Introduction Envision our digital world as a sprawling universe filled with glittering constellations of encrypted data, each packet of information ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

1 Department of Information Technology and Computer Science, School of Computing and Mathematics, The Cooperative University of Kenya, Nairobi, Kenya. 2 Department of Computing and Informatics, School ...

Abstract: Class imbalance occurs frequently in machine learning, particularly in binary classification tasks where the majority class has a significantly larger number of samples than the minority ...

Cancer machine learning research is often limited by overparameterization and overfitting, which arise because cancer ‘omic’ variables significantly outnumber patient samples. Traditional feature ...

Build interactive web applications with Streamlit and Python Train Logistic Regression, Random Forest, and Support Vector Classifiers using scikit-learn Plot evaluation metrics for binary ...

Cluster analysis can be used on symptom and behavior data to identify groups of similar individuals who may share underlying disease etiology or health risks. However, there are few clustering methods ...

Quantum machine learning is considered one of the current research fields with great potential. In recent years, Havlíček et al. [Nature (London) 567, 209 (2019)] have proposed a quantum machine ...

Dr. James McCaffrey from Microsoft Research presents a C# program that illustrates using the AdaBoost algorithm to perform binary classification for spam detection. Compared to other classification ...