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. …"

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. …"

Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF حسب Haidi Wang (9694004)

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

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

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)

Latent state estimations for experimental data with no stressors for subject 3. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 15. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 7. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 1. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 15. حسب Samiul Alam (18563323)