Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM
Traffic flow prediction is important for urban planning and traffic congestion alleviation as
well as for intelligent traffic management systems. Due to the periodic characteristics and …
well as for intelligent traffic management systems. Due to the periodic characteristics and …
A temporal-aware lstm enhanced by loss-switch mechanism for traffic flow forecasting
Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …
Automatic detection and classification of mammograms using improved extreme learning machine with deep learning
SRS Chakravarthy, H Rajaguru - Irbm, 2022 - Elsevier
Background and objective Breast cancer, the most intrusive form of cancer affecting women
globally. Next to lung cancer, breast cancer is the one that provides a greater number of …
globally. Next to lung cancer, breast cancer is the one that provides a greater number of …
Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine
J Wang, Q Cui, M He - Chaos, Solitons & Fractals, 2022 - Elsevier
As the climate problem continues to worsen, carbon trading markets for energy conservation
and emission reduction have been established in many countries. Accurate forecasting of …
and emission reduction have been established in many countries. Accurate forecasting of …
A state of health estimation framework based on real-world electric vehicles operating data
X Zhao, J Hu, G Hu, H Qiu - Journal of Energy Storage, 2023 - Elsevier
The accurate estimation of battery state of health (SOH) is essential to alleviate mileage
anxiety for the drivers of electric vehicles (EVs) and achieve echelon use of the battery. The …
anxiety for the drivers of electric vehicles (EVs) and achieve echelon use of the battery. The …
St-trafficnet: A spatial-temporal deep learning network for traffic forecasting
This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for
traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined …
traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined …
Δfree-LSTM: An error distribution free deep learning for short-term traffic flow forecasting
Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term
memory (LSTM) network is considered qualified to capture the long-term temporal …
memory (LSTM) network is considered qualified to capture the long-term temporal …
Short-term prediction of carbon emissions based on the EEMD-PSOBP model
W Sun, C Ren - Environmental Science and Pollution Research, 2021 - Springer
The recovery of carbon emissions in the past 2 years has alerted us that carbon emissions
are a long-term process, and setting short-term emission reduction targets can more …
are a long-term process, and setting short-term emission reduction targets can more …
A Sample-Rebalanced Outlier-Rejected -Nearest Neighbor Regression Model for Short-Term Traffic Flow Forecasting
Short-term traffic flow forecasting is a fundamental and challenging task due to the stochastic
dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a …
dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a …