A time series can often be characterized using machine learning techniques, which require feature vectors as input. The quality of the feature vectors reflects the accuracy of the utilized machine learning techniques. We propose a method for combining features extracted from two popular...

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural...

This study introduces an integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities with application to regional scrap steel prices in east, north, south, central, northeast, and southwest China. We use daily data and combine the Peter and...

This study proposes a logical Petri net model to leverage the modeling advantages of Petri nets in handling batch processing and uncertainty in value passing and to integrate relevant game elements from multi-agent game processes for modeling multi-agent decision problems and resolving...

This study focuses on forming planar subgraphs in fuzzy graphs where the planarity value is not equal to 1. The paper commences by introducing the concepts of vertex-deletion (VD) and edge-deletion (ED) operations applied to fuzzy graphs. These operations aim to derive the fuzzy planar subgraphs...

The communities in a network have distinct characteristics and interrelationships. Community discovery methods based on node embedding and deep learning have surpassed spectral clustering and statistical inference as the preferred methods for handling high-dimensional network data. Community...

People have recently begun communicating their thoughts and viewpoints through user-generated multimedia material on social networking websites. This information can be images, text, videos, or audio. With the help of knowledge graphs, it is possible to extract organized knowledge from texts and...

There has been significant interest in integrating sentiment analysis with graph neural networks (GNNs) for stock prediction tasks. This article thoroughly reviews the application of GNNs in conjunction with sentiment analysis for stock prediction. This study introduces the fundamental concepts...

Community detection can help uncover and understand complex networks' underlying patterns and structures. It involves identifying cohesive groups with similar entities while being separated from other groups. Social networks are a prime example of an area where community detection is...