Published Paper Details:

A subfield of artificial intelligence, machine learning is concerned with the development of algorithms for computers that can make judgements independently by analysing historical data and experience. Computer simulation of intelligent human behaviour is a fundamental component of the ever-expanding field of data science. The principal aim is to empower computers to engage in autonomous learning processes and adjust their behaviours without requiring human intervention. A foundational discipline of mathematics, linear algebra is critical for comprehending the mechanisms underlying machine learning algorithms, including the gradient descent algorithm. Calculus is utilised to characterise the process of learning and refining our models, which is present in virtually all models. The aim of machine learning is to construct predictive algorithms capable of acquiring knowledge from data, encompassing instances such as the future cost of fuels in a given country, the visual attributes of an object within an image, or the most effective combination of pharmaceuticals to cure a specific ailment. A vast network comprised of the Internet and telephone lines permits users to exchange data via websites and telephones. The responsibility of network administrators is to establish connections between the originator and receiver while ensuring that the capacity of each link is not exceeded. Ensuring a dependable service is unattainable in the absence of the mathematical principles that underpin queuing theory. The inclusion of Poisson processes in mathematical models guarantees the existence of a contact tone throughout a telephone conversation. The complexity of Internet connection routing arises from the unpredictability surrounding the timing and volume of incoming inquiries. Consequently, the development of packet switching occurred, wherein data is partitioned into more manageable "packets" for autonomous transmission. While this promotes improved network performance and resilience, it is not uncommon for routers to encounter connection failures due to excessive packet burdens. There is a viewpoint that fractional mathematics may eventually play a role in the advancement of an internet service that is considerably more dependable.