Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach
Social media is used to categorise products or services, but analysing vast comments is time-
consuming. Researchers use sentiment analysis via natural language processing …
consuming. Researchers use sentiment analysis via natural language processing …
MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-Based deep learning model for social media sentiment analysis
MS Başarslan, F Kayaalp - Journal of Cloud Computing, 2023 - Springer
Today, internet and social media is used by many people, both for communication and for
expressing opinions about various topics in many domains of life. Various artificial …
expressing opinions about various topics in many domains of life. Various artificial …
New hybrid deep learning approach using BiGRU-BiLSTM and multilayered dilated CNN to detect arrhythmia
Deep learning methods have shown early progress in analyzing complicated ECG signals,
especially in heartbeat classification and arrhythmia detection. However, there is still a long …
especially in heartbeat classification and arrhythmia detection. However, there is still a long …
Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression …
Automatic leaf disease detection techniques are effective for reducing the time-consuming
effort of monitoring large crop farms and early identification of disease symptoms of plant …
effort of monitoring large crop farms and early identification of disease symptoms of plant …
Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit
Vehicles play a pivotal role in the current era of Industry 4.0 by providing passengers with
excellent mobility, comfort, and safety while strengthening national and international …
excellent mobility, comfort, and safety while strengthening national and international …
Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection
This study presents a comprehensive review of the potential of multimodal deep learning
(DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of …
(DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of …
KHACDD: a knowledge-based hybrid method for multilabel sentiment analysis on complex sentences using attentive capsule and dual structured recurrent network
Using a machine to mine public opinion saves money and time. Traditional sentiment
analysis approaches are typically unable to handle multi-meaning phrases, syntactically …
analysis approaches are typically unable to handle multi-meaning phrases, syntactically …
Predicting patients' sentiments about medications using artificial intelligence techniques
The increasing development of technology has led to the increase of digital data in various
fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to …
fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to …
State-of-charge estimation of sodium-ion batteries: A fusion deep learning approach
Sodium-ion batteries (SIBs) have shown great promise as an alternative to lithium-ion
batteries (LIBs) due to abundant sodium resources. Accurate and universal state of charge …
batteries (LIBs) due to abundant sodium resources. Accurate and universal state of charge …
Machine Learning for a Payment Security Evaluation System for Mobile Networks
Consumers can now access services using a wide range of handheld devices due to the
recent explosive expansion of various mobile network payment gateways. The method used …
recent explosive expansion of various mobile network payment gateways. The method used …