The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

A) Ranking of models trained on different feature sets across all tested algorithms, sorted by their F1-score. by Jose Cleydson F. Silva (8685594)

RMSE comparison in all test function (a) F1 (b) F2 (c) F3 (d) F4 (e) F5 (f) F6 (g) F7 (h) F8 (i) F9 (j) F10. by Jin Ding (11522)

Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

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Results of each algorithm. by Guilin Yang (583364)

“…<div><p>To address the issue of poor performance in the chimp optimization (ChOA) algorithm, a new algorithm called the manta ray-based chimpa optimization algorithm (MChOA) was developed. …”

FaaSr: Serverless Workflows in R by Renato Figueiredo (3684430)

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Top-ranked hub genes identified by 5 cytoHubba algorithms. by Jun Li (6494)

Control parameters of the SOMA algorithm. by Quoc Bao Diep (18452207)

“…To optimize this cost function, we employ the self-organizing migrating algorithm, a swarm intelligence algorithm inspired by the cooperative and competitive behaviors observed in natural organisms. …”

List of 3D plugins and functionalities. by Helmut Ahammer (315536)

The recurrent T3D algorithm. by Neythen J. Treloar (8698251)

Backbone dimension () as a function of relative age (). by Jonathan E Kings (20818367)

A bar chart for the number of function evaluations—Phase III. by Fadi K. Dib (5204807)

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

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CEC2017 basic functions. by Tengfei Ma (597633)

“…<div><p>To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. …”

Classification thresholds and prediction algorithms for novel PGx variants. by Ewa Goljan (3463487)

Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems by Dongge Lei (6836915)

“…The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.…”

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

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