Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM
Sign Up to like & getrecommendations! Published in 2021 at "Soft Computing"
DOI: 10.1007/s00500-021-06075-8
Abstract: The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and… read more here.
Keywords: bat algorithm; optimized lstm; covid; lstm ... See more keywords
Selection of optimal wavelet features for epileptic EEG signal classification with LSTM
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05666-0
Abstract: Epilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing… read more here.
Keywords: epileptic eeg; selection optimal; lstm; classification ... See more keywords
RecogNet-LSTM+CNN: a hybrid network with attention mechanism for aspect categorization and sentiment classification
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Intelligent Information Systems"
DOI: 10.1007/s10844-021-00692-3
Abstract: Sentiment analysis for user reviews has received substantial heed in recent years. There are many deep learning models for natural language processing (NLP) applications. Long-short term memory (LSTM) and Convolutional neural network (CNN) based models… read more here.
Keywords: sentiment; aspect categorization; lstm; cnn ... See more keywords
Air quality prediction using CNN+LSTM-based hybrid deep learning architecture
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DOI: 10.1007/s11356-021-16227-w
Abstract: Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships between these variables by sophisticated methods… read more here.
Keywords: network; pollution; air; model ... See more keywords
Investigating the dynamic memory effect of human drivers via ON-LSTM
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DOI: 10.1007/s11432-019-2844-3
Abstract: It is a widely accepted view that considering the memory effects of historical information (driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However, many commonly used models (e.g., long short-term… read more here.
Keywords: investigating dynamic; trajectory prediction; memory effects; lstm ... See more keywords
Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-019-01591-w
Abstract: In this paper, we proposed Multi-model LSTM-based Pre-trained Convolutional Neural Networks (MLP-CNNs) as an ensemble majority voting classifier for the detection of plant diseases and pests. The proposed hybrid model is based on the combination… read more here.
Keywords: detection; multi model; model; apple ... See more keywords
Improving Text Summarization using Ensembled Approach based on Fuzzy with LSTM
Sign Up to like & getrecommendations! Published in 2020 at "Arabian Journal for Science and Engineering"
DOI: 10.1007/s13369-020-04827-6
Abstract: Abstractive text summarization using attentional recurrent neural network (sequence-to-sequence) models have proven to be very effective. In this paper, a novel hybrid approach is presented for generating abstractive text summaries by combining fuzzy logic rules… read more here.
Keywords: flstm model; text summarization; approach; summarization using ... See more keywords
Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition
Sign Up to like & getrecommendations! Published in 2021 at "Applied Energy"
DOI: 10.1016/j.apenergy.2021.117193
Abstract: Abstract Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments. Traditional forecasting methods find it relatively difficult to obtain a reliable solar energy monitoring system… read more here.
Keywords: term; short term; model; lstm ... See more keywords
Adversarial training based lattice LSTM for Chinese clinical named entity recognition
Sign Up to like & getrecommendations! Published in 2019 at "Journal of biomedical informatics"
DOI: 10.1016/j.jbi.2019.103290
Abstract: Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used… read more here.
Keywords: clinical named; entity recognition; adversarial training; lattice lstm ... See more keywords
Using a stacked residual LSTM model for sentiment intensity prediction
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DOI: 10.1016/j.neucom.2018.09.049
Abstract: Abstract The sentiment intensity of a text indicates the strength of its association with positive sentiment, which is a continuous real-value between 0 and 1. Compared to polarity classification, predicting sentiment intensities for texts can… read more here.
Keywords: sentiment intensity; lstm; sentiment; intensity prediction ... See more keywords
Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models.
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DOI: 10.1016/j.watres.2021.117001
Abstract: Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in this… read more here.
Keywords: occurrence recreational; beach; antibiotic resistance; lstm ... See more keywords