Data streams are viewed as a sequence of relational tuples (e.g., sensor readings,call records, web page visits) that continuously arrive at time-varying and possibly unbound streams.These data streams are potentially huge in size and thus it is impossible to process many data mining techniques and approaches. Classi cation techniques fail to successfully process data streams because of two factors: their overwhelming volume and their distinctive feature known as concept drift. Concept drift is a term used to describe changes in the learned structure that occur over time. The occurance of concept drift leads to a drastic drop in classi cation accuracy. The recognition of concept drift in data streams has led to sliding-window approaches also di erent approaches to mining data streams with concept drift include instance selection methods, drift detection, ensemble classi ers, option trees and using Hoe ding boundaries to estimate classi er performance. This paper describes the various types of concept drifts that affect the data examples and discusses various approaches inorder to handle concept drift scenarios.The aim of this paper is to review and compare single classi er and ensemble approaches to data stream mining respectively.

Blockchain and Internet of Things are considered as most disruptive technologies of the decade. Internet of Things has established its existence in several areas including manufacturing, smart home system to IT enabled Services on the other several use cases are available for blockchain mentioning its successful application in finances to supply change management, electronic health care record etc. Researchers are also trying to integrate blockchain and Internet of Things. This paper introduces the primary work carried to integrate blockchain and internet of things. To integrate blockchain and internet of things it is essential that all the participating devices work in an environment that allows them to communicate and initiate transactions thereby allowing the successful creation of block and blockchain.The major contribution of this paper includes development of a private blockchain that allows various users of system to perform their activities as per the rules or smart contracts defined while they are the part of blockchain. We have developed a private blockchain framework that utilizes a novel method to create the blocks and blockchain using SHA-256 algorithm, QR Codes and stores the information in blockchain at a particular timeframe. The proposed private blockchain framework is explained in terms of use case taken for marking attendance of students using mobile phones and teacher's laptop which participate in the blockchain creation. The rest of the paper is organized in five sections. Initially a short introduction of the proposed system is given then in second section related work is presented. Third section describes the proposed system architecture, implementation details are highlighted then in last section conclusion and directions to future work are given.

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