Performance comparison of different algorithms. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

The performance of the contralateral temporo-frontal setup across virtual time. by Janne J. Luppi (17804327)

“…<p>All stimulation setups were begun at t = 10, in order to give the ADD algorithm time to cause intitial damage. …”

The performance of the contralateral parieto-frontal setups across virtual time. by Janne J. Luppi (17804327)

“…<p>All stimulation setups were begun at t = 10, in order to give the ADD algorithm time to cause intitial damage. …”

This illustration compares the AUC for a Random Forest classifier trained using various feature sets. by Mahmoud Zeydabadinezhad (12289570)

Subjects:

This illustration compares the AUC for a Random Forest classifier trained using various feature sets. by Mahmoud Zeydabadinezhad (12289570)

Subjects:

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art CEA-ES algorithms using CEC2014 functions. by Yang Cao (53545)

“…<p>Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art CEA-ES algorithms using CEC2014 functions.…”

Wilcoxon’s test results for EBJADE algorithms and other algorithms using CEC2014 functions for D = 30, 50 and 100. by Yang Cao (53545)

“…<p>Wilcoxon’s test results for EBJADE algorithms and other algorithms using CEC2014 functions for D = 30, 50 and 100.…”

Benchmark functions T-test for the proposed and compared algorithms. by El-Sayed M. El-kenawy (14581088)

Proposed algorithm convergence curves versus other competing algorithms for the seven benchmark functions. by El-Sayed M. El-kenawy (14581088)

Experimental setup. by María Victoria Waks-Serra (21578611)

Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

“…To choose the next pipeline configuration to evaluate, the BO algorithm uses an Expected Improvement function to trade off maximisation of QS with the need to fully learn the GP. …”

Results of the PSO-Kmeans-ANMS algorithm for the Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

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Results of the PSO-Kmeans-ANMS algorithm for the Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

Data_Sheet_1_SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.docx by Ekaterina Mikhaylets (17865407)

“…<p>The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. …”

Graph of convergence of PSO algorithm fitness function. by Mohammed F. Elnaggar (18297954)

Convergence curves of the voltage profile improvement by using (a) the HFPSO algorithm and (b) the PSO algorithm. by Abdullah Khan (377292)

Scenarios for algorithms using IEEE 33 bus. by Bachirou Bogno (20485724)

Scenarios for algorithms using IEEE 69 bus. by Bachirou Bogno (20485724)

AIF and VOF derived features used to feed the machine learning algorithms. by Ezequiel de la Rosa (11361108)