Graphic representation of the CAP A events identified by the algorithm (blue line) and by the scorer (red line). by Maria Paola Tramonti Fantozzi (11785619)

Large-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

Medium-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

Small-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

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. …”

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

Algorithm parameters. by Ruyi Dong (9038174)

“…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”

Complex traffic line and track point diagram. by Mingkang Sun (6498791)

Complex traffic line and track point diagram. by Mingkang Sun (6498791)

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

Service Coverage Optimization for Facility Location: Considering Line-Of-Sight Coverage in Continuous Demand Space by XIAOYA MA (12164486)

Steady-State Simulation Performance at 1000 <i>W</i>/<i>m</i>² (a) Conventional P&O Algorithm, (b) Proposed SF Algorithm. by Rana Ahmed (4923127)

Assessing the overall accuracy of the solutions obtained by the algorithms. by Nasiru Salihu (14803855)

Transient Simulation Performance at 800<i>W</i>/<i>m</i>² and 400<i>W</i>/<i>m</i>² (a) Conventional P&O Algorithm, (b) Proposed SF Algorithm. by Rana Ahmed (4923127)

Automated Discovery and Optimization of 3D Topological Photonic Crystals by Samuel Kim (1682104)

“…They are typically designed by hand based on the careful analysis of their bands and mode profiles, but recent theoretical advances have revealed new and powerful insights into the connection between band symmetry, connectivity, and topology. Here we propose a combined global and local optimization framework that integrates a flexible symmetry-constrained level-set parametrization with standard gradient-free optimization algorithms to optimize topological photonic crystals, a problem setting where the objective function may be highly nonconvex and noncontinuous. …”

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. …”