Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm.

Importance of the carbon trading has been escalating expeditiously not only because of the environmentalist purposes to mitigate the adverse effects of climate change but also the increasing diversification benefits of the carbon emission contracts due to the low correlation between the emission, equity, and commodity markets. In accordance with the promptly rising significance of accurate carbon price prediction, this paper develops and compares 48 hybrid machine learning models by using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Permutation Entropy (PE), and multiple types of Machine Learning (ML) models optimized by Genetic Algorithm (GA). The outcomes of this study present the performances of the implemented models at different levels of mode decomposition and the impact of genetic algorithm optimization by comparing the key performance indicators that the CEEMDAN-VMD-BPNN-GA optimized double decomposition hybrid model outperforms the others with a striking R value of 0.

The quality of antenatal care in Turkey and the role of ultrasonography in the antenatal care system.

Objective: The aim of this study was to determine the quality of antenatal care and the role of routine obstetric ultrasonography (US) in Turkey's antenatal care program.

Materials And Method: Two surveys consisting of 11 questions were conducted on 295 patients without pregnancy associated risk in 1995 and on 208 patients in 2000, during the first 24 h after delivery. The results of the two surveys were compared.