The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

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<i>De Novo</i> Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization by Alberga Domenico (9356272)

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Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

Face recognition algorithm flow based on the PCA. by Na Lin (69470)

Summary of classification algorithms. by Stephen R. Piccolo (8367780)

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Feature selection process. by Balraj Preet Kaur (20370832)

Classification algorithm hyperparameter combinations. by Stephen R. Piccolo (8367780)

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Summary of feature-selection algorithms. by Stephen R. Piccolo (8367780)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

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Library of the 55 different classification and regression machine-learning algorithms used by the ensemble predictor SuperLearner (SL.library) in the CBDA 2.0 implementation. by Simeone Marino (81051)

Feature-selection algorithm hyperparameter combinations. by Stephen R. Piccolo (8367780)

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An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

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Comparison of ranks for classification algorithms across performance metrics. by Stephen R. Piccolo (8367780)

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Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

Tradeoff between execution time and predictive performance for classification algorithms. by Stephen R. Piccolo (8367780)

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RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms by Yidan Tang (6623693)

“…The Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm and reaction-driven molecule proposal to enhance structure-based <i>de novo</i> drug discovery. …”

Relative classification performance per combination of feature-selection and classification algorithm. by Stephen R. Piccolo (8367780)

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Schematic of iteration process of IDE-IIGA. by Ling Zhao (111365)

Comparison based on hard instances from [79]. by Xiang Tian (4369285)

“…Secondly, based on the data libraries of the IPMMPO, two tuple sets suitable for constraint programming modeling are further designed as data preprocessing. …”

Train test validation libraries. by Muhammad Usman Tariq (11022141)

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