Two important architectures are Artificial Neural Networks and Long Short-Term Memory networks. LSTM networks are especially useful for financial applications because they are designed to work with ...
Abstract: Dempster–Shafer (DS) evidence theory provides a powerful framework for modeling uncertainty, reasoning, and combining information from multiple sources. However, it may yield ...
A hierarchical clustering approach to dissect behavioral symptoms in early-stage breast cancer (BC).
Autoimmune conditions and ‘breast implant illness’ in breast cancer patients with implant-based breast reconstructions. Proportions of patients with clinically meaningful symptoms by CL at Y1 (may not ...
ABSTRACT: Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of ...
There is a way to use a collection of M4 Mac minis in a cluster, but the benefits only really exist when you use high-end Macs. While most people think of having a more powerful computer means buying ...
ABSTRACT: The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific ...
The main challenge in solving clustering problems using mathematical optimization techniques is the non-smoothness of the distance measure used. To overcome this challenge, we used Nesterov's ...
Abstract: Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. However, classic non-hierarchical MARL algorithms still cannot address various complex ...
A Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback