CPU runtime of each algorithm: (a) Running Time Comparison of Different Algorithms on F1-F10 and (b) Running Time Comparison of Different Algorithms on F11-F20. by Ruyi Dong (9038174)

Subjects: “…dimensional benchmark functions…”

Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

“…These implementations need to utilize high-precision arithmetic, and are programmed in the Python programming language. A thorough comparison with existing R functions allows the identification of cases when the latter fail, and provide further guidance to their use.…”

Line charts of the elbow method and the k-value selection algorithm: (A) elbow method; (B) k-value selection algorithm. by Jingnan Yan (20727331)

Subjects:

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…We then evaluate the ability to identify lead compounds among a large chemical space for the proposed flexible receptor docking algorithm using a subset of the DUD-E containing receptor targets MCR, GCR, and ANDR. …”

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…We then evaluate the ability to identify lead compounds among a large chemical space for the proposed flexible receptor docking algorithm using a subset of the DUD-E containing receptor targets MCR, GCR, and ANDR. …”

Supplementary file 1_Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules.docx by Fatima Wardi (22052360)

“…<p>In this paper, we deal with the use of the earthworm optimization algorithm (EOA) in foraging to estimate and extract the intrinsic electrical parameters of single-, double-, and triple-diode solar cells and photovoltaic modules across different technologies. …”

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)

Subjects:

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

Subjects:

Benchmark functions. by Yun Qi (560401)

NC statistics versus total clients over 50 runs per scenario using the MVO algorithm with various approaches. by Le Huu Binh (18340233)

Subjects:

Evolutionary processes on special functions (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP. by Jin Ding (11522)

Subjects:

Type-1 membership function for distance. by Seung-Min Ryu (21463891)

Subjects:

Type-1 membership function for speed. by Seung-Min Ryu (21463891)

Subjects:

Illustration of the DLNN used in this study, including 10 inputs, three hidden layers, and one output variable. by Tuan Anh Pham (1346049)

Data_Sheet_1_Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways.zip by Maxim Sorokin (4379848)

“…We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. …”

Population diversity comparison on all function (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP. by Jin Ding (11522)

Subjects:

The empirical distribution function and fitted distribution functions for data sets <i>Q</i> and <i>T</i>. by Salem A. Alyami (10572611)

Comparison of deconvolution and optimization algorithms on a batch of data. by Ali-Kemal Aydin (10968731)

“…Both experimental data have been resampled at 50ms and used to compute a set of TFs (in orange) either with direct deconvolution approaches (Fourier or Toeplitz methods, middle-upper panel TFs) or with 1-Γ function optimization performed by 3 different algorithms (middle-lower panel TFs). …”

Visualizing the scatter plot of the drought (red) versus no-drought (green) and the support vectors (black) using the SVM section 4 classification algorithm. by J. Emmanuel Johnson (9579567)

Results of Wilcoxon signed-rank test for <i>W</i> objective function. by Serdar Ekinci (15927455)