Performance of SVM model for various kernel functions. by Xueliang Guo (4797057)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... by Hassan Mehboob (8960273)

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …”

The skeleton algorithm identifies mitochondrial density in sample images from cells. by Margarita M. Ivanova (11604141)

Table_1_Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.DOCX by Gaoyan Zhang (450697)

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Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

Summary of the five most indicative brain areas and networks in the classification of healthy (HC) and early mild cognitive impairment (EMCI). by Jafar Zamani (12902504)

Logistic kernel: Power and Type I error for cluster evaluation metric simulations. by Alexandria M. Jensen (6494957)

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ABIDE dataset subject demographics. by Alexandria M. Jensen (6494957)

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Linear kernel: Power and Type I error for cluster evaluation metric simulations. by Alexandria M. Jensen (6494957)

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Examples of between- and within-community Beta distributions. by Alexandria M. Jensen (6494957)

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P-values from analysis of ABIDE dataset for binary outcome of diagnostic group. by Alexandria M. Jensen (6494957)

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Examples of four simulated networks and adjacency matrices. by Alexandria M. Jensen (6494957)

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P-values from analysis of ABIDE dataset for continuous outcome of VIQ. by Alexandria M. Jensen (6494957)

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Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network by Rajesh Kumar Thangavel (20591164)

“…<p>Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The PS-VAE enables a detailed brain region-to-behavior mapping in the two-view mouse dataset. by Matthew R. Whiteway (11467528)

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