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<p>According to Global Adult Tobacco Survey 2016-17, 61.9% of people quitting tobacco the reason was the warnings displayed on the product covers. The focus of this paper is to automatically display warning messages in video clips. This paper explains the development of a system to automatically detect the smoking scenes using image recognition approach in video clips and then add the warning message to the viewer.  The approach aims to detect the cigarette object using Tensorflow’s object detection API. Tensorflow is an open source software library for machine learning provided by Google which is broadly used in the field image recognition. At present, Faster R-CNN with Inception ResNet is theTensorflow’s slowest but most accurate model. Faster R-CNN with Inception Resnet v2 model is used to detect smoking scenes by training the model with cigarette as an object.</p><p><em><br /></em></p>

Extremely large data sets often known as "Big Data" are analyzed for interesting patterns, trends, and associations, especially those relating to human behavior and interactions. Extraction of meaningful and useful information needs to be done in parallel using advanced clustering algorithms. In this paper, effort has been made to tweak in changes to the existing K-means algorithm so as to work in parallel using MapReduce paradigm. K-means due to its gradient descent nature is highly sensitive to the initial placement of the cluster centers. This random initialization of cluster centers results in empty clusters and slower convergence. In this paper, an overview of existing methods with emphasis on computational efficiency is presented. Comparison of three well known linear time complexity initialization methods has been presented here. These methods are analyzed on two different data sets. The experimental results are recorded and presented with insights on different initialization methods for practitioners.

<span>Diabetic retinopathy (DR) is a diabetic impairment that affects the eyes and if not treated could lead to permanent vision impairment. Traditionally, Ophthalmologists perform diagnosis of DR by checking for existence and any seriousness of some subtle features in the fundus images. This process is not very efficient as it takes a lot of time and resources. DR testing of all the patients, a lot of which are undiagnosed or untreated, is a big task due to the inefficiency of the traditional method. This paper was written with the aim to propose a classification system based on an efficient deep convolution neural network (DCNN) model which is computationally efficient. Amongst other supervised algorithms involved, proposed solution is to find a way to efficiently classify the fundus images into 5 different levels of severity. Application of segmentation after the pre-processing and then use of deep convolutional neural networks on the dataset results in a high accuracy of 91.52%. The result achieved is high given the limitations of the dataset and computational powers.</span>