Feature selection is one of the most efficient procedures for reducing the dimensionality of
high-dimensional data by choosing a practical subset of features. Since labeled samples are …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

We propose a CNN-BiLSTM-Attention classifier to classify online short messages in Chinese
posted by users on government web portals, so that a message can be directed to one or …

Abstract The rise of Large Language Models (LLMs) has brought about a notable shift,
rendering them increasingly ubiquitous and readily accessible. This accessibility has …

One of the most widely used content types in internet misinformation campaigns is memes.
Since they can readily reach a big number of users on social media sites, they are most …

In this work, we address the challenge of identifying the inference relation between a plain
language statement and Clinical Trial Reports (CTRs) by using a T5-large model …

Feature selection problem represents the field of study that requires approximate algorithms
to identify discriminative and optimally combined features. The evaluation and suitability of …

Ant colony optimization (ACO) is a metaheuristic algorithm that handles complex
optimization problems. ACO simulates the behavior of ants foraging in the real world to find …

The EDiReF shared task at SemEval 2024 comprises three subtasks: Emotion Recognition
in Conversation (ERC) in Hindi-English code-mixed conversations, Emotion Flip Reasoning …

A multi-objective optimization problem (MOP) refers to a methodology employed to identify
optimal solutions for issues that fall under the category of NP-hard. This method yields a …