Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. حسب Laurentius O. Osapoetra (9931759)

Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset. حسب Jiaxi Wang (545353)

The image of the Aekley function. حسب Zhengyu Xu (8550660)

The ALO algorithm optimization flowchart. حسب Wenjing Wang (181404)

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The IALO algorithm solution flowchart. حسب Wenjing Wang (181404)

الموضوعات:

Functional enrichment of gene clusters identified in up-regulated drought tolerant network (uDTN) using MCL algorithm. حسب Sanchari Sircar (198485)

Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. حسب Adam L. Tyson (10892822)

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Continuous Probability Distributions generated by the PIPE Algorithm حسب LUIS G.B. PINHO (14073372)

"…<div><p>Abstract We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. …"

Benchmark functions. حسب Peiwen Zhang (1863202)

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional حسب Ryan Stocks (16867476)

"…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …"

WGCNA and machine learning algorithms. حسب Shushu Wang (6870191)

Functional enrichment of 17 gene clusters identified in down-regulated drought tolerant network (dDTN) using MCL algorithm. حسب Sanchari Sircar (198485)

Optimization flow of the co-simulation method. حسب Shangyang Jin (21772466)

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CEC2022 and CEC2017 benchmark functions. حسب Chengcheng Ding (9013862)

The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

PSO algorithm flowchart. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

Prediction performance of different optimization algorithms. حسب Ali-Kemal Aydin (10968731)

"…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …"

The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms. حسب Hamed Khodadadi (21446172)

"…<p>The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms.…"

Standard functions to test. حسب Kemal Gökhan Nalbant (18858009)

Comparison of different algorithms. حسب Dawei Wang (471687)

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