PORM: Predictive Optimization of Risk Management to control Uncertainty Problems in Software Engineering

Irrespective of different research-based approaches toward risk management, developing a precise model towards risk management is found to be a computationally challenging task owing to critical and vague definition of the origination of the problems. This research work introduces a model called as PROM i.e. Predictive Optimization of Risk Management with the perspective of software engineering. The significant contribution of PORM is to offer a reliable computation of risk analysis by considering generalized practical scenario of software development practices in Information Technology (IT) industry. The proposed PORM system is also designed and equipped with better risk factor assessment with an aid of machine learning approach without having more involvement of iteration. The study outcome shows that PORM system offers computationally cost effective analysis of risk factor as assessed with respect to different quality standards of object oriented system involved in every software projects

3LRM-3 Layer Risk Mitigation Modelling of ICT Software Development Projects

With the adoption of new technology and quality standards, the software development firms are still encountering the critical issues of risk modelling. With the changing dynamics of customer needs, potential competition has being mushrooming in the global IT markets to relay a new standard of software engineering which has higher capability of sustaining risk.  However, till date, it is still theoretical to large extent from research viewpoint. Hence, this paper presents a mathematical model called as 3LRM that is designed with the simple approach keeping in mind the real-time issues of risk factors in software engineering for ICT software development project. The study has also identified requirement volatility as one of the prominent source of risk and hence, the framework intends to identify a risk as well as mitigating the risk to a large extent. The paper is illustrated with some of the simple statistical approaches of random probability

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches