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)

A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm by Anonymous authors (for blind review purpose) (6473313)

“…In particular, the first folder also contains the addition data applicable to deterministic model (indicated by the notes in the name of the file, i.e., "for deterministic model"), which directly gives the processing time and processing cost of the end-of-life products on the corresponding processing line.…”

Energy consumption in the PPO process. by Yunhu Huang (21402795)

Running time in the PPO process. by Yunhu Huang (21402795)

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. …”

Parameter estimation process and performance. by Gennady Gorin (8466255)

Robustness of the optimization process in a real dataset. by Neta Ravid Tannenbaum (2940351)

“…The illustration is done for patient number 1 which is identified in a hypovolemic shock state and is presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010835#pcbi.1010835.g005" target="_blank">Fig 5</a>. Panels A-E present the extracted observables. Panels F-H present the optimal parameters obtained for realizations of the optimization process with different start points which are supplied to the optimization function. …”

Matching percentages of pairwise comparisons within repeats of the same scan settings for D (default setting) and C (chosen optimal setting). by Melvin Geubbelmans (19565593)

Minimum-time entrainment cases with the tuned sleep schedule in Eq (18) (left column) and controllable sleep schedule (right column). by Jiawei Yin (8149467)

An Example of a WPT-MEC Network. 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. …”

Related Work Summary. 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. …”