Narendra Rao Tadapaneni

Previously cloud computing networks was highly centralized where data was gathered on the outermost edges and transmitted back to the main servers for processing. This made the local devices found on the network edges to lack computational power and storage capacity to analyze the collected data. These devices were able to connect to cellular and WI-FI networks were relatively limited by their hardware capabilities. Therefore the concept of edge computing will help to move some of the processes towards the edge of the networks to harness computational capabilities that are currently untapped in edge nodes. This research paper will present the general overview of edge computing as well as its challenges and opportunities that have revealed it.

Technological advancements have rapidly changed the global mode of communication as well as the traditional model of performing business operations in many organizations. Mainly, the evolution of superpower computers, as well as high-speed Internet connectivity have transformed the world into a global village where anyone can socialize or provide goods and services to the global community. Significantly, this digital wave has also not spared the traditional Information Technology (IT) departments and on-premise data centers. Indeed, advancements in technology have led to the evolution of cloud computing which has eliminated the need for maintaining an on-premise data center. Depending on the service provider, there are different types of cloud computing models which are categorized according to the service provided. Thus, this paper discusses the different types of cloud service models.

Fog computing locates certain resources and transactions at the edge of a network. Therefore locating resources closer to the devices, it allows internet users to aggregate bandwidth at access points. This reduces the overall need for bandwidth since less data is being transmitted away from data centers. Therefore Fog computing is new technology to the edge of the network which has led to the implementation of a new breed of applications and services [1]. This research paper will present the characteristics of Fog computing and its role in the Internet of Things.

The 21st century has witnessed accelerating growth in the utilization of digitized financial services. There is a consistent effort to simulate, augment, enhance, and replicate human intelligence using artificial technologies, to form intelligent machines. Artificial intelligence, which has its foundations in computer science, linguistics, psychology, mathematics, and philosophy has proved to be a powerful tool in financial services. Firms are now utilizing analytical tools such as machine learning and ANN to analyse data collected over time. AI enhances adaptive pattern recognition using modern statistical methods and large volumes of data to provide the 'best guess' answer to any specific and narrowly-defined problem set. AI has a high potential for positive impact when deployed with care, prudence, and diligence. This paper seeks to expound on the application of AI tools in finance and the implications of implementation to financial stability. Important considerations for AI use include benefits to clients and firms, data privacy and protection, elimination of human capital, and sustainability of financial services.

In the last few years, there has been a growing surge for the interest in the application of artificial intelligence in issues in Software Engineering (SE). Artificial Intelligence has become a much powerful and easy to use as they are increasingly deployed for different key components in the software system [1]. It enables the new functionalities and even allows much better adaptation to different user needs. This also creates additional issues for different software engineers and even exposes different firms to risks. There is a huge number of daunting challenges related to design, building and testing for an engineering system. Software engineers have one advantage over the engineers is that their own material and software can be used for attacking the challenges resulted due to the production of this system [2]. AI algorithm is very much suited for this kind of complex issues in software engineering as they are designed to deal with some of the growing challenges. Software engineers have implemented, exploited most of the practical algorithms, techniques and methods. It has emerged from the community of artificial intelligence [3]. All these activities have a vital and effective application which create an impact on different areas of software engineering.