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

“…<p dir="ltr">This study presents an innovative material classification system involving an inductive tactile sensor and machine learning algorithms. A simple-structured sensor based on the principle of electromagnetic induction was developed to capture varying inductance signals induced by different materials with distinct magnetic properties, facilitating material detection and distinction. …”

High-Precision Landing on a Moving Platform Based on Drone Vision Using Yolo Algorithm by Satoshi Suzuki (20437556)

“…To overcome the challenge of the flight altitude is too high to detect the landing target, this paper fi rst detects large-volume targets, followed by the precise identifi cation of smaller targets, achieving enhanced recognition accuracy and speed through an improved YOLOv8 OBB algorithm. To maintain the UAV’s safety and stability throughout the landing process, This paper applies a position control approach using a reference model based sliding mode controller (RMSMC). …”

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

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

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…These classification algorithms often requires conversion of a medical data to another space in which the original data is reduced to important values or moments. …”

Computational cost of anchor-free algorithms. by Olarewaju Mubashiru Lawal (14710251)

Data Sheet 1_i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.docx by Sakshi Gautam (10305897)

Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes by Vijay Subramanian (20718933)

“…Morpheus exhaustively curates and uses fragment structural data from the protein data bank as well as the AlphaFold Protein Structure Database. …”

Data_Sheet_1_Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.CSV by Jie Shen (31533)

Data_Sheet_2_Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.docx by Jie Shen (31533)

DataSheet1_Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm.docx by Qiyuan Cui (19729288)

“…Objective<p>The aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study.…”

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

RFEcv algorithm flowchart. by Xinrui Wang (488330)

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

Overview of the Cell2Spatial algorithm. by Huamei Li (8815955)

“…SC and spatial transcriptomics (ST) data were standardized using <i>SCTransform</i> in Seurat, with cell-type-specific genes identified through a modified entropy-based method (Step 1). …”

Data Sheet 3_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 2_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 4_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”