| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| Rui Chen, Lei Hei, Yi La. Object detection in optical imaging of the Internet of Things based on deep learning. PeerJ. Computer science. vol 9. 2024-01-09. PMID:38192473. |
a dynamic image target detection training model is established through the convolutional neural network (cnn) algorithm within the framework of deep learning (dl). |
2024-01-09 |
2024-01-10 |
Not clear |
| Rui Chen, Lei Hei, Yi La. Object detection in optical imaging of the Internet of Things based on deep learning. PeerJ. Computer science. vol 9. 2024-01-09. PMID:38192473. |
finally, an attention mechanism is incorporated within the dl framework, leading to the construction of an attention mechanism cnn target detection model that operates at three difficulty levels: simple, intermediate, and challenging. |
2024-01-09 |
2024-01-10 |
Not clear |
| Yanlong Liu, Peiyun Cheng, Jie L. Application interface design of Chongqing intangible cultural heritage based on deep learning. Heliyon. vol 9. issue 11. 2023-12-04. PMID:38045196. |
the experimental results reveal: (1) the model for recognizing ich images using the convolutional neural network (cnn) has higher recognition accuracy, recall, and f1 value than the model without cnns; (2) after incorporating transfer learning (tl) into the model, the recognition accuracy, recall, and f1 value of the model have further improved; (3) the survey results show that the chongqing ich app interface system based on dl technology, user habits, and visual perception performs better in terms of user experience, usability, and other aspects. |
2023-12-04 |
2023-12-10 |
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 |
| Seunghyeon Lee, Heewon Jeong, Seok Min Hong, Daeun Yun, Jiye Lee, Eunju Kim, Kyung Hwa Ch. Automatic classification of microplastics and natural organic matter mixtures using a deep learning model. Water research. vol 246. 2023-10-19. PMID:37857009. |
a convolutional neural network (cnn)-based dl model with a spatial attention mechanism was adopted to classify substances from their raman spectra. |
2023-10-19 |
2023-11-08 |
Not clear |
| Shahd A Alajaji, Rula Amarin, Radi Masri, Tiffany Tavares, Vandana Kumar, Jeffery B Price, Ahmed S Sulta. Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach. Oral surgery, oral medicine, oral pathology and oral radiology. 2023-09-28. PMID:37770329. |
we leveraged an artificial intelligence deep-learning convolutional neural network (dl cnn) to detect calcified carotid artery atheromas (ccaas) on cone beam computed tomography (cbct) images. |
2023-09-28 |
2023-10-07 |
Not clear |
| Muhammad Hassan Jamal, Muazzam A Khan, Safi Ullah, Mohammed S Alshehri, Sultan Almakdi, Umer Rashid, Abdulwahab Alazeb, Jawad Ahma. Multi-step attack detection in industrial networks using a hybrid deep learning architecture. Mathematical biosciences and engineering : MBE. vol 20. issue 8. 2023-09-07. PMID:37679112. |
this study proposes a hybrid model that combines two dl algorithms, namely convolutional neural networks (cnn) and deep belief networks (dbn), for intrusion detection in industrial networks. |
2023-09-07 |
2023-10-07 |
Not clear |
| Summiya Batool, Syed Omer Gilani, Asim Waris, Khawaja Fahad Iqbal, Niaz B Khan, M Ijaz Khan, Sayed M Eldin, Fuad A Awwa. Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images. Scientific reports. vol 13. issue 1. 2023-09-02. PMID:37660096. |
auto-encoders, sparse coding, and limited boltzmann machines were used as a few past deep learning (dl) techniques and features for the classification of dr. convolutional neural networks (cnn) have been identified as a promising solution for detecting and classifying dr. we employ the deep learning capabilities of efficient net batch normalization (bns) pre-trained models to automatically acquire discriminative features from fundus images. |
2023-09-02 |
2023-09-07 |
Not clear |
| Ruofan Wang, Qiguang He, Chunxiao Han, Haodong Wang, Lianshuan Shi, Yanqiu Ch. A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network. Frontiers in neuroscience. vol 17. 2023-08-24. PMID:37614342. |
the convolutional neural network (cnn) is a mainstream deep learning (dl) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as alzheimer's disease (ad). |
2023-08-24 |
2023-09-07 |
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 |
| Zahid Rasheed, Yong-Kui Ma, Inam Ullah, Tamara Al Shloul, Ahsan Bin Tufail, Yazeed Yasin Ghadi, Muhammad Zubair Khan, Heba G Mohame. Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning. Brain sciences. vol 13. issue 4. 2023-05-16. PMID:37190567. |
convolutional neural network (cnn) is one of the most prominent dl methods for visual learning and image classification tasks. |
2023-05-16 |
2023-08-14 |
Not clear |
| Seung Yeon Seo, Jungsu S Oh, Jinwha Chung, Seog-Young Kim, Jae Seung Ki. MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization. Nuclear medicine and molecular imaging. vol 57. issue 2. 2023-03-31. PMID:36998592. |
to resolve this issue, we propose a deep learning (dl)-based individual-brain-specific vois (i.e., cortex, hippocampus, striatum, thalamus, and cerebellum) directly generated from pet images using the inverse-spatial-normalization (isn)-based voi labels and deep convolutional neural network model (deep cnn). |
2023-03-31 |
2023-08-14 |
mouse |
| 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 |
| Joomee Song, Juyoung Hahm, Jisoo Lee, Chae Yeon Lim, Myung Jin Chung, Jinyoung Youn, Jin Whan Cho, Jong Hyeon Ahn, Kyungsu Ki. Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes. Scientific reports. vol 13. issue 1. 2023-03-01. PMID:36859498. |
using the gold-standard non-dl model, freesurfer (fs), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for dl models, the representative convolutional neural network (cnn) and vision transformer (vit)-based models. |
2023-03-01 |
2023-08-14 |
Not clear |
| Milagros Jaén-Vargas, Karla Miriam Reyes Leiva, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmed. Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models. PeerJ. Computer science. vol 8. 2022-09-12. PMID:36091986. |
the objective of this research was to analyze the performance of four dl models: a simple deep neural network (dnn); a convolutional neural network (cnn); a long short-term memory network (lstm); and a hybrid model (cnn-lstm), when variating the sliding window size using fixed overlapped windows to identify an optimal window size for har. |
2022-09-12 |
2023-08-14 |
human |
| Mohammed F Alsharek. Facial Emotion Recognition in Verbal Communication Based on Deep Learning. Sensors (Basel, Switzerland). vol 22. issue 16. 2022-08-26. PMID:36015866. |
to address this issue, we propose an efficient dl technique using a cnn model to classify emotions from facial images. |
2022-08-26 |
2023-08-14 |
human |
| Adrian L Breto, Benjamin Spieler, Olmo Zavala-Romero, Mohammad Alhusseini, Nirav V Patel, David A Asher, Isaac R Xu, Jacqueline B Baikovitz, Eric A Mellon, John C Ford, Radka Stoyanova, Lorraine Portelanc. Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer. Frontiers in oncology. vol 12. 2022-06-06. PMID:35664789. |
we hypothesized that deep learning (dl) convolutional neural networks (cnn) can be trained to accurately segment gross tumor volume (gtv) and oars both in planning and daily fractions' mri scans. |
2022-06-06 |
2023-08-14 |
Not clear |