Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

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Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. by Laurentius O. Osapoetra (9931759)

Microkinetic Model Fitted with a Genetic Algorithm to Experimental XPS Coverages at High Pressure–CO Hydrogenation on Rh(111) by Mikael Valter-Lithander (18955053)

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Statistical results of various algorithms. by ZeSheng Lin (20501356)

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Fitness function parameters. by Ankita Sharma (3183054)

Images of partial benchmark functions. by ZeSheng Lin (20501356)

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Penalty functions used in different methods. by Souvik Seal (12437166)

Predictive Mixing for Density Functional Theory (and Other Fixed-Point Problems) by L. D. Marks (1949689)

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Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …”

Algorithm operation steps. by Junyan Wang (4738518)

Step by step comparison of enrichment algorithms. by Nicholas Markarian (18251668)

Step-by- step workflow of applied pipeline in current study. by Bahman Panahi (7323578)

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Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

Overlays of the gates drawn for the same 12 samples sorted both by hand and with the automation show similar gated populations. by Diane M. Wiener (1956583)

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The main steps of the general algorithm. by Dror Mughaz (18017583)

Preservation analysis of co-expressed modules of control networks were tested in the stress condition based on the median rank (A) and Zsummary (B) algorithms. by Bahman Panahi (7323578)

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If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

The number of inactive nodes as a function of time steps in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)

Steps in the proposed Apriori algorithm. by Yongkang Ding (21807539)