IEEE

Lightweight Parallel Convolutional Neural Network With SVM Classifier for Satellite Imagery Classification

Abstract: Satellite image classification is crucial for various applications, driving advancements in convolutional neural networks (CNNs). While CNNs have proven effective, deep models often ...
GitHub

bhumikabhat/SVM

🧠 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 ...
Frontiers

Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review

Department of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang, China Background: Effective connectivity (EC) refers to the ...
C&EN

Amplification-Free Quantification of Endogenous Mitochondrial DNA Copy Number Using Solid-State Nanopores

Mitochondrial DNA (mtDNA) quantification is crucial in understanding mitochondrial dysfunction, which is linked to a variety of diseases, including cancer and neurodegenerative disorders. Traditional ...
Frontiers

Logging-data-driven lithology identification in complex reservoirs: an example from the Niuxintuo block of the Liaohe oilfield

For lithologic oil reservoirs, lithology identification plays a significant guiding role in exploration targeting, reservoir evaluation, well network adjustment and optimization, and the establishment ...
Scientific Research Publishing

Bazi, Y. and Melgani, F. (2006) Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 44 ...

ABSTRACT: Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over ...
IEEE

One-Class SVM Probabilistic Outputs

Abstract: One-class support vector machine (SVM) is an extension of SVM to handle unlabeled data. As a mature technique for outlier detection, one-class SVM has been widely used in many applications.