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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.
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.