Values of the objective function and its non-normalized terms , obtained by using different weights in the optimization process. by Roberto Castro-Medina (21484990)

CNN’s optimal hyperparameters utilizing the APSO–CNN algorithm. by Ahmed Bahaa (12887063)

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

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

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Internal symmetry in crystallin proteins. by Spencer E. Bliven (6672755)

Exponent in memory kernel and intensity function. by Mariko I. Ito (5007323)

Algorithm of the main experiment targeted to measure the perceptual point spread function (pPSF) treating patients visual system including its optics, physiology and psychology as an integrated imaging system, and patient’s perceptions as its output signal. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “Ic” denotes the intensity of the central diode (Ic = 40 cd); “DIST(i)” is a randomly sorted list of “D” angular stimuli positions distributed equally as a function of distance from 0.24° to 7.67° from the central point (D = 10), while “i” is an index corresponding to the current distance of a probe diode (“d”); “N” denotes the number of trials for each stimuli position (N = 20); “s” denotes the perceptual brightness value transformed to diode luminous intensity by an array “I(s)” corresponds to the table “scale (level)” determined by the algorithm presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0306331#pone.0306331.g003" target="_blank">Fig 3</a>; “cnt” is a counter of trials for the current probe diode’s distance, array threshold (d), and slope (d), i.e., it denotes the intensity of the single point of the pPSF and its uncertainty. …”

Benchmarking functions. by Guilin Yang (583364)

“…Introducing nonlinear convergence factors based on positive cut functions to changing the convergence of algorithms, the early survey capabilities and later development capabilities of the algorithm are balanced. …”

Band width overhead ratio. by Xumin Zhao (18261643)

“…Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. …”

Broadband overhead comparison. by Xumin Zhao (18261643)

“…Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. …”

Accessing present data blocks (Hit successful). by Xumin Zhao (18261643)

“…Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. …”

Summary of related work in storage optimization. by Xumin Zhao (18261643)

“…Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. …”

The fog calculates the average response speed. by Xumin Zhao (18261643)

“…Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. …”

Accessing present data blocks (Hit successful). by Xumin Zhao (18261643)

“…Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. …”

The fog calculates the one-hop hit rate. by Xumin Zhao (18261643)

“…Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. …”

The results of Friedman and Kruskal-Wallis tests for DEGH and its variants with different <i>CR</i> values. by Xuxu Zhong (10099054)

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”

Comparison of average precision means of different algorithms. by Haisong Xu (141729)

Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …”

Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

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