Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

Consistency of model-derived predictive patterns as a function of sample diversity. by Oualid Benkarim (9707986)

Overview of the initial cell candidate detection steps, from raw data to a cuboid of data fed into the classification network. by Adam L. Tyson (10892822)

Subjects:

Fractal dimension of cortical functional connectivity networks & severity of disorders of consciousness by Thomas F. Varley (8446899)

“…<div><p>Recent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain and that consciousness emerges in a critical zone between low and high-entropy states. …”

Out-of-distribution prediction and performance of predictive models as a function of sample diversity. by Oualid Benkarim (9707986)

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

“…These implementations need to utilize high-precision arithmetic, and are programmed in the Python programming language. A thorough comparison with existing R functions allows the identification of cases when the latter fail, and provide further guidance to their use.…”

Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. by Arian Ashourvan (6685232)

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

Subjects:

Representative neurons and their corresponding step functions. by Reem Khalil (10898163)

Subjects:

Explained variance as a function of number of inner dimensions, <i>Q</i>. by Kai J. Miller (8846117)

Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. by Adam L. Tyson (10892822)

Subjects:

Comparison of the performance of the emulators as a function of the size of the training set (resolution) for the agent-based cellular automata (CA) model of brain tumours. by Andreas Christ Sølvsten Jørgensen (13911723)

Comparison of the performance of the emulators as a function of the size of the training set (resolution) for the agent-based cellular automata (CA) model of brain tumours. by Andreas Christ Sølvsten Jørgensen (13911723)

Comparison between algorithm and expert cell counting. by Adam L. Tyson (10892822)

Subjects:

Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Classification accuracy and information transfer rate (ITR) as a function of the measurement set-up and the number of combined trials. by Dovilė Kurmanavičiūtė (21072549)

Data_Sheet_1_Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: a data-based study.docx by Hui Dang (4396087)

“…</p>Conclusion<p>The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.…”

Data_Sheet_1_Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: A data-based study.docx by Hui Dang (4396087)

“…</p>Conclusion<p>The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.…”