Artículos de revistas sobre el tema "HYBRID CNN-RNN MODEL"
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Zaheer, Shahzad, Nadeem Anjum, Saddam Hussain, Abeer D. Algarni, Jawaid Iqbal, Sami Bourouis y Syed Sajid Ullah. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model". Mathematics 11, n.º 3 (22 de enero de 2023): 590. http://dx.doi.org/10.3390/math11030590.
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 completoKrishnan, V. Gokula, M. V. Vijaya Saradhi, T. A. Mohana Prakash, K. Gokul Kannan y AG Noorul Julaiha. "Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection". International Journal on Recent and Innovation Trends in Computing and Communication 10, n.º 12 (31 de diciembre de 2022): 133–39. http://dx.doi.org/10.17762/ijritcc.v10i12.5894.
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 completoBehera, Bibhuti Bhusana, Binod Kumar Pattanayak y Rajani Kanta Mohanty. "Deep Ensemble Model for Detecting Attacks in Industrial IoT". International Journal of Information Security and Privacy 16, n.º 1 (1 de enero de 2022): 1–29. http://dx.doi.org/10.4018/ijisp.311467.
Texto completoCheng, Yepeng, Zuren Liu y Yasuhiko Morimoto. "Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting". Information 11, n.º 6 (5 de junio de 2020): 305. http://dx.doi.org/10.3390/info11060305.
Texto completoPawar, Mahendra Eknath, Rais Allauddin Mulla, Sanjivani H. Kulkarni, Sajeeda Shikalgar, Harikrishna B. Jethva y Gunvant A. Patel. "A Novel Hybrid AI Federated ML/DL Models for Classification of Soil Components". International Journal on Recent and Innovation Trends in Computing and Communication 10, n.º 1s (10 de diciembre de 2022): 190–99. http://dx.doi.org/10.17762/ijritcc.v10i1s.5823.
Texto completoUTKU, Anıl. "Kentsel Trafik Tahminine Yönelik Derin Öğrenme Tabanlı Verimli Bir Hibrit Model". Bilişim Teknolojileri Dergisi 16, n.º 2 (30 de abril de 2023): 107–17. http://dx.doi.org/10.17671/gazibtd.1167140.
Texto completoLiang, Youzhi, Wen Liang y Jianguo Jia. "Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN". Advances in Artificial Intelligence and Machine Learning 03, n.º 02 (2023): 1110–22. http://dx.doi.org/10.54364/aaiml.2023.1165.
Texto completoZhang, Langlang, Jun Xie, Xinxiu Liu, Wenbo Zhang y Pan Geng. "Research on water quality prediction based on PE-CNN-GRU hybrid model". E3S Web of Conferences 393 (2023): 02014. http://dx.doi.org/10.1051/e3sconf/202339302014.
Texto completoKhamparia, Aditya, Babita Pandey, Shrasti Tiwari, Deepak Gupta, Ashish Khanna y Joel J. P. C. Rodrigues. "An Integrated Hybrid CNN–RNN Model for Visual Description and Generation of Captions". Circuits, Systems, and Signal Processing 39, n.º 2 (11 de noviembre de 2019): 776–88. http://dx.doi.org/10.1007/s00034-019-01306-8.
Texto completoUly, Novem, Hendry Hendry y Ade Iriani. "CNN-RNN Hybrid Model for Diagnosis of COVID-19 on X-Ray Imagery". Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 14, n.º 1 (27 de mayo de 2023): 57–67. http://dx.doi.org/10.31849/digitalzone.v14i1.13668.
Texto completoArshad, Muhammad Zeeshan, Ankhzaya Jamsrandorj, Jinwook Kim y Kyung-Ryoul Mun. "Gait Events Prediction Using Hybrid CNN-RNN-Based Deep Learning Models through a Single Waist-Worn Wearable Sensor". Sensors 22, n.º 21 (27 de octubre de 2022): 8226. http://dx.doi.org/10.3390/s22218226.
Texto completoGong, Liyun, Miao Yu, Vassilis Cutsuridis, Stefanos Kollias y Simon Pearson. "A Novel Model Fusion Approach for Greenhouse Crop Yield Prediction". Horticulturae 9, n.º 1 (20 de diciembre de 2022): 5. http://dx.doi.org/10.3390/horticulturae9010005.
Texto completoKang, Taehyung, Dae Yeong Lim, Hilal Tayara y Kil To Chong. "Forecasting of Power Demands Using Deep Learning". Applied Sciences 10, n.º 20 (16 de octubre de 2020): 7241. http://dx.doi.org/10.3390/app10207241.
Texto completoHasbullah, Sumayyah, Mohd Soperi Mohd Zahid y Satria Mandala. "Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network". BioMedInformatics 3, n.º 2 (15 de junio de 2023): 478–92. http://dx.doi.org/10.3390/biomedinformatics3020033.
Texto completoRong, Guangzhi, Kaiwei Li, Yulin Su, Zhijun Tong, Xingpeng Liu, Jiquan Zhang, Yichen Zhang y Tiantao Li. "Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment". Remote Sensing 13, n.º 22 (20 de noviembre de 2021): 4694. http://dx.doi.org/10.3390/rs13224694.
Texto completoSharma, Richa, Sudha Morwal y Basant Agarwal. "Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text". International Journal of Cognitive Informatics and Natural Intelligence 15, n.º 3 (julio de 2021): 1–11. http://dx.doi.org/10.4018/ijcini.20210701.oa1.
Texto completoGuo, Yanan, Xiaoqun Cao, Bainian Liu y Kecheng Peng. "El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition". Symmetry 12, n.º 6 (1 de junio de 2020): 893. http://dx.doi.org/10.3390/sym12060893.
Texto completoMas-Pujol, Sergi, Esther Salamí y Enric Pastor. "RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic". Aerospace 9, n.º 2 (10 de febrero de 2022): 93. http://dx.doi.org/10.3390/aerospace9020093.
Texto completoLapa, Paulo, Mauro Castelli, Ivo Gonçalves, Evis Sala y Leonardo Rundo. "A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI". Applied Sciences 10, n.º 1 (2 de enero de 2020): 338. http://dx.doi.org/10.3390/app10010338.
Texto completoBeseiso, Majdi. "Word and Character Information Aware Neural Model for Emotional Analysis". Recent Patents on Computer Science 12, n.º 2 (25 de febrero de 2019): 142–47. http://dx.doi.org/10.2174/2213275911666181119112645.
Texto completoAmer, Rusul y Ahmed Al Tmeme. "Hybrid Deep Learning Model for Singing Voice Separation". MENDEL 27, n.º 2 (21 de diciembre de 2021): 44–50. http://dx.doi.org/10.13164/mendel.2021.2.044.
Texto completoZhang, Dong y Qichuan Tian. "A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition". Elektronika ir Elektrotechnika 27, n.º 5 (27 de octubre de 2021): 67–74. http://dx.doi.org/10.5755/j02.eie.29648.
Texto completoWang, Yu, Yining Sun, Zuchang Ma, Lisheng Gao y Yang Xu. "A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records". ACM Transactions on Asian and Low-Resource Language Information Processing 20, n.º 2 (23 de abril de 2021): 1–12. http://dx.doi.org/10.1145/3436819.
Texto completoRoy, Bishwajit, Lokesh Malviya, Radhikesh Kumar, Sandip Mal, Amrendra Kumar, Tanmay Bhowmik y Jong Wan Hu. "Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals". Diagnostics 13, n.º 11 (1 de junio de 2023): 1936. http://dx.doi.org/10.3390/diagnostics13111936.
Texto completoYadav, Omprakash, Rachael Dsouza, Rhea Dsouza y Janice Jose. "Soccer Action video Classification using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junio de 2022): 1060–63. http://dx.doi.org/10.22214/ijraset.2022.43929.
Texto completoMekruksavanich, Sakorn y Anuchit Jitpattanakul. "Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data". Electronics 10, n.º 14 (14 de julio de 2021): 1685. http://dx.doi.org/10.3390/electronics10141685.
Texto completoFarid, Ahmed Bahaa, Enas Mohamed Fathy, Ahmed Sharaf Eldin y Laila A. Abd-Elmegid. "Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM)". PeerJ Computer Science 7 (16 de noviembre de 2021): e739. http://dx.doi.org/10.7717/peerj-cs.739.
Texto completoÇAVDAR, İsmail y Vahid FARYAD. "New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid". Energies 12, n.º 7 (29 de marzo de 2019): 1217. http://dx.doi.org/10.3390/en12071217.
Texto completoWEN, HAO, WENJIAN YU, YUANQING WU, SHUAI YANG y XIAOLONG LIU. "A SCALABLE HYBRID MODEL FOR ATRIAL FIBRILLATION DETECTION". Journal of Mechanics in Medicine and Biology 21, n.º 05 (17 de abril de 2021): 2140021. http://dx.doi.org/10.1142/s0219519421400212.
Texto completoRafi, Quazi Ghulam, Mohammed Noman, Sadia Zahin Prodhan, Sabrina Alam y Dip Nandi. "Comparative Analysis of Three Improved Deep Learning Architectures for Music Genre Classification". International Journal of Information Technology and Computer Science 13, n.º 2 (8 de abril de 2021): 1–14. http://dx.doi.org/10.5815/ijitcs.2021.02.01.
Texto completoDhar, Puja, Vijay Kumar Garg y Mohammad Anisur Rahman. "Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals". Journal of Healthcare Engineering 2022 (16 de marzo de 2022): 1–14. http://dx.doi.org/10.1155/2022/3491828.
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 completoUmair, Muhammad, Muhammad Zubair, Farhan Dawood, Sarim Ashfaq, Muhammad Shahid Bhatti, Mohammad Hijji y Abid Sohail. "A Multi-Layer Holistic Approach for Cursive Text Recognition". Applied Sciences 12, n.º 24 (9 de diciembre de 2022): 12652. http://dx.doi.org/10.3390/app122412652.
Texto completoMoradzadeh, Arash, Sahar Zakeri, Waleed A. Oraibi, Behnam Mohammadi-Ivatloo, Zulkurnain Abdul-Malek y Reza Ghorbani. "Non-Intrusive Load Monitoring of Residential Loads via Laplacian Eigenmaps and Hybrid Deep Learning Procedures". Sustainability 14, n.º 22 (11 de noviembre de 2022): 14898. http://dx.doi.org/10.3390/su142214898.
Texto completoBao, Zhengyi, Jiahao Jiang, Chunxiang Zhu y Mingyu Gao. "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery". Energies 15, n.º 12 (16 de junio de 2022): 4399. http://dx.doi.org/10.3390/en15124399.
Texto completoAlrasheedi, Abdullah y Abdulaziz Almalaq. "Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting". Mathematics 10, n.º 15 (28 de julio de 2022): 2666. http://dx.doi.org/10.3390/math10152666.
Texto completoTran Quang, Duy y Sang Hoon Bae. "A Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index". Promet - Traffic&Transportation 33, n.º 3 (31 de mayo de 2021): 373–85. http://dx.doi.org/10.7307/ptt.v33i3.3657.
Texto completoHong, Taekeun, Jin-A. Choi, Kiho Lim y Pankoo Kim. "Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks". Sensors 21, n.º 1 (30 de diciembre de 2020): 199. http://dx.doi.org/10.3390/s21010199.
Texto completoRajagukguk, Rial A., Raden A. A. Ramadhan y Hyun-Jin Lee. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power". Energies 13, n.º 24 (15 de diciembre de 2020): 6623. http://dx.doi.org/10.3390/en13246623.
Texto completoSelvarani, Renjith Vijayakumar y Paul Subha Hency Jose. "A Label-Free Marker Based Breast Cancer Detection using Hybrid Deep Learning Models and Raman Spectroscopy". Trends in Sciences 20, n.º 4 (22 de enero de 2023): 6299. http://dx.doi.org/10.48048/tis.2023.6299.
Texto completoChung, Jaewon y Beakcheol Jang. "Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data". PLOS ONE 17, n.º 11 (23 de noviembre de 2022): e0278071. http://dx.doi.org/10.1371/journal.pone.0278071.
Texto completoGeng, Boting. "Open Relation Extraction in Patent Claims with a Hybrid Network". Wireless Communications and Mobile Computing 2021 (28 de abril de 2021): 1–7. http://dx.doi.org/10.1155/2021/5547281.
Texto completoAl Duhayyim, Mesfer, Hanan Abdullah Mengash, Radwa Marzouk, Mohamed K. Nour, Hany Mahgoub, Fahd Althukair y Abdullah Mohamed. "Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification". Computational Intelligence and Neuroscience 2022 (30 de junio de 2022): 1–11. http://dx.doi.org/10.1155/2022/6162445.
Texto completoSong, Fuquan, Heying Ding, Yongzheng Wang, Shiming Zhang y Jinbiao Yu. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network". Energies 16, n.º 6 (21 de marzo de 2023): 2904. http://dx.doi.org/10.3390/en16062904.
Texto completoAltalak, Maha, Mohammad Ammad uddin, Amal Alajmi y Alwaseemah Rizg. "Smart Agriculture Applications Using Deep Learning Technologies: A Survey". Applied Sciences 12, n.º 12 (10 de junio de 2022): 5919. http://dx.doi.org/10.3390/app12125919.
Texto completoLee, Chien-Hsing, Phuong Nguyen Thanh, Chao-Tsung Yeh y Ming-Yuan Cho. "Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant". International Transactions on Electrical Energy Systems 2022 (16 de septiembre de 2022): 1–15. http://dx.doi.org/10.1155/2022/2870668.
Texto completoJishan, Md Asifuzzaman, Khan Raqib Mahmud, Abul Kalam Al Azad, Md Shahabub Alam y Anif Minhaz Khan. "Hybrid deep neural network for Bangla automated image descriptor". International Journal of Advances in Intelligent Informatics 6, n.º 2 (12 de julio de 2020): 109. http://dx.doi.org/10.26555/ijain.v6i2.499.
Texto completoKhortsriwong, Nonthawat, Promphak Boonraksa, Terapong Boonraksa, Thipwan Fangsuwannarak, Asada Boonsrirat, Watcharakorn Pinthurat y Boonruang Marungsri. "Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant". Energies 16, n.º 5 (22 de febrero de 2023): 2119. http://dx.doi.org/10.3390/en16052119.
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