بدائل البحث:
process optimization » model optimization (توسيع البحث)
series process » species process (توسيع البحث), series cross (توسيع البحث)
binary series » primary series (توسيع البحث), webinar series (توسيع البحث), binary relief (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data global » daily global (توسيع البحث)
process optimization » model optimization (توسيع البحث)
series process » species process (توسيع البحث), series cross (توسيع البحث)
binary series » primary series (توسيع البحث), webinar series (توسيع البحث), binary relief (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data global » daily global (توسيع البحث)
-
1
-
2
-
3
-
4
-
5
-
6
-
7
-
8
-
9
The Pseudo-Code of the IRBMO Algorithm.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
10
IRBMO vs. meta-heuristic algorithms boxplot.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
11
IRBMO vs. feature selection algorithm boxplot.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
12
Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results
منشور في 2025"…The percentage mean absolute residuals of the activity coefficients obtained using DEA, NMM, and the parameter estimation tool in Aspen Plus were in the ranges of 0.05–16.69, 0.05–16.69, and 0.09–326.77%, respectively. This in-house algorithm will be helpful for obtaining more accurate NRTL parameters in a timely manner and will facilitate the simulation of biochemical processes for process optimization, energy consumption estimation, and life cycle assessment.…"
-
13
IRBMO vs. variant comparison adaptation data.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
14
Pseudo Code of RBMO.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
15
P-value on CEC-2017(Dim = 30).
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
16
Memory storage behavior.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
17
Elite search behavior.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
18
Description of the datasets.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
19
S and V shaped transfer functions.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
-
20
S- and V-Type transfer function diagrams.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"