Indirect and direct anthropogenic greenhouse gas based optical communication model toward carbon footprint in quantum networks

An accurate measurement of greenhouse gas emissions serves as foundation for creation of adequate mitigation measures. In this context, a significant prerequisite is the development of protocols for measuring emissions and methods for data analysis that can extrapolate precise annual emission values. Quantifying the emissions of greenhouse gases (GHGs) from irrigated paddy fields is crucial for combating climate change. We present an electro-optic hardware platform for optical neural networks’ nonlinear activation functions. With no slowdown in processing speed, the optical-to-optical nonlinearity works by transforming a small amount of the input optical signal into an analogue electric signal, which is then utilised to intensity-modulate the original optical signal. Using machine learning methods, this study proposes a novel method for detecting anthropogenic greenhouse gases from food products and managing carbon footprints. Multilayer kernel vector expectile regression (MKVEx) is utilized to examine presence of anthropogenic greenhouse gases in this food product as an input. After that, quadrature convolutional genetic neural networks (QCGNN) are used to developed explicitly for use on unstructured data like carbon footprints and cut down on energy and carbon emissions. In terms of sensitivity, positive predictive value, negative predictive value, training accuracy, and precision, various food product greenhouse gas datasets have been the subject of experimental analysis. The proposed technique attained sensitivity of 65%, positive predictive value of 59%, negative predictive value of 59%, training accuracy of 98%, precision of 95%.