| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Damilola D Olatinwo, Adnan Abu-Mahfouz, Gerhard Hancke, Hermanus Myburg. IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients. Sensors (Basel, Switzerland). vol 23. issue 6. 2023-03-30. PMID:36991659. |
we developed a hybrid deep learning model, i.e., convolutional neural network (cnn) and bidirectional long short-term memory (bilstm), and a regularized cnn model. |
2023-03-30 |
2023-08-14 |
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 |
| Jagjeet Singh, Lakshmi Babu Saheer, Oliver Faus. Speech Emotion Recognition Using Attention Model. International journal of environmental research and public health. vol 20. issue 6. 2023-03-29. PMID:36982048. |
this paper proposes a self-attention-based deep learning model that was created by combining a two-dimensional convolutional neural network (cnn) and a long short-term memory (lstm) network. |
2023-03-29 |
2023-08-14 |
Not clear |
| Min Li, Jiale Wang, Shiqi Yang, Jun Xie, Guanghua Xu, Shan Lu. A CNN-LSTM model for six human ankle movements classification on different loads. Frontiers in human neuroscience. vol 17. 2023-03-27. PMID:36968785. |
to achieve this, a convolutional neural network (cnn)-long short-term memory (lstm) model, a time-domain feature selection method of the semg, and a two-step method are proposed. |
2023-03-27 |
2023-08-14 |
human |
| Najeeb Ur Rehman Malik, Usman Ullah Sheikh, Syed Abdul Rahman Abu-Bakar, Asma Chann. Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique. Sensors (Basel, Switzerland). vol 23. issue 5. 2023-03-11. PMID:36904953. |
even though this area is well-researched, har algorithms such as 3d convolution neural networks (cnn), two-stream networks, and cnn-lstm (long short-term memory) suffer from highly complex models. |
2023-03-11 |
2023-08-14 |
human |
| Chengqing Liang, Lei Liu, Chen Li. Multi-UAV autonomous collision avoidance based on PPO-GIC algorithm with CNN-LSTM fusion network. Neural networks : the official journal of the International Neural Network Society. vol 162. 2023-03-06. PMID:36878168. |
next, the cnn-lstm (cl) fusion network is constructed by fusing the convolutional neural network (cnn) and the long short-term memory network (lstm), which realizes the feature interaction among the information of multi-uav. |
2023-03-06 |
2023-08-14 |
Not clear |
| Mostafa Haghi, Arman Ershadi, Thomas M Desern. Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking. Sensors (Basel, Switzerland). vol 23. issue 4. 2023-02-28. PMID:36850664. |
we implement and compare the performance of three neural networks: long short-term memory (lstm), convolutional neural network (cnn), and a hybrid model (cnn-lstm). |
2023-02-28 |
2023-08-14 |
human |
| Yung-Chung Wang, Yi-Chun Houng, Han-Xuan Chen, Shu-Ming Tsen. Network Anomaly Intrusion Detection Based on Deep Learning Approach. Sensors (Basel, Switzerland). vol 23. issue 4. 2023-02-28. PMID:36850768. |
after preprocessing the dataset, six models-deep neural network (dnn), convolutional neural network (cnn), recurrent neural network (rnn), long short-term memory (lstm), cnn + rnn and cnn + lstm-were constructed to judge whether network traffic comprised a malicious attack. |
2023-02-28 |
2023-08-14 |
Not clear |
| Wenli Liu, Tianxiang Liu, Zihan Liu, Hanbin Luo, Hanmin Pe. A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction. Environmental research. 2023-02-26. PMID:36842699. |
specifically, convolutional neural network (cnn), long short-term memory (lstm), and temporal convolutional network (tcn) combined to build a hybrid deep learning model cnn-lstm-tcn (clt). |
2023-02-26 |
2023-08-14 |
Not clear |
| J Quetzalcóatl Toledo-Marín, Taqdir Ali, Tibor van Rooij, Matthias Görges, Wyeth W Wasserma. Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks. Journal of clinical medicine. vol 12. issue 4. 2023-02-25. PMID:36836230. |
using the blood glucose risk score formula proposed by kovatchev et al., models with different architectures were trained, including, a recurrent neural network (rnn), a gated recurrent unit (gru), a long short-term memory (lstm) network, and an encoder-like convolutional neural network (cnn). |
2023-02-25 |
2023-08-14 |
Not clear |
| Zhongyi Luo, Di Wu, Peilei Zhang, Xin Ye, Haichuan Shi, Xiaoyu Cai, Yingtao Tia. Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network. Materials (Basel, Switzerland). vol 16. issue 4. 2023-02-25. PMID:36837245. |
lastly, a novel hybrid model combing cnn (convolutional neural network) and lstm (long short term memory) was designed to deeply mine the spatial and temporal acoustic features from the extracted frequency components. |
2023-02-25 |
2023-08-14 |
Not clear |
| Yilin Mao, He Li, Yu Wang, Kai Fan, Jiazhi Shen, Jie Zhang, Xiao Han, Yujie Song, Caihong Bi, Litao Sun, Zhaotang Din. Low temperature response index for monitoring freezing injury of tea plant. Frontiers in plant science. vol 14. 2023-02-23. PMID:36818866. |
the ltri and seven indexes of tea plant were modeled by partial least squares (pls), support vector machine (svm), random forests (rf), back propagation (bp) machine learning methods and convolutional neural networks (cnn), long short-term memory (lstm) deep learning methods. |
2023-02-23 |
2023-08-14 |
Not clear |
| Fu-Xiang Rikudo Chen, Chia-Yu Lin, Hui-Ying Siao, Cheng-Yuan Jian, Yong-Cheng Yang, Chun-Liang Li. Deep learning based atomic defect detection framework for two-dimensional materials. Scientific data. vol 10. issue 1. 2023-02-14. PMID:36788235. |
the long analysis time of stm for locating defects in images has been solved by combining feature detection with convolutional neural networks (cnn). |
2023-02-14 |
2023-08-14 |
Not clear |
| Isoon Kanjanasurat, Kasi Tenghongsakul, Boonchana Purahong, Attasit Lasaku. CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images. Sensors (Basel, Switzerland). vol 23. issue 3. 2023-02-11. PMID:36772394. |
cnn models vgg19, resnet152v2, and densenet121 were combined with long short-term memory (lstm) and gated recurrent unit (gru) rnn models, which are convenient to develop because these networks are all available as features on many platforms. |
2023-02-11 |
2023-08-14 |
Not clear |
| Franziska Hakansson, Dan Børge Jense. Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods. Frontiers in veterinary science. vol 9. 2023-01-30. PMID:36713870. |
two secondary frameworks were utilized, being a long short-term memory (lstm) network applied to sequences of image features (cnn-lstm), and a cnn applied to image representations of sequences (cnn-cnn). |
2023-01-30 |
2023-08-14 |
Not clear |
| Xiaoming Zhao, Yuehui Liao, Zhiwei Tang, Yicheng Xu, Xin Tao, Dandan Wang, Guoyu Wang, Hongsheng L. Integrating audio and visual modalities for multimodal personality trait recognition Frontiers in neuroscience. vol 16. 2023-01-23. PMID:36685221. |
to effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (cnn), bi-directional long short-term memory network (bi-lstm), and the transformer network. |
2023-01-23 |
2023-08-14 |
Not clear |
| Cansu Buyuk, Burcin Arican Alpay, Fusun E. Detection of the separated root canal instrument on panoramic radiograph: A comparison of LSTM and CNN deep learning methods. Dento maxillo facial radiology. 2023-01-23. PMID:36688738. |
the purpose of this study was to compare two deep learning methods that are convolutional neural network (cnn) and long short-term memory (lstm) to detect the separated endodontic instruments on dental radiographs. |
2023-01-23 |
2023-08-14 |
Not clear |
| Elham Kiyani, Steven Silber, Mahdi Kooshkbaghi, Mikko Karttune. Machine-learning-based data-driven discovery of nonlinear phase-field dynamics. Physical review. E. vol 106. issue 6-2. 2023-01-21. PMID:36671129. |
in this paper, we present data-driven architectures based on a multilayer perceptron, a convolutional neural network (cnn), and a combination of a cnn and long short-term memory structures for discovering the nonlinear equations of motion for phase-field models with nonconserved and conserved order parameters. |
2023-01-21 |
2023-08-14 |
Not clear |
| Sami Azam, A K M Rakibul Haque Rafid, Sidratul Montaha, Asif Karim, Mirjam Jonkman, Friso De Boe. Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches to Classify Lung Diseases. Biomedicines. vol 11. issue 1. 2023-01-21. PMID:36672641. |
to classify the ct scans into three classes, two deep learning architectures, (a) a convolutional neural network (cnn) and (b) a cnn with long short-term memory (lstm) and an attention mechanism, are considered. |
2023-01-21 |
2023-08-14 |
Not clear |
| Tooba Rashid, Muhammad Sultan Zia, Najam-Ur-Rehman, Talha Meraj, Hafiz Tayyab Rauf, Seifedine Kadr. A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture. Life (Basel, Switzerland). vol 13. issue 1. 2023-01-21. PMID:36676082. |
in this research, a fused model of the deep learning method, a convolutional neural network (cnn), and long short-term memory (lstm) is proposed to detect wrist fractures from x-ray images. |
2023-01-21 |
2023-08-14 |
human |