| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Amal Kammoun, Philippe Ravier, Olivier Buttell. Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles. Sensors (Basel, Switzerland). vol 24. issue 16. 2024-08-29. PMID:39205012. |
in this paper, we compare the accuracy of estimating grf components for both feet using six methods: three deep learning (dl) methods (artificial neural network, long short-term memory, and convolutional neural network) and three supervised machine learning (sml) methods (least squares, support vector regression, and random forest (rf)). |
2024-08-29 |
2024-09-04 |
human |
| Dinesh Komarasamy, Siva Malar Ramaganthan, Dharani Molapalayam Kandaswamy, Gokuldhev Mon. Deep learning and optimization enabled multi-objective for task scheduling in cloud computing. Network (Bristol, England). 2024-08-20. PMID:39163538. |
thereafter, task scheduling is accomplished based on dl using the proposed deep feedforward neural network fused long short-term memory (dfnn-lstm), which is the combination of dfnn and lstm. |
2024-08-20 |
2024-08-23 |
Not clear |
| Nek Dil Khan, Javed Ali Khan, Jianqiang Li, Tahir Ullah, Qing Zha. Mining software insights: uncovering the frequently occurring issues in low-rating software applications. PeerJ. Computer science. vol 10. 2024-08-15. PMID:39145243. |
also, an experimental study comparing various ml and dl algorithms, including multinomial naive bayes (mnb), logistic regression (lr), random forest (rf), multi-layer perception (mlp), k-nearest neighbors (knn), adaboost, voting, convolutional neural network (cnn), long short-term memory (lstm), bidirectional long short term memory (bilstm), gated recurrent unit (gru), bidirectional gated recurrent unit (bigru), and recurrent neural network (rnn) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. |
2024-08-15 |
2024-08-17 |
Not clear |
| Hamid Anwar, Afed Ullah Khan, Basir Ullah, Abubakr Taha Bakheit Taha, Taoufik Najeh, Muhammad Usman Badshah, Abdulnoor A J Ghanim, Muhammad Irfa. Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6. Scientific reports. vol 14. issue 1. 2024-07-30. PMID:39080322. |
this research was carried out to predict daily streamflow for the swat river basin, pakistan through four deep learning (dl) models: feed forward artificial neural networks (ffann), seasonal artificial neural networks (sann), time lag artificial neural networks (tlann) and long short-term memory (lstm) under two shared socioeconomic pathways (ssps) 585 and 245. |
2024-07-30 |
2024-08-02 |
Not clear |
| Youngsin Kim, Mihyung Moon, Seokwhwan Moon, Wonkyu Moo. Effects of precise cardio sounds on the success rate of phonocardiography. PloS one. vol 19. issue 7. 2024-07-15. PMID:39008512. |
we used a convolutional neural network (cnn) and deep-learning (dl) for image classification, and a cnn equipped with long short-term memory to enable sequential feature extraction. |
2024-07-15 |
2024-07-18 |
Not clear |
| Chengshuai Liu, Wenzhong Li, Caihong Hu, Tianning Xie, Yunqiu Jiang, Runxi Li, Shan-E-Hyder Soomro, Yuanhao X. Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting. Journal of environmental management. vol 362. 2024-06-04. PMID:38833924. |
in this study, we propose a runoff process vectorization (rpv) method and integrate it with three deep learning (dl) models, namely long short-term memory (lstm), temporal convolutional network (tcn), and transformer, to develop a series of rpv-dl flood forecasting models, namely rpv-lstm, rpv-tcn, and rpv-transformer models. |
2024-06-04 |
2024-06-07 |
Not clear |
| Shoaib Sattar, Rafia Mumtaz, Mamoon Qadir, Sadaf Mumtaz, Muhammad Ajmal Khan, Timo De Waele, Eli De Poorter, Ingrid Moerman, Adnan Shahi. Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets. Sensors (Basel, Switzerland). vol 24. issue 8. 2024-04-27. PMID:38676101. |
multiple dl models, including a convolutional neural network (cnn), a long short-term memory (lstm) network, and a self-supervised learning (ssl)-based model using autoencoders are explored and compared in this study. |
2024-04-27 |
2024-04-29 |
human |
| Zeinab Mohammadi-Raigani, Hamid Gholami, Aliakbar Mohamadifar, Aliakbar Nazari Samani, Biswajeet Pradha. Using an interpretable deep learning model for the prediction of riverine suspended sediment load. Environmental science and pollution research international. 2024-04-24. PMID:38656723. |
this paper investigates the abilities of four dl models, including dense deep neural networks (ddnn), long short-term memory (lstm), gated recurrent unit (gru), and simple recurrent neural network (rnn) models for the prediction of daily ssl using river discharge and rainfall data at a daily time scale in the taleghan river watershed, northwestern tehran, iran. |
2024-04-24 |
2024-04-28 |
Not clear |
| Gourab Saha, Chaopeng Shen, Jonathan Duncan, Raj Cibi. Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins. Journal of environmental management. vol 357. 2024-04-02. PMID:38565027. |
a long short-term memory (lstm) based deep learning (dl) modeling framework was developed to predict daily nitrate concentrations. |
2024-04-02 |
2024-04-05 |
Not clear |
| Sarina Aminizadeh, Arash Heidari, Mahshid Dehghan, Shiva Toumaj, Mahsa Rezaei, Nima Jafari Navimipour, Fabio Stroppa, Mehmet Una. Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artificial intelligence in medicine. vol 149. 2024-03-10. PMID:38462281. |
the studies under examination employ a diverse range of ml and dl methods, along with distributed systems, with convolutional neural networks (cnns) being the most commonly used (16.7 %), followed by long short-term memory (lstm) networks (14.6 %) and shallow learning networks (12.5 %). |
2024-03-10 |
2024-03-14 |
Not clear |
| Jagdeep Rahul, Diksha Sharma, Lakhan Dev Sharma, Umakanta Nanda, Achintya Kumar Sarka. A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Frontiers in human neuroscience. vol 18. 2024-02-29. PMID:38419961. |
in contrast, dl techniques, which use neural networks such as convolutional neural networks (cnns) and long short-term memory networks (lstms), are more adaptable to intricate eeg patterns but require significant data and computational power. |
2024-02-29 |
2024-03-02 |
Not clear |
| Laura Fontes, Pedro Machado, Doratha Vinkemeier, Salisu Yahaya, Jordan J Bird, Isibor Kennedy Ihianl. Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques. Sensors (Basel, Switzerland). vol 24. issue 4. 2024-02-24. PMID:38400254. |
in contrast to the research on wearable devices, this paper proposes novel hybrid deep learning (dl) networks for stress detection based on remote photoplethysmography (rppg), employing (long short-term memory (lstm), gated recurrent units (gru), 1d convolutional neural network (1d-cnn)) models with hyperparameter optimisation and augmentation techniques to enhance performance. |
2024-02-24 |
2024-02-26 |
human |
| 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 |