Relevant bibliographies by topics / Long short-term memory

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Long short-term memory'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Long short-term memory"

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Zannatul, Ferdoush, University Brac, Chakrabarty Amitabha, and Uddin Jia. "A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 763–71. https://doi.org/10.11591/ijece.v11i1.pp763-771.

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In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hyb
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Hochreiter, Sepp, and Jürgen Schmidhuber. "Long Short-Term Memory." Neural Computation 9, no. 8 (1997): 1735–80. http://dx.doi.org/10.1162/neco.1997.9.8.1735.

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Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to
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Upadhyay, Devanshu, and Soumya Sharma. "Stock Price Prediction Using Concept of Long-Short Term Memory." International Journal of Scientific Engineering and Research 9, no. 12 (2021): 7–11. https://doi.org/10.70729/se211221110402.

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Schweickert, Richard. "Short-Term Memory; Long-Term Goals." Contemporary Psychology: A Journal of Reviews 32, no. 11 (1987): 940–42. http://dx.doi.org/10.1037/026488.

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Dani, Ninad. "Analysis of Financial Market Forecasting using Long Short-Term Memory (LSTM)." International Journal of Science and Research (IJSR) 11, no. 8 (2022): 1099–105. http://dx.doi.org/10.21275/sr22817190830.

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Izquierdo, I. "SHORT- AND LONG-TERM MEMORY." Behavioural Pharmacology 9, no. 1 (1998): S46. http://dx.doi.org/10.1097/00008877-199808000-00094.

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Izquierdo, I. "SHORT- AND LONG-TERM MEMORY." Behavioural Pharmacology 9, Supplement (1998): S46. http://dx.doi.org/10.1097/00008877-199808001-00094.

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Izquierdo, I. "SHORT- AND LONG-TERM MEMORY." Behavioural Pharmacology 9, no. 1 (1998): S46. http://dx.doi.org/10.1097/00008877-199812001-00094.

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Minton, Kirsty. "Short- and long-term memory." Nature Reviews Immunology 11, no. 3 (2011): 161. http://dx.doi.org/10.1038/nri2951.

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Caldicott, C. E. J. "On short-term and long-term memory." Huguenot Society Journal 27, no. 2 (1999): 279–80. http://dx.doi.org/10.3828/huguenot.1999.27.02.279.

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Dissertations / Theses on the topic "Long short-term memory"

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Gers, Félix. "Long short-term memory in recurrent neural networks /." [S.l.] : [s.n.], 2001. http://library.epfl.ch/theses/?nr=2366.

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Cumming, N. "The Hebb effect : investigating long-term learning from short-term memory." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598214.

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How do we learn a sequence of items so we can remember it not only over the short-term, as in hearing a phone-number and repeating it back, but over the long term? Ten experiments are presented that investigate this problem using the Hebb repetition effect (Hebb, 1961). In a canonical Hebb effect experiment, lists of familiar items are presented in an immediate serial recall task and one list is repeatedly presented at regular intervals. This leads to an improvement in recall for the repeating list over baseline performance. Existing models of serial order learning are tested; Chapter 2 provid
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Simner, Julia Claire. "Engaging long and short term memory during anaphor comprehension." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368489.

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This research investigates how memory representations are activated and associated when making inferences in language, and in particular during the comprehension of anaphors (Le. co-referring expressions). Experiments 1 to 6 investigate 'do it' comprehension (e.g. John bought a newspaper. He did it while the others were out). Experiments 1 and 2 (offline sentence-completion tasks) show that 'do it' processing is sensitive to both NPs (a newspaper) and VPs (bought a newspaper) in the preceding context, and to specific lexical properties of the preceding NPs. With similar tasks, Experiments 3 an
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Shojaee, Ali B. S. "Bacteria Growth Modeling using Long-Short-Term-Memory Networks." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105038908441.

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Yang, Tianshu. "Electric Load Forecasting Using Long Short-term Memory Algorithm." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6027.

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Abstract Power system load forecasting refers to the study or uses a mathematical method to process past and future loads systematically, taking into account important system operating characteristics, capacity expansion decisions, natural conditions, and social impacts, to meet specific accuracy requirements. Dependence of this, determine the load value at a specific moment in the future. Improving the level of load forecasting technology is conducive to the planned power management, which is conducive to rationally arranging the grid operation mode and unit maintenance plan, and is conducive
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Wood, Noelle L. "Memory for recent words : a matter of short-term memory storage or long-term distinctiveness? /." free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737848.

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Bailey, Tony J. "Neuromorphic Architecture with Heterogeneously Integrated Short-Term and Long-Term Learning Paradigms." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1554217105047975.

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Yangyang, Wen. "Sensor numerical prediction based on long-term and short-term memory neural network." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39165.

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Many sensor nodes are scattered in the sensor network,which are used in all aspects of life due to their small size, low power consumption, and multiple functions. With the advent of the Internet of Things, more small sensor devices will appear in our lives. The research of deep learning neural networks is generally based on large and medium-sized devices such as servers and computers, and it is rarely heard about the research of neural networks based on small Internet of Things devices. In this study, the Internet of Things devices are divided into three types: large, medium, and small in ter
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Fukuda, Keisuke, and Keisuke Fukuda. "The Capacity of Visual Short Term Memory Determines the Bandwidth of Information Transfer into Visual Long Term Memory." Thesis, University of Oregon, 2012. http://hdl.handle.net/1794/12391.

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Visual long term memory (VLTM) research has shown that we are capable of learning a virtually infinite amount of visual information. At the same time, visual short term memory (VSTM) research has shown that there is a severe limitation in the amount of information we can simultaneously apprehend at a given time. How does the severe capacity limitation in the initial uptake of information influence the encoding of information into VLTM? To this date, there has been no direct test of such influence, and the effect of such limitation has been unclear. Here, we demonstrate that, across wide variet
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Morales, Dawn A. "Memory for color over brief intervals : one capacity or two? /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3089470.

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Books on the topic "Long short-term memory"

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1972-, Thorn Annabel, and Page Mike 1966-, eds. Interactions between short-term and long-term memory in the verbal domain. Psychology Press, 2008.

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1972-, Thorn Annabel, and Page Mike 1966-, eds. Interactions between short-term and long-term memory in the verbal domain. Psychology Press, 2008.

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Dehn, Milton J. Long-term memory problems in children and adolescents: Assessment, intervention, and effective instruction. Wiley, 2010.

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1963-, Oakes Lisa M., and Bauer Patricia J, eds. Short-and long-term memory in infancy and early childhood: Taking the first steps toward remembering. Oxford University Press, 2007.

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Grabowski, Peter. The effects of three dimensional text imagery on short and long term memory. Laurentian University, Department of Psychology, 2000.

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Eitelman, Paul. A non-random walk revisited: Short- and long-term memory in asset prices. Federal Reserve Board, 2008.

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LaCroix, Connie Lynn. Short-term and long-term memory for intentional versus incidental learning: Does rhyme or reason make a difference? Laurentian University, Department of Psychology, 2001.

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Muzia, Mary M. Social facilitation and memory task complexity: Does experimenter presence exert a differential effect on short- and long-term word recognition and recall? Laurentian University, Department of Psychology, 1996.

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Page, Mike, and Annabel Thorn. Interactions Between Short-Term and Long-Term Memory in the Verbal Domain. Taylor & Francis Group, 2013.

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Page, Mike, and Annabel Thorn. Interactions Between Short-Term and Long-Term Memory in the Verbal Domain. Taylor & Francis Group, 2008.

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Book chapters on the topic "Long short-term memory"

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Knudson, Kim, Joel Fernandes, Richard Holbert, Robert Averbuch, and Uma Suryadevara. "Short-Term/Long-Term Memory." In Encyclopedia of Gerontology and Population Aging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69892-2_702-1.

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Knudson, Kim, Joel Fernandes, Richard Holbert, Robert Averbuch, and Uma Suryadevara. "Short-Term/Long-Term Memory." In Encyclopedia of Gerontology and Population Aging. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-22009-9_702.

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Graves, Alex. "Long Short-Term Memory." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24797-2_4.

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Ushiku, Yoshitaka. "Long Short-Term Memory." In Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_856.

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Ushiku, Yoshitaka. "Long Short-Term Memory." In Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_856-1.

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Widrow, Bernard, and Edward P. Katz. "Long-Term and Short-Term Memory." In Springer Series on Bio- and Neurosystems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80939-2_21.

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Alla, Sridhar, and Suman Kalyan Adari. "Long Short-Term Memory Models." In Beginning Anomaly Detection Using Python-Based Deep Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5177-5_6.

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Parker, Rolland S. "Memory: Short- and Long-Term." In Traumatic Brain Injury and Neuropsychological Impairment. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3398-5_10.

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Adari, Suman Kalyan, and Sridhar Alla. "Long Short-Term Memory Models." In Beginning Anomaly Detection Using Python-Based Deep Learning. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0008-5_8.

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Baddeley, A. D., C. Papagno, and G. Vallar. "When long-term learning depends on short-term storage." In Exploring Working Memory. Routledge, 2017. http://dx.doi.org/10.4324/9781315111261-13.

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Conference papers on the topic "Long short-term memory"

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Hsu, Yu-Chao, Tai-Yu Li, and Kuan-Cheng Chen. "Quantum Kernel-Based Long Short-term Memory." In 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2025. https://doi.org/10.1109/icasspw65056.2025.11011010.

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Salman, Ali, Gilbert El Mir, Lea Diab, Elias El Haber, Gaby Abou Haidar, and Roger Achkar. "Earthquake Analysis Using Long-Short Term Memory - LSTM." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10927849.

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J, Sathyaraj, and Sankardoss V. "Short-Term Wind Speed Forecasting using Long Shot-Term Memory Algorithms." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841597.

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Yang, Zihao, and Yi Zhuang. "Transformer-based Multi-Object Tacking Long-term and Short-term Memory." In 2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2025. https://doi.org/10.1109/iccece65250.2025.10984843.

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Smorodnikov, Grigory, Ruslan Zolotarev, Aleksandra Rykova, Artem Sabutkevich, and Aleksandr Samochadin. "Elastic cloud resource allocation using short-term long short-term memory-based workload prediction." In Fourth International Conference on Optics, Computer Applications, and Materials Science (CMSD-IV 2024), edited by Arthur Gibadullin and Ramazon Abdullozoda. SPIE, 2025. https://doi.org/10.1117/12.3060861.

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WALZ, ROGER, MONICA R. M. VIANNA, JORGE H. MEDINA, and IVAN IZQUIERDO. "SHORT AND LONG-TERM MEMORY." In Proceedings of the International School of Biocybernetics. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776563_0022.

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Chen, Samuel Yen-Chi, Shinjae Yoo, and Yao-Lung L. Fang. "Quantum Long Short-Term Memory." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747369.

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Dong, Yi, Yang Chen, Xingyu Zhao, and Xiaowei Huang. "Short-term Load Forecasting with Distributed Long Short-Term Memory." In 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2023. http://dx.doi.org/10.1109/isgt51731.2023.10066368.

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Wand, Michael, Jan Koutnik, and Jurgen Schmidhuber. "Lipreading with long short-term memory." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472852.

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Ding, Shuoyang, and Philipp Koehn. "Parallelizable Stack Long Short-Term Memory." In Proceedings of the Third Workshop on Structured Prediction for. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-1501.

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Reports on the topic "Long short-term memory"

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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the ot
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Ly, Racine, Fousseini Traore, and Khadim Dia. Forecasting commodity prices using long-short-term memory neural networks. International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134265.

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Han, Shangxuan. Stock Prediction with Random Forests and Long Short-term Memory. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1334.

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Carew, Thomas J. A Parallel Processing Hypothesis for Short-Term and Long-Term Memory in Aplysia. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada284101.

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Vold, Andrew. Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1529330.

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Ankel, Victoria, Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Anthonie Cilliers, and Alexander Heifetz. One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1760289.

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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successfu
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