Shallow Model and Deep Learning Model for Features Extraction of Images

Authors

  • Saba Qasim Hasan Northern Technical University

DOI:

https://doi.org/10.56286/ntujet.v2i3.449

Keywords:

Artificial Intelligence (AI), Machine Learning (ML), Features Extraction(FE), Features Selection, Classificationm, Supervised learning, Unsupervised learning, Reinforcement learning

Abstract

Applications trend today on artificial intelligence (AI). The latest development in the field of machine learning (ML) comes from deep learning which is expected to cause a powerful improvement in the field of artificial intelligence. Features Extraction(FE) is very important.  These properties make it possible to characterize the issue and create models that explain a system or process. A variety of image preparation techniques or data sets, Different approaches are done to obtain a feature that will be used for artificial intelligence (AI) algorithms that projects involving ML or the trendiest and most well-liked fields, including deep learning. Algorithm selection techniques are vital in academic machine-learning research. This article discusses different categorization algorithms and new efforts to increase classification accuracy.

Additional Files

Published

2023-11-20

How to Cite

[1]
S. . Qasim Hasan, “Shallow Model and Deep Learning Model for Features Extraction of Images”, NTU-JET, vol. 2, no. 3, Nov. 2023.

Issue

Section

Articles