Anomaly Detection for Vertical Plant Wall System using Novel Support Vector Machine in Comparison with Artificial Neural Network for improving accuracy

Aim: New Support Vector Machine and Artificial Neural Network are used to construct a plant wall system for indoor climate management. Materials and Methods: To detect indoor climatic changes in vertical plant wall systems, a Machine Learning Method that compares Artificial Neural Network and Innovative Support Vector Machine was created. The G Power calculator yielded 534 samples. 1068 samples and 80% Pretest Power. Results: Innovative Support vector machine had the highest accuracy in indoor changes at 68.42% and the lowest mean error at 58.58%. Groups are statistically insignificant. Conclusion: New Support Vector Machine technique outperforms Artificial Neural Network in indoor control.