Iteration curve of the optimization process. by Meijun Shang (22806461)

An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Related Work Summary. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Simulation parameters. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Training losses for N = 10. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”

MLP vs classification algorithms. by Mohd Mustaqeem (19106494)

“…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. …”

Load data or power demand. by M. Nasrallah (19066121)

Mean scores of the last 100 episodes using PPO and DQN in Unity environment and ViZDoom. by Enoch Solomon (21416703)

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Mean reward of using visual information, visual information with raw audio samples in Unity ML environment. by Enoch Solomon (21416703)

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Proposed reinforcement learning architecture. by Enoch Solomon (21416703)

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The scenario used in Unity environment. Both the agent and target are spawned randomly at the start of the game anywhere in the game environment for each episode. by Enoch Solomon (21416703)

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The images shown correspond the downscaled images an agent receives in Unity environment. by Enoch Solomon (21416703)

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Mean reward of using only pixels and pixels with raw audio samples in ViZDoom environment. by Enoch Solomon (21416703)

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The mean and standard deviation of the test results are presented for the use of both visual information and raw audio samples in the Unity ML environment. by Enoch Solomon (21416703)

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Average success rate (%) of the test experiment on ViZDoom and Unity environments. by Enoch Solomon (21416703)

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The mean reward results of using visual information and audio samples in the ViZDoom environment. by Enoch Solomon (21416703)

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Box plot of Unity ML experiments using visual information, audio samples and combination of visual information and audio samples. by Enoch Solomon (21416703)

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Wilcoxon’s rank sum test results. by Guangwei Liu (181992)