Code snippets to add user-specific custom functionality in PyNoetic. حسب Gursimran Singh (575288)

الموضوعات:

The convergence curves of the test functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

Single-peaked reference functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

IEEE CEC 2017 benchmark function specifications. حسب Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. حسب Kankan Tang (21474968)

Study proposed algorithm. حسب Ainhoa Pérez-Guerrero (21377457)

"…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …"

The benchmark functions. حسب Shuhui Yu (1535500)

Modular architecture design of PyNoetic showing all its constituent functions. حسب Gursimran Singh (575288)

الموضوعات:

Flowchart for the unsupervised SOM algorithm modified from Seifert et al. [56] with permission. حسب Sergio Montalt-García (17696115)

Multi-algorithm comparison figure. حسب Dawei Wang (471687)

"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"

Test results of multimodal benchmark functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

Fixed-dimensional multimodal reference functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

Test results of multimodal benchmark functions. حسب Ruiyu Zhan (21602031)

"…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …"

Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx حسب Peng Liu (120506)

"…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …"

Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff حسب Peng Liu (120506)

"…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …"

Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff حسب Peng Liu (120506)

"…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …"

Image 5_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff حسب Peng Liu (120506)

"…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …"

Image 3_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff حسب Peng Liu (120506)

"…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …"

Image 2_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff حسب Peng Liu (120506)

"…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …"

Test function results. حسب Ruyi Dong (9038174)

"…From the statistical results of mean, best and variance of different algorithms, the LLSKSO algorithm outperforms the other algorithms. …"