Nowadays, machine learning affects practically every industry, but the effectiveness of these systems depends on the accessibility of training data sets. Every device now produces data, and that data can serve as the foundation for upcoming technologies. Traditional machine learning systems need centralised data for their training, but the availability of valid and good amounts of data is not always possible due to various privacy risks. But federated learning can solve this issue [78]. In a federated learning (FL) environment, a model can be trained on decentralised datasets by involving a large number of participants, such as mobile devices or entire enterprises. Researchers are using this technique in various fields and getting great responses. The importance of using federated learning in the healthcare industry is highlighted in this paper since there is a wealth of data available in hospitals or electronic health records that may be used to train medical systems but cannot be shared due to privacy issues. The main contribution of this paper is to highlight the role of federated learning in the medical field. It also presents a list of frameworks available to implement federated learning models. The paper also listed the evaluation metrics used to check the efficiency of a federated learning model. Broadly used evaluation metrics are accuracy, precision, recall, and F1-score. Open issues for research in this area are also discussed at the end of this paper.

Smart cities are the emerging paradigm to improve living standards. Smart cities exploit numerous technologies to provide good health, better transportation facilities, education, and uninterrupted power and water supply that lead to higher levels of comfort. For practical implementations of these kinds of projects, huge amounts of data will initially be required and an ample amount of data will be generated thereafter the implementation of the project. In the present digital world, the biggest challenge that organizations are facing is the analysis of such a large amount of data. Direct analysis and exploitation is pivotal key factor of the data for accomplishment in numerous business and service domains, as well as the smart city domain. In this paper special focus is given to those applications of big data that support smart cities along with the advantages of including big data applications for smart cities and need of secure graph database that can be used to analyze the large data. In addition to it is also concluded that graph analytics can easily regulate the connections between many different data points of the smart city data: even those that at first do not appear to be connected.

Cloud computing is becoming an adoptable technology for many of the organizations with its dynamic scalability and usage of virtualized resources as a service through the Internet. Cloud computing is the delivery of computing services over the Internet. Cloud services allow individuals and businesses to use software and hardware that are managed by third parties at remote locations. Cloud Computing is a computing model, in which customer plug into the “cloud” to access IT resources which are priced and provided “on-demand”. The major challenges that prevent Cloud Computing from being adopted are recognized by organizations are security issues. Many techniques for securing the data in cloud are proposed by researcher but almost all methods have some drawbacks and till date no appropriate method has not been proposed that Cloud service providers can win the trust of customer. In this research paper the various security issues are reviewed along with cloud computing service providers which will give a deep insight for cloud service providers as well as researchers to work on the areas and make cloud computing a“trusted computing “and hardening the confidence of organizations towards cloud computing migration.

In the present digitization era, almost everything is available online, at just one click away from us, which offer a lot of opportunities, like saving a lot of time, but also many challenges, due to the existence of many cyber-attacks, more complex and difficult to be detected. The cyber-attacks effects can be data theft, modification, or alteration. In recent time, cybersecurity is very important also in the academic field, because schools and universities systems are connected online. To protect our data from various attacks, cybersecurity plays the most important key role. Cybersecurity helps in ensuring the safety of data, personally identifiable information, and intellectual property. Cybersecurity is not only for individuals, a specific group or organization, but it is for all the people and for the government to keep data integrity, confidentiality, and availability. This paper presents the cybersecurity concept, analyzing different cyber-attacks and the specific preventions measures.

Neuroscience is the study of the brain and its impact on behavior and cognitive functions. Computational neuroscience is the subfield that deals with the study of the ability of the brain to think and compute. It also analyzes various electrical and chemical signals that take place in the brain to represent and process the information. In this chapter, a special focus will be given on the processing of signals by the brain to solve the problems. In the second section of the chapter, the role of graph theory is discussed to analyze the pattern of neurons. Graph-based analysis reveals meaningful information about the topological architecture of human brain networks. The graph-based analysis also discloses the networks in which most nodes are not neighbors of each other but can be reached from every other node by a small number of steps. In the end, it is concluded that by using the various operations of graph theory, the vertex centrality, betweenness, etc. can be computed to identify the dominant neurons for solving different types of computational problems.