy is one of the complications of diabetes mellitus that occurs to the eye. It damages the blood vessels, which cause the leaking of the blood and other fluids due to the elevated blood glucose level. Diabetic Retinopathy is a quiet ailment that patients may not discover until abnormalities in the retina have progressed to the point that medication is difficult or impossible. It can also result in patients losing their sight completely. However, an automated screening machine may help overcome this problem by helping the ophthalmologist diagnose diabetic retinopathy patients as soon as possible. Hence, this research investigates the effectiveness of automatic screening machine by employing the Transfer Learning model such as VGG16 to extract the features and fed them to the Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and Random Forest (RF) for the classification. It was shown that the VGG16-SVM pipeline displayed the most promising performance on the classification of Diabetic Retinopathy.
Evaluation of the Machine Learning Classifier in Wafer Defects Classification
Jessnor Arif Mat Jizat,Anwar P.P. Abdul Majeed,Ahmad Fakhri Ab. Nasir,Zahari Taha,Edmund Yuen 한국통신학회 2021 ICT Express Vol.7 No.4
In this paper, an evaluation of machine learning classifiers to be applied in wafer defect detection is described. The objective is to establish the best machine learning classifier for Wafer Defect Detection application. k-Nearest Neighbours (k-NN), Logistic Regression, Stochastic Gradient Descent, and Support Vector Machine were evaluated with 3 defects categories and one non-defect category. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train, test and validate the classifier. Each image went through the embedding process by InceptionV3 algorithms before the evaluated classifier classifies the images.
Automated Gastrointestinal Tract Classification Via Deep Learning and The Ensemble Method
Omair Rashed Abdulwareth Alman,Mohd Azraai Mohd Razman,Ismail Mohd Khairuddin,Muhammad Amirul Abdullah,Anwar P.P. Abdul Majeed 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Colorectal cancer is a leading cause of death among the cancer family with a record of almost a million moralities in 2020 alone. While the treatment of colorectal cancer is very difficult, early diagnosis can help immensely with treatment, eliminating the risks, and recovery. In most cases early diagnosis is possible by catching any of the precursors of the disease, many of which appear on the Gastrointestinal tract. The use of machine learning to automate the process of gastrointestinal tract examination could accelerate the process of diagnosis, and increase its efficiency. This study suggests the use of the stacking ensemble method with multiple pre-trained CNN models for an accurate classification of GI tract using the publicly available dataset Kvasir. The pre-trained models used in this study were ResNet50, MobileNetV2, and Xception, all of which were ensembled and trained on a subset of the data and tested on another to eliminate bias, and evaluates the model’s capacity for generalization. Overall, the model demonstrated impressive performance at 99.2% accuracy, 0.9977 AUC, and 99.29% F1-score, especially compared to the individual constituent models and other models discussed in the review section of the study.
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.
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.
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.