History of Process Transformation by de Wit Coetsee (21380672)

Overview of the cohort definition process. by Nicola Lazzarini (3528197)

ROC area for the ML algorithms applied on Dataset 3. by Murad Al-Rajab (10661360)

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Classification accuracy for the ML algorithms applied to Dataset 3. by Murad Al-Rajab (10661360)

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Classification accuracy for the ML algorithms applied to Dataset 2. by Murad Al-Rajab (10661360)

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ROC area for the ML algorithms applied to Dataset 1. by Murad Al-Rajab (10661360)

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ROC area for the ML algorithms applied to Dataset 2. by Murad Al-Rajab (10661360)

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Confusion matrix for the ML algorithms applied to Dataset 2. by Murad Al-Rajab (10661360)

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Classification accuracy for the ML algorithms applied to Dataset 1. by Murad Al-Rajab (10661360)

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ML confusion matrix for the ML algorithms applied to Dataset 1. by Murad Al-Rajab (10661360)

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ML confusion matrix for the ML algorithms applied to Dataset 3. by Murad Al-Rajab (10661360)

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Summary of ROC area for the ML algorithms applied to all datasets. by Murad Al-Rajab (10661360)

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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. …”

Robustness of the optimization process in a real dataset. by Neta Ravid Tannenbaum (2940351)

History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>. by Karl W. Schulz (14309461)

Predictive model-building process. by Mukhtar Ijaiya (18935122)

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. …”

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms. by Aref Andishgar (18155608)

Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization by Sandeep Jagonda Patil (22048337)

“…The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.…”