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. …”

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. …”

Image1_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

“…This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. …”

Image2_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

“…This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. …”

Image4_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.jpg by Wisnu Ananta Kusuma (9276182)

“…This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. …”

Image5_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.jpg by Wisnu Ananta Kusuma (9276182)

“…This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. …”

Image3_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

“…This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. …”

DataSheet1_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.docx by Wisnu Ananta Kusuma (9276182)

“…This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. …”

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. …”

Simulation parameters. 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. …”

Training losses for N = 10. 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. …”

Normalized computation rate for N = 10. 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. …”

Summary of Notations Used in this paper. 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. …”

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports by Olivier Q. Groot (9370461)

“…<p> The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. …”