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The global development industries progress towards meeting the ever evolving contemporary and future demands. This transformative evolution introduced phenomena such as Industry 4.0 and 5.0 which are facilitated by both information and operational technologies: collaborative robotics, IoT, AI. Their integration into a hyper-connected system facilitates the production of goods and services. In addition, these industries are characterized by automation, as well as by unmatched levels of data exchange throughout the value chain. Cyber security risks are crucial as the prevalence of these information and operation technologies has changed the appearance of cyber threats. Addressing the premises and realities of cyber security in Industries 4.0 and 5.0 is crucial. Risk mitigation strategies provided by various organizations are crucial for lowering risks. Given the loopholes and vulnerabilities generated by interconnections, cyber security is vital for the advancement of digital industrial transformation.

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The Game Theory model provides revolutionary grounds for tackling problems in an optimal manner by considering various constraints and conditions. This research paper proposes a novel idea of monitoring, diagnosis and treatment of hypertension using game theory model using systematic review methodology. The theoretical framework for designing software called Hypertension Management System (HMS) is proposed using underlying principles of game theory by considering patients and doctors as players. The system is installed in the smartphones of players and its functioning follows the hierarchy of Big Data mining and extraction. The theoretical framework of HMS starts from data sensing through physical sensors, proceeds along layers for data processing and reduction, and finally arrives at the decision-making step to assist doctors in the treatment of disease. This novel system will reduce the mortality due to chronic diseases like hypertension by enabling game patients and doctors to deal with its symptoms in a timely manner according to feedback from previous data.

Cities have grown in development and sophistication throughout human history. Smart cities are the current incarnation of this process, with increased complexity and social importance. This complexity has come to involve significant digital components and has thus come to raise the associated cybersecurity concerns. Major security relevant events can cascade into the connected systems making up a smart city, causing significant disruption of function and economic damage. The present paper aims to survey the landscape of scientific publication related to cybersecurity-related issues in relation to smart cities. Relevant papers were selected based on the number of citations and the quality of the publishing journal as a proxy indicator for scientific relevance. Cybersecurity will be shown to be reflected in the selected literature as an extremely relevant concern in the operation of smart cities. Generally, cybersecurity is implemented in actual cities through the concerted application of both mature existing technologies and emerging new approaches.

It represents a calculation of how one probability distribution diverges from another one, expected probability distribution. Kullback-Leibler divergence has a lot of real-time applications. Even though there is a good progress in the field of medicine, there is a need for a statistical analysis for supporting the emerging requirements. In this paper, we are discussing the application of Kullback-Leibler divergence as a possible method for predicting hypertension by using chest sound recordings and machine learning algorithms. It would have a major outreached benefit in emergency health care systems. Decoding the chest sound pattern has a wide degree in distinguishing different irregularities and wellbeing states of a person in the medicinal field. The proposed method for the estimation of blood pressure is chest sound analysis using a method that creates a record of sounds delivered by the contracting heart, coming about because of valves and related vessels vibration and analyzing it with the help of Kullback-Leibler divergence and machine algorithm. An analysis using the Kullback-Leibler divergence method will allow finding the difference in chest sound recordings which can be evaluated by a machine learning algorithm. The report also proposes the method for analysis of chest sound recordings in Kullback-Leibler divergence class.