All Relations between short term memory and cnn

Publication Sentence Publish Date Extraction Date Species
Youngsin Kim, Mihyung Moon, Seokwhwan Moon, Wonkyu Moo. Effects of precise cardio sounds on the success rate of phonocardiography. PloS one. vol 19. issue 7. 2024-07-15. PMID:39008512. we used a convolutional neural network (cnn) and deep-learning (dl) for image classification, and a cnn equipped with long short-term memory to enable sequential feature extraction. 2024-07-15 2024-07-18 Not clear
Gayathri R, Maheswari S, Sandeep Kumar Mathivanan, Basu Dev Shivahare, Radha Raman Chandan, Mohd Asif Sha. A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT. Scientific reports. vol 14. issue 1. 2024-07-08. PMID:38977848. the goal of this research is to create an ensemble deep learning model for internet of things (iot) applications that specifically target remote patient monitoring (rpm) by integrating long short-term memory (lstm) networks and convolutional neural networks (cnn). 2024-07-08 2024-07-12 Not clear
Amin Mashayekhi Shams, Sepideh Jabbar. A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory. Biomedical engineering letters. vol 14. issue 4. 2024-07-01. PMID:38946814. this study attempts to develop a novel, end-to-end approach based on a 15-layers convolutional neural network (cnn) and a 16-layers cnn- long short-term memory (lstm) to help psychiatrists automatically diagnose sz from electroencephalogram (eeg) signals. 2024-07-01 2024-07-03 human
Yi-Cheng Huang, Po-Chen Che. Failure Diagnosis for Dental Air Turbine Handpiece with Payload Using Feature Engineering and Temporal Convolution Network. Bioengineering (Basel, Switzerland). vol 11. issue 6. 2024-06-27. PMID:38927791. these data were then utilized to create a diagnostic health classification (dhc) for further developing a tcn, a 1d convolutional neural network (cnn), and long short-term memory (lstm) prediction models. 2024-06-27 2024-06-29 Not clear
Alex Mirugwe, Clare Ashaba, Alice Namale, Evelyn Akello, Edward Bichetero, Edgar Kansiime, Juwa Nyirend. Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers. Life (Basel, Switzerland). vol 14. issue 6. 2024-06-27. PMID:38929691. the techniques examined included a 6-layer convolutional neural network (cnn), a 6-layer long short-term memory model (lstm), and an 8-layer bidirectional encoder representations from transformers (bert) model. 2024-06-27 2024-06-29 Not clear
Ali Mokhtar, Hongming He, Mohsen Nabil, Saber Kouadri, Ali Salem, Ahmed Elbeltag. Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models. Scientific reports. vol 14. issue 1. 2024-06-26. PMID:38926368. these models were random forest (rf), extreme gradient boosting (xgb), conventional neural network (cnn) and long short-term memory (lstm), and the hybridization of rf with xgb and cnn with lstm based on eleven combinations (scenarios) of input variables. 2024-06-26 2024-06-29 Not clear
Berat Bozkurt, Kerem Coskun, Gokhan Baka. Building a challenging medical dataset for comparative evaluation of classifier capabilities. Computers in biology and medicine. vol 178. 2024-06-20. PMID:38901188. we built widely used machine-learning (logistic regression, xgboost, catboost, and random forest classifiers) and modern deep-learning (convolutional neural networks - cnn, long short-term memory - lstm, and gated recurrent unit - gru) models. 2024-06-20 2024-06-23 Not clear
Renteng Yuan, Mohamed Abdel-Aty, Qiaojun Xian. A study on diversion behavior in weaving segments: Individualized traffic conflict prediction and causal mechanism analysis. Accident; analysis and prevention. vol 205. 2024-06-19. PMID:38897142. moreover, machine learning methods, including convolutional neural networks (cnn), long short-term memory (lstm), attention-based lstm, extreme gradient boosting (xgb), support vector machine (svm), and multilayer perceptron (mlp), are employed for real-time traffic conflict prediction. 2024-06-19 2024-06-22 Not clear
Yuqian Yuan, Xiaozhu Tang, Hongyan Li, Xufeng Lang, Yihua Song, Ye Yang, Zuojian Zho. BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs. Molecules (Basel, Switzerland). vol 29. issue 11. 2024-06-19. PMID:38893304. this study presents a novel hybrid model combining a convolutional neural network (cnn) and a bidirectional long short-term memory network (bilstm) for precise m6a methylation site prediction in circular rnas (circrnas) based on data from hek293 cells. 2024-06-19 2024-06-21 Not clear
Yuanyuan Liao, Shouqian Lu, Gang Yi. Short-Term and Imminent Rainfall Prediction Model Based on ConvLSTM and SmaAT-UNet. Sensors (Basel, Switzerland). vol 24. issue 11. 2024-06-19. PMID:38894366. the convlstm model is a fusion of a cnn (convolutional neural network) and lstm (long short-term memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional lstm models cannot accomplish. 2024-06-19 2024-06-21 Not clear
Talal H Noor, Ayman Noor, Ahmed F Alharbi, Ahmed Faisal, Rakan Alrashidi, Ahmed S Alsaedi, Ghada Alharbi, Tawfeeq Alsanoosy, Abdullah Alsaeed. Real-Time Arabic Sign Language Recognition Using a Hybrid Deep Learning Model. Sensors (Basel, Switzerland). vol 24. issue 11. 2024-06-19. PMID:38894473. the hybrid model consists of a convolutional neural network (cnn) classifier to extract spatial features from sign language data and a long short-term memory (lstm) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). 2024-06-19 2024-06-21 Not clear
Loes Verhaeghe, Jan Verwaeren, Gamze Kirim, Saba Daneshgar, Peter A Vanrolleghem, Elena Torf. Towards good modelling practice for parallel hybrid models for wastewater treatment processes. Water science and technology : a journal of the International Association on Water Pollution Research. vol 89. issue 11. 2024-06-15. PMID:38877625. both long short-term memory (lstm) and convolutional neural network (cnn) are tested as data-driven components, with a cnn hm (root-mean-squared error (rmse) = 1.58 mg no 2024-06-15 2024-06-17 Not clear
Zhanjun Hao, Zhizhou Sun, Fenfang Li, Ruidong Wang, Jianxiang Pen. Millimeter wave gesture recognition using multi-feature fusion models in complex scenes. Scientific reports. vol 14. issue 1. 2024-06-14. PMID:38877076. this model consists of three convolutional neural network (cnn) for three obtained features and one long short-term memory (lstm) for temporal features. 2024-06-14 2024-06-17 Not clear
Enes Efe, Emrehan Yavsa. AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals. Mathematical biosciences and engineering : MBE. vol 21. issue 4. 2024-06-14. PMID:38872562. attbilfnet integrates a bidirectional long short-term memory (bilstm) network with a convolutional neural network (cnn) and incorporates an attention mechanism using the focal loss function. 2024-06-14 2024-06-16 Not clear
Rahul Gupta, Anil Kumar Yadav, S K Jh. Harnessing the power of hybrid deep learning algorithm for the estimation of global horizontal irradiance. The Science of the total environment. 2024-06-13. PMID:38871320. to address this challenge, this study proposes a forecasting framework using an integrated model of the convolutional neural network (cnn), long short-term memory (lstm), and gated recurrent unit (gru). 2024-06-13 2024-06-16 Not clear
Adi Alhudhaif, Kemal Pola. Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data. PeerJ. Computer science. vol 10. 2024-06-10. PMID:38855251. this study proposes a method combining a convolutional neural network (cnn) and a long short-term memory network (lstm) for analyzing and compensating spatiotemporal features in residents' travel data. 2024-06-10 2024-06-14 Not clear
Abdurrahman Nasr, Khalil Mohamed, Ayman Elshenawy, Mohamed Zak. A Siamese deep learning framework for efficient hardware Trojan detection using power side-channel data. Scientific reports. vol 14. issue 1. 2024-06-08. PMID:38844523. to obtain the best results, different neural network models such as convolutional neural network (cnn), gated recurrent unit (gru), and long short-term memory (lstm) are integrated individually with snn. 2024-06-08 2024-06-10 Not clear
Jilei Yang, Xuefeng Hu, Lihang Feng, Zhiyuan Liu, Adil Murtazt, Weiwei Qin, Ming Zhou, Jiaming Liu, Yali Bi, Jingui Qian, Wei Zhan. AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture. ACS sensors. 2024-06-05. PMID:38836922. the experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (cnn) and long short-term memory (lstm). 2024-06-05 2024-06-07 Not clear
Jiang Lu, Changming Xiao, Changshui Zhan. Meta-Modulation: A General Learning Framework for Cross-Task Adaptation. IEEE transactions on neural networks and learning systems. vol PP. 2024-06-05. PMID:38837924. our framework is scalable for various base learner models like multi-layer perceptron (mlp), long short-term memory (lstm), convolutional neural network (cnn), and transformer, and applicable to different learning problems like language modeling and image recognition. 2024-06-05 2024-06-08 Not clear
Hey Wing Liu, Shuo Wang, Shelley Xiuli Ton. DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task. Brain sciences. vol 14. issue 5. 2024-05-25. PMID:38790423. by combining transfer learning of convolutional neural network (cnn) and positional encoding with the temporal-sequential encoding of long short-term memory (lstm) and attention mechanism, we trained and tested the model with handwriting images of 100,000 chinese characters from 1064 children in grades 2-6 (dd = 483; typically developing [td] = 581). 2024-05-25 2024-05-27 Not clear