Artículos de revistas sobre el tema "CNN AND LSTM NETWORKS"
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Garcia, Carlos Iturrino, Francesco Grasso, Antonio Luchetta, Maria Cristina Piccirilli, Libero Paolucci y Giacomo Talluri. "A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM". Applied Sciences 10, n.º 19 (27 de septiembre de 2020): 6755. http://dx.doi.org/10.3390/app10196755.
Texto completoXu-Nan Tan, Xu-Nan Tan. "Human Activity Recognition Based on CNN and LSTM". 電腦學刊 34, n.º 3 (junio de 2023): 221–35. http://dx.doi.org/10.53106/199115992023063403016.
Texto completoLiu, Tianyuan, Jinsong Bao, Junliang Wang y Yiming Zhang. "A Hybrid CNN–LSTM Algorithm for Online Defect Recognition of CO2 Welding". Sensors 18, n.º 12 (10 de diciembre de 2018): 4369. http://dx.doi.org/10.3390/s18124369.
Texto completoGeng, Yue, Lingling Su, Yunhong Jia y Ce Han. "Seismic Events Prediction Using Deep Temporal Convolution Networks". Journal of Electrical and Computer Engineering 2019 (2 de abril de 2019): 1–14. http://dx.doi.org/10.1155/2019/7343784.
Texto completoBanda, Anish. "Image Captioning using CNN and LSTM". International Journal for Research in Applied Science and Engineering Technology 9, n.º 8 (31 de agosto de 2021): 2666–69. http://dx.doi.org/10.22214/ijraset.2021.37846.
Texto completoReddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy y Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM". International Journal for Research in Applied Science and Engineering Technology 10, n.º 4 (30 de abril de 2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.
Texto completoZhang, Jilin, Lishi Ye y Yongzeng Lai. "Stock Price Prediction Using CNN-BiLSTM-Attention Model". Mathematics 11, n.º 9 (23 de abril de 2023): 1985. http://dx.doi.org/10.3390/math11091985.
Texto completoYang, Xingyu y Zhongrong Zhang. "A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China". Water 14, n.º 15 (31 de julio de 2022): 2377. http://dx.doi.org/10.3390/w14152377.
Texto completoSridhar, C. y Aniruddha Kanhe. "Performance Comparison of Various Neural Networks for Speech Recognition". Journal of Physics: Conference Series 2466, n.º 1 (1 de marzo de 2023): 012008. http://dx.doi.org/10.1088/1742-6596/2466/1/012008.
Texto completoXu, Lingfeng, Xiang Chen, Shuai Cao, Xu Zhang y Xun Chen. "Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation". Sensors 18, n.º 10 (25 de septiembre de 2018): 3226. http://dx.doi.org/10.3390/s18103226.
Texto completoKłosowski, Grzegorz, Anna Hoła, Tomasz Rymarczyk, Mariusz Mazurek, Konrad Niderla y Magdalena Rzemieniak. "Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection". Energies 16, n.º 4 (11 de febrero de 2023): 1818. http://dx.doi.org/10.3390/en16041818.
Texto completoNguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi y Yong-Hwa Kim. "NLOS Identification in WLANs Using Deep LSTM with CNN Features". Sensors 18, n.º 11 (20 de noviembre de 2018): 4057. http://dx.doi.org/10.3390/s18114057.
Texto completoBilgera, Christian, Akifumi Yamamoto, Maki Sawano, Haruka Matsukura y Hiroshi Ishida. "Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments". Sensors 18, n.º 12 (18 de diciembre de 2018): 4484. http://dx.doi.org/10.3390/s18124484.
Texto completoYu, Dian y Shouqian Sun. "A Systematic Exploration of Deep Neural Networks for EDA-Based Emotion Recognition". Information 11, n.º 4 (15 de abril de 2020): 212. http://dx.doi.org/10.3390/info11040212.
Texto completoKumar, M. Pranay. "Image Captioning Generator Using CNN and LSTM". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junio de 2022): 2847–51. http://dx.doi.org/10.22214/ijraset.2022.44502.
Texto completoBen Ismail, Mohamed Maher. "Insult detection using a partitional CNN-LSTM model". Computer Science and Information Technologies 1, n.º 2 (1 de julio de 2020): 84–92. http://dx.doi.org/10.11591/csit.v1i2.p84-92.
Texto completoHe, Yijuan, Jidong Lv, Hongjie Liu y Tao Tang. "Toward the Trajectory Predictor for Automatic Train Operation System Using CNN–LSTM Network". Actuators 11, n.º 9 (31 de agosto de 2022): 247. http://dx.doi.org/10.3390/act11090247.
Texto completoMing, Ye, Hu Qian y Liu Guangyuan. "CNN-LSTM Facial Expression Recognition Method Fused with Two-Layer Attention Mechanism". Computational Intelligence and Neuroscience 2022 (13 de octubre de 2022): 1–9. http://dx.doi.org/10.1155/2022/7450637.
Texto completoAlamri, Nawaf Mohammad H., Michael Packianather y Samuel Bigot. "Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm". Applied Sciences 13, n.º 4 (16 de febrero de 2023): 2536. http://dx.doi.org/10.3390/app13042536.
Texto completoK A, Shirien, Neethu George y Surekha Mariam Varghese. "Descriptive Answer Script Grading System using CNN-BiLSTM Network". International Journal of Recent Technology and Engineering 9, n.º 5 (30 de enero de 2021): 139–44. http://dx.doi.org/10.35940/ijrte.e5212.019521.
Texto completoShen, Qianqiao, Guiyong Wang, Yuhua Wang, Boshun Zeng, Xuan Yu y Shuchao He. "Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network". Energies 16, n.º 14 (13 de julio de 2023): 5347. http://dx.doi.org/10.3390/en16145347.
Texto completoYao, Ruizhe, Ning Wang, Zhihui Liu, Peng Chen y Xianjun Sheng. "Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach". Sensors 21, n.º 2 (18 de enero de 2021): 626. http://dx.doi.org/10.3390/s21020626.
Texto completoZhang, Chun-Xiang, Shu-Yang Pang, Xue-Yao Gao, Jia-Qi Lu y Bo Yu. "Attention Neural Network for Biomedical Word Sense Disambiguation". Discrete Dynamics in Nature and Society 2022 (10 de enero de 2022): 1–14. http://dx.doi.org/10.1155/2022/6182058.
Texto completoWei, Jun, Fan Yang, Xiao-Chen Ren y Silin Zou. "A Short-Term Prediction Model of PM2.5 Concentration Based on Deep Learning and Mode Decomposition Methods". Applied Sciences 11, n.º 15 (27 de julio de 2021): 6915. http://dx.doi.org/10.3390/app11156915.
Texto completoAlshingiti, Zainab, Rabeah Alaqel, Jalal Al-Muhtadi, Qazi Emad Ul Haq, Kashif Saleem y Muhammad Hamza Faheem. "A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN". Electronics 12, n.º 1 (3 de enero de 2023): 232. http://dx.doi.org/10.3390/electronics12010232.
Texto completoMou, Hanlin y Junsheng Yu. "CNN-LSTM Prediction Method for Blood Pressure Based on Pulse Wave". Electronics 10, n.º 14 (13 de julio de 2021): 1664. http://dx.doi.org/10.3390/electronics10141664.
Texto completoWang, Changyuan, Ting Yan y Hongbo Jia. "Spatial-Temporal Feature Representation Learning for Facial Fatigue Detection". International Journal of Pattern Recognition and Artificial Intelligence 32, n.º 12 (27 de agosto de 2018): 1856018. http://dx.doi.org/10.1142/s0218001418560189.
Texto completoSun, Jiaqi, Jiarong Wang, Zhicheng Hao, Ming Zhu, Haijiang Sun, Ming Wei y Kun Dong. "AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM". Remote Sensing 14, n.º 13 (4 de julio de 2022): 3221. http://dx.doi.org/10.3390/rs14133221.
Texto completoAshraf, Mohsin, Fazeel Abid, Ikram Ud Din, Jawad Rasheed, Mirsat Yesiltepe, Sook Fern Yeo y Merve T. Ersoy. "A Hybrid CNN and RNN Variant Model for Music Classification". Applied Sciences 13, n.º 3 (22 de enero de 2023): 1476. http://dx.doi.org/10.3390/app13031476.
Texto completoAlam, Muhammad S., AKM B. Hossain y Farhan B. Mohamed. "Performance Evaluation of Recurrent Neural Networks Applied to Indoor Camera Localization". International Journal of Emerging Technology and Advanced Engineering 12, n.º 8 (2 de agosto de 2022): 116–24. http://dx.doi.org/10.46338/ijetae0822_15.
Texto completoKim, Tae-Young y Sung-Bae Cho. "Predicting residential energy consumption using CNN-LSTM neural networks". Energy 182 (septiembre de 2019): 72–81. http://dx.doi.org/10.1016/j.energy.2019.05.230.
Texto completoLi, Shuyan, Zhixiang Chen, Xiu Li, Jiwen Lu y Jie Zhou. "Unsupervised Variational Video Hashing With 1D-CNN-LSTM Networks". IEEE Transactions on Multimedia 22, n.º 6 (junio de 2020): 1542–54. http://dx.doi.org/10.1109/tmm.2019.2946096.
Texto completoSperandio Nascimento, Erick Giovani, Júnia Ortiz, Adhvan Novais Furtado y Diego Frias. "Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks". PLOS ONE 18, n.º 4 (6 de abril de 2023): e0282621. http://dx.doi.org/10.1371/journal.pone.0282621.
Texto completoZhang, Yilin. "Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors". Mathematical Problems in Engineering 2022 (14 de mayo de 2022): 1–10. http://dx.doi.org/10.1155/2022/2871889.
Texto completoZhang, Chen, Qingxu Li y Xue Cheng. "Text Sentiment Classification Based on Feature Fusion". Revue d'Intelligence Artificielle 34, n.º 4 (30 de septiembre de 2020): 515–20. http://dx.doi.org/10.18280/ria.340418.
Texto completoAlam, Md Shahinur, Ki-Chul Kwon, Shariar Md Imtiaz, Md Biddut Hossain, Bong-Gyun Kang y Nam Kim. "TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network". Human Behavior and Emerging Technologies 2022 (24 de septiembre de 2022): 1–13. http://dx.doi.org/10.1155/2022/6063779.
Texto completoBarua, Arnab, Daniel Fuller, Sumayyah Musa y Xianta Jiang. "Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition". Biosensors 12, n.º 7 (21 de julio de 2022): 549. http://dx.doi.org/10.3390/bios12070549.
Texto completoDu, Wenjun, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu y Tuo Sun. "Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion". Journal of Advanced Transportation 2021 (9 de julio de 2021): 1–16. http://dx.doi.org/10.1155/2021/9512501.
Texto completoJing, Xin, Jungang Luo, Shangyao Zhang y Na Wei. "Runoff forecasting model based on variational mode decomposition and artificial neural networks". Mathematical Biosciences and Engineering 19, n.º 2 (2021): 1633–48. http://dx.doi.org/10.3934/mbe.2022076.
Texto completoLiu, Kun, Yong Liu, Shuo Ji, Chi Gao, Shizhong Zhang y Jun Fu. "A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors". Sensors 23, n.º 13 (26 de junio de 2023): 5905. http://dx.doi.org/10.3390/s23135905.
Texto completoArif, Sheeraz, Jing Wang, Tehseen Ul Hassan y Zesong Fei. "3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition". Future Internet 11, n.º 2 (13 de febrero de 2019): 42. http://dx.doi.org/10.3390/fi11020042.
Texto completoSun, Tuo, Chenwei Yang, Ke Han, Wanjing Ma y Fan Zhang. "Bidirectional Spatial–Temporal Network for Traffic Prediction with Multisource Data". Transportation Research Record: Journal of the Transportation Research Board 2674, n.º 8 (5 de julio de 2020): 78–89. http://dx.doi.org/10.1177/0361198120927393.
Texto completoLivieris, Ioannis E., Niki Kiriakidou, Stavros Stavroyiannis y Panagiotis Pintelas. "An Advanced CNN-LSTM Model for Cryptocurrency Forecasting". Electronics 10, n.º 3 (26 de enero de 2021): 287. http://dx.doi.org/10.3390/electronics10030287.
Texto completoZhou, Xiu, Xutao Wu, Pei Ding, Xiuguang Li, Ninghui He, Guozhi Zhang y Xiaoxing Zhang. "Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm". Energies 13, n.º 1 (20 de diciembre de 2019): 61. http://dx.doi.org/10.3390/en13010061.
Texto completoDu, Shaohui, Zhenghan Chen, Haoyan Wu, Yihong Tang y YuanQing Li. "Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks". Complexity 2021 (2 de julio de 2021): 1–9. http://dx.doi.org/10.1155/2021/5196190.
Texto completoLu, Wenxing, Haidong Rui, Changyong Liang, Li Jiang, Shuping Zhao y Keqing Li. "A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots". Entropy 22, n.º 3 (25 de febrero de 2020): 261. http://dx.doi.org/10.3390/e22030261.
Texto completoChen, Ningyan. "Visual recognition and prediction analysis of China’s real estate index and stock trend based on CNN-LSTM algorithm optimized by neural networks". PLOS ONE 18, n.º 2 (24 de febrero de 2023): e0282159. http://dx.doi.org/10.1371/journal.pone.0282159.
Texto completoChuang, Chia-Chun, Chien-Ching Lee, Chia-Hong Yeng, Edmund-Cheung So y Yeou-Jiunn Chen. "Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation". Applied Sciences 11, n.º 24 (17 de diciembre de 2021): 12019. http://dx.doi.org/10.3390/app112412019.
Texto completoLu, Yi-Xiang, Xiao-Bo Jin, Dong-Jie Liu, Xin-Chang Zhang y Guang-Gang Geng. "Anomaly Detection Using Multiscale C-LSTM for Univariate Time-Series". Security and Communication Networks 2023 (23 de enero de 2023): 1–12. http://dx.doi.org/10.1155/2023/6597623.
Texto completoFu, Lei, Qizhi Tang, Peng Gao, Jingzhou Xin y Jianting Zhou. "Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network". Algorithms 14, n.º 6 (8 de junio de 2021): 180. http://dx.doi.org/10.3390/a14060180.
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