Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

Artificial intelligence (AI) has significantly impacted various fields. Large language models
(LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-1 Jumbo etc., have …

Learning human languages is a difficult task for a computer. However, Deep Learning (DL)
techniques have enhanced performance significantly for almost all-natural language …

This paper presents, TextConvoNet, a novel Convolutional Neural Network (CNN) based
architecture for binary and multi-class text classification problems. Most of the existing CNN …

Arabic text classification is a process to simultaneously categorize the different contextual
Arabic contents into a proper category. In this paper, a novel deep learning Arabic text …

Convolutional neural networks (CNN), recurrent neural networks (RNN), attention, and their
variants are extensively applied in the sentiment analysis, and the effect of fusion model is …

Nowadays, trends in deep learning for text classification are addressed to create complex
models to deal with huge datasets. Deeper models are usually based on cutting edge neural …

We study a two-player zero-sum game (matrix game for short) with the objective of finding
the saddle point and its value. We develop a novel convolutional neural network (CNN for …

Deep learning models, despite their potential, often function as “black boxes”, posing
significant challenges to interpretability, particularly in sensitive fields such as healthcare …