Search alternatives:
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
lines based » lens based (Expand Search), genes based (Expand Search), lines used (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
lines based » lens based (Expand Search), genes based (Expand Search), lines used (Expand Search)
-
121
IRBMO vs. meta-heuristic algorithms boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
-
122
IRBMO vs. feature selection algorithm boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
-
123
-
124
Comparative discussion on database-1.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
125
Schematic view of fog layer.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
126
Simulation parameter.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
127
Systematic model of fog-cloud COVID prediction.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
128
Comparative discussion on database-2.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
129
Solution encoding.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
130
Architectural view of DNFN.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
131
Modelled view of cloud layer.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
132
Architecture of Deep LSTM.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
133
Architectural view of PSP-Net.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
134
Simulation parameters.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
-
135
-
136
-
137
-
138
-
139
-
140
Flowchart scheme of the ML-based model.
Published 2024“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”