Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

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The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Final solutions of a string problem for multiple algorithms runs. by Pavel Kodytek (10140509)

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Function graph and algorithm iterative graph. by WanRu Zhao (18980374)

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Results of proposed algorithms and algorithms in [9] for 18 instances. by Jin Zhang (53297)

Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform by Zhehong Ai (17937324)

Subjects: “…customized multiobjective functions…”

Illustration of methods based on Green’s function first-passage algorithms. by Jean Marc Tregan (15195173)

“…<p>Illustration of methods based on Green’s function first-passage algorithms.</p>…”

APSO–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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APSO–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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Optimization results of test functions for different algorithm improvement points. by WanRu Zhao (18980374)

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Test functions. by Kejia Liu (5699651)

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Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<p>Among 46 DEGs whose expression significantly changed in A549 and BEAS-2B cell lines, the core genes were selected using three algorithms: MCC, MNC, and DEGREE. <b>(A)</b> The top 10 genes were prioritized using MCC analysis, which identifies the largest clique within the PPI network and selects the central proteins within the clique. …”

The optimization result of 10 benchmark functions for different algorithms (D = 10). by Yu Li (183836)

“…<p>The optimization result of 10 benchmark functions for different algorithms (D = 10).</p>…”

The optimization result of 10 benchmark functions for different algorithms (D = 30). by Yu Li (183836)

“…<p>The optimization result of 10 benchmark functions for different algorithms (D = 30).</p>…”

The result of CEC2010 standard functions for different algorithms (D = 30). by Yu Li (183836)

“…<p>The result of CEC2010 standard functions for different algorithms (D = 30).</p>…”

The optimization result of 10 benchmark functions for different algorithms (D = 100). by Yu Li (183836)

“…<p>The optimization result of 10 benchmark functions for different algorithms (D = 100).</p>…”

The convergence curves of the function in different dimensions. by Yu Li (183836)

APSO–WOA–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

Subjects: