Relevant bibliographies by topics / LSTM (Long Short-Term Memory)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'LSTM (Long Short-Term Memory)'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "LSTM (Long Short-Term Memory)"

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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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Halim, Kevin Yudhaprawira, Dodon Turianto Nugrahadi, Mohammad Reza Faisal, Rudy Herteno, and Irwan Budiman. "Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 3 (2023): 606–18. https://doi.org/10.26555/jiteki.v9i3.26354.

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Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two
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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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Simanihuruk, Laurensia, and Hari Suparwito. "Long Short-Term Memory and Bidirectional Long Short-Term Memory Algorithms for Sentiment Analysis of Skintific Product Reviews." ITM Web of Conferences 71 (2025): 01016. https://doi.org/10.1051/itmconf/20257101016.

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In the era of ever-evolving digital technology, conducting customer sentiment analysis through product reviews has become crucial for businesses to improve their offerings and increase customer satisfaction. This research aims to analyze the sentiment of SKINTIFIC skincare products on the Shopee online store platform using advanced deep learning models: Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). These models were evaluated using learning rate, number of units, and dropout rate. The dataset consists of 9,184 product reviews extracted through the Shopee API
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Zoremsanga, Chawngthu, and Jamal Hussain. "An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India." Indian Journal Of Science And Technology 17, no. 18 (2024): 1828–37. http://dx.doi.org/10.17485/ijst/v17i18.2505.

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Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30)
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Mirza, Arsalan R., and Abdulbasit K. Al-Talabani. "Time Series-Based Spoof Speech Detection Using Long Short-Term Memory and Bidirectional Long Short-Term Memory." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 12, no. 2 (2024): 119–29. http://dx.doi.org/10.14500/aro.11636.

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Detecting fake speech in voice-based authentication systems is crucial for reliability. Traditional methods often struggle because they can't handle the complex patterns over time. Our study introduces an advanced approach using deep learning, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models, tailored for identifying fake speech based on its temporal characteristics. We use speech signals with cepstral features like Mel-frequency cepstral coefficients (MFCC), Constant Q cepstral coefficients (CQCC), and open-source Speech and Music Interpretation by Large-space
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Chen Wang, Chen Wang, Bingchun Liu Chen Wang, Jiali Chen Bingchun Liu, and Xiaogang Yu Jiali Chen. "Air Quality Index Prediction Based on a Long Short-Term Memory Artificial Neural Network Model." 電腦學刊 34, no. 2 (2023): 069–79. http://dx.doi.org/10.53106/199115992023043402006.

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<p>Air pollution has become one of the important challenges restricting the sustainable development of cities. Therefore, it is of great significance to achieve accurate prediction of Air Quality Index (AQI). Long Short Term Memory (LSTM) is a deep learning method suitable for learning time series data. Considering its superiority in processing time series data, this study established an LSTM forecasting model suitable for air quality index forecasting. First, we focus on optimizing the feature metrics of the model input through Information Gain (IG). Second, the prediction results of th
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Seng, Hansun, Perdana Putri Farica, Q. M. Khaliq Abdul, and Hugeng Hugeng. "On searching the best mode for forex forecasting: bidirectional long short-term memory default mode is not enough." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1596–606. https://doi.org/10.11591/ijai.v11.i4.pp1596-1606.

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Presently, the Forex market has become the world’s largest financial market with more than US$5 trillion daily volume. Therefore, it attracts many researchers to learn its traded currency pairs characteristics and predict their future values. Here, we propose simple three layers Bidirectional long shortterm memory (Bi-LSTM) networks for Forex forecasting with four different merge modes. Moreover, the proposed model is also compared to the conventional long short-term memory (LSTM) networks with the same architecture. Five major Forex currency pairs, namely AUD/USD, EUR/USD, GBP/USD, USD/
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Septiadi, Jaka, Budi Warsito, and Adi Wibowo. "Human Activity Prediction using Long Short Term Memory." E3S Web of Conferences 202 (2020): 15008. http://dx.doi.org/10.1051/e3sconf/202020215008.

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Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be
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Singh, Arjun, Shashi Kant Dargar, Amit Gupta, et al. "Evolving Long Short-Term Memory Network-Based Text Classification." Computational Intelligence and Neuroscience 2022 (February 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/4725639.

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Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a we
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Dissertations / Theses on the topic "LSTM (Long Short-Term Memory)"

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Singh, Akash. "Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215723.

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We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on LSTM prediction and detection performance. LSTMs are also compared to feed-forward neural networks wit
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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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Valluru, Aravind-Deshikh. "Realization of LSTM Based Cognitive Radio Network." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538697/.

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This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.
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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level
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Gustafsson, Anton, and Julian Sjödal. "Energy Predictions of Multiple Buildings using Bi-directional Long short-term Memory." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43552.

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The process of energy consumption and monitoring of a buildingis time-consuming. Therefore, an feasible approach for using trans-fer learning is presented to decrease the necessary time to extract re-quired large dataset. The technique applies a bidirectional long shortterm memory recurrent neural network using sequence to sequenceprediction. The idea involves a training phase that extracts informa-tion and patterns of a building that is presented with a reasonablysized dataset. The validation phase uses a dataset that is not sufficientin size. This dataset was acquired through a related paper
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Corni, Gabriele. "A study on the applicability of Long Short-Term Memory networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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This thesis summarises the research-oriented study of applicability of Long Short-Term Memory Recurrent Neural Networks (LSTMs) to industrial Optical Character Recognition (OCR) problems. Traditionally solved through Convolutional Neural Network-based approaches (CNNs), the reported work aims to detect the OCR aspects that could be improved by exploiting recurrent patterns among pixel intensities, and speed up the overall character detection process. Accuracy, speed and complexity act as the main key performance indicators. After studying the core Deep Learning foundations, the best train
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van, der Westhuizen Jos. "Biological applications, visualizations, and extensions of the long short-term memory network." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287476.

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Sequences are ubiquitous in the domain of biology. One of the current best machine learning techniques for analysing sequences is the long short-term memory (LSTM) network. Owing to significant barriers to adoption in biology, focussed efforts are required to realize the use of LSTMs in practice. Thus, the aim of this work is to improve the state of LSTMs for biology, and we focus on biological tasks pertaining to physiological signals, peripheral neural signals, and molecules. This goal drives the three subplots in this thesis: biological applications, visualizations, and extensions. We start
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Nawaz, Sabeen. "Analysis of Transactional Data with Long Short-Term Memory Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281282.

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An issue authorities and banks face is fraud related to payments and transactions where huge monetary losses occur to a party or where money laundering schemes are carried out. Previous work in the field of machine learning for fraud detection has addressed the issue as a supervised learning problem. In this thesis, we propose a model which can be used in a fraud detection system with transactions and payments that are unlabeled. The proposed modelis a Long Short-term Memory in an auto-encoder decoder network (LSTMAED)which is trained and tested on transformed data. The data is transformed by
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Verner, Alexander. "LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1074.

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In order to ensure the validity of sensor data, it must be thoroughly analyzed for various types of anomalies. Traditional machine learning methods of anomaly detections in sensor data are based on domain-specific feature engineering. A typical approach is to use domain knowledge to analyze sensor data and manually create statistics-based features, which are then used to train the machine learning models to detect and classify the anomalies. Although this methodology is used in practice, it has a significant drawback due to the fact that feature extraction is usually labor intensive and requir
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Svanberg, John. "Anomaly detection for non-recurring traffic congestions using Long short-term memory networks (LSTMs)." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234465.

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In this master thesis, we implement a two-step anomaly detection mechanism for non-recurrent traffic congestions with data collected from public transport buses in Stockholm. We investigate the use of machine learning to model time series data with LSTMs and evaluate the results with a baseline prediction model. The anomaly detection algorithm embodies both collective and contextual expressivity, meaning it is capable of findingcollections of delayed buses and also takes the temporality of the data into account. Results show that the anomaly detection performance benefits from the lower predic
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Books on the topic "LSTM (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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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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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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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 "LSTM (Long Short-Term Memory)"

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Hvitfeldt, Emil, and Julia Silge. "Long short-term memory (LSTM) networks." In Supervised Machine Learning for Text Analysis in R. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003093459-14.

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Nandam, Srinivasa Rao, Adouthu Vamshi, and Inapanuri Sucharitha. "CAN Intrusion Detection Using Long Short-Term Memory (LSTM)." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1976-3_36.

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Salem, Fathi M. "Gated RNN: The Long Short-Term Memory (LSTM) RNN." In Recurrent Neural Networks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89929-5_4.

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Pal, Debasmita, and Partha Pratim Deb. "Stock Index Forecasting Using Stacked Long Short-Term Memory (LSTM)." In Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0. CRC Press, 2022. http://dx.doi.org/10.1201/9781003279044-7.

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Swarnkar, Suman Kumar, and Yogesh Kumar Rathore. "Music Genre Classification Using Long Short-Term Memory (LSTM) Networks." In Machine Learning in Multimedia. CRC Press, 2024. http://dx.doi.org/10.1201/9781003477280-6.

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Barone, Ben, David Coar, Ashley Shafer, Jinhong K. Guo, Brad Galego, and James Allen. "Interpreting Pilot Behavior Using Long Short-Term Memory (LSTM) Models." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80624-8_8.

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Ghosh, Sreyan, Kunj Pahuja, Joshua Mammen Jiji, Antony Puthussery, Samiksha Shukla, and Aynur Unal. "Classifying Bipolar Personality Disorder (BPD) Using Long Short-Term Memory (LSTM)." In Data Science and Security. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5309-7_17.

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Mukhtar, Harun, Muhammad Akmal Remli, Khairul Nizar Syazwan Wan Salihin Wong, and Yoze Rizki. "Long-Short Term Memory (LSTM) Based Architecture for Forecasting Tourist Arrivals." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49544-1_52.

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Abimannan, Satheesh, Deepak Jayaswal, Yue-Shan Chang, and K. Thirunavukkarasu. "Evolution of Long Short-Term Memory (LSTM) in Air Pollution Forecasting." In Handbook of Research on Machine Learning. Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003277330-18.

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Grósz, Tamás, and Mikko Kurimo. "LSTM-XL: Attention Enhanced Long-Term Memory for LSTM Cells." In Text, Speech, and Dialogue. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83527-9_32.

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Conference papers on the topic "LSTM (Long Short-Term Memory)"

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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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Gupta, Anish, Lalit Kumar Tyagi, and Vibhash Singh Sisodia. "Long Short-Term Memory (LSTM) Networks For Stock Market Prediction." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10939100.

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Kajita, Yukihide, Ryo Morishige, Jeong Moon Kyeong, and Taiji Mazda. "Displacement Prediction by Acceleration of Bridge Pier Using Long Short-Term Memory." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2360.

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<p>After a large earthquake, prompt assessment of damage to seismic isolation bearings, especially in bridges, is critical. Although accelerometers are commonly used in structural health monitoring, displacement response calculations based on time integration of acceleration data are often inaccurate due to numerical errors. In this paper, using dynamic analysis results on a bridge model with seismic isolation bearings, a long short-term memory (LSTM) model was employed to predict displacement more accurately. Results showed that the LSTM model reduced the maximum response displacement e
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Damanik, Agung Althaaf Emha, Hilal H. Nuha, Niken Dwi Wahyu Cahyani, Setyorini, and Mohd Arfian Bin Ismail. "Email Spam Detection using Long Short-Term Memory (LSTM) Network Method." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836586.

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Maylawati, Dian Sa'adillah, Karima Marwazia Shalih, Muhammad Ali Ramdhani, Cepy Slamet, and Diena Rauda Ramdania. "Indonesian Abstractive Text Summarization with Bidirectional Long Short-Term Memory (Bi-LSTM)." In 2024 12th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2024. https://doi.org/10.1109/citsm64103.2024.10775593.

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Wang, Yan, and Weidi Guo. "Long Short-Term Memory Network (LSTM) is used to Model Action Sequences." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934377.

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Ashtagi, Rashmi, Ranjeet Vasant Bidwe, Aditya Fukate, Ojas Kulkarni, Pratik Jadhav, and Sakshi Patil. "Sentiment Analysis on YouTube Comments using Long Short-Term Memory (LSTM) Networks." In 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). IEEE, 2025. https://doi.org/10.1109/icmcsi64620.2025.10883113.

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Nafiyanto, Mohamad Irfan, and Erwin Budi Setiawan. "Parameter Optimization for Long Short-Term Memory (LSTM) and Bi-LSTM in Netflix Recommendation System." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913010.

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Azizah, Nur, Eko Mulyanto Yuniarno, and Mauridhi Hery Purnomo. "Lip Reading Using Spatio Temporal Convolutions (STCNN) And Long Short Term Memory (LSTM)." In 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2024. http://dx.doi.org/10.1109/isitia63062.2024.10667885.

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Fadzli, Mohd Faizul Hanif Mohd, and Shahrani Shahbudin. "Hyperparameter Analysis-Based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification." In 2024 IEEE International Conference on Power and Energy (PECon). IEEE, 2024. https://doi.org/10.1109/pecon62060.2024.10827412.

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Reports on the topic "LSTM (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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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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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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