| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Hazrat Bilal, Yibin Tian, Ahmad Ali, Yar Muhammad, Abid Yahya, Basem Abu Izneid, Inam Ulla. An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques. Bioengineering (Basel, Switzerland). vol 11. issue 12. 2025-01-08. PMID:39768108. |
in addition to the proposed model, three other hybrid dl models, such as convolutional neural network + recurrent neural network (cnn-rnn), convolutional neural network + long short-term memory (cnn-lstm), and convolutional neural network + bidirectional long short-term memory (cnn-blstm), were also investigated. |
2025-01-08 |
2025-01-13 |
Not clear |
| Md Amir Hamja, Mahmudul Hasan, Md Abdur Rashid, Md Tanvir Hasan Shouro. Exploring happiness factors with explainable ensemble learning in a global pandemic. PloS one. vol 20. issue 1. 2025-01-02. PMID:39746025. |
we design two ensemble ml and dl models using blending and stacking ensemble techniques, namely, blending rgmll, which combines ridge regression (rr), gradient boosting (gb), multilayer perceptron (mlp), long short-term memory (lstm), and linear regression (lr), and stacking lrgr, which combines lr, random forest (rf), gb, and rr. |
2025-01-02 |
2025-01-05 |
Not clear |
| Haoyu Wang, Yaoping Yu, Xiaoguang Liu, Kun Wei, Wangshu Zhang, Youjie Ye, Yulin Zho. Deep learning based signal processing and detection for multiple medical devices OFDM systems. Scientific reports. vol 14. issue 1. 2024-11-29. PMID:39613798. |
in this work, we evaluate three deep learning (dl) algorithms: fully connected deep neural networks, convolutional neural networks, and long short-term memory neural networks for signal processing and detection in uncoded multiple medical devices ofdm communications systems. |
2024-11-29 |
2024-12-02 |
Not clear |
| Owais Ali Wani, Syed Sheraz Mahdi, Md Yeasin, Shamal Shasang Kumar, Alexandre S Gagnon, Faizan Danish, Nadhir Al-Ansari, Salah El-Hendawy, Mohamed A Matta. Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas. Scientific reports. vol 14. issue 1. 2024-11-13. PMID:39537701. |
to address this, our study proposes the application of advanced machine learning (ml) algorithms, including random forest (rf), support vector regression (svr), artificial neural network (ann), and k-nearest neighbour (knn) along with various deep learning (dl) algorithms such as long short-term memory (lstm), bi-directional lstm, deep lstm, gated recurrent unit (gru), and simple recurrent neural network (rnn). |
2024-11-13 |
2024-11-17 |
human |
| Elham Kalantari, Hamid Gholami, Hossein Malakooti, Ali Reza Nafarzadegan, Vahid Moosav. Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning? Environmental science and pollution research international. 2024-10-29. PMID:39467867. |
in this study, several machine learning (ml) models consisting of shallow learning (sl) models (e.g., random forest (rf), k-nearest neighbor (knn), weighted k-nearest neighbor (wknn), support vector machine (svm), artificial neural network (ann), and deep learning (dl) models (e.g., long short-term memory (lstm), gated recurrent unit (gru), recurrent neural network (rnn), and convolutional neural network (cnn)) have been employed for predicting air pollution and its classification. |
2024-10-29 |
2024-10-31 |
Not clear |
| Naveed Ur Rehman Junejo, Qingsheng Huang, Xiaoqing Dong, Chang Wang, Adnan Zeb, Mahammad Humayoo, Gengzhong Zhen. SAPPNet: students' academic performance prediction during COVID-19 using neural network. Scientific reports. vol 14. issue 1. 2024-10-19. PMID:39427025. |
additionally, we also try to implement classical machine learning (ml) models including support vector machine, k nearest neighbor, decision tree, and random forest, and dl models named artificial neural network, convolutional neural network, long short-term memory, and students learning prediction network. |
2024-10-19 |
2024-10-22 |
Not clear |
| Zhiwen Zhu, Shaoxuan Yuan, Quan Yang, Hao Jiang, Fengru Zheng, Jiayi Lu, Qiang Su. Autonomous Scanning Tunneling Microscopy Imaging via Deep Learning. Journal of the American Chemical Society. 2024-10-09. PMID:39382312. |
in this study, we developed an autonomous stm framework powered by dl to enable autonomous operations of the stm without human interventions. |
2024-10-09 |
2024-10-11 |
human |
| Jie Y. Evaluation of influencing factors of China university teaching quality based on fuzzy logic and deep learning technology. PloS one. vol 19. issue 9. 2024-09-06. PMID:39240954. |
combining fuzzy logic and dl can provide a powerful approach for assessing the influencing factors of college and university teaching effects by implementing the sequential intuitionistic fuzzy (sif) assisted long short-term memory (lstm) model proposed. |
2024-09-06 |
2024-09-09 |
Not clear |
| 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 |