Green Software

Green Software, Machine learning, Software Architecture  

Theobjective is toaddress the issue of rising energy consumption and carbon emissions caused by the growing dependence on technology. The aim is to develop environmentally sustainable software byleveraging machine learning techniques. The primaryobjective is to enhance the green performance of machine learning algorithms, andsubsequently explore how they can beutilized to optimize energy consumption and reduce waste. 

The increasing reliance on technology has led to a significant increase in energy consumption and carbon emissions, making it imperative to environmentally sustainable design software. One promising approach to achieving this goal is making software “green" using machine learning. Machine learning algorithms can optimize software design and minimize energy consumption and waste, thereby reducing the environmental impact of software. 

This research aims to explore the potential of machine learning in creating environmentally sustainable software. The first step will be investigating ways to make machine learning “green" by optimizing energy consumption and reducing waste. The second phase of the research will focus on using machine learning algorithms to optimize energy consumption and reduce waste in the software development process. 

Energy efficiency, Green AI, CO2 emission, Accuracy