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

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Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods. by Hend Bayoumi (22693738)

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The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames. by Hend Bayoumi (22693738)

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

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CDF of task latency, approximated as the inverse of the achieved computation rate. by Hend Bayoumi (22693738)

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Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate. by Hend Bayoumi (22693738)

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

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Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Summary of LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

SHAP analysis for LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

Comparison of intrusion detection systems. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

Parameter setting for CBOA and PSO. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

NSL-KDD dataset description. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

The architecture of LSTM cell. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

The architecture of ILSTM. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

Parameter setting for LSTM. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

LITNET-2020 data splitting approach. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

Transformation of symbolic features in NSL-KDD. by Asmaa Ahmed Awad (16726315)

“…The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. …”

Classification performance after optimization. by Amal H. Alharbi (21755906)

“…<div><p>Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”

ANOVA test for optimization results. by Amal H. Alharbi (21755906)

“…<div><p>Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. …”