Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques

In biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste management system. While screening the biomass wastage management system, prediction of activated carbon’s quality and understanding of the mechanism of adsorption of CO2 are a complicated task. Many research works have been developed; the main issues are inaccurate and inefficient prediction of carbon available in the various feedstock of biomass wastage. To overcome these issues, this paper proposed gene expression programming (GEP) with K -nearest neighbour (GEP-KNN). The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different K values produced the highest accuracy at K = 9 and k = 10 of 95.12% and 95.67%; the lowest accuracy is K = 1 of 65.34%.