All Relations between short term memory and dl

Publication Sentence Publish Date Extraction Date Species
S V Hemanth, Saravanan Alagarsamy, T Dhiliphan Rajkuma. A novel deep learning model for diabetic retinopathy detection in retinal fundus images using pre-trained CNN and HWBLSTM. Journal of biomolecular structure & dynamics. 2024-02-19. PMID:38373067. this study develops a novel deep learning (dl) approach called he weighted bi-directional long short-term memory (hwblstm) with an effective transfer learning technique for detecting dr from the rfi. 2024-02-19 2024-02-22 Not clear
Hussam Eldin Elzain, Osman A Abdalla, Mohammed Abdallah, Ali Al-Maktoumi, Mohamed Eltayeb, Sani I Abb. Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study. Journal of environmental management. vol 354. 2024-02-15. PMID:38359624. a novel approach of the sl model, the catboost regressor (cbr) and three dl models: 1d-convolutional neural networks (1d-cnn), long short-term memory (lstm), and gated recurrent unit (gru) were adopted and coupled with a semi-supervised pseudo-labeling (pl) technique. 2024-02-15 2024-02-18 Not clear
Reinier Herrera-Casanova, Arturo Conde, Carlos Santos-Pére. Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions. Sensors (Basel, Switzerland). vol 24. issue 3. 2024-02-10. PMID:38339599. this paper introduces a bidirectional long short-term memory (bilstm) deep learning (dl) model designed for forecasting photovoltaic power one hour ahead. 2024-02-10 2024-02-12 Not clear
Abdulaziz Aldaej, Tariq Ahamed Ahanger, Imdad Ulla. Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments. Sensors (Basel, Switzerland). vol 23. issue 24. 2023-12-23. PMID:38139716. next, dl is used to train an attack detection recurrent neural network, which consists of a recurrent neural network (rnn) and bidirectional long short-term memory (lstm). 2023-12-23 2023-12-25 Not clear
Xuejiao Chen, Zhaonan Chen, Mu Zhang, Zixuan Wang, Minyao Liu, Mengyi Fu, Pan Wan. A remaining useful life estimation method based on long short-term memory and federated learning for electric vehicles in smart cities. PeerJ. Computer science. vol 9. 2023-12-11. PMID:38077580. in this article, we propose an rul estimation method utilizing a deep learning (dl) approach based on long short-term memory (lstm) and federated learning (fl) to predict the rul of lithium batteries. 2023-12-11 2023-12-17 Not clear
Mengfang Li, Yuanyuan Jiang, Yanzhou Zhang, Haisheng Zh. Medical image analysis using deep learning algorithms. Frontiers in public health. vol 11. 2023-12-01. PMID:38026291. through a systematic categorization of state-of-the-art dl techniques, such as convolutional neural networks (cnns), recurrent neural networks (rnns), generative adversarial networks (gans), long short-term memory (lstm) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. 2023-12-01 2023-12-10 Not clear
Yanbu Wang, Linqing Liu, Chao Wan. Trends in using deep learning algorithms in biomedical prediction systems. Frontiers in neuroscience. vol 17. 2023-12-01. PMID:38027475. by categorizing cutting-edge dl approaches into distinct categories, including convolutional neural networks (cnns), recurrent neural networks (rnns), generative adversarial networks (gans), long short-term memory (lstm) models, support vector machine (svm), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. 2023-12-01 2023-12-10 Not clear
Mohammad G Zamani, Mohammad Reza Nikoo, Sina Jahanshahi, Rahim Barzegar, Amirreza Meydan. Forecasting water quality variable using deep learning and weighted averaging ensemble models. Environmental science and pollution research international. 2023-11-23. PMID:37996598. thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (dl) models - namely, recurrent neural network (rnn), long short-term memory (lstm), gated recurrence unit (gru), and temporal convolutional network (tcn) - in forecasting chl-a concentrations; (2) the incorporation of these dl models into ensemble models (ems) employing genetic algorithm (ga) and non-dominated sorting genetic algorithm (nsga-ii) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed ems. 2023-11-23 2023-11-29 Not clear
Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa T Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xian. Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection. IEEE transactions on neural networks and learning systems. vol PP. 2023-11-15. PMID:37962997. existing approaches for this problem mostly employ either statistical models which cannot capture the nonlinear relations well or conventional deep learning (dl) models e.g., convolutional neural network (cnn) and long short-term memory (lstm) that do not explicitly learn the pairwise correlations among variables. 2023-11-15 2023-11-20 Not clear
Gang Li, Zhangjun Liu, Jingwen Zhang, Huiming Han, Zhangkang Sh. Bayesian model averaging by combining deep learning models to improve lake water level prediction. The Science of the total environment. 2023-10-13. PMID:37832688. in this study, three deep learning (dl) models, including long short-term memory (lstm), the gated recurrent unit (gru), and the temporal convolutional network (tcn), were used to predict wls at five stations of poyang lake for different forecast periods (1-day ahead, 3-day ahead, and 7-day ahead). 2023-10-13 2023-10-15 Not clear
Abeer Saber, Abdelazim G Hussien, Wael A Awad, Amena Mahmoud, Alaa Allakan. Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images. Scientific reports. vol 13. issue 1. 2023-09-09. PMID:37689757. a new deep-learning (dl) model based on a combination of transfer-learning (tl) and long short-term memory (lstm) is proposed in this study to adequately facilitate the automatic detection and diagnosis of the bc suspicious region using the 80-20 method. 2023-09-09 2023-10-07 Not clear
Iva Matetić, Ivan Štajduhar, Igor Wolf, Sandi Ljubi. Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection. Sensors (Basel, Switzerland). vol 23. issue 15. 2023-08-12. PMID:37571501. we tested three contemporary dl modeling approaches: convolutional neural network (cnn), long short-term memory network (lstm), and a combination of cnn and gated recurrent unit (gru). 2023-08-12 2023-08-16 Not clear
Sameer Sayyad, Satish Kumar, Arunkumar Bongale, Ketan Kotecha, Ajith Abraha. Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning Models. Sensors (Basel, Switzerland). vol 23. issue 12. 2023-07-08. PMID:37420825. in this work, the authors considers the time-frequency domain (tfd) features such as short-time fourier-transform (stft) and different wavelet transforms (wt) along with deep learning (dl) models such as long short-term memory (lstm), different variants of lstn, convolutional neural network (cnn), and hybrid models that are a combination of ccn with lstm variants for rul estimation. 2023-07-08 2023-08-14 Not clear
Rajendhar Junjuri, Ali Saghi, Lasse Lensu, Erik M Vartiaine. Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra. Physical chemistry chemical physics : PCCP. 2023-06-08. PMID:37287325. in this work, a bidirectional lstm (bi-lstm) neural network is explored for the first time to remove the nrb in the cars spectra automatically, and the results are compared with those of three dl models reported in the literature, namely, convolutional neural network (cnn), long short-term memory (lstm) neural network, and very deep convolutional autoencoders (vector). 2023-06-08 2023-08-14 Not clear
Ameen A Hai, Mark G Weiner, Anuradha Paranjape, Alice Livshits, Jeremiah R Brown, Zoran Obradovic, Daniel J Rubi. Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes. AMIA ... Annual Symposium proceedings. AMIA Symposium. vol 2022. 2023-05-02. PMID:37128461. in 2,836,569 encounters of 36,641 diabetes patients, deep learning (dl) long short-term memory (lstm) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models. 2023-05-02 2023-08-14 Not clear
Wafa Alotaibi, Faye Alomary, Raouia Mokn. COVID-19 vaccine rejection causes based on Twitter people's opinions analysis using deep learning. Social network analysis and mining. vol 13. issue 1. 2023-04-10. PMID:37033473. in this paper, we used multi-class sentiment analysis to classify people's opinions from extracted tweets about covid-19 vaccines, using firstly different machine learning (ml) classifiers such as logistic regression (lr), stochastic gradient descent, support vector machine, k-nearest neighbors, decision tree (dt), multinomial naïve bayes, random forest and gradient boosting and secondly various deep learning (dl) models such as recurrent neural network (rnn), long short term memory (lstm), gated recurrent unit (gru), rnn-lstm and rnn-gru. 2023-04-10 2023-08-14 Not clear
Sajid Ali, Shaker El-Sappagh, Farman Ali, Muhammad Imran, Tamer Abuhme. Multitask Deep Learning for Cost-Effective Prediction of Patient's Length of Stay and Readmission State Using Multimodal Physical Activity Sensory Data. IEEE journal of biomedical and health informatics. vol 26. issue 12. 2023-04-06. PMID:36037451. this study develops multimodal multitasking long short-term memory (lstm) deep learning (dl) model that can predict both los and readmission for patients using multi-sensory data from 47 patients. 2023-04-06 2023-08-16 Not clear
Erkan Bostanci, Engin Kocak, Metehan Unal, Mehmet Serdar Guzel, Koray Acici, Tunc Asurogl. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. Sensors (Basel, Switzerland). vol 23. issue 6. 2023-03-30. PMID:36991790. in addition, to compare the performance with canonical ml models, one-dimensional convolutional neural network (1-d cnn), long short-term memory (lstm), and bidirectional lstm (bilstm) dl models are utilized. 2023-03-30 2023-08-14 Not clear
Salem Alkhalaf, Fahad Alturise, Adel Aboud Bahaddad, Bushra M Elamin Elnaim, Samah Shabana, Sayed Abdel-Khalek, Romany F Mansou. Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging. Cancers. vol 15. issue 5. 2023-03-11. PMID:36900283. for cancer classification, the majority weighted voting ensemble model with three dl classifiers, namely recurrent neural network (rnn), gated recurrent unit (gru), and bidirectional long short-term memory (bilstm). 2023-03-11 2023-08-14 Not clear
Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, Abel Henriot, Delphine Allier, Lisa Baulo. A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability. The Science of the total environment. 2023-01-01. PMID:36587693. in this study, we assess the capabilities of three deep learning (dl) models (long short-term memory (lstm), gated recurrent unit (gru), and bidirectional lstm (bilstm)) in simulating three types of gwls affected by varying low-frequency behavior: inertial (dominated by low-frequency), annual (dominated by annual cyclicity) and mixed (in which both annual and low-frequency variations have high amplitude). 2023-01-01 2023-08-14 Not clear