| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Mohsen Sadat Shahabi, Ahmad Shalbaf, Reza Rostam. Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal. Cognitive neurodynamics. vol 17. issue 4. 2023-07-31. PMID:37522037. |
in this work, we proposed a hybrid model created by pre-trained convolutional neural networks (cnn) models and bidirectional long short-term memory (blstm) cells to predict response to rtms treatment from raw eeg signal. |
2023-07-31 |
2023-08-14 |
Not clear |
| Rudresh Deepak Shirwaikar, Iram Sarwari, Mehwish Najam, Shama H . Has Machine Learning Enhanced the Diagnosis of Autism Spectrum Disorder? Critical reviews in biomedical engineering. vol 51. issue 1. 2023-07-31. PMID:37522537. |
diagnosis of autism has recently made substantial use of long short term memory (lstm), convolutional neural network (cnn) and its variants, the random forest (rf) and naive bayes (nb) machine learning techniques. |
2023-07-31 |
2023-08-14 |
Not clear |
| Turki Aljrees, Xiaochun Cheng, Mian Muhammad Ahmed, Muhammad Umer, Rizwan Majeed, Khaled Alnowaiser, Nihal Abuzinadah, Imran Ashra. Fake news stance detection using selective features and FakeNET. PloS one. vol 18. issue 7. 2023-07-31. PMID:37523404. |
these methods are employed with a hybrid neural network architecture of convolutional neural network (cnn) and long short-term memory (lstm) model called fakenet. |
2023-07-31 |
2023-08-14 |
Not clear |
| Amir Djenna, Ezedin Barka, Achouak Benchikh, Karima Khadi. Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics. Sensors (Basel, Switzerland). vol 23. issue 14. 2023-07-29. PMID:37514596. |
this study provides a new collaborative deep learning approach based on unsupervised long short-term memory (lstm) and supervised convolutional neural network (cnn) models for the early identification and detection of botnet attacks. |
2023-07-29 |
2023-08-14 |
Not clear |
| Md Muntasir Zitu, Shijun Zhang, Dwight H Owen, Chienwei Chiang, Lang L. Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records. Frontiers in pharmacology. vol 14. 2023-07-28. PMID:37502211. |
we applied classical machine learning (support vector machine (svm)), deep learning (convolutional neural network (cnn) and bidirectional long short-term memory (bilstm)), and state-of-the-art transformer-based (bidirectional encoder representations from transformers (bert) and clinicalbert) methods trained and tested in the two different corpora and compared performance among them to detect drug-ade relationships. |
2023-07-28 |
2023-08-14 |
Not clear |
| Yanan Lu, Kun L. Multistation collaborative prediction of air pollutants based on the CNN-BiLSTM model. Environmental science and pollution research international. 2023-07-25. PMID:37490250. |
a hybrid deep learning model consisting of a convolutional neural network (cnn) and bidirectional long short-term memory (bilstm) is proposed to predict pollutant concentrations. |
2023-07-25 |
2023-08-14 |
Not clear |
| Muhammad Usman Tariq, Shuhaida Binti Ismail, Muhammad Babar, Ashir Ahma. Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PloS one. vol 18. issue 7. 2023-07-20. PMID:37471397. |
we evaluate the performance of long short-term memory (lstm), bi-directional lstm, convolutional neural networks (cnn), cnn-lstm, multilayer perceptron, gated recurrent unit (gru), and recurrent neural networks (rnn). |
2023-07-20 |
2023-08-14 |
Not clear |
| Kun Liu, Yong Liu, Shuo Ji, Chi Gao, Shizhong Zhang, Jun F. A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors. Sensors (Basel, Switzerland). vol 23. issue 13. 2023-07-14. PMID:37447755. |
the advantages of long short-term memory (lstm) and convolutional neural network (cnn) are combined (lstm-cnn) in this paper, then a gait phase recognition method based on lstm-cnn neural network model is proposed. |
2023-07-14 |
2023-08-14 |
Not clear |
| Wei Zhong, Chen Qian, Wanjun Liu, Liping Zhu, Runze L. Feature Screening for Interval-Valued Response with Application to Study Association between Posted Salary and Required Skills. Journal of the American Statistical Association. vol 118. issue 542. 2023-07-14. PMID:37448462. |
we find that the skill words like optimization, long short-term memory (lstm), convolutional neural networks (cnn), collaborative filtering, are positively correlated with the salary while the words like excel, office, data collection, may negatively contribute to the salary. |
2023-07-14 |
2023-08-14 |
Not clear |
| Jing Chen, Jiping Wang, Qun Yuan, Zhao Yan. CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise. IEEE journal of translational engineering in health and medicine. vol 11. 2023-07-12. PMID:37435544. |
we first developed a combined convolutional neural network (cnn) and long short-term memory (lstm) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of baduanjin. |
2023-07-12 |
2023-08-14 |
human |
| Gi-Taek An, Jung-Min Park, Kyung-Soon Le. Contrastive Learning-Based Anomaly Detection for Actual Corporate Environments. Sensors (Basel, Switzerland). vol 23. issue 10. 2023-07-11. PMID:37430676. |
to evaluate the effectiveness of the proposed method, we compared it with traditional deep learning models, such as the convolutional neural network (cnn) and long short-term memory (lstm). |
2023-07-11 |
2023-08-14 |
Not clear |
| Yufeng Zheng, Erik Blasc. Facial Micro-Expression Recognition Enhanced by Score Fusion and a Hybrid Model from Convolutional LSTM and Vision Transformer. Sensors (Basel, Switzerland). vol 23. issue 12. 2023-07-08. PMID:37420815. |
then, a hybrid nn model is created by combining a convolutional neural network (cnn), a recurrent neural network (rnn, e.g., long short-term memory (lstm)), and a vision transformer. |
2023-07-08 |
2023-08-14 |
human |
| Sameer Sayyad, Satish Kumar, Arunkumar Bongale, Ketan Kotecha, Ajith Abraha. Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning Models. Sensors (Basel, Switzerland). vol 23. issue 12. 2023-07-08. PMID:37420825. |
in this work, the authors considers the time-frequency domain (tfd) features such as short-time fourier-transform (stft) and different wavelet transforms (wt) along with deep learning (dl) models such as long short-term memory (lstm), different variants of lstn, convolutional neural network (cnn), and hybrid models that are a combination of ccn with lstm variants for rul estimation. |
2023-07-08 |
2023-08-14 |
Not clear |
| Arash Gharehbaghi, Elaheh Partovi, Ankica Babi. Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification. Studies in health technology and informatics. vol 305. 2023-06-30. PMID:37387059. |
the study considers various combinations of parallel and cascaded integration of cnn with gated recurrent network (grn) as well as long- short term memory (lstm) and explores the accuracy and sensitivity of each integration independently, using the physionet dataset of heart sound recordings. |
2023-06-30 |
2023-08-14 |
Not clear |
| Yingchao Xue, Hui Jian. Monitoring of Chlorpyrifos Residues in Corn Oil Based on Raman Spectral Deep-Learning Model. Foods (Basel, Switzerland). vol 12. issue 12. 2023-06-28. PMID:37372614. |
this study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through raman spectroscopy using a combined long short-term memory network (lstm) and convolutional neural network (cnn) architecture. |
2023-06-28 |
2023-08-14 |
Not clear |
| Antong Sun, Xiang Chen, Mengjuan Xu, Xu Zhang, Xun Che. Feasibility study on the application of a spiking neural network in myoelectric control systems. Frontiers in neuroscience. vol 17. 2023-06-28. PMID:37378016. |
compared with a convolutional neural network (cnn), long short-term memory network (lstm) and linear discriminant analysis (lda), snn can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1-2 orders of magnitude. |
2023-06-28 |
2023-08-14 |
Not clear |
| Pengfei Xie, Jujuan Zhuang, Geng Tian, Jialiang Yan. Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction. Biosafety and health. vol 5. issue 3. 2023-06-27. PMID:37362223. |
in this work, we present an embedding-based neural framework with convolutional neural network (cnn) and bi-directional long short-term memory unit (bi-lstm) architecture, named emvirus, to predict human-virus ppis (including human-sars-cov-2 ppis). |
2023-06-27 |
2023-08-14 |
human |
| M Chithambarathanu, M K Jeyakuma. Survey on crop pest detection using deep learning and machine learning approaches. Multimedia tools and applications. 2023-06-26. PMID:37362671. |
a clear overview of recent research in the area of crop pests and pathogens identification using techniques in machine learning techniques like random forest (rf), support vector machine (svm), and decision tree (dt), naive bayes (nb), and also some deep learning methods like convolutional neural network (cnn), long short-term memory (lstm), deep convolutional neural network (dcnn), deep belief network (dbn) was presented. |
2023-06-26 |
2023-08-14 |
human |
| Amin Zabardast, Elif Güney Tamer, Yeşim Aydın Son, Arif Yılma. An automated framework for evaluation of deep learning models for splice site predictions. Scientific reports. vol 13. issue 1. 2023-06-23. PMID:37353532. |
convolutional neural network (cnn), long short-term memory (lstm) and its bidirectional version (blstm), gated recurrent unit (gru), and its bidirectional version (bgru) are promising models. |
2023-06-23 |
2023-08-14 |
human |
| Shoaib Ahmed, Dost Muhammad Khan, Saima Sadiq, Muhammad Umer, Faisal Shahzad, Khalid Mahmood, Heba Mohsen, Imran Ashra. Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques. PeerJ. Computer science. vol 9. 2023-06-22. PMID:37346678. |
the influence of term frequency-inverse document frequency, bag of words (bow), word2vec, and combination of tf-idf and bow are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (etc), logistic regression, naïve bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (cnn), bidirectional encoder representations from transformers (bert), long short-term memory (lstm), and recurrent neural network (rnn). |
2023-06-22 |
2023-08-14 |
Not clear |