Homomorphic binary tree. by Jianhua He (341366)

Proposed Algorithm. by Hend Bayoumi (22693738)

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Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

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<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i>_<i>δ</i>) based on this metric. <b>(E)</b> Starting from the interaction at the top of <i>I</i>_<i>δ</i>, <i>hi</i>PRS constructs <i>I</i>_<i>K</i>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …”

Schematic of iteration process of IDE-IIGA. by Ling Zhao (111365)

“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”

A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

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The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Datasets and their properties. by Olaide N. Oyelade (14047002)

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Parameter settings. by Olaide N. Oyelade (14047002)

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Fig 4 - by Olaide N. Oyelade (14047002)

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Fig 7 - by Olaide N. Oyelade (14047002)

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Comparison between ILSTM and machine learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

Comparison between ILSTM and deep learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

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

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Related Work Summary. by Hend Bayoumi (22693738)

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Simulation parameters. by Hend Bayoumi (22693738)

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

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

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

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One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)