Software process and development are the fundamental activities in software engineering. Increasing software usage either develops in-house or outsourcing requires improving the software process accordingly to minimise the adverse effects on the environment. The resources and power consumption controlled by hardware affected the software to move the process that causes high emission of power and energy. Thus, most of the existing work on software development is aimed at the efficiency of hardware operation through CPU, memory, and processor. Although sustainability is still initial in software engineering, the green software process can be achieved through sustainable development that concerns the preservation of the environment. However, there is still a lack of study and effort in the software process that emphasises sustainability perspectives and software waste elimination. Therefore, this study proposes the green factors for the software process that consider the sustainability elements and waste reduction during development. The green factors are the benchmark to measure a sustainable and green software process. Besides, this paper also presents the qualitative interview design and pilot study. The pilot study analysis has demonstrated the reliability of the interview protocol. Therefore, the actual interview and data analysis are currently in progress.
Dengue fever is one of the neglected tropical diseases (NTDs) in the Southeast Asian Countries (ASEAN), almost 70 million cases of dengue fever occur annually. This infection is now one of the most economically important NTDs in the region. Hence, there are urgent needs to spread public awareness on NTDs, the prevention, the treatment and the clinical cost involved. An innovation in health services and management system is needed to cater this issue. Intelligent Ecosystem for Healthcare Service and Management (IEHSM) is an integration of healthcare management, health knowledge base and data reference, control and elimination tools, and clinical costing. The main objective of this research is to provide a model for the improvement of fundamental understanding of public health especially NTDs and at the same time improve the existing healthcare services and management system. IEHSM adapts the optimization of prevention emphasizing on therapeutic approaches through Big Data Analytics, Artificial Intelligent, Cloud Computing, Machine Learning and Information Centric Networking. The IEHSM framework is based on Casemix system, a system that aggregates information about patients and associated procedures based on the type and mix of patients' treatment.
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