Path data for different algorithms in four different environments. by Tengfei Ma (597633)

Subjects:

Continuous Flow Chemistry and Bayesian Optimization for Polymer-Functionalized Carbon Nanotube-Based Chemiresistive Methane Sensors by John H. Dunlap (5954933)

Comparison results of the loss functions. by Jia Li (160557)

“…To address this problem, this paper proposes a YOLOv8-based insulator defect detection algorithm, YOLOv8-SSF. …”

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

“…The algorithm employs recurrent neural networks to capture and process historical information. …”

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

“…The algorithm employs recurrent neural networks to capture and process historical information. …”

Test results of different algorithms. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

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

Comparison of different optimization algorithms. by Hang Zhao (143592)

Depiction of how the label of is assigned as a function of whether are identical (row =), nested (rows ), or partially overlapping (row “else”), and as a function of the labels of... by Jordan Moutet (21800145)

Time Series Based Methods. by Ferhat Yuna (21519851)

“…The mathematical model was transformed into a fitness function and a solution was provided with the Tabu Search Algorithm and Simulated Annealing. …”

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

Subjects:

Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

Subjects:

Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

Subjects:

A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

The data for Example 5 are sampled from the peaks function. by Yushi Liu (173457)

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Subjects:

Proportion of simulated data with improved likelihood from using multiple restarts (Algorithm 1). by Jesse Wheeler (18462457)

Subjects: “…routinely applicable algorithm…”

Comparative experimental data of loss functions. by Huiying Zhang (200681)

“…<div><p>An MDCFVit-YOLO model based on the YOLOv8 algorithm is proposed to address issues in nighttime infrared object detection such as low visibility, high interference, and low precision in detecting small objects. …”

Filled contour plot based on histograms of the occurence-frequency of binned values of and as a function of rate ratio . by Paul Kinsler (3975620)

Routing policy based on path satisfaction. by Yang Yu (4292)

“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. …”