The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results.

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The neutron-induced degradation of depth images acquired using a Kinect sensor is investigated through the standard metrics. The evaluated metrics indicate saturation of degradation after several hours of exposure, which is recovered through annealing.

Map Reduce has gained remarkable significance as a prominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytics where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using MapReduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the MapReduce framework. In this survey, different MapReduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on MapReduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.

Abstract-A rapid growth of data in recent time, Industries and academia required an intelligent data analysis tool that would be helpful to satisfy the need to analysis a huge amount of data. MapReduce framework is basically designed to compute data intensive applications to support effective decision making. Since its introduction, remarkable research efforts have been put to make it more familiar to the users subsequently utilized to support the execution of massive data intensive applications.Our survey paper emphasizes the state of the art in improving the performance of various applications using recent MapReduce models and how it is useful to process large scale dataset. A comparative study of given models corresponds to Apache Hadoop and Phoenix will be discussed primarily based on execution time and fault tolerance. At the end, a high-level discussion will be done about the enhancement of the MapReduce computation in specific problem area such as Iterative computation, continuous query processing, hybrid database etc.

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