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Density Based Spatial Clustering of Applications with Noise (DBSCAN), a well-known Density-Based Clustering Algorithm is a advanced data clustering method with various applications in numerous fields like Satellites images, X-ray crystallography, Anomaly Detection in Temperature Data. But its run time R(n 2 ) complexity draws a major challenge. In this research paper, we propose a new unique algorithm called Real Time Density Based Clustering RTDBC to minimize the problems in DBSCAN. In proposed algorithms, objects are allotted into clusters using labels representatives than the method of propagating directly to reduce propagation time of label considerably. In contrast, RTDBC produce fast result and continuous process of runtime and additionally users are permitted to suspend for testing the result and continue as to enhance good results.

One of several traditional megaprojects is underground construction, given its long building time high building expense and possible risks. In tunnel engineering, trench boring devices are generally used to increase work performance and safety. During the tunnelling process, system has recorded vast volumes of tracking data to ensure building safety. The processing of vast real-time surveillance data also lacks successful techniques, and, in many situations, it must be performed manually that pose possible safety hazards. This paper suggests an approach for hybrid data mining (DM) to automatically process the TBM data for real-time tracking. Three separate DM strategies are merged in order to improve the operation of mining also to help security management. The sequential pattern method is executed to remove connections between TBM parameters in order to give people the expertise needed for an irregular on-site judging. A random forest model is built to identify training data in order to complement knowledge needed for building decision-making system. Finally, neural network models measure the penetration rate (ROP) in order to detect irregular data and to alert early. In the case of a tunnel project in China, the suggested technique was applied, and the findings of the application concluded that the approach offered a reliable and effective way of evaluating TBM protection management data in real time during buildings.

The second revolution in blockchain technology is smart contracts. Smart contracts are used in most of the blockchain applications like cryptocurrency, Health care, banking sectors, supply chain and IOT with different platforms like Fabric, Ethereum, Corda etc. In Ethereum blockchain, due to lack of inefficiency of the knowledge of technical developers and insecure programming languages for smart contracts, the attackers have exploited the smart contracts and the end users have lost millions of dollars like re-entrancy, king of ether throne attack, DoS, forcefully send ethers, multisig wallet, unexpected ether and poly network attack etc. In the year 2016, the attackers have exploited approximately $289 million US dollars with the help of re-entrancy vulnerability. The attackers have also attacked the smart contracts and broke the execution of that particular contracts through king of ether throne attack. In this paper, we propose a novel prevention and detection mechanisms for re-entrancy and king of ether throne attacks using time mechanisms and also implementing the same with proof of concepts for these vulnerabilities.

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