Parameters of the machine learning algorithms used in the study. by Xiaoyan Yang (259084)

Subjects:

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

Performance of machine learning algorithms. by Wenxue Liu (6877754)

Machine learning algorithms hyperparameter values. by Mahbub E. Sobhani (22278967)

ML algorithms used in this study. by Toheeb Salahudeen (21368040)

“…This study employed advanced machine learning techniques to classify major depressive disorders based on clinical indicators and mitochondrial oxidative stress markers. …”

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.</p><p>Materials and methods</p><p>This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017–18 data. …”

Prediction of pharmaceuticals occurrence based on sales data and Machine learning algorithms. by Júlia Bijos (14489543)

“…</p><p dir="ltr"><b>Antibioticos/Carbamazepina</b>: contains the main codes of the prediction models to classify the occurrence concentrations of some antibiotics and Carbamazepine, by tree boosting algorithms.</p><p dir="ltr"><b><u>Sensitivity Analysis</u></b></p><p dir="ltr"><b>cargas25_50_75_AS:</b> contains the pre processed updated dataset of pharmaceuticals sales data for the 3 scenarium conditions.…”

Flowchart of the SSA-based optimization of the BPNN algorithm. by Yeping Shi (14279118)

Subjects:

Machine learning prediction algorithm flow chart. by Ahmad M. Al-Omari (20460931)

Data used in "Material Classification System using Inductive Tactile Sensors and Machine Learning Algorithms" by Yuning Jiang (19758561)

“…Various machine learning algorithms were employed to train classification models, and an aggregated model was presented based on the individual classifiers. …”

Receiver operator characteristic (ROC) curves of the machine learning algorithms. by Yue Lv (549862)

<b>Discrimination of gold deposit types based on big data analysis and machine learning techniques</b> by Lihao Feng (20837036)

Comparison of information on predicting survival outcomes or metastasis of cancer patients using machine learning algorithms in the past. by Jingru Dong (14076094)

Subjects:

Decision tree algorithms. by Mahbub E. Sobhani (22278967)

“…We have used Random Forest, Bagging, and Boosting (AdaBoost) algorithms and have compared their performances. …”

Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms by Jie Zhu (126574)

“…Six different machine learning algorithms were employed to develop prognostic models based on the clinical features during the acute phase, which were reduced using Lasso regression.…”

Confusion Matrix for Subspace algorithm (3000 Learning Cycles) providing insight into the performance evaluation of Subspace algorithm. by Sadia Nazim (21440115)

Comparison of ROC curve between deep learning and 12 handcrafted features using four machine learning algorithms. by Rui-Si Hu (5337227)

Comparison of different optimization algorithms. by Hang Zhao (143592)

Subjects: “…crayfish optimization algorithm…”

Surface Tension Prediction of Fuel Additives Based on Machine Learning Model with Subtraction-Average-Based Optimizer Algorithm by Jianan Wang (359744)

“…To address the demand for thermophysical data of fuel additives, 574 surface tension data for 22 fuel additives were extensively collected and evaluated using empirical models. …”

Data Sheet 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”