The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

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

Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. by Konstantina Charmpi (9901053)

Screening MMRG gene variables associated with UC based on the optimal machine learning model. by Li Li (14993)

Proposed Algorithm. by Hend Bayoumi (22693738)

“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”

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

“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”

Optimal ALARM BN discovered by CausNet. by Nand Sharma (21519845)

“…<div><p>In our recent work, we developed a novel dynamic programming algorithm to find optimal Bayesian networks with parent set constraints. …”

DataSheet_1_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.docx by Daniela Tizabi (10297654)

DataSheet_2_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.pdf by Daniela Tizabi (10297654)

Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF by Deniz Seçilmiş (13028616)

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF by Deniz Seçilmiş (13028616)

Expression values and standard errors of genes within each patient group under the optimal feature filtering method (ALOGA < 128) used for (blue bars) distinguishing each individua... by Song Cui (553627)

Table 1_Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma.docx by Bingxin Zhang (13011397)

DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP by Liangliang Wang (580395)

“…</p><p>Results: Immune cluster analysis based on ssGSEA could well distinguish the TIME characteristics of patients with HCC. …”

Summary of datasets used. by Stephen R. Piccolo (8367780)

SHAP bar plot. by Meng Cao (105914)

“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”

Sample screening flowchart. by Meng Cao (105914)

“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”

Descriptive statistics for variables. by Meng Cao (105914)

“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”