Simulation parameters. 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. …"

Training losses for N = 10. 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. …"

Normalized computation rate for N = 10. 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. …"

Dataset 2: Zip file containing the Tables of the presented method and results حسب Suchismita Behera (22027316)

Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data حسب Fei Xue (24567)

"…Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. …"

Summary of literature review. حسب Muhammad Ayyaz Sheikh (18610943)

"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"

Topic description. حسب Muhammad Ayyaz Sheikh (18610943)

"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"

Notations along with their descriptions. حسب Muhammad Ayyaz Sheikh (18610943)

"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"

Detail of the topics extracted from DUC2002. حسب Muhammad Ayyaz Sheikh (18610943)

"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"

Experimental observation of gastruloids and shape quantification using LOCO-EFA. حسب Martijn A. de Jong (17896732)

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. حسب Ramsés Noguez Imm (6658637)

Latent state estimations for simulated data. حسب Samiul Alam (18563323)

Contextual Dynamic Pricing with Strategic Buyers حسب Pangpang Liu (18886419)

"…In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. …"

Latent state estimations for experimental data with no stressors. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 18. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 22. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 17. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 13. حسب Samiul Alam (18563323)