Structural changes of images after denoising using different algorithms. حسب Bingbing Li (461702)

Supporting data of the PET image reconstruction using multi-parametric anato-functional priors حسب Abolfazl Mehranian (11386115)

Convergence curves of three algorithms under different test functions. حسب Jianpeng Zhang (528185)

الموضوعات:

PR curves of three algorithms under different test functions. حسب Jianpeng Zhang (528185)

الموضوعات:

The optimal path in the improvement gird map using FIQL algorithm. حسب Bing Hao (2095699)

Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). حسب Solomon Shiferaw Beyene (9143178)

Comparison of results of different algorithms. حسب Zhongzhong Guo (12133402)

الموضوعات:

The benchmark functions. حسب Shuhui Yu (1535500)

Final assignment algorithm. حسب Negin Alisoltani (11949357)

Accuracy, sensitivity, specificity and F-score. حسب Solomon Shiferaw Beyene (9143178)

The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. حسب Solomon Shiferaw Beyene (9143178)

Comparison of PSOLFWM with PSO families algorithms using t-Test. حسب Yong Gao (29929)

Convergence curve of real power transmission line loss minimization by using (a) the HFPSO algorithm and (b) the PSO algorithm. حسب Abdullah Khan (377292)

Function graph and algorithm iterative graph. حسب WanRu Zhao (18980374)

"…This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. …"

Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. حسب Alexis M. Thornton (11068403)

الموضوعات:

The convergence curve of each optimization algorithm under the test function. حسب Hao Yang (328526)

Accuracy of the support vector machines for different proportion of test samples and different length of nodes used for training. حسب Abeer Aljohani (18497914)

الموضوعات:

Algorithm testing. حسب Ziad M. Ali (12516532)

الموضوعات:

Pre-trained protein language model. حسب Youzhi Zhang (728859)

"…Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. …"

Box plots of different algorithms on CEC2017 test function. حسب Tengfei Ma (597633)

الموضوعات: