Sentiment analysis with ensemble hybrid deep learning model
The rapid development of mobile technologies has made social media a vital platform for
people to express their feelings and opinions. Understanding the public opinions can be …
people to express their feelings and opinions. Understanding the public opinions can be …
A survey of recent advances in deep learning models for detecting malware in desktop and mobile platforms
Malware is one of the most common and severe cyber threats today. Malware infects
millions of devices and can perform several malicious activities including compromising …
millions of devices and can perform several malicious activities including compromising …
A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition
Recent experiments show that deep bidirectional long short-term memory (BLSTM) recurrent
neural network acoustic models outperform feedforward neural networks for automatic …
neural network acoustic models outperform feedforward neural networks for automatic …
An experimental approach towards the performance assessment of various optimizers on convolutional neural network
S Vani, TVM Rao - 2019 3rd international conference on trends …, 2019 - ieeexplore.ieee.org
Artificial Intelligence is a technique of modeling a computer, a computer administered-robot,
in the indistinguishable manner the acute humans reflect. Machine Learning is a mechanism …
in the indistinguishable manner the acute humans reflect. Machine Learning is a mechanism …
A deep neural network approach towards real-time on-branch fruit recognition for precision horticulture
Real-time and accurate on-branch fruit recognition in an uncontrolled/unstructured
environment of orchards could facilitate Precision Horticulture (PH) practices. These …
environment of orchards could facilitate Precision Horticulture (PH) practices. These …
Air pollution concentration forecast method based on the deep ensemble neural network
The global environment has become more polluted due to the rapid development of
industrial technology. However, the existing machine learning prediction methods of air …
industrial technology. However, the existing machine learning prediction methods of air …
A multiscale neural network based on hierarchical matrices
In this work we introduce a new multiscale artificial neural network based on the structure of
H-matrices. This network generalizes the latter to the nonlinear case by introducing a local …
H-matrices. This network generalizes the latter to the nonlinear case by introducing a local …
MF-TCPV: a machine learning and fuzzy comprehensive evaluation-based framework for traffic congestion prediction and visualization
L Li, H Lin, J Wan, Z Ma, H Wang - IEEE Access, 2020 - ieeexplore.ieee.org
A framework for traffic congestion prediction and visualization based on machine learning
and Fuzzy Comprehensive Evaluation named MF-TCPV is proposed in this paper. The …
and Fuzzy Comprehensive Evaluation named MF-TCPV is proposed in this paper. The …
MIMO: A unified spatio-temporal model for multi-scale sea surface temperature prediction
Sea surface temperature (SST) is a crucial factor that affects global climate and marine
activities. Predicting SST at different temporal scales benefits various applications, from …
activities. Predicting SST at different temporal scales benefits various applications, from …
Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding
Brain tumor detection in clinical applications is a complex and challenging task due to the
intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely …
intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely …