[HTML][HTML] A systematic review of social media-based sentiment analysis: Emerging trends and challenges
In the present information age, a wide and significant variety of social media platforms have
been developed and become an important part of modern life. Massive amounts of user …
been developed and become an important part of modern life. Massive amounts of user …
Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …
and deep learning to advance scientific computing in many fields, including fluid mechanics …
[HTML][HTML] Sentiment analysis: A survey on design framework, applications and future scopes
M Bordoloi, SK Biswas - Artificial intelligence review, 2023 - Springer
Sentiment analysis is a solution that enables the extraction of a summarized opinion or
minute sentimental details regarding any topic or context from a voluminous source of data …
minute sentimental details regarding any topic or context from a voluminous source of data …
[HTML][HTML] Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: A review and new modeling results
We review the latest modeling techniques and propose new hybrid SAELSTM framework
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …
[HTML][HTML] Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction
Global solar radiation (GSR) prediction plays an essential role in planning, controlling and
monitoring solar power systems. However, its stochastic behaviour is a significant challenge …
monitoring solar power systems. However, its stochastic behaviour is a significant challenge …
Spatial–temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism
Z Chen, Z Lu, Q Chen, H Zhong, Y Zhang, J Xue… - Information Sciences, 2022 - Elsevier
Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays
an important role in traffic management. The graph convolution network (GCN) is widely …
an important role in traffic management. The graph convolution network (GCN) is widely …
Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis
Abstract Machine learning is considered advantageous smart cities through sentiment
analysis using social media reviews. Social media reviews can be helpful inside smart cities …
analysis using social media reviews. Social media reviews can be helpful inside smart cities …
Deep learning with data preprocessing methods for water quality prediction in ultrafiltration
Ultrafiltration (UF) has been widely used to remove colloidal substances and suspended
solids in feed water. However, UF membrane breakage can lead to downstream impurities …
solids in feed water. However, UF membrane breakage can lead to downstream impurities …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
SA Faroughi, NM Pawar… - Journal of …, 2024 - asmedigitalcollection.asme.org
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
[HTML][HTML] A deep learning approach in predicting products' sentiment ratings: a comparative analysis
We present a benchmark comparison of several deep learning models including
Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short …
Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short …