Factorial Algorithms for Efficient Big Data Computing in AI and ML Models and their Implementation in Python

Factorial algorithms encompass a spectrum of computational methods, and their efficiency and practical viability depends on the specific techniques employed during implementation process in the programming language of choice. Among these methods, the iterative approach has demonstrated commendable e...

Full description

Saved in:
Bibliographic Details
Published in2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE) pp. 1 - 6
Main Authors Nair, Sowparnika, V., Shynu S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 30.12.2023
Subjects
AI and ML
big data
Computational efficiency
Computational modeling
Data models
data science
factorial algorithms
GPU accelerated computing
Iterative methods
Memory management
Python
Time complexity
Online AccessGet full text
DOI10.1109/ICECCE61019.2023.10442073

Cover

More Information
Summary:Factorial algorithms encompass a spectrum of computational methods, and their efficiency and practical viability depends on the specific techniques employed during implementation process in the programming language of choice. Among these methods, the iterative approach has demonstrated commendable efficiency. Nevertheless, the optimization of factorial computations can be taken to greater heights by harnessing advanced algorithms tailored to particular computational demands. In contrast to the recursive model, which exhibits conceptual simplicity, it is not immune to the drawback of repetitive calculations and large memory requirements. This redundancy can be effectively eliminated through the utilization of the iterative algorithm, thereby furnishing a swifter solution in the context of factorial calculations. The implementations of all cases in this paper are elaborated in such a way that both time complexity and memory requirements are taken into account for large values of the samples.
DOI:10.1109/ICECCE61019.2023.10442073