| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| R Janani Abinaya, G Rajakuma. Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization. Journal of imaging informatics in medicine. 2024-02-13. PMID:38351226. |
this research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (mrmr) feature selection, bidirectional long short-term memory (bilstm), and convolutional neural networks (cnn). |
2024-02-13 |
2024-02-16 |
Not clear |
| Bin Wu, Xinyu Wu, Peng Li, Youbing Gao, Jiangbo Si, Naofal Al-Dhahi. Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition. Sensors (Basel, Switzerland). vol 24. issue 3. 2024-02-10. PMID:38339606. |
to tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (fpga), and implements a one-dimensional (1d) convolutional neural network (cnn) and long short-term memory (lstm) neural network (nn) model for radar emitter signal recognition, directly targeting the intermediate frequency (if) data of radar emitter signal for classification and recognition. |
2024-02-10 |
2024-02-12 |
Not clear |
| Xiangnan Dang, Wentao Li, Jasmine Zou, Brian Cong, Yuanfang Gua. Assessing the impact of body location on the accuracy of detecting daily activities with accelerometer data. iScience. vol 27. issue 2. 2024-02-06. PMID:38318391. |
here, we conducted a trial focusing on the impact of sensor placement in predicting 21 common activities using convolutional neural networks (cnn) and long short-term memory networks (lstm). |
2024-02-06 |
2024-02-09 |
Not clear |
| Musa Aslan, Muhammet Baykara, Talha Burak Alaku. LieWaves: dataset for lie detection based on EEG signals and wavelets. Medical & biological engineering & computing. 2024-02-04. PMID:38311647. |
in the last stage, each obtained feature vector was classified separately using convolutional neural network (cnn), long short-term memory (lstm), and cnnlstm hybrid algorithms. |
2024-02-04 |
2024-02-07 |
human |
| Liguo Zhang, Liangyu Zhao, Yongtao Ya. A hybrid neural network-based intelligent body posture estimation system in sports scenes. Mathematical biosciences and engineering : MBE. vol 21. issue 1. 2024-02-02. PMID:38303452. |
specifically, a cnn unit and a long short-term memory (lstm) unit are employed as the backbone network in order to extract key-point information and temporal information from video frames, respectively. |
2024-02-02 |
2024-02-04 |
Not clear |
| Chang June Lee, Jung Keun Le. IMU-Based Energy Expenditure Estimation for Various Walking Conditions Using a Hybrid CNN-LSTM Model. Sensors (Basel, Switzerland). vol 24. issue 2. 2024-01-23. PMID:38257507. |
in this study, we present a hybrid model comprising a convolutional neural network (cnn) and long short-term memory (lstm) to estimate the steady-state energy expenditure under various walking conditions based solely on imu data. |
2024-01-23 |
2024-01-25 |
Not clear |
| Tabish Saeed, Aneeqa Ijaz, Ismail Sadiq, Haneya Naeem Qureshi, Ali Rizwan, Ali Imra. An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio. Bioengineering (Basel, Switzerland). vol 11. issue 1. 2024-01-22. PMID:38247932. |
a hybrid of a convolutional neural networks (cnn) and long short-term memory (lstm) networks is proposed for the feature encoder module of rbf-net. |
2024-01-22 |
2024-01-24 |
Not clear |
| Zhaohua Wang, Longzhen Duan, Dongsheng Shuai, Taorong Qi. Research on water environmental indicators prediction method based on EEMD decomposition with CNN-BiLSTM. Scientific reports. vol 14. issue 1. 2024-01-19. PMID:38243034. |
to address this issue, this paper introduces a hybrid water quality index prediction model based on ensemble empirical mode decomposition (eemd), combined with convolutional neural network (cnn) and bidirectional long short-term memory network (bilstm). |
2024-01-19 |
2024-01-22 |
Not clear |
| Honglei Wang, Tao Huang, Dong Wang, Wenliang Zeng, Yanjing Sun, Lin Zhan. MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction. BMC bioinformatics. vol 25. issue 1. 2024-01-17. PMID:38233745. |
bidirectional long short-term memory (bilstm), convolutional neural network (cnn), and the transformer have demonstrated achievements in modification site prediction. |
2024-01-17 |
2024-01-20 |
Not clear |
| Junbo Niu, Bin Miao, Jiaxu Guo, Zhifeng Ding, Yin He, Zhiyu Chi, Feilong Wang, Xinxin M. Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness. Materials (Basel, Switzerland). vol 17. issue 1. 2024-01-11. PMID:38204003. |
by conducting rigorous experimentation and obtaining corresponding nanoindentation data, we evaluated the performance of four distinct neural network architectures: multilayer perceptron (mlp), convolutional neural network (cnn), long short-term memory network (lstm), and transformer. |
2024-01-11 |
2024-01-13 |
Not clear |
| Dmitrii Kaplun, Surajit Deka, Arunabh Bora, Nupur Choudhury, Jyotishman Basistha, Bhaswadeep Purkayastha, Ifthikaruz Zaman Mazumder, Vyacheslav Gulvanskii, Kandarpa Kumar Sarma, Debashis Dev Misr. An intelligent agriculture management system for rainfall prediction and fruit health monitoring. Scientific reports. vol 14. issue 1. 2024-01-04. PMID:38177254. |
the proposed system based on an ai aided model makes use of a convolutional neural network (cnn) with long short-term memory (lstm) layer for rainfall prediction and a cnn with softmax layer along with a few deep learning pre-trained models for fruit health monitoring. |
2024-01-04 |
2024-01-07 |
Not clear |
| Chen Huang, Ye Zhou, Tao Wu, Mingyue Zhang, Yu Qi. A cellular automata model coupled with partitioning CNN-LSTM and PLUS models for urban land change simulation. Journal of environmental management. vol 351. 2023-12-22. PMID:38134506. |
to address these gaps, this study proposes a novel model called kclp-ca, which integrates k-means, a convolutional neural network (cnn), a long and short-term memory neural network (lstm), and the popular patch-generation land use model (plus). |
2023-12-22 |
2023-12-25 |
Not clear |
| Xing Wei, Shitao Cheng, Rui Chen, Zijian Wang, Yanjun L. ANN deformation prediction model for deep foundation pit with considering the influence of rainfall. Scientific reports. vol 13. issue 1. 2023-12-19. PMID:38114655. |
in the study, an ann model is proposed based on the wave transform (wt), copula method, convolutional neural network (cnn) and long short-term memory neural network (lstm). |
2023-12-19 |
2023-12-23 |
Not clear |
| Xizheng Ke, Qingyang Zhang, Huanhuan Qi. CNN neural network temporal feature storage structure fusion for the visible channel equalization algorithm. Applied optics. vol 62. issue 35. 2023-12-18. PMID:38108694. |
this paper proposes an equalization algorithm based on the structure of a convolutional neural network (cnn), combining time series feature length and long short-term memory (lstm), and adding a residual structure. |
2023-12-18 |
2023-12-21 |
Not clear |
| Zilu Wang, Ian Daly, Junhua L. An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2023. 2023-12-12. PMID:38082609. |
recent research has demonstrated that deep learning models, such as convolutional neural network (cnn) and long short-term memory (lstm), are successful in a wide range of classification applications. |
2023-12-12 |
2023-12-17 |
Not clear |
| Anant Jain, Lalan Kuma. EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2023. 2023-12-12. PMID:38082886. |
a residual convolutional neural network (cnn) - long short-term memory (lstm) based kinematics decoding model is proposed that utilizes motor neural information present in pre-movement brain activity. |
2023-12-12 |
2023-12-17 |
Not clear |
| Subha S, Baghavathi Priya Sankaralingam, Anitha Gurusamy, Sountharrajan Sehar, Durga Prasad Bavirisett. Personalization-based deep hybrid E-learning model for online course recommendation system. PeerJ. Computer science. vol 9. 2023-12-11. PMID:38077588. |
a hybrid deep learning (hdl) model using convolutional neural network (cnn), residual network (resnet) and long short term memory (lstm) is proposed for better course selection of the enrolled candidates in an online learning platform. |
2023-12-11 |
2023-12-17 |
human |
| Dina Saif, Amany M Sarhan, Nada M Elshennaw. Deep-kidney: an effective deep learning framework for chronic kidney disease prediction. Health information science and systems. vol 12. issue 1. 2023-12-04. PMID:38045020. |
therefore, the contribution of the current paper is proposing three predictive models to predict ckd possible occurrence within 6 or 12 months before disease existence namely; convolutional neural network (cnn), long short-term memory (lstm) model, and deep ensemble model. |
2023-12-04 |
2023-12-10 |
Not clear |
| Ashfia Jannat Keya, Hasibul Hossain Shajeeb, Md Saifur Rahman, M F Mridh. FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection. PloS one. vol 18. issue 12. 2023-12-01. PMID:38039283. |
the model combines the power of pre-trained bidirectional encoder representation of transformers (bert) embeddings with a deep convolutional neural network (cnn) having skip convolution block and long short-term memory (lstm). |
2023-12-01 |
2023-12-10 |
Not clear |
| Xinxing Hou, Chao Ju, Bo Wan. Prediction of solar irradiance using convolutional neural network and attention mechanism-based long short-term memory network based on similar day analysis and an attention mechanism. Heliyon. vol 9. issue 11. 2023-12-01. PMID:38027694. |
this paper proposes a convolutional neural network (cnn) and attention mechanism-based long short-term memory network (a-lstm) to predict solar irradiance the next day. |
2023-12-01 |
2023-12-10 |
Not clear |