Modelled view of cloud layer. by Shanthi Amgothu (17692146)

Systematic model of fog-cloud COVID prediction. by Shanthi Amgothu (17692146)

Table_1_Optimizing DUS testing for Chimonanthus praecox using feature selection based on a genetic algorithm.xlsx by Ting Zhu (460268)

Nested CV procedure for model development and evaluation. by María Goñi (12963621)

Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

“…If the identical blob that was detected in panel J (bottom) is found in any of the new subtracted binary images (cyan arrow), the animal is considered as having left its original position, and the algorithm continues. …”

Data_Sheet_1_Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms.docx by Yuanyuan Chen (165812)

Metaheuristic Solutions to Order-of-Addition Design Problems by Zack Stokes (17333870)

“…To this end, we employ two exemplary nature-inspired metaheuristic algorithms, Differential Evolution (DE) and Particle Swarm Optimization (PSO), to search for efficient order-of-addition designs for two classes of important inferential problems: (a) estimating parameters in an imprecisely specified model, and (b) constructing space-filling designs without specifying a model. …”

Incremental Inverse Design of Desired Soybean Phenotypes by Joseph Zavorskas (19761296)

“…A random forest (RF) is used to model the genotype-to-phenotype relationship, and a genetic algorithm is used to query the RF until a feasible genotype with desired phenotype is discovered. …”

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

DataSheet_1_Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.xlsx by Abiel Roche-Lima (5079422)

“…ML algorithms were trained with the training set to obtain the models. …”

CPM - Cotton Production Model by USDA Agricultural Research Service (17476221)

“…The algorithms that simulate crop growth are derived in part from the best of each of the previous models, and they incorporate new physiological information as well. …”

Table 1_Development of a diagnostic model for MASLD and identification of daidzein as the potential drug using bioinformatics analysis and experiments.xls by Tao Wang (12008)

“…</p>Results<p>62 MASLD-DEGs were finally identified. The optimal predictive model for MASLD was the 17-gene signature (IGFBP1, ENO3, SOCS2, GADD45G, NR4A2, RTP4, RAB26, CRYAA, PPP1R3C,MCAM, IL6, IER3, RTP3, NR4A1, CCL5, FOS, JUNB) selected through combined glmBoost+GBM algorithms, which was demonstrated robust predictive performance. …”

Image 1_Development of a diagnostic model for MASLD and identification of daidzein as the potential drug using bioinformatics analysis and experiments.tif by Tao Wang (12008)

“…</p>Results<p>62 MASLD-DEGs were finally identified. The optimal predictive model for MASLD was the 17-gene signature (IGFBP1, ENO3, SOCS2, GADD45G, NR4A2, RTP4, RAB26, CRYAA, PPP1R3C,MCAM, IL6, IER3, RTP3, NR4A1, CCL5, FOS, JUNB) selected through combined glmBoost+GBM algorithms, which was demonstrated robust predictive performance. …”

Data Sheet 1_Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study.docx by Tao Sha (18829884)

“…Background<p>Sepsis-induced coagulopathy (SIC) is a fatal complication in ICU patients, yet early risk prediction remains challenging. This study aimed to develop an interpretable machine learning model for predicting SIC within seven days of ICU admission.…”

Data_Sheet_1_Neurons as Canonical Correlation Analyzers.pdf by Cengiz Pehlevan (526705)

“…To model networks of pyramidal neurons, we introduce a novel multi-channel CCA objective function, and derive from it an online gradient-based optimization algorithm whose steps can be interpreted as the operation of a pyramidal neuron network including its architecture, dynamics, and synaptic learning rules. …”

Table_2_Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study.do... by Xiangming Cai (9117378)

“…Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. …”

Table_5_Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study.do... by Xiangming Cai (9117378)

“…Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. …”

Table_1_Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study.do... by Xiangming Cai (9117378)

“…Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. …”

Image_3_Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study.ti... by Xiangming Cai (9117378)

“…Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. …”

Table_3_Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study.do... by Xiangming Cai (9117378)

“…Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. …”