A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews
Consumer sentiment analysis is a recent fad for social media-related applications such as
healthcare, crime, finance, travel, and in academia. Disentangling consumer perception to …
healthcare, crime, finance, travel, and in academia. Disentangling consumer perception to …
An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting
T Peng, C Zhang, J Zhou, MS Nazir - Energy, 2021 - Elsevier
Accurate and reliable solar radiation forecasting is of great significance for the management
and utilization of solar energy. This study proposes a deep learning model based on Bi …
and utilization of solar energy. This study proposes a deep learning model based on Bi …
An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction
Accurate prediction of wind speed is of great significance to the stable operation of wind
power equipment. In this study, a hybrid deep learning model based on convolutional neural …
power equipment. In this study, a hybrid deep learning model based on convolutional neural …
A study of optimization in deep neural networks for regression
Due to rapid development in information technology in both hardware and software, deep
neural networks for regression have become widely used in many fields. The optimization of …
neural networks for regression have become widely used in many fields. The optimization of …
Landslide displacement forecasting using deep learning and monitoring data across selected sites
Accurate early warning systems for landslides are a reliable risk-reduction strategy that may
significantly reduce fatalities and economic losses. Several machine learning methods have …
significantly reduce fatalities and economic losses. Several machine learning methods have …
Changes in tourist mobility after COVID-19 outbreaks
L Yu, P Zhao, J Tang, L Pang - Annals of Tourism Research, 2023 - Elsevier
We comparatively examined tourist mobility changes in the entire country and explicitly
covered two distinct waves of COVID-19 outbreaks, based on mobile phone data from …
covered two distinct waves of COVID-19 outbreaks, based on mobile phone data from …
Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information
Due to flexible and clean nature, distributed photovoltaic (PV) power plants in micro-grid are
essential for solving energy and environmental problems. However, because of the high …
essential for solving energy and environmental problems. However, because of the high …
Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture
LP Joseph, EA Joseph, R Prasad - Computers in Biology and Medicine, 2022 - Elsevier
Diabetes is a deadly chronic disease that occurs when the pancreas is not able to produce
ample insulin or when the body cannot use insulin effectively. If undetected, it may lead to a …
ample insulin or when the body cannot use insulin effectively. If undetected, it may lead to a …
Improving time series forecasting using LSTM and attention models
H Abbasimehr, R Paki - Journal of Ambient Intelligence and Humanized …, 2022 - Springer
Accurate time series forecasting has been recognized as an essential task in many
application domains. Real-world time series data often consist of non-linear patterns with …
application domains. Real-world time series data often consist of non-linear patterns with …
Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform
Y Lin, K Chen, X Zhang, B Tan, Q Lu - Applied Soft Computing, 2022 - Elsevier
In this study, a novel hybrid model for forecasting crude oil futures price time series is
proposed. The combination of Bidirectional long short-term memory network (BiLSTM) …
proposed. The combination of Bidirectional long short-term memory network (BiLSTM) …