Abstract: The quest for a top-rated similarity metric is inherently mission-specific, with no universally ”great” metric relevant across all domain names. Notably, the efficacy of a similarity metric is regularly contingent on the character of the challenge and the characteristics of the records at hand. This paper introduces an innovative mathematical model called MCESTA, a versatile and effective technique designed to enhance similarity learning via the combination of multiple similarity functions. Each characteristic within it is assigned a selected weight, tailor-made to the necessities of the given project and data type. This adaptive weighting mechanism enables it to outperform conventional methods by providing an extra nuanced approach to measuring similarity. The technique demonstrates significant enhancements in numerous machine learning tasks, highlighting the adaptability and effectiveness of our model in diverse applications.

Mohamedou Cheikh Tourad, Abdali Abdelmounaim, Mohamed Dhleima, Cheikh Abdelkader Ahmed Telmoud and Mohamed Lachgar, “DeepSL: Deep Neural Network-based Similarity Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01503136

@article{Tourad2024,
title = {DeepSL: Deep Neural Network-based Similarity Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01503136},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01503136},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Mohamedou Cheikh Tourad and Abdali Abdelmounaim and Mohamed Dhleima and Cheikh Abdelkader Ahmed Telmoud and Mohamed Lachgar}
}