Sensor value scenario for average value algorithm. by Gourab Saha (8987405)

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Baseline characteristics of recipients’ comparison before and after data match by the unsupervised machine learning algorithm. by Mahmoudreza Moein (22127153)

Water consumption due to average value and fuzzy logic algorithm. by Gourab Saha (8987405)

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

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Statistical results of various algorithms. by ZeSheng Lin (20501356)

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Automatic translation error detection based on fuzzy decision tree algorithm. by Danqiangyu Zhou (22146196)

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Analysis of ABIDE dataset: Conventional regression frameworks for optimized algorithmic value. by Alexandria M. Jensen (6494957)

Subjects: “…community detection algorithms…”

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…First, physico-chemical inputs as bulk density (BD), initial water content (W_i), saturated water content (W_s), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

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

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The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

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Automated Analysis of Astrocyte Cell Connectivity After Laser Ablation Using Machine Learning and Path-Finding Algorithms by Connor Lee (22327243)

Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms by Hossein Mashhadimoslem (11456548)

“…In this research study, we created a data set and collected data points from porous adsorbents (2789) from 21 published papers, including carbon-based, porous polymers, metal–organic frameworks (MOFs), and zeolites, to understand their characteristics for CO₂ adsorption. Different machine learning (ML) algorithms, such as NN, MLP-GWO, XGBoost, RF, DT, and SVM, have been applied to display the CO₂ adsorption performance as a function of characteristics and adsorption isotherm parameters. …”

Optimization Performance Comparison of Metaheuristic Algorithms. by Xie Qiu (13243770)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... by Hassan Mehboob (8960273)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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Comparison of classification accuracy across different machine learning models using the objective function metric. by Amal H. Alharbi (21755906)

The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

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Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx by 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 by 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 by 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). …”