We compare the proposed model with eighteen different neural network models, as well as the PSO-GRU | LSTM and GA-GRU | LSTM models, which refer to optimized deep learning models using the Particle Swarm Optimi
Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing tech...
2021 Superiority of CEEMDAN as compared to EMD in forecasting stock index price. 2022 Superiority of the EEMD over EMD for intelligent chatter detection. 2022 Effectiveness of the EEMD algorithm for improving the prediction accuracy of the individual models in predicting the hourly urban water ...
Our proposed model (BiCuDNNLSTM-1dCNN) is compared with other hybrid DL-based models and state of the art models for verification using five stock price datasets. The predicted results show that the proposed hybrid model is efficient for accurate prediction of stock price and reliable for ...
Wind power forecasting is decisive for the incorporation of wind energy into the power grid, as it enables wind farms to optimize their operational efficiency and contributes to maintaining grid stability. This study evaluates three deep learning models namely Long Short-Term Memory (LSTM), Gated Re...
learning tools, Long Short-Term Memory (LSTM) networks3, a specialized breed of recurrent neural networks (RNNs), have carved a niche for themselves ("long short-term memory" (LSTM), a novel recurrent network architecture in conjunction with an appropriate gradient-based learning algorithm. ...
Stochastic gradient descent algorithm was applied to optimize the model performance. CNN-LSTM architecture The growth and development of plants are a dynamic process not only related to spatial, but also associated with temporal information, which are not considered in conventional CNN model. As a ...
In addition, we compared the results of the proposed models with state-of-the-art time series, ML, and DL models, such as ARIMA, SVR, LSTM, and GRU. We propose a classification approach to predict the trend instead of the value of the cloud workload. We study the effect of using com...
Stock investment is an economic activity characterized by high risks and high returns. Therefore, the prediction of stock prices or fluctuations is of great importance to investors. Stock price prediction is a challenging task due to the nonlinearity and
In this process, the model weights are optimized by the genetic algorithm, and the structural features of the model are determined to provide the best performance. The study evaluates the accuracy and performance of the COVID-19 predictions using the GDCNN model. The model makes predictions ...