Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles
through interactions with proteins. However, only a few plant lncRNAs have been …

In this work we produce a framework for constructing universal function approximators on
graph isomorphism classes. We prove how this framework comes with a collection of …

Recently, network representation has attracted many research works mostly concentrating
on representing of nodes in a dense low-dimensional vector. There exist some network …

Today, with the proliferation of complex networks and their large amounts of data,
researchers have great concerns about the accurate community detection methods. The …

The importance of employing appropriate pricing strategies for perishable products within
the supply chain cannot be overstated. Pricing is a cross-functional driver of each supply …

Abstract Representation learning is a method of representing the intrinsic information of
research object as a dense low dimensional real valued vector. The main purpose is to find …

This research was conducted with the purpose of analyzing physicochemical properties of
molecular systems using an artificial intelligence paradigm known as Machine Learning …

Background: Sequence graphics could be used to address the lacunae of drawing skill
development in medical undergraduates. Objectives: The present study aimed to use …

Search engines have a complete dependency on the query auto-completion. Query auto-
completion is a process that suggests a group of words for every click dynamically. Query …