Classification of Babylonian Numbers with Convolutional Neural Networks

AUTHOR(S):

Loay Alzubaidi, Aymen Ahmed, Arif Al Nahdi

TITLE

Classification of Babylonian Numbers with Convolutional Neural Networks

ABSTRACT

This paper introduces an innovative approach for classifying Babylonian numerals using Convolutional Neural Networks (CNNs). The methodology involves feature extraction through the analysis of vertical and horizontal angles of cuneiform symbols, enhanced by deep learning techniques. By leveraging CNNs, the proposed system achieves high accuracy in recognizing and interpreting ancient Babylonian numbers. The research not only facilitates the automated classification of these historical symbols but also contributes to the preservation and study of ancient mathematical texts. The experimental results demonstrate the model's effectiveness, with a classification accuracy of 98.33%, showcasing the potential of deep learning in historical data analysis and preservation.

KEYWORDS

Babylonian Numbers, Feature Extraction, Convolutional Neural Networks, Deep Learning, Cuneiform Symbols, Image Processing

Cite this paper

Loay Alzubaidi, Aymen Ahmed, Arif Al Nahdi. (2025) Classification of Babylonian Numbers with Convolutional Neural Networks. International Journal of Computers, 10, 220-225