Review of Automata Processor of KNN Algorithm Applications in Machine Learning
DOI:
https://doi.org/10.69996/jcai.2025025Keywords:
Automata Processor, KNN, Similarity, NFA, ApplicationsAbstract
This paper examined the concepts and application of the kNN algorithm relating to machine learning characteristics on automata processes. The algorithm applications focused on network congestion, demand, and supply relating to price and time, and finally, the usage of transaction mode in financial transactions in terms of deposit and withdrawal. The study reviewed machine learning as an aspect of artificial intelligence in computing systems that improves and automatically applies to a variety of
complex mathematical computations to address problems using similarity search on query techniques with a given dataset. This was applied with k-nearest neighbors (kNN) developing an effective design of the automata processor (AP)) related to non-von Neumann architecture that focused on nondeterministic finite automaton (NFA) driven by the execution model; According to the research, KNN performance is affected by a number of factors, including parameter normalization, the value of K, and the distance
between the two points on the graph. The study also revealed some challenges of the kNN algorithm, such as computational efficiency, selecting the optimal value of k, high-dimensional data handling, and finally, noise and outlier sensitivity. The results of the kNN algorithm indicated common values of each MSE for both demand and supply prediction, and the network congestion. The optimal k-value is 2, with a
corresponding 66.67% without congestion. Finally, with regard to the financial transaction, the kNN algorithm computes each customer transaction frequency with the selected bank account number carried
out within the range of the query provided.
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