Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

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

Game-relationship-based Remanufacturing Scheduling Model with Sequence-dependent Setup Times Using Improved Discrete Particle Swarm Algorithm by Anonymous Authors (for blind review purpose) (11523120)

Most informative genes for predicting cell-line responses for Gefitinib. by Sofia P. Miranda (11418930)

Subjects:

Gear meshing line. by Linyue Qiu (17319793)

Operation curve comparison among the five algorithms in the 95 s trip. by Yunhu Huang (21402795)

Subjects:

Operation curve comparison among the five algorithms in the 115 s trip. by Yunhu Huang (21402795)

Subjects:

Operation curve comparison among the five algorithms in the 101s trip. by Yunhu Huang (21402795)

Subjects:

Histograms for each drug based on drug response (IC₅₀ values) for the GDSC dataset. by Sofia P. Miranda (11418930)

Subjects:

The power magnitude of lines in scenario 1. by Dai Wan (20108122)

The power magnitude of lines in scenario 2. by Dai Wan (20108122)

Pareto-optimized solution set under essential maintenance strategy at 1-inspection maintenance. by Dapeng Wang (250459)

Subjects:

Pareto-optimized solution set under preventive maintenance strategy at 2-inspection maintenance. by Dapeng Wang (250459)

Subjects:

Pareto-optimized solution set under essential maintenance strategy at 2-inspection maintenance. by Dapeng Wang (250459)

Subjects:

Pareto-optimized solution set under preventive maintenance strategy at 1-inspection maintenance. by Dapeng Wang (250459)

Subjects:

Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction by E. Tejera (7470644)

“…<div><p>The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. …”

Descriptions of the algorithms we tested and hyperparameters that we evaluated via nested cross validation. by Sofia P. Miranda (11418930)

Subjects:

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

SUNRISE hybrids obtained from 36 maintainer lines (females, rows) crossed to 36 restorer lines (males, columns). by Brigitte Mangin (336874)

Model selection by BO based on RCV(nv) from the simulated data with different noise levels. by László Zimányi (2649973)