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The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number of Euclidean shapes and recursive shapes.

For annotating an image, it is very easy, if the image is categorized as indoor or outdoor. Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object.

This work is carried out on the standard image data sets. The data sets are Microsoft Research Cambridge (MRC) object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. This work is carried out on the standard image data sets. The data sets are MRC object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

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.

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.

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).

This paper fulfills the necessity of intelligent personal assistant for PCs using verbalizer.