Model-based clustering based on parameterized finite Gaussian mixture models. Models are estimated by EM algorithm initialized by hierarchical model-based agglomerative clustering. The optimal model ...

Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

Abstract: The Gaussian Mixture Model (GMM) is widely used in anomaly detection due to its flexibility in handling complex data distributions and its soft classification mechanism. However, its ...

What if the tools you already use could do more than you ever imagined? Picture this: you’re working on a massive dataset in Excel, trying to make sense of endless rows and columns. It’s slow, ...

Here we describe the process of generating the clustering analysis from cells with TDP-43 knockdown and the activation of TDP-REG reporter as in our manuscript (Fig.S5F) To use, orient to the folder ...

If you’ve ever found yourself staring at a messy spreadsheet of survey data, wondering how to make sense of it all, you’re not alone. From split headers to inconsistent blanks, the challenges of ...

1 Institute of Geology and Geophysics, Ministry of Science and Education, Baku, Azerbaijan 2 School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan In recent years, seismological ...

A massive part of the conversation around sports ratings and all their attendant sports business impacts on rights deals, advertising prices, and beyond is how they’re measured. While there are ...