Abstract: Sorting algorithms have always been the primary focus of data organization and have been the same since they were
discovered. They play a vital role in reducing work and maximizing efficiency as well as accuracy. This paper aims at comparing
and examining traditional sorting algorithms, usually applied by programmers as opposed to AI-based methods like Decision Trees
and Neural Networks. The performance of these sorting techniques would be evaluated on time over such data executions…
Read moreAbstract: Sorting algorithms have always been the primary focus of data organization and have been the same since they were
discovered. They play a vital role in reducing work and maximizing efficiency as well as accuracy. This paper aims at comparing
and examining traditional sorting algorithms, usually applied by programmers as opposed to AI-based methods like Decision Trees
and Neural Networks. The performance of these sorting techniques would be evaluated on time over such data executions
considering memory requirement and accuracy on different datasets. Most traditional methods could sort structured data even of a
smaller dataset, but they cannot work well when it comes to scaling and unstructured data [4]. AI-based algorithms, however, seem
to perform better in terms of accuracy, which is superior in its implementation to complex and unstructured datasets, at a great cost
of and accuracy; thus, making possible insights into the practical implementations. These findings show the potentials of hybrid
methods regarding modern data sorting application challenges and pave the way for further future optimization and real-world
implementation [8].
