Literatura académica sobre el tema "RNN NETWORK"
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Artículos de revistas sobre el tema "RNN NETWORK"
Yin, Qiwei, Ruixun Zhang y XiuLi Shao. "CNN and RNN mixed model for image classification". MATEC Web of Conferences 277 (2019): 02001. http://dx.doi.org/10.1051/matecconf/201927702001.
Texto completoTridarma, Panggih y Sukmawati Nur Endah. "Pengenalan Ucapan Bahasa Indonesia Menggunakan MFCC dan Recurrent Neural Network". JURNAL MASYARAKAT INFORMATIKA 11, n.º 2 (17 de noviembre de 2020): 36–44. http://dx.doi.org/10.14710/jmasif.11.2.34874.
Texto completoMa, Qianli, Zhenxi Lin, Enhuan Chen y Garrison Cottrell. "Temporal Pyramid Recurrent Neural Network". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5061–68. http://dx.doi.org/10.1609/aaai.v34i04.5947.
Texto completoMosavat, Majid y Guido Montorsi. "Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology Using Recurrent Neural Network". Electronics 11, n.º 19 (29 de septiembre de 2022): 3130. http://dx.doi.org/10.3390/electronics11193130.
Texto completoDu, Xiuli, Xiaohui Ding y Fan Tao. "Network Security Situation Prediction Based on Optimized Clock-Cycle Recurrent Neural Network for Sensor-Enabled Networks". Sensors 23, n.º 13 (1 de julio de 2023): 6087. http://dx.doi.org/10.3390/s23136087.
Texto completoChoi, Seongjin, Hwasoo Yeo y Jiwon Kim. "Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks using Deep Learning". Transportation Research Record: Journal of the Transportation Research Board 2672, n.º 45 (7 de septiembre de 2018): 173–84. http://dx.doi.org/10.1177/0361198118794735.
Texto completoNowak, Mateusz P. y Piotr Pecka. "Routing Algorithms Simulation for Self-Aware SDN". Electronics 11, n.º 1 (29 de diciembre de 2021): 104. http://dx.doi.org/10.3390/electronics11010104.
Texto completoMuhuri, Pramita Sree, Prosenjit Chatterjee, Xiaohong Yuan, Kaushik Roy y Albert Esterline. "Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks". Information 11, n.º 5 (1 de mayo de 2020): 243. http://dx.doi.org/10.3390/info11050243.
Texto completoParamasivan, Senthil Kumar. "Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review". Revue d'Intelligence Artificielle 35, n.º 1 (28 de febrero de 2021): 1–10. http://dx.doi.org/10.18280/ria.350101.
Texto completoYan, Jiapeng, Huifang Kong y Zhihong Man. "Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles". Energies 15, n.º 24 (14 de diciembre de 2022): 9486. http://dx.doi.org/10.3390/en15249486.
Texto completoTesis sobre el tema "RNN NETWORK"
Bäärnhielm, Arvid. "Multiple time-series forecasting on mobile network data using an RNN-RBM model". Thesis, Uppsala universitet, Datalogi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-315782.
Texto completoVikström, Filip. "A recurrent neural network approach to quantification of risks surrounding the Swedish property market". Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-126192.
Texto completoBillingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.
Texto completoLiu, Chang. "Data Analysis of Minimally-Structured Heterogeneous Logs : An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191334.
Texto completoLi, Edwin. "LSTM Neural Network Models for Market Movement Prediction". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231627.
Texto completoAtt förstå och kunna förutsäga hur index varierar med tiden och andra parametrar är ett viktigt problem inom kapitalmarknader. Tidsserieanalys med autoregressiva metoder har funnits sedan årtionden tillbaka, och har oftast gett goda resultat. Dessa metoder saknar dock möjligheten att förklara trender och cykliska variationer i tidsserien, något som kan karaktäriseras av tidsvarierande samband, men även samband mellan parametrar som indexet beror utav. Syftet med denna studie är att undersöka om recurrent neural networks (RNN) med long short-term memory-celler (LSTM) kan användas för att fånga dessa samband, för att slutligen användas som en modell för att komplettera indexhandel. Experimenten är gjorda mot en modifierad S&P-500 datamängd, och två distinkta modeller har tagits fram. Den ena är en multivariat regressionsmodell för att förutspå exakta värden, och den andra modellen är en multivariat klassifierare som förutspår riktningen på nästa dags indexrörelse. Experimenten visar för den konfiguration som presenteras i rapporten att LSTM RNN inte passar för att förutspå exakta värden för indexet, men ger tillfredsställande resultat när modellen ska förutsäga indexets framtida riktning.
Ďuriš, Denis. "Detekce ohně a kouře z obrazového signálu". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412968.
Texto completoRacette, Olsén Michael. "Electrocardiographic deviation detection : Using long short-term memory recurrent neural networks to detect deviations within electrocardiographic records". Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-76411.
Texto completoCarman, Benjamin Andrew. "Translating LaTeX to Coq: A Recurrent Neural Network Approach to Formalizing Natural Language Proofs". Ohio University Honors Tutorial College / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors161919616626269.
Texto completoLjungehed, Jesper. "Predicting Customer Churn Using Recurrent Neural Networks". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210670.
Texto completoIllojalitet prediktering används för att identifiera kunder som är påväg att bli mindre lojala och är ett hjälpsamt verktyg för att ett företag ska kunna driva en konkurrenskraftig verksamhet. I detaljhandel behöves en dynamisk definition av illojalitet för att korrekt kunna identifera illojala kunder. Kundens livstidsvärde är ett mått på monetärt värde av en kundrelation. En avstannad förändring av detta värde indikerar en minskning av kundens lojalitet. Denna rapport föreslår en ny metod för att utföra illojalitet prediktering. Den föreslagna metoden består av ett återkommande neuralt nätverk som används för att identifiera illojalitet hos kunder genom att prediktera kunders livstidsvärde. Resultaten visar att den föreslagna modellen presterar bättre jämfört med slumpmässig metod. Rapporten undersöker också användningen av en k-medelvärdesalgoritm som ett substitut för en regelextraktionsalgoritm. K-medelsalgoritm bidrog till en mer omfattande analys av illojalitet predikteringen.
Смішний, Денис Миколайович. "Система прогнозування економічних показників". Master's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/30950.
Texto completoMaster's Thesis: 88 pp., 20 figs., 27 tables, 1 appendix, 33 sources. The urgency of the problem. Globalization and population growth are con-tributing to the development of the global economy and, consequently, to the emergence of new types of economic activity and new players in the labor market. When implementing your own business it is important to properly evaluate the risks of the market, analyzing and trying to predict the movement of quotations in the near future for minimal financial losses. Relationship with working with scientific programs, plans, topics. Cur-rently, it has no specific links to scientific programs or plans. The purpose and objectives of the study. The purpose of this work is re-search possibility of forecasting the economic parameters of enterprises on the ex-ample of stock prices of companies on the stock exchange. The purpose is to de-velop a system based on a neural network, capable of analyzing specified economic indicators and, based on the data obtained, to predict their dynamics. Object of study. The process of forecasting economic performance using neural network elements. Subject of study. Methods of analysis and processing of economic data for a certain period. Novelty. Obtaining a software product capable of predicting economic fluc-tuations. Investigation of the possibility of creating a universal model based on a neural network, which would not require specialization and would be able to work effectively with any set of input data without further training.
Libros sobre el tema "RNN NETWORK"
Collins, Lesley J. RNA infrastructure and networks. New York, N.Y: Springer Science+Business Media, 2011.
Buscar texto completoNational Education Association of the United States. Professional and Organizational Development. y National Education Association of the United States. Research Division., eds. Research computer network: Operators handbook. Washington, D.C: The Association, 1989.
Buscar texto completoCollins, Lesley J., ed. RNA Infrastructure and Networks. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0332-6.
Texto completoMaselnikova, Alice. The AIM Network: Artists' Initiatives Meetings. Stockholm: AIM Network/Supermarket, 2017.
Buscar texto completoYawen, Xiao, ed. Chao you xiao lian shu ji ke shu: Bu hua qian jiu neng ju ji liu lan ren chao de jing ren fang fa. Taibei Shi: Tian xia za zhi gu fen you xian gong si, 2012.
Buscar texto completoPeng, Mugen, Zhongyuan Zhao y Yaohua Sun. Fog Radio Access Networks (F-RAN). Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50735-0.
Texto completoZhongguo ren mai. Beijing Shi: Shi jie zhi shi chu ban she, 2010.
Buscar texto completoWang: Zhangguo shi ren mai = The network : relationship in China. Wuhan: Wuhan da xue chu ban she, 2006.
Buscar texto completoShanghai zheng da yan jiu suo, ed. Xin Shanghai ren. Beijing: Dong fang chu ban she, 2002.
Buscar texto completoRen gong shen jing wang luo ji chu. Ha'erbin: Ha'erbin gong cheng da xue chu ban she, 2008.
Buscar texto completoCapítulos de libros sobre el tema "RNN NETWORK"
Das, Susmita, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy y Imon Banerjee. "Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research". En Machine Learning for Brain Disorders, 117–38. New York, NY: Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_4.
Texto completoYellin, Daniel M. y Gail Weiss. "Synthesizing Context-free Grammars from Recurrent Neural Networks". En Tools and Algorithms for the Construction and Analysis of Systems, 351–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_19.
Texto completoKobayashi, Naoki y Minchao Wu. "Neural Network-Guided Synthesis of Recursive List Functions". En Tools and Algorithms for the Construction and Analysis of Systems, 227–45. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30823-9_12.
Texto completoKanumuri, Saketh, Vinay Teja Kantipudi, A. Viji Amutha Mary y Mercy Paul Selvan. "Detection of Ransomware Based on Recurrent Neural Network (RNN)". En Lecture Notes in Electrical Engineering, 569–75. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7511-2_57.
Texto completoJayashree, D., O. Pandithurai, S. Shreevathsav y P. Shyamala. "Generation of Handwriting Applying RNN with Mixture Density Network". En Advances in Automation, Signal Processing, Instrumentation, and Control, 2593–601. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_241.
Texto completoAl Mamun, S. M. Abdullah y Mehmet Beyaz. "LSTM Recurrent Neural Network (RNN) for Anomaly Detection in Cellular Mobile Networks". En Machine Learning for Networking, 222–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19945-6_15.
Texto completoJi, Jinbao, Zongxiang Hu, Weiqi Zhang y Sen Yang. "Development of Deep Learning Algorithms, Frameworks and Hardwares". En Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 696–710. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_71.
Texto completoJin, Jialong, Wei Zhou y Baichen Jiang. "Maritime Target Trajectory Prediction Model Based on the RNN Network". En Lecture Notes in Electrical Engineering, 334–42. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0187-6_39.
Texto completoLiu, Enhan, Yan Chu, Lan Luan, Guang Li y Zhengkui Wang. "Mixing-RNN: A Recommendation Algorithm Based on Recurrent Neural Network". En Knowledge Science, Engineering and Management, 109–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29551-6_10.
Texto completoWang, Qi. "RNN Neural Network for Recovery Characteristic System of Resistant Polymer". En 2021 International Conference on Applications and Techniques in Cyber Intelligence, 725–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79200-8_108.
Texto completoActas de conferencias sobre el tema "RNN NETWORK"
Ghodsi, Mohammadreza, Xiaofeng Liu, James Apfel, Rodrigo Cabrera y Eugene Weinstein. "Rnn-Transducer with Stateless Prediction Network". En ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054419.
Texto completoTseng, Yung-Chiao, Chao-Chun Chen, Chiang Lee y Yuan-Ko Huang. "Incremental In-Network RNN Search in Wireless Sensor Networks". En 2007 International Conference on Parallel Processing Workshops (ICPPW 2007). IEEE, 2007. http://dx.doi.org/10.1109/icppw.2007.47.
Texto completoPark, Shin Hyuk, Hyun Jae Park y Young-June Choi. "RNN-based Prediction for Network Intrusion Detection". En 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065249.
Texto completoVenturini, M. "Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models". En ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68030.
Texto completoXia, Rui, Mengran Zhang y Zixiang Ding. "RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/734.
Texto completoLi, Xuelong, Bin Zhao y Xiaoqiang Lu. "MAM-RNN: Multi-level Attention Model Based RNN for Video Captioning". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/307.
Texto completoLyu, Muyang, Ruixuan Liu y Junyi Wang. "Solving Raven's Progressive Matrices Using RNN Reasoning Network". En 2022 7th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2022. http://dx.doi.org/10.1109/iccia55271.2022.9828445.
Texto completoLi, Dailun, Zeying Tian y Yining Duan. "Self-attention on RNN-based text classification". En International Conference on Computer Network Security and Software Engineering (CNSSE 2022), editado por Wenshun Sheng y Yongquan Yan. SPIE, 2022. http://dx.doi.org/10.1117/12.2641031.
Texto completoNam, Sukhyun, Jiyoon Lim, Jae-Hyoung Yoo y James Won-Ki Hong. "Network Anomaly Detection Based on In-band Network Telemetry with RNN". En 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2020. http://dx.doi.org/10.1109/icce-asia49877.2020.9276768.
Texto completoLuo, Donghao, Bingbing Ni, Yichao Yan y Xiaokang Yang. "Image Matching via Loopy RNN". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/335.
Texto completoInformes sobre el tema "RNN NETWORK"
Brooks, Richard. Reactive Sensor Networks (RSN). Fort Belvoir, VA: Defense Technical Information Center, octubre de 2003. http://dx.doi.org/10.21236/ada419219.
Texto completoAllende López, Marcos, Diego López, Sergio Cerón, Antonio Leal, Adrián Pareja, Marcelo Da Silva, Alejandro Pardo et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, junio de 2021. http://dx.doi.org/10.18235/0003313.
Texto completoYeates, Jessica. The Foundations of Network Dynamics in an RNA Recombinase System. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.2915.
Texto completoRobert, J. y Michael Forte. Field evaluation of GNSS/GPS based RTK, RTN, and RTX correction systems. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/41864.
Texto completoCiobanu, Catalin Irinel. A Neural networks search for single top quark production in CDF Run I data. Office of Scientific and Technical Information (OSTI), agosto de 2002. http://dx.doi.org/10.2172/1420934.
Texto completoHotsur, Oksana. SOCIAL NETWORKS AND BLOGS AS TOOLS PR-CAMPAIGN IMPLEMENTATIONS. Ivan Franko National University of Lviv, marzo de 2021. http://dx.doi.org/10.30970/vjo.2021.50.11110.
Texto completoFarhi, Edward y Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, diciembre de 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.
Texto completoELECTRONIC SYSTEMS CENTER HANSCOM AFB MA. Environmental Assessment Ground Wave Emergency Network for Northwestern Indiana Relay Node Site NO. RN 8C902IN. Fort Belvoir, VA: Defense Technical Information Center, marzo de 1993. http://dx.doi.org/10.21236/ada267849.
Texto completoELECTRONIC SYSTEMS CENTER HANSCOM AFB MA. Ground Wave Emergency Network Final Operational Capability: Environmental Assessment for Central Utah Relay Node, Site Number RN 8C920UT. Fort Belvoir, VA: Defense Technical Information Center, abril de 1993. http://dx.doi.org/10.21236/ada267628.
Texto completoELECTRONIC SYSTEMS CENTER HANSCOM AFB MA. Ground Wave Emergency Network Final Operational Capability: Environmental Assessment for Northwestern Nebraska Relay Node, Site Number RN 8C930NE. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1993. http://dx.doi.org/10.21236/ada267629.
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