Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process
This study assessed the performance of single and modified algorithms based on machine learning and deep learning for wastewater treatment process. More specifically, this study adopted support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for machine learning as well...
Main Authors: | Jaeil Kim, Hyo Sub Lee, Jinuk Jang, Yongtae Ahn, Seo Jin Ki, Hyun-Geoun Park |
---|---|
Format: | Article |
Language: | English |
Published: |
Korean Society of Environmental Engineers
2023-12-01
|
Series: | 대한환경공학회지 |
Subjects: | |
Online Access: | http://www.jksee.or.kr/upload/pdf/KSEE-2023-45-12-587.pdf |
Similar Items
-
Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset
by: Ummey Hany Ainan, et al.
Published: (2024-01-01) -
Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach
by: Junwei Ma, et al.
Published: (2022-11-01) -
Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting
by: Wilson Bakasa, et al.
Published: (2023-10-01) -
Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis
by: Enas Elgeldawi, et al.
Published: (2021-11-01) -
A novel hybrid ensemble convolutional neural network for face recognition by optimizing hyperparameters
by: Anwarul Shahina, et al.
Published: (2023-06-01)