IGA parameters selection. by Dingming Yang (8505990)

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The model of network adversarial attack. by Dingming Yang (8505990)

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Pathway activity and single-cell level functional states of CEDGs in cancer. by Yuxi Gu (11386081)

Changes in the relative abundances of signaling functions across initial GA-LR pairs. by Erick Armingol (5125595)

“…Changes were computed from the fold change (FC) between the relative abundance in each of the 100 runs of the genetic algorithm (GA) with respect to the corresponding relative abundance in the complete list of LR pairs (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010715#pcbi.1010715.s002" target="_blank">S1 Table</a>), and shown as the log10(FC+1) transformation (x-axis). …”

Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

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Accuracy, in terms of the Jaccard index, as a function of the number of iterations for the first four retinal image registrations (shown in different colours) with INSPIRE. by Johan Öfverstedt (14185817)

Type-1 membership function for distance. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

Type-1 membership function for speed. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

Illustration of how (1 + 1)-WEA_v3 improves the target set when solving TSS. by Alexander Andreev (16822333)

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Convergence plots for solving IM for p2p-Gnutella06_uni_1-1000_uni_0.75-1 via (1 + 1)-WEA. by Alexander Andreev (16822333)

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Functions of most frequently mutated genes. by Anda Stan (18056225)

“…Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. …”

Fraction of stripes considered as a function of aspect ratio cut-off value for individual stripe measurements. by Pratik Mullick (12851011)

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The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Effects of processing rate and algorithms performance on estimates of biodiversity. by Valentine Fleuré (12822137)

Number of offspring as a function of the number of mismatches for the best couple (blue) and the second-best couple (red) found by APIS parentage assignment. by Julien Roche (611898)

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? by Minyi Su (1425976)

“…In order to examine the true power of machine-learning algorithms in scoring function formulation, we have conducted a systematic study of six off-the-shelf machine-learning algorithms, including Bayesian Ridge Regression (BRR), Decision Tree (DT), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Linear Support Vector Regression (L-SVR), and Random Forest (RF). …”

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Fig 1 - by Yuh-Chin T. Huang (17867207)

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