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1 – 1 of 1Iwin Thanakumar Joseph Swamidason, Sravanthy Tatiparthi, Karunakaran Velswamy and S. Velliangiri
An intelligent personal assistant for personal computers (PCs) is a vital application for the current generation. The current computer personal assistant services checking…
Abstract
Purpose
An intelligent personal assistant for personal computers (PCs) is a vital application for the current generation. The current computer personal assistant services checking frameworks are not proficient at removing significant data from PCs and long-range informal communication information.
Design/methodology/approach
The proposed verbalizers use long short-term memory to classify the user task and give proper guidelines to the users. The outcomes show that the proposed method determinedly handles heterogeneous information and improves precision. The main advantage of long short-term memory is that handle the long-term dependencies in the input data.
Findings
The proposed model gives the 22% mean absolute error. The proposed method reduces mean square error than support vector machine (SVM), convolutional neural network (CNN), multilayer perceptron (MLP) and K-nearest neighbors (KNN).
Originality/value
This paper fulfills the necessity of intelligent personal assistant for PCs using verbalizer.