Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined. by Yuanchen Zhao (12905580)

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Fluctuation of the objective function of our mathematical model with respect to shifts in one input signal of a data sample. by Sebastian Sager (16205)

Optimised formula constants for the SRKT, the Hoffer Q, the Holladay 1, Haigis (with optimised a0 and preset values a1 = 0.4 / a2 = 0.1, Haigis1; and with optimised a0 / a1 / a2 co... by Achim Langenbucher (3156741)

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Distinct functional composition between age groups. by Elio L. Herzog (11576876)

Subjects: “…applying independent algorithms…”

Cumulative density function (CDF) of prediction error (PE, achieved spherical equivalent–formula predicted spherical equivalent) for the SRKT, Hoffer Q, Holladay1, Haigis (with opt... by Achim Langenbucher (3156741)

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Algorithm membership function. by Mohamed Raef Smaoui (9865830)

“…<p>(Top) Input Membership Function. The algorithm classifies glucose input into 4 sets: low, medium, high, and ex_high. …”

Distinct microbial and functional composition between age groups. by Elio L. Herzog (11576876)

Subjects: “…applying independent algorithms…”

Constructing the dynamical law. by Robert Marsland (8616483)

Function values by the two rescheduling measures. by Yuxuan Long (17720796)

S1 File - by Yuh-Chin T. Huang (17867207)

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S1 Dataset - by Yuh-Chin T. Huang (17867207)

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Highly interrelated genes and their biological functions among 21 cancers. by Qunlun Shen (11668968)

Performance comparison of RWDisEnh on the heterogeneous network of diseases and enhancers and that on the enhancer functional interaction network and the disease similarity network... by Duc-Hau Le (603529)

Cellular frustration algorithms for anomaly detection applications by Bruno Faria (6928079)

“…Likewise it has been hypothesized that these models could provide inspiration to develop new artificial intelligence algorithms for data mining applications. …”

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

The proposed method for protein function prediction. by Seyyede Fatemeh Seyyedsalehi (10196767)

Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval. by Achim Langenbucher (3156741)

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Prediction error (PE) as the difference between achieved and formula predicted spherical equivalent for 8 different statistical metrics of formula constant optimisation and various... by Achim Langenbucher (3156741)

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Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

“…With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. …”

A flow diagram to explain the algorithm named 2BDP (Biomarker Discovery Process at Binomial Decision Point) that we used to find the best fitting model to predict RRiF. by Nabarun Chakraborty (535772)