Parameter definitions and estimation for model (1). by Mengqi He (5753525)

The set of sleep measures computed by ActiLife, including definitions for each measure and the aggregation methods utilized when multiple sleep periods were detected within the sam... by Louis Faust (6807728)

Computational complexity of various algorithms in the pipeline. by Ali M. S. Alfosool (12936678)

Parameter definitions and estimation for functions <i>c</i>_<i>i</i>(<i>t</i>) and <i>q</i>_<i>i</i>(<i>t</i>) (i = 1,2,3). by Mengqi He (5753525)

G4mismatch and extant methods performance in prediction of mismatch scores of PQs. by Mira Barshai (8680773)

Example stride. by Arne Küderle (10919821)

Conditional Spectral Analysis of Replicated Multiple Time Series With Application to Nocturnal Physiology by Robert T. Krafty (3685369)

“…Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. …”

Riemann Hypothesis Solution: A Structured Hilbert-Polya Operator Realization via Entropy Geometry by Jennifer Bulyaki (22283308)

Data_Sheet_1_A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology.PDF by Fortunato Bianconi (6938102)

“…CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. …”

Study flow diagram. by Shahrokh Mousavi (21989418)

“…We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.…”

Optimization framework. by Nisha Ann Viswan (20371285)

“…However, we may have cross-interactions or feedback (arrows with dashed lines), which may require the pipeline to repeat one or more levels. C: pseudocode for definition of the HOSS pipeline. Within each level we can have multiple pathways, each of which needs a list of experiments, parameters and optionally parameter bounds. …”

Table_1_Machine learning segmentation of core and penumbra from acute stroke CT perfusion data.DOCX by Freda Werdiger (7327274)

“…We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously.</p>Methods<p>We used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. …”

Table_1_Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment.DOCX by Jelena Skorucak (8349912)

“…No correlations between MSE measures in the MWT and driving performance measures were found. …”

Assessing the effectiveness of a melanopsin-based signal for colour constancy - ICVS Presentation 2019 by Daniel Garside (3367640)

“…<br><br>Using this measurement, effectiveness of a melanopsin-based signal was compared to a basic ‘grey-world’ algorithm. …”

Image_1_Test-retest reliability of modular-relevant analysis in brain functional network.PDF by Xuyun Wen (414454)

“…Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results.…”

Optimising pipeline configuration through an interactive machine learning approach. by Lisa Laux (9367681)

“…<p>The conventional approach to optimising a set of cell profiling parameters (or ‘configuration’) requires the user to change multiple settings in a trial and error manner. This is a slow and tedious process, with quality of the image processing pipelines usually only measured after analysis of the entire dataset. …”

DataSheet_1_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.pdf by GuoHua You (17140984)

“…Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN).…”

Table_3_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.xls by GuoHua You (17140984)

“…Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN).…”

Table_2_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.xls by GuoHua You (17140984)

“…Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN).…”

Table_1_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.xls by GuoHua You (17140984)

“…Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN).…”