SciELO

Obesity classification: a comparative study of machine learning models excluding weight and height data

OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity ...
GitHub

GitHub - kossisoroyce/timber: Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code ...

Timber takes a trained ML model — XGBoost, LightGBM, scikit-learn, CatBoost, ONNX (tree ensembles, linear models, SVMs, k-NN, Naive Bayes, GPR, Isolation Forest), or a URDF robot description — runs it ...
Nature

Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models

Surgical site infections (SSIs), among the most frequent healthcare-associated infections, require surveillance, but traditional methods are labour-intensive. We developed machine learning (ML) and ...
LinkedIn

Building a Spam Detector with Python: A Step-by-Step Machine Learning Journey

As I wrapped up a late Sunday evening (July 6, 2025) exploring new ways to leverage machine learning, I dove into creating a spam detector using Python. This project, inspired by my recent sentiment ...
Scientific Research Publishing

Extra Trees Model for Heart Disease Prediction ()

1 Computer Science Department, Babcock University, Ilishan-Remo, Ogun State, Nigeria. 2 Computer Science Department, Adeleke University, Ede, Osun State, Nigeria. 3 Department of Applied Mathematics, ...
GitHub

email-spam-classifier

Implemented Preprocessing steps, Feature Extraction techniques and Naive Bayes Classifier in C++. Moreover, we have also implemented all the steps using python for comparative analysis. One of the ...
Psychology Today

Brain-Computer Interfaces Boosted by Novel AI Algorithm

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...
IEEE

Cybersecurity Empirics: Evaluating Machine Learning Techniques for Phishing Detection

Abstract: TIn an age dominated by internet usage, the threat of phishing attacks continues to plague users and organizations alike. Traditional cybersecurity mechanisms often fail to cope with the ...
Nature

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

Detecting code smells may be highly helpful for reducing maintenance costs and raising source code quality. Code smells facilitate developers or researchers to understand several types of design flaws ...
IEEE

Machine Learning Model for Measuring Cyclomatic Complexity of Source Code

Abstract: Software developers strive to build high-performance and quality software with a very high degree of coherence among its components, simplified structure, and reduced complexity. A ...
Visual Studio Magazine

Multinomial Naive Bayes Classification Using the scikit Library

A full-code demo from Dr. James McCaffrey of Microsoft Research shows how to predict the type of a college course by analyzing grade counts for each type of course. General naive Bayes classification ...