Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM
Load forecasting in power microgrids and load management systems is still a challenge and
needs an accurate method. Although in recent years, short-term load forecasting is done by …
needs an accurate method. Although in recent years, short-term load forecasting is done by …
Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks
ABSTRACT Student dropout in Engineering Education is an important problem which has
been studied from different perspectives, as well as using different techniques. This …
been studied from different perspectives, as well as using different techniques. This …
[HTML][HTML] Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model
Per-and polyfluoroalkyl substances (PFAS) are emerging contaminants that pose significant
challenges in mechanistic fate and transport modeling due to their diverse and complex …
challenges in mechanistic fate and transport modeling due to their diverse and complex …
Application of Chi-square discretization algorithms to ensemble classification methods
N Peker, C Kubat - Expert Systems with Applications, 2021 - Elsevier
Classification is one of the important tasks in data mining and machine learning.
Classification performance depends on many factors as well as data characteristics. Some …
Classification performance depends on many factors as well as data characteristics. Some …
Causative analysis of freight railway accident in specific scenes using a data-driven Bayesian network
As the freight railway system is a typical complex system, freight railway accidents have
various and complex accident scenes. A Data-Driven Bayesian Network (DDBN) with …
various and complex accident scenes. A Data-Driven Bayesian Network (DDBN) with …
Expert knowledge–guided Bayesian belief networks for predicting bridge pile capacity
Bridge pile capacity is a vital criterion used to assure the durability and stability of a bridge
pile foundation. In fact, reliably predicting the pile capacity plays a significant role in …
pile foundation. In fact, reliably predicting the pile capacity plays a significant role in …
Selective unsupervised learning-based Wi-Fi fingerprint system using autoencoder and GAN
JH Seong, DH Seo - IEEE Internet of Things Journal, 2019 - ieeexplore.ieee.org
In this article, we propose an automatic Wi-Fi fingerprint system that combines an
unsupervised dual radio map** (UDRM) algorithm with the aim of reducing the time-cost …
unsupervised dual radio map** (UDRM) algorithm with the aim of reducing the time-cost …
Identifying spatial influence of urban elements on road-deposited sediment and the associated phosphorus by coupling Geodetector and Bayesian Networks
Elevated particles and phosphorus washed from road-deposited sediment (RDS) are
noteworthy causes of eutrophication in urban water bodies. Identifying how urban elements …
noteworthy causes of eutrophication in urban water bodies. Identifying how urban elements …
Detecting social-ecological resilience thresholds of cultural landscapes along an urban–rural gradient: a methodological approach based on Bayesian Networks
Context The difficulty of analysing resilience and threshold responses to changing
environmental drivers becomes evident in the social-ecological systems framework due to …
environmental drivers becomes evident in the social-ecological systems framework due to …
Bayesian network applications for sustainable holistic water resources management: modeling opportunities for South Africa
Anthropogenic transformation of land globally is threatening water resources in terms of
quality and availability. Managing water resources to ensure sustainable utilization is …
quality and availability. Managing water resources to ensure sustainable utilization is …