| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Guoce Feng, Lei Zhang, Feifan Ai, Yirui Zhang, Yupeng Ho. An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages. Entropy (Basel, Switzerland). vol 24. issue 8. 2022-08-26. PMID:36010775. |
that makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (arima), convolutional neural network (cnn), and long short-term memory network (lstm), to fully capture the interaction between these variables and provide accurate prediction results. |
2022-08-26 |
2023-08-14 |
Not clear |
| Asif Iqbal Middya, Sarbani Ro. Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models. Technological forecasting and social change. vol 183. 2022-08-08. PMID:35938066. |
various widely applied deep learning models namely cnn (convolutional neural network), rnn (recurrent neural network), vanilla lstm (long short-term memory), lstm autoencoder, and bidirectional lstm are considered to investigate their spatio-temporal performance variation. |
2022-08-08 |
2023-08-14 |
Not clear |
| Conor Wall, Li Zhang, Yonghong Yu, Akshi Kumar, Rong Ga. A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs. Sensors (Basel, Switzerland). vol 22. issue 15. 2022-07-28. PMID:35898070. |
specifically, four base deep networks are proposed, which include attention-based convolutional recurrent neural network (a-crnn), attention-based bidirectional long short-term memory (a-bilstm), attention-based bidirectional gated recurrent unit (a-bigru), as well as convolutional neural network (cnn). |
2022-07-28 |
2023-08-14 |
Not clear |
| Mohammed Hasan Ali Al-Abyadh, Mohamed A M Iesa, Hani Abdel Hafeez Abdel Azeem, Devesh Pratap Singh, Pardeep Kumar, Mohamed Abdulamir, Asadullah Jalal. Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model. Computational intelligence and neuroscience. vol 2022. 2022-07-28. PMID:35898769. |
the support vector machine (svm), long short-term memory (lstm), and ghost model convolution neural network (cnn) are combined to get the hybrid model. |
2022-07-28 |
2023-08-14 |
Not clear |
| Arnab Barua, Daniel Fuller, Sumayyah Musa, Xianta Jian. Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition. Biosensors. vol 12. issue 7. 2022-07-27. PMID:35884354. |
multiple types of deep neural networks, such as convolutional neural networks (cnn), long short-term memory (lstm), or their hybridization (cnn-lstm), have been implemented. |
2022-07-27 |
2023-08-14 |
human |
| Mingxin Liu, Jing Feng, Yongtian Wang, Zhaohui L. Classification of overlapping spikes using convolutional neural networks and long short term memory. Computers in biology and medicine. vol 148. 2022-07-25. PMID:35872414. |
in this paper, we propose a novel deep learning approach based on convolutional neural networks (cnn) and long short term memory (lstm) to implement overlapping spike sorting. |
2022-07-25 |
2023-08-14 |
monkey |
| Tejal Tiwary, Rajendra Prasad Mahapatr. An accurate generation of image captions for blind people using extended convolutional atom neural network. Multimedia tools and applications. 2022-07-20. PMID:35855372. |
ecann model combines the cnn (convolutional neural network) and lstm (long short-term memory) architectures to perform the caption reusable system to select the most accurate caption. |
2022-07-20 |
2023-08-14 |
Not clear |
| Youngseob Eum, Eun-Hye Yo. Imputation of missing time-activity data with long-term gaps: A multi-scale residual CNN-LSTM network model. Computers, environment and urban systems. vol 95. 2022-07-11. PMID:35812524. |
the method consists of two steps: (1) the continuous bag-of-words word2vec model to convert daily ta sequences into a low-dimensional numerical representation to reduce complexity; (2) a multi-scale residual convolutional neural network (cnn)-stacked long short-term memory (lstm) model to capture multi-scale temporal dependencies across historical observations and to predict the missing tas. |
2022-07-11 |
2023-08-14 |
human |
| Wei Li. Simulation Training Auxiliary Model Based on Neural Network and Virtual Reality Technology. Computational intelligence and neuroscience. vol 2022. 2022-07-11. PMID:35814575. |
this paper proposes an id model based on cnn (convolutional neural networks) and lstm to address these issues (long short-term memory networks). |
2022-07-11 |
2023-08-14 |
Not clear |
| Theyazn H H Aldhyani, Hasan Alkahtan. Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments. Sensors (Basel, Switzerland). vol 22. issue 13. 2022-07-09. PMID:35808184. |
these include hard-threshold, machine, and deep learning, support vector machine (svm), k-nearest neighbors (knn), random forest (rf) tree algorithms, namely convolutional neural network (cnn), and long short-term memory (lstm). |
2022-07-09 |
2023-08-14 |
Not clear |
| Manzura Jorayeva, Akhan Akbulut, Cagatay Catal, Alok Mishr. Deep Learning-Based Defect Prediction for Mobile Applications. Sensors (Basel, Switzerland). vol 22. issue 13. 2022-07-09. PMID:35808230. |
we performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (cnn) and long short term memory (lstm) to develop a defect prediction model for android-based applications. |
2022-07-09 |
2023-08-14 |
Not clear |
| Yan Yan, Guangyu Ye, Fei Fen. Application of Deep Learning Model in the Avoidance of Investment Risk of Multinational Enterprises. Computational intelligence and neuroscience. vol 2022. 2022-07-08. PMID:35800687. |
combining the advantages of long short-term memory (lstm) and convolutional neural network (cnn), the lstm-cnn (long short-term memory-convolutional neural network) model is proposed to predict the volatility trend of stocks. |
2022-07-08 |
2023-08-14 |
Not clear |
| XiangDong Huang, Hao Li, Jiajia Liu, FengChun Liu, Jian Wang, BaoShan Xie, BaoPing Chen, Qi Zhang, Tao Xu. A Malicious Domain Detection Model Based on Improved Deep Learning. Computational intelligence and neuroscience. vol 2022. 2022-07-07. PMID:35795747. |
this article proposes a malicious domain name detection model based on improved deep learning, which can combine the advantages of three different network models, convolutional neural network (cnn), temporal convolutional network (tcn), and long short-term memory network (lstm) in malicious domain name detection, to obtain a better detection effect than that of the original single or two models. |
2022-07-07 |
2023-08-14 |
Not clear |
| Shahab Ahmad, Tahir Ullah, Ijaz Ahmad, Abdulkarem Al-Sharabi, Kalim Ullah, Rehan Ali Khan, Saim Rasheed, Inam Ullah, Md Nasir Uddin, Md Sadek Al. A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection. Computational intelligence and neuroscience. vol 2022. 2022-07-05. PMID:35785076. |
this study is tested and compared among three models: convolutional neural network gru (cnn-gru), cnn long short-term memory (cnn-lstm), and the proposed alexnet-gru. |
2022-07-05 |
2023-08-14 |
Not clear |
| Majid Ramezani, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafa. Knowledge Graph-Enabled Text-Based Automatic Personality Prediction. Computational intelligence and neuroscience. vol 2022. 2022-06-30. PMID:35769270. |
finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (cnn), simple recurrent neural network (rnn), long short-term memory (lstm), and bidirectional long short-term memory (bilstm). |
2022-06-30 |
2023-08-14 |
human |
| Xiaoguang Ji. Music Emotion Classification Method Based on Deep Learning and Improved Attention Mechanism. Computational intelligence and neuroscience. vol 2022. 2022-06-30. PMID:35769273. |
then, by using the feature extraction ability of convolutional neural network (cnn) and the ability of long short-term memory (lstm) network to process the serialized data, and integrating the matching attention mechanism, an emotion analysis model based on cnn-lstm is constructed. |
2022-06-30 |
2023-08-14 |
Not clear |
| Subhrajit Dey, Rajdeep Bhattacharya, Samir Malakar, Friedhelm Schwenker, Ram Sarka. CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays. Expert systems with applications. vol 206. 2022-06-27. PMID:35754941. |
in this paper, convolutional long short-term memory (convlstm) layer is used in order to encode the spatial dependency among the feature maps obtained from the last convolutional layer of the cnn and to improve the image representational capability of the model. |
2022-06-27 |
2023-08-14 |
Not clear |
| Renjie Zhou, Yanyan Zhan. Reconstruction of missing spring discharge by using deep learning models with ensemble empirical mode decomposition of precipitation. Environmental science and pollution research international. 2022-06-25. PMID:35751724. |
using eemd, the local precipitation data is decomposed into several intrinsic mode functions (imfs) from high to low frequencies and a residual function, which are served as the input of convolutional neural network (cnn), long short-term memory (lstm), and hybrid cnn-lstm models to reconstruct the missing discharge data. |
2022-06-25 |
2023-08-14 |
Not clear |
| Yuzhi Guo, Jiaxiang Wu, Hehuan Ma, Sheng Wang, Junzhou Huan. Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction. Biomolecules. vol 12. issue 6. 2022-06-24. PMID:35740899. |
several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as long short-term memory (lstm) network and convolutional neural network (cnn). |
2022-06-24 |
2023-08-14 |
Not clear |
| You Li, Xueyong Li, Yuewu Liu, Yuhua Yao, Guohua Huan. MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides. Pharmaceuticals (Basel, Switzerland). vol 15. issue 6. 2022-06-24. PMID:35745625. |
we proposed a convolution neural network (cnn) and bi-directional long short-term memory (bi-lstm)-based deep learning method (called mpmabp) for recognizing multi-activities of bioactive peptides. |
2022-06-24 |
2023-08-14 |
Not clear |