Search alternatives:
process optimization » model optimization (Expand Search)
models optimization » model optimization (Expand Search), wolf optimization (Expand Search), codon optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
a process » _ process (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
process optimization » model optimization (Expand Search)
models optimization » model optimization (Expand Search), wolf optimization (Expand Search), codon optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
a process » _ process (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
-
21
-
22
-
23
-
24
-
25
-
26
Event-driven data flow processing.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
-
27
-
28
Proposed Algorithm.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
-
29
-
30
MCLP_quantum_annealer_V0.5
Published 2025“…This paper presents a quantum computing path for Transformation-to-Sampling-to-Verification of geospatial optimization problems, adaptable to the controlled qubit scale and coherence constraints under current NISQ conditions. …”
-
31
Comparisons between ADAM and NADAM optimizers.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
-
32
Dynamic resource allocation process.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
-
33
Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
Published 2022“…In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. …”
-
34
The comparison of the accuracy score of the benchmark and the proposed models.
Published 2025Subjects: -
35
-
36
The Pseudo-Code of the IRBMO Algorithm.
Published 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. …”
-
37
-
38
Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results
Published 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.…”
-
39
-
40
Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
Published 2025Subjects: