All Relations between short term memory and cnn

Publication Sentence Publish Date Extraction Date Species
Anderson P Avila Santos, Breno L S de Almeida, Robson P Bonidia, Peter F Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S Sanches, André C P L F de Carvalh. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA biology. vol 21. issue 1. 2024-03-26. PMID:38528797. this study presents biodeepfuse, a hybrid deep learning framework integrating convolutional neural networks (cnn) or bidirectional long short-term memory (bilstm) networks with handcrafted features for enhanced accuracy. 2024-03-26 2024-03-28 Not clear
Yuanfang Gou, Cheng Guo, Risheng Qi. Ultra short term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi head attention mechanism. PloS one. vol 19. issue 3. 2024-03-22. PMID:38517854. convolutional neural networks(cnn) combined with bidirectional long short term memory(bilstm) networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. 2024-03-22 2024-03-25 Not clear
Supriya Mahadevkar, Shruti Patil, Ketan Kotech. Enhancement of handwritten text recognition using AI-based hybrid approach. MethodsX. vol 12. 2024-03-21. PMID:38510932. through the integration of convolutional neural networks (cnn) and bidirectional long short-term memory (bilstm) with a connectionist temporal classification (ctc) decoder, the results indicate substantial improvement. 2024-03-21 2024-03-23 Not clear
Muhammad Usman Tariq, Shuhaida Binti Ismai. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PloS one. vol 19. issue 3. 2024-03-14. PMID:38483948. in this study, we compared several cutting-edge deep learning models, including long short-term memory (lstm), bidirectional lstm, convolutional neural networks (cnn), hybrid cnn-lstm, multilayer perceptron's, and recurrent neural networks (rnn), to project covid-19 cases in the aforementioned regions. 2024-03-14 2024-03-17 Not clear
Shengjun Zhao, Tong An, Qi Wang, Fei Qi. Using Machine Learning and Finite Element Analysis to Extract Traction-Separation Relations at Bonding Wire Interfaces of Insulated Gate Bipolar Transistor Modules. Materials (Basel, Switzerland). vol 17. issue 5. 2024-03-13. PMID:38473474. a novel machine learning (ml) architecture integrating a convolutional neural network (cnn) and a long short-term memory (lstm) network is used to identify the shape and parameters of the traction separation law (tsl) of the fe-czm model accurately and efficiently. 2024-03-13 2024-03-15 Not clear
Guanghua Fu, Qingjuan Wei, Yongsheng Yan. Bearing fault diagnosis with parallel CNN and LSTM. Mathematical biosciences and engineering : MBE. vol 21. issue 2. 2024-03-08. PMID:38454688. to enhance the quality of feature extraction from bearing vibration signals and the robustness of the model, we construct a fault diagnostic model based on convolutional neural network (cnn) and long short-term memory (lstm) parallel network to extract their temporal and spatial features from two perspectives. 2024-03-08 2024-03-10 Not clear
Amirhossein Amini, Robab Kalantar. Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning. PloS one. vol 19. issue 3. 2024-03-07. PMID:38452043. in this paper, different architectures of deep neural network (dnn) have been proposed based on long short-term memory (lstm) and convolutional-based neural networks (cnn) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. 2024-03-07 2024-03-10 Not clear
Danyal Khan, Mohammed Alonazi, Maha Abdelhaq, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Hui Li. Robust human locomotion and localization activity recognition over multisensory. Frontiers in physiology. vol 15. 2024-03-07. PMID:38449788. to achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (cnn) and long short-term memory (lstm), have been explored. 2024-03-07 2024-03-09 human
Theofrida Julius Maginga, Emmanuel Masabo, Pierre Bakunzibake, Kwang Soo Kim, Jimmy Nseng. Using wavelet transform and hybrid CNN - LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection. Heliyon. vol 10. issue 4. 2024-02-29. PMID:38420424. utilizing convolutional neural networks (cnn) and long short term memory (lstm) models, nonvisual measurements of total volatile organic compounds (vocs) and ultrasound emissions from maize plants were captured and analyzed. 2024-02-29 2024-03-02 Not clear
Haichen Tian, Weijun Gong, Wei Li, Yurong Qia. PASTFNet: a paralleled attention spatio-temporal fusion network for micro-expression recognition. Medical & biological engineering & computing. 2024-02-27. PMID:38413518. inspired by the composite architecture of the convolutional neural network (cnn) and long short-term memory (lstm) for temporal modeling, we propose a novel attention-based multi-scale feature fusion network (amfnet) to encode features of sequential frames, which can learn more expressive facial-detailed features for it implements the integrated use of attention and multi-scale feature fusion, then design an aggregation block to aggregate and acquire temporal features. 2024-02-27 2024-03-01 Not clear
Doaa A Abdel Hady, Tarek Abd El-Hafee. Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning. Scientific reports. vol 14. issue 1. 2024-02-27. PMID:38413786. we evaluated the performance of multiple models, including multi-layer perceptron (mlp), long short-term memory (lstm), convolutional neural network (cnn), recurrent neural network (rnn), elasticnetcv, random forest regressor, svr, and bagging regressor. 2024-02-27 2024-03-01 Not clear
Umesh Kumar Lilhore, Surjeet Dalal, Neeraj Varshney, Yogesh Kumar Sharma, K B V Brahma Rao, V V R Maheswara Rao, Roobaea Alroobaea, Sarita Simaiya, Martin Margala, Prasun Chakrabart. Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model. Scientific reports. vol 14. issue 1. 2024-02-24. PMID:38402249. this research proposes a hybrid ppdd framework that combines improved bi-directional long short-term memory (ibi-lstm) with transfer learning (tl) based on two convolutional neural network (cnn) architectures, respectively cnn-text and cnn audio. 2024-02-24 2024-02-27 Not clear
Junde Chen, Yuxin Wen, Michael Pokojovy, Tzu-Liang Bill Tseng, Peter McCaffrey, Alexander Vo, Eric Walser, Scott Moe. Multi-modal learning for inpatient length of stay prediction. Computers in biology and medicine. vol 171. 2024-02-21. PMID:38382388. specifically, a convolutional neural network (cnn) model, which we termed crxmdl, is designed for chest x-ray (cxr) image data, two long short-term memory networks are used to extract features from long text data, and a novel attention-embedded 1d convolutional neural network is developed to extract useful information from numerical data. 2024-02-21 2024-02-24 Not clear
Serkan Kartal, Muzaffer Can Iban, Aliihsan Sekerteki. Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series. Environmental science and pollution research international. 2024-02-14. PMID:38353824. to achieve this objective, the study proposes employing a combined convolutional neural network (cnn) and a specific type of recurrent neural network (rnn) called long short-term memory (lstm), known as convlstm. 2024-02-14 2024-02-16 Not clear
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