Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Performance comparison based on feature groups and their combinations. by Rinrada Tanthanathewin (18611171)

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Accuracy comparison between the proposed algorithm and existing algorithms. by Xin Li (51274)

The main workflow of the proposed algorithm. by Xin Li (51274)

Illustrations of distances used as the 5^th to 7^th features (f5-f7) from a blob (orange circle) to the fovea. by Rinrada Tanthanathewin (18611171)

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Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …”

Examples of results from two different OCT images shown in the left and right columns. by Rinrada Tanthanathewin (18611171)

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Illustration of Area of interest (left) and all HRF blobs used as EX-AN candidates (right). by Rinrada Tanthanathewin (18611171)

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Examples of the fovea locations (stars) a typical case (left) and an abnormal case (right). by Rinrada Tanthanathewin (18611171)

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ILM (pink line) and RPE (green line) layers in a normal OCT image. by Rinrada Tanthanathewin (18611171)

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Examples of input (first column) and areas between ILM and RPE layers of ground truth (second column) and U-Net (third columns) and U-Net++ (fourth column) of four OCT cases. by Rinrada Tanthanathewin (18611171)

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