Classified maps from LISS IV, Sentinel 2A and Integrated images using ML (Maximum Likelihood), GMM (Gaussian mixed model), KNN (K-nearest neighbour), SVM (Support Vector Machine) and RF (Random Forest) classifier algorithms. by Ritam Dutta (13132451)

“…<p>Classified maps from LISS IV, Sentinel 2A and Integrated images using ML (Maximum Likelihood), GMM (Gaussian mixed model), KNN (K-nearest neighbour), SVM (Support Vector Machine) and RF (Random Forest) classifier algorithms.…”

ROC curve analysis of machine learning algorithms with balanced data before tuning (A) and after optimized with hyper parameter tuning (B). by Alemu Birara Zemariam (17540938)

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Analysis of variable importance to single-family housing prices using the SHAP method. by Jaewon Han (8660295)

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Global explanation using SHAP value plot. by Md Amir Hamja (20495951)

“…This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. …”

Global explanation using SHAP bar plot. by Md Amir Hamja (20495951)

Global explanation using SHAP bar plot. by Md Amir Hamja (20495951)

Global explanation using SHAP bar plot. by Md Amir Hamja (20495951)

“…This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. …”

Flowchart diagram illustrating the data analysis process using machine learning models. by Isabela Bittencourt Basso (14178739)

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Architecture of the EfficientNetB3-based regression model used for cattle weight prediction. by Md Junayed Hossain (22615268)

<b>Comparing Different Supervised Classifications</b><b> </b><b>Methods for Land Use and Land Cover Mapping </b><b>with Sentinel-2 Data in Google Earth Engine</b> by Omid Shobairi (22317805)

“…Four machine learning algorithms—support vector machine (SVM), random forest (RF), classification and regression trees (CART), and minimum distance (MD)—were employed to evaluate their performance. …”

S1 Syntax - Application of machine learning algorithms to predict early childhood development in children aged 24–59 months across three East African countries by Tsion Mulat Tebeje (13955254)

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Model performance in ROC space across different algorithms and task conditions. by Yuqi Cheng (428511)

Table 4_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.xlsx by Fan Bu (1563562)

Table 3_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.xlsx by Fan Bu (1563562)

Table 1_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.docx by Fan Bu (1563562)

Table 2_Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.xlsx by Fan Bu (1563562)

Evaluation of classification models using ROC curves. by Isabela Bittencourt Basso (14178739)

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Image 2_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.jpeg by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Table 2_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Table 6_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”