The phases of automated BHV monitoring. by Bernhard Vennemann (7434353)

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …”

One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

Table_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.DOCX by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Damping oscillation function (before adding random numbers). by Fujun Wang (96184)

Kinematics timeries and features of cluster 1 before and one year after surgery. by Xavier Gasparutto (8374578)

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Kinematics timeries and features of cluster 3 before and one year after surgery. by Xavier Gasparutto (8374578)

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Kinematics timeries and features of cluster 2 before and one year after surgery. by Xavier Gasparutto (8374578)

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Spatiotemporal parameters for patients clusters (before and one year after surgery) and control group. by Xavier Gasparutto (8374578)

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Speech signal waveforms (a) before and (b) after hiding the encrypted text message. by Mustafa A. Al Sibahee (17729627)

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Data_Sheet_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.PDF by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Characteristics and patients reported outcomes for patient clusters before and one year after surgery compared to the control group. by Xavier Gasparutto (8374578)

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PSO algorithms flowchart. by Mohammed F. Elnaggar (18297954)

Kinematic features for all patients before (PRE) and one year after surgery (P1Y) as well as all control group values. by Xavier Gasparutto (8374578)

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Diagram of the algorithm used during the training phase. by Ana P. Arantes (17131753)

The comparison of TL before and after optimization. by Shangyang Jin (21772466)

The comparison of TL before and after optimization. by Shangyang Jin (21772466)

The number of inactive nodes as a function of time steps in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)

Comparison of loss function curves before and after improvement. by Xiaozhou Feng (2918222)

Flowchart of the phase optimization algorithm. by Maxim Terekhov (3429614)

“…<p>At stage II, phase vectors {Φ^top} providing cost function values F_c above 90% of maximum are selected as initial condition {Φ₀} for iterative NLS-search and selection of the optimum vectors {Φ^opt}. …”