A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral analysis and SVM classification, the approach improves ...

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for millions of deaths each year according to the World Health Organization (WHO). Early detection of these diseases ...

🧠 Support Vector Machines (SVM) for Breast Cancer Classification 📌 Objective Use Support Vector Machines (SVMs) for binary classification of breast cancer (Malignant vs. Benign). The project ...

1 Key Laboratory of Tobacco Chemistry, Zhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, China 2 Technology Center, China Tobacco Jilin Industrial Co., Ltd.

I propose adding a Multiple Kernel Learning (MKL) module for kernel optimization in kernel-based methods (such as SVM) to scikit-learn. MKL is a more advanced approach compared to GridSearchCV, ...

Abstract: Support Vector Machine (SVM) is a widely used algorithm for classification, valued for its flexibility with kernels that effectively handle non-linear problems and high-dimensional data.

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, ...

Epilepsy detection using artificial intelligence (AI) networks has gained significant attention. However, existing methods face challenges in accuracy, computational cost, and speed. CNN excel in ...