Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. by Laurentius O. Osapoetra (9931759)

Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. by Adam L. Tyson (10892822)

Subjects:

Functional enrichment of gene clusters identified in up-regulated drought tolerant network (uDTN) using MCL algorithm. by Sanchari Sircar (198485)

Functional enrichment of 17 gene clusters identified in down-regulated drought tolerant network (dDTN) using MCL algorithm. by Sanchari Sircar (198485)

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

Subjects:

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Functional enrichment analyses of Co-genes. by Qingcong Zheng (11169094)

Completion times for different algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

Decision Graph to compute clusters according to Density Peak Clustering algorithm (Ref. [92]). by Rajdeep Kaur Grewal (12563150)

“…[<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010114#pcbi.1010114.ref092" target="_blank">92</a>]. For both, Model 1 (A) and Model 2 (B), we observed only one such cluster center to exist implying the presence of a single minimum cost function in the parameter range explored by the PSO. …”

Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

Immune infiltration analysis by CIBERSORT algorithm. by Lanying Shen (22755930)

Functional enrichment of protein subnetworks in HIV-1 producer cells after co-culture with uninfected cells. by James W. Bruce (10204626)

Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …”

Sub-model-specific gene sets with functional annotations and weights. by Josh L. Espinoza (10492448)

Comparison of deconvolution and optimization algorithms on a batch of data. by Ali-Kemal Aydin (10968731)

“…Both experimental data have been resampled at 50ms and used to compute a set of TFs (in orange) either with direct deconvolution approaches (Fourier or Toeplitz methods, middle-upper panel TFs) or with 1-Γ function optimization performed by 3 different algorithms (middle-lower panel TFs). …”