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

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

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

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

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

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

Training process. حسب Ning Zhao (84707)

Proposed Algorithm. حسب 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. …"

Quantitative experimental results of the present algorithm and other methods. حسب Tingting Liu (267387)

The Pseudo-Code of the IRBMO Algorithm. حسب 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. …"

Fusion results of the proposed algorithm and other methods on Lytro-05. حسب Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. حسب Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. حسب Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-01. حسب Tingting Liu (267387)

Comparisons between ADAM and NADAM optimizers. حسب 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. …"

Flow chart of particle swarm algorithm. حسب Nour Eldeen Mahmoud Khalifa (19259450)

"…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…"

Pseudo-color results of our algorithm versus other methods on Lytro-05. حسب Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. حسب Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-01. حسب Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. حسب Tingting Liu (267387)