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Existing land registration systems have several issues like dual ownership, fraudulent or incomplete registries. The existing system also includes a lot of middle men which makes it prone to malicious acts and results in a vulnerable system. Keeping track of who owns which part of land is a big challenge. To overcome this problem blockchain technology is used. Use of blockchain will remove middle men, increase trust, efficiency of the system and reduce corruption. This paper highlights the issues related to the current system such as authenticity, centralization, reliability, transparency, etc and proposes a better method using ethereum blockchain and smart contracts to overcome this problem. Blockchain method provides identification of true ownership of land, authenticity and transparency in the system. [ABSTRACT FROM AUTHOR]

This study examines how flexible optical networks may be used to combine adaptive software-defined networking (SDN) with vehicle-to-vehicle (V2V) communication in the Fifth Generation (5G) era. Commercial V2V (C-V2V) technology offers great potential to revolutionize current wireless infrastructure, possibly eliminating the need for specific short-range interaction mechanisms in the V2V systems as the automobile sector moves closer to full-scale 5G integration. These networks' improved capacity and programmability significantly affect the deployment of V2V functions in future intelligent mobility systems and the design of advanced vehicular network architectures. Each vehicle may connect to a wide range of entities using SDN in a flexible, dependable, and secure way. These entities include people, objects, processes, data, and numerous commodities. These mobile systems' capabilities will be strengthened by 5G technology, expanding the range of services they can provide. This paper explains the most recent findings concerning V2V network observations in the 60-GHz band, provides a thorough analysis of current practices in fiber optic V2V network observations and modeling, and discusses the difficulties that still need to be overcome to advance fiber optic V2V network observations and modeling. [ABSTRACT FROM AUTHOR]

Coronavirus 2 virus is responsible for the spread of the infectious disease COVID-19 (also known as Coronavirus disease). People around the globe who got infected with the virus experienced a respiratory illness that could become as serious as leading someone to lose their life. However, the upside of the pandemic is that it has led to numerous types of research and explorations, majorly in the medical science field. Since a systematic survey of previous research activities and bibliometric analysis gives a brief idea about such contributions and acts as a reference to future research, this study aims to cover the research related to COVID-19 in the computer technology domain. It is limited to the works accepted and accessible with the keywords – Covid-19, prediction, and pandemic, in the Scopus search engine to justify the scope of this survey. Further, the paper highlights a few prior works used for predictive analysis and presents a quantitative angle on their algorithms. Earlier works showcase Time Series Analysis using ARIMA/SARIMA models for predicting the vaccination rates, and Extreme Gradient Boosting (XGBoost), Xtremely Boosted Network (XBNet) Regression, and Recurrent Neural Network (RNN) for Confirmed, Cured, and Death cases. Amongst the algorithms used in the latter use case, XBNet regression performed better than XGBoost regressor. [ABSTRACT FROM AUTHOR]