In this paper, the use of high-resolution images for identification of urban features through pixel-based image fusion techniques is discussed. Fusion techniques are used to merge high spatial resolution panchromatic (PAN) image with low spatial resolution multispectral (MS) image to enhance the visual quality/appearance of some of the urban features present in the image. In this paper nine pixel-based fusion techniques, viz., Principal Component Analysis (PCA), multiplicative, Brovey transformation, wavelet analysis, subtractive, HPF, modified IHS, Ehlers, and hyperspherical color space, are discussed for the fusion of PAN and MS IKONOS images. The fused images are interpreted on the basis of visual comparison, correlation coefficients and histogram statistics. All the nine fusion techniques improved the resolution and the visual appearance of the original MS image. These are comparable to the resolution of the original PAN image. All the three resampling methods (nearest neighborhood, bilinear interpolation and the cubic convolution) do not have any significant effect on the final visual appearance of the fused images. All fusion techniques result in a change of the statistical parameters of the original images. The multiplicative technique results in major changes of the statistical parameters than the other techniques. After doing visual comparison and analyzing the statistical parameters, the Brovey and the PCA techniques seem to gather more information of urban features in fused PAN and MS IKONOS images.

Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.

Interconnection of various "things" are resulting into many issues related to security and privacy of IoT platform. With the advancement in technology, many e-businesses and retail stores are using IoT based solutions for their sale, marketing, productivity and promotions. These IoT based solutions are much helpful in providing various benefits to owner and customers. However, these solutions are vulnerable to many security and privacy based concerns. This paper addressed the rise of cyber threats in IoT, Enterprise view of IoT for E-Business & Retail Security, advancements in e-business and retail because of IoT, flow of threat agents related to security and privacy concerns in e-business and retail.