An ensemble learning based classification approach for the prediction of household solid waste generation
With the increase in urbanization and smart cities initiatives, the management of waste
generation has become a fundamental task. Recent studies have started applying machine …
generation has become a fundamental task. Recent studies have started applying machine …
Solar radiation forecasting using machine learning and ensemble feature selection
ES Solano, P Dehghanian, CM Affonso - Energies, 2022 - mdpi.com
Accurate solar radiation forecasting is essential to operate power systems safely under high
shares of photovoltaic generation. This paper compares the performance of several machine …
shares of photovoltaic generation. This paper compares the performance of several machine …
Solar radiation forecasting with deep learning techniques integrating geostationary satellite images
The prediction of solar radiation allows estimating photovoltaic systems' power production in
advance, guaranteeing a more reliable and stable energy supply. In this work, we present a …
advance, guaranteeing a more reliable and stable energy supply. In this work, we present a …
The intrusion detection system by deep learning methods: Issues and challenges
Intrusion Detection Systems (IDS) are one of the major research application problems in the
computer security domain. With the increasing number of advanced network attacks, the …
computer security domain. With the increasing number of advanced network attacks, the …
In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning
The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease
impacts economic, political, and cultural sectors, which causes social implications. Across …
impacts economic, political, and cultural sectors, which causes social implications. Across …
An Arima-Lstm Model for Predicting Volatile Agricultural Price Series with Random Forest Technique
Abstract Machine learning mechanism is establishing itself as a promising area for
modelling and forecasting of complex time series over conventional statistical models. In this …
modelling and forecasting of complex time series over conventional statistical models. In this …
[PDF][PDF] Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection. Energies 2022, 15, 7049
ES Solano, P Dehghanian, CM Affonso - 2022 - psecommunity.org
Accurate solar radiation forecasting is essential to operate power systems safely under high
shares of photovoltaic generation. This paper compares the performance of several machine …
shares of photovoltaic generation. This paper compares the performance of several machine …
Enhancing Tidal Wave Predictions for the Estuary of the Nakdong River Using a Fixed-Lag Smoother
H Choi, B Kim, G Lee, SJ Noh - Energies, 2022 - mdpi.com
The prediction of tidal waves is essential for improving not only our understanding of the
hydrological cycle at the boundary between the land and ocean but also energy production …
hydrological cycle at the boundary between the land and ocean but also energy production …
[PDF][PDF] Investigating Prediction Models for Vehicle Demand in a Service Industry.
A Alzaidi, S Shakya, H Khargharia - IJCCI, 2022 - scitepress.org
Demand prediction is an important part of resource management. Higher forecasting
accuracy leads to better decision taking capabilities, especially in a competitive service …
accuracy leads to better decision taking capabilities, especially in a competitive service …
[PDF][PDF] Using LSTM and XGBoost for streamflow prediction based on meteorological time series data
M Afshari Hemmatalikeykha - 2022 - studenttheses.uu.nl
Streamflow prediction, as one of the most critical issues in hydrological studies, plays a
crucial role in water resources management namely reservoir operation, water allocation …
crucial role in water resources management namely reservoir operation, water allocation …