Data Sheet 1_Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical information database network.pdf by Ryusuke Inoue (4617505)

“…The highest sensitivity at 89.53% was observed for Algorithm 9. This algorithm was constructed using a combination of disease codes entered in electronic medical records, DPC, or health insurance claims data, abnormal BNP values in the moderate or high range (≥100 pg/mL), and medications for acute heart failure. …”

Flowchart for GR codes. by R. Sakthivel (2589547)

“…The algorithm was developed and coded in Verilog and simulated using Modelsim. …”

Voltage trace of the target data and the traces corresponding to the best parameters found by the Covariance Matrix Adaptation Evolution Strategy (CMAES) and Random Search algorithms in Use Case 5 (extended integrate-and-fire model). by Máté Mohácsi (20469514)

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Voltage trace of the target data and the traces corresponding to the best parameters found by the Covariance Matrix Adaptation Evolution Strategy (CMAES) and Random Search algorithms in Use Case 3 (passive, anatomically detailed neuron). by Máté Mohácsi (20469514)

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The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

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Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

Distance matrix from pairwise comparisons of behavior sequences of the two cichlid species <i>Neolamprologus multifasciatus</i> and <i>Lamprologus ocellatus</i> (S4–S17 Datasets),... by Nicolai Kraus (19949667)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

“…By leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) algorithm, sensor data can be efficiently analyzed in both spatial and temporal dimensions, achieving a promising recognition accuracy of 97.14%. …”

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

“…By leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) algorithm, sensor data can be efficiently analyzed in both spatial and temporal dimensions, achieving a promising recognition accuracy of 97.14%. …”

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

“…By leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) algorithm, sensor data can be efficiently analyzed in both spatial and temporal dimensions, achieving a promising recognition accuracy of 97.14%. …”

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

“…By leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) algorithm, sensor data can be efficiently analyzed in both spatial and temporal dimensions, achieving a promising recognition accuracy of 97.14%. …”

S1 Data - A GNSS receiver positioning algorithm based on weighting the statistical properties of discontinuous signals at lower rotation speeds by Jiahui Gan (22793753)

“…Weighted Kalman Filter Algorithm in C Code. S3 File. NavSolveKalman.h. Weighted Kalman Filter Algorithm in C Code. …”

The used datasets with their details. by Javad Hemmatian (20700209)

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In this figure we show the output of the half-loop biclustering algorithm applied to the BDRN cohort in arm-1 (limited to those SNPs with maf ≥0.25). by Caroline C. McGrouther (5216153)

Subjects: “…corrected biclustering algorithm…”

Oresme by Mehmet Keçeci (14301782)

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Data&Codes.rar by anonymous author (20478659)

Table 1_Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical information database network.pdf by Ryusuke Inoue (4617505)

“…The highest sensitivity at 89.53% was observed for Algorithm 9. This algorithm was constructed using a combination of disease codes entered in electronic medical records, DPC, or health insurance claims data, abnormal BNP values in the moderate or high range (≥100 pg/mL), and medications for acute heart failure. …”

All Code and MoCap data for Cutaneous Rectilinear Algorithm tests by Henry Astley (22155685)

ASTCT_data & code by Ju Peng (13922445)

MD5 signature for a whole file by Paul A. Gagniuc (1818325)

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