IEEE

Hyperspectral Unmixing for Additive Nonlinear Models With a 3-D-CNN Autoencoder Network

Abstract: Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed aiming at analyzing the material ...
GitHub

SolidBin: Improving Metagenome Binning with Semi-supervised Normalized Cut

CheckM relies on a number of precalculated data files which can be downloaded from https://data.ace.uq.edu.au/public/CheckM_databases/. (More details are available at ...
Scientific Research Publishing

An Explainable Wavelet-Based Feature Decomposition and Machine Learning Framework for Land Cover Classification ()

1 Department of Geomatic Engineering, University of Mines and Technology, Tarkwa, Ghana. 2 Department of Geomatic and Civil Engineering, University of Mines and Technology, Essikado, Ghana. Accurate ...
C&EN

Creation of an Open-Access High-Resolution Tandem Mass Spectral Library of 1000 Food Toxicants

Spectral library searching is a key method for compound annotation in mass spectrometry; however, existing libraries often suffer from high data heterogeneity, varying spectral quality, or limited ...
GitHub

STAG: Spectral Toolkit of Algorithms for Graphs

STAG is a C++ library providing efficient spectral algorithms for the analysis of massive graphs. There is also an accompanying STAG Python library. The library provides a simple and convenient ...
Visual Studio Magazine

DBSCAN Data Clustering from Scratch Using C#

Compared to other clustering techniques, DBSCAN does not require you to explicitly specify how many data clusters to use, explains Dr. James McCaffrey of Microsoft Research in this full-code, ...
Analytics India Magazine

Guide To BIRCH Clustering Algorithm(With Python Codes)

BIRCH is an alternative to MinibatchKMeans and is designed for large datasets. The algorithm converts data into a tree structure, facilitating efficient clustering. BIRCH allows for initial clustering ...