Block chain is generic name to describe the technology used by Bitcoin and other digital currency to record and secure transaction. This technology enables a highly accessible ledger with greatly reduced risk for tampering. The dynamic immutable, data ledger makes ideal for real time monitoring of the shipment of goods. Cloud is an important of distributed storage system of networking. Cloud system need for security, storage management, minimize the cloud cost and fast storage could be improved. The security using new most development security system of block chain technology is used to improve the cloud security. The data owner to be uploads the data on web page and access the folder. The user has been accessing the data on cloud storage using encryption and decryption using block chain based cyber security system. the problem for security in cloud storage because data transmission and data sharing, the alternate for security solution using Post Quantum-proof cryptography algorithm is used to improve the encryption and decryption process and more tight security for block chain technology for the cloud system. The SHA-3 512 Hash function algorithm automatically generate the key for data security enhancements of cloud networks. Post Quantum-proof cryptography algorithm has been improved the encryption Performance and reduced the power consumption, and increase the Latency performance, and boost up the security performances. Finally Post Quantum-proof cryptography algorithm for well support for security system of the cloud networks. Even some passwords, which are often cited as the weakest link in cyber security, may not be necessary.

A graph G admits a fuzzy graceful labeling and if all the vertex labelings are distinct then we can say G is a fuzzy vertex graceful graph. Here, we discuss the fuzzy vertex graceful labeling on certain classes of double fan graphs and double wheel graphs.

In this research paper, we have study about the heat and mass transfer effects of rotation and porosity of the medium on unsteady convection flow of a viscous, incompressible, and electrically conducting flow past a started vertical plate under the influence of transversely applied uniform mass diffusion. The Laplace-transform technique is used to solve the governing equations in this investigation. The results are described using graphs for various factors such as the thermal Grashof number, the mass Grashof number, the Prandtl number, the permeability parameter, and the Schmidt number. The goal of this research is to look at the rotation and porosity of the medium in a flow model. The velocity in the boundary layer was found to be higher in the study. The non-dimensional equations are solved and programmed in MATLAB R2020. Finally, it can be shown that the speed increases when warm Grashof or mass value evaluations are made. The significance of the problem can be observed in the cooling of nuclear reactor electronic components.

Machine learning is one of the fundamental approaches to nature the data produced by Internet of Things (IoT) and has drastic enhancements to enhance the decision-making fields such as education, security, business, and especially the healthcare sector. This integration of ML with IoT makes it possible for healthcare systems to generate patterns from big data which can improve the efficiency of predictive analytics and referrals. It also helps in the development of templates for medical records and real-time patient care, and the identification of patient diagnosis electronically. Though, performance of the ML algorithms can be affected by the dataset on the basis of which clinical decisions are made. Such variations clearly demonstrate the requirement to gain a deeper understanding of how several ML approaches work in terms of IoT data analysis for healthcare. In the proposed study we are testing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms for tracking health parameters. We first employ the above-mentioned approach for training and validation of the ML models on the database available at UCI. In the testing phase, the IoT devices record actual data of heart rate, blood pressure, posture, and temperature to forecast a health anomaly. Subsequently, an examination of accuracy of the aforementioned prediction is performed on the cloud stored data with reference to those produced by K-nearest neighbour (KNN) algorithm. Such an approach makes it possible to give adequate consideration to the offered assessment of the role of ML in increasing the efficiency of IoT-based healthcare systems with regard to early detection and comprehensive intervention practices. In this way, this research help to consider variability in prognostic outcomes for more accurate and precise clinical decision-making processes, and thus contribute in enhancing patient care and health in IoT environment.

Contents IJAM: Volume 31, No. 6 (2018)