Random parameter factor. حسب Guangwei Liu (181992)

"…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …"

Hyperbolic tangent row domain. حسب Guangwei Liu (181992)

"…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …"

Parameter settings. حسب Guangwei Liu (181992)

"…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …"

Nonlinear fast convergence factor. حسب Guangwei Liu (181992)

"…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …"

CEC2019 benchmark functions. حسب Guangwei Liu (181992)

"…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …"

Data_Sheet_1_Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning.docx حسب Pan Ma (400070)

DataSheet1_Primary sarcopenia is associated with elevated spontaneous NET formation.pdf حسب Irina Balazs (18102253)

Table 1_Optimization of diagnosis-related groups for patients with acute appendicitis using a machine learning model.docx حسب Xinlong Gu (22163251)

Table_1_One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline.DOCX حسب Paul Grzesik (11136582)

"…When optimizing culture media for primary cells used in cell and gene therapy, traditional DoE approaches that depend on interpretable models will not always provide reliable predictions due to high donor variability. …"

Anonymized data. حسب Michael Bonert (3751348)

"…Among 4,625 cases of brain surgical resection specimens, 854 were classified as probable metastasis by the algorithm. On report review, 538/854 cases were confirmed as metastasis with a known primary site. …"

A new fast filtering algorithm for a 3D point cloud based on RGB-D information حسب Chaochuan Jia (7256237)

"…This method aligns the color image to the depth image, and the color mapping image is converted to an HSV image. Then, the optimal segmentation threshold of the V image that is calculated by using the Otsu algorithm is applied to segment the color mapping image into a binary image, which is used to extract the valid point cloud from the original point cloud with outliers. …"

Datasets used for the study and their sources. حسب Peter N-jonaam Mahama (15347793)

Data_Sheet_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.PDF حسب Michael Moor (10887888)

DataSheet1_Development of an Intelligent Power Management System for Solar PV-Wind-Battery-Fuel-Cell Integrated System.docx حسب Vincent Anayochukwu Ani (10322801)

Data_Sheet_1_Adaptive restraint design for a diverse population through machine learning.pdf حسب Wenbo Sun (531308)

DEM error verified by airborne data. حسب Qinghua Li (398885)

"…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …"

Error of ICESat-2 with respect to airborne data. حسب Qinghua Li (398885)

"…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …"

Symmetry test result. حسب Jiyong Moon (16819302)

Optimal Timings Codebook.xlsx حسب Emma Spillane (11249121)

Block diagram of MSMO classifier. حسب Muhammad Asim Shahid (15285640)

"…Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. …"