The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually. In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as "Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)" model. The DDRGRM model consists of 3 stages. In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff. In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts. In stage 3, Cooccurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification. Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features. To prove the efficiency of the proposed method, it is tested on different stone texture image databases. The proposed method resulted in high classification rate when compared with the other existing methods.

Analyzing the big textual information manually is tougher and time-consuming. Sentiment analysis is a automated process that uses computing (AI) to spot positive and negative opinions from the text. Sentiment analysis is widely used for getting insights from social media comments, survey responses, and merchandise reviews to create data-driven decisions. Sentiment analysis systems are accustomed to add up to the unstructured text by automating business processes and saving hours of manual processing. In recent years, Deep Learning (DL) has garnered increasing attention within the industry and academic world for its high performance in various domains. Today, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are the foremost popular types of DL architectures used. We do sentiment analysis on text reviews by using Long Short-Term Memory (LSTM). Recently, thanks to their ability to handle large amounts of knowledge, neural networks have achieved a good success on sentiment classification. Especially long STM networks.

No abstract

No abstract

No abstract