Deep Learning Classification Algorithms Applications: A Review

Abstract

This paper examines the recent articles on classification tasks, particularly focusing on deep learning Algorithms. The process of categorizing data into distinct classes based on specific features is essential for tasks such as image recognition, sentiment analysis, disease diagnosis, and more. This article the fundamental concepts of deep learning, including neural network architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants. It explores the significance of feature selection techniques in improving classification model performance. Additionally, this article provides a detailed literature review, aiming to foster the development of more effective and efficient classification algorithms and methodologies and highlighting their applications in fields such as healthcare, agriculture, disaster response, and beyond. Through this review, this article underscores the transformative impact of deep learning approaches in enabling automated decision-making, pattern recognition, and data-driven insights, offering valuable insights for researchers, practitioners, and policymakers involved in classification tasks, this article aims to facilitate the development of more effective and efficient classification algorithms and methodologies.