This research work focuses on analyzing the performance of a proposed random forest (RF) method with that of Gaussian Naive Bayes in predicting software problems. The database utilized in this ...

In the proposed framework, a statistical data aggregation method (MEAN function) is deployed at local gateway devices to remove redundant data to minimize communication latency and energy usage.

A new paper published in Nature Communications reveals how the way tree species are arranged in a forest can help optimize ...

With Apache Spark Declarative Pipelines, engineers describe what their pipeline should do using SQL or Python, and Apache Spark handles the execution.

A comprehensive comparative analysis of XGBoost and Random Forest algorithms for distinguishing breast cancer tissue from normal tissue using TCGA gene expression data.

Health research is increasingly turning to high-throughput molecular datasets (also known as ‘omic’ datasets) to discover novel biomarkers of disease risk and outcome. Unfortunately, the size and ...

This section provides general guidance to authors on their responsibilities and the code of conduct they should follow when submitting to a Royal Society of Chemistry journal. It also describes what ...

A Python-based interactive tool that compares multiple classification algorithms (e.g., SVM, KNN, Random Forest, Naive Bayes) on real-world datasets and visualizes their performance.

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Python software was utilized for data processing and machine learning model building. We employed four ML algorithms, such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), ...