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Software reliability is an essential part of software engineering to ensure the quality of a system. There are various techniques, which can be used in building models for predicting quality attributes. This paper presents a Fuzzy model for software reliability prediction. We have proposed three parameters Availability, Failure Probability and Recoverability as an integrated measure of software reliability. Fuzzy Model provides a way to arrive at a discrete Reliability Non-functional requirement (NFR) in contrast to imprecise, vague and ambiguous. This model will help us to evolve intermediate stages between reliable state and unreliable state of a system. Results obtained by proposed model show that this is suitable for predicting software reliability of the software.

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Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.