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Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multiview data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and L 1 -norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution.INDEX TERMS Non-negative matrix factorization, multi-view data, manifold structure, nearest neighbor.

Nowadays over speeding is one of the most common traffic violations. Generally, over speeding is the result of restless and bad behavior of drivers. As the accident rates are increasing it is important to develop and implement a system which can automatically detect and report over speeding to the traffic control authorities as early as possible. Nearly all the roads are marked with speed limits depending upon the size of moving vehicles and heaviness of traffic, but some drivers habitually ignore this speed limit. The advancement in technology has replaced most of the manual or semi-automatic systems with an automated system. To add on to various systems in place, this research is making the use of Internet of Things to detect and report over speeding of the vehicle on which the device has been preinstalled. IoT is a technique to integrate various devices to exchange data among themselves. This research proposes the design, development and functioning of a smart device that helps in automatically detect and report to competitive authority, when so ever the subject vehicle exceeds the speed limit. The device has been developed based on the Global Positioning System (GPS) Technology using Raspberry Pi hardware and Android OS and has been practically tested on real time basis by installing it in a car.

Chaotic systems with complicated characteristics of ergodicity, impredictability as well as sensitivity to beginning stages are commonly utilized in the world of cryptography. A 2D logistic-adjusted-sine (LS) map is implemented in this article. Performance assessments reveal superior ergodicity as well as unpredictable and even a broader spectrum of chaotics than numerous previous chaotic maps. This research also develops a 2D-LS-based image encryption system and proposed LS-IES. The notion of diffusion as well as confusion is properly complied with enabled encryption functions. Research outcomes as well as security analyses demonstrate that LS-IES can swiftly encrypting different parameters in various images with a great resistance towards security threats.

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