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

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Xiang Xu, Xiang Xu, 李儀 Xiang Xu, and Yi-Fan Wang Yi Li. "Particle Swarm Optimization with Long and Short Term Memory in Feature Selection." 電腦學刊 33, no. 5 (2022): 121–33. http://dx.doi.org/10.53106/199115992022103305011.

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<p>Taking each iteration of Particle swarm optimization (PSO) algorithm as a time node, the change of population in PSO algorithm can be regarded as a time series model. Particle population learns and evolves in multiple time nodes, which can be regarded as a dependent behavior on leader particles. In the traditional particle swarm optimization algorithm, this dependence behavior is independent of time, and its consideration standard is only the fitness value of particles. We deeply study the leadership mechanism of PSO algorithm in order to find a more robust leadership mechanism and im

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12

Wagle, Aumkar. "Deep Learning for Financial Time Series using Long Short-Term Memory Model." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1944–72. http://dx.doi.org/10.21275/sr24418141736.

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13

Salman, Afan Galih, Yaya Heryadi, Edi Abdurahman, and Wayan Suparta. "Weather Forecasting Using Merged Long Short-term Memory Model." Bulletin of Electrical Engineering and Informatics 7, no. 3 (2018): 377–85. https://doi.org/10.11591/eei.v7i3.1181.

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Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The pro

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14

Norris, Dennis. "Short-term memory and long-term memory are still different." Psychological Bulletin 143, no. 9 (2017): 992–1009. http://dx.doi.org/10.1037/bul0000108.

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15

Oh, Youngkyo, and Dongyoung Koo. "Evaluation of Korean Reviews Automatically Generated using Long Short-Term Memory Unit." Journal of KIISE 46, no. 6 (2019): 515–25. http://dx.doi.org/10.5626/jok.2019.46.6.515.

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16

Shreyas, Pagare. "Human Action Recognition using Long Short-Term Memory and Convolutional Neural Network Model." International Journal of Soft Computing and Engineering (IJSCE) 14, no. 2 (2024): 20–26. https://doi.org/10.35940/ijsce.I9697.14020524.

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<strong>Abstract: </strong>Human Action Recognition (HAR) is the difficulty of quickly identifying strenuous exercise performed by people. It is feasible to sample some measures of a body's tangential acceleration and speed using inertial sensors and exercise them only to learn model skills of incorrectly categorizing behavior into the relevant categories. In detecting human activities, the use of detectors in personal and portable devices has increased to better understand and anticipate human behavior. Many specialists are working toward developing a classification that can distinguish betwe

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17

A, Manasvin. "Analysis of Long Short Term Memory (LSTM) Network for Rice Crop Yield Prediction." International Journal of Science and Research (IJSR) 11, no. 11 (2022): 1338–42. http://dx.doi.org/10.21275/sr221125154150.

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18

Poomka, Pumrapee, Wattana Pongsena, Nittaya Kerdprasop, and Kittisak Kerdprasop. "SMS Spam Detection Based on Long Short-Term Memory and Gated Recurrent Unit." International Journal of Future Computer and Communication 8, no. 1 (2019): 11–15. http://dx.doi.org/10.18178/ijfcc.2019.8.1.532.

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19

Cammarota, Martín, Lia R. M. Bevilaqua, Janine I. Rossato, Maria Ramirez, Jorge H. Medina, and Iván Izquierdo. "Relationship between short- and long-term memory and short- and long-term extinction." Neurobiology of Learning and Memory 84, no. 1 (2005): 25–32. http://dx.doi.org/10.1016/j.nlm.2005.03.002.

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20

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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21

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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22

Siregar, Indra Rivaldi, Adhiyatma Nugraha, Khairil Anwar Notodiputro, Yenni Angraini, and Laily Nissa Atul Mualifah. "THE COMPARISON OF LONG SHORT-TERM MEMORY AND BIDIRECTIONAL LONG SHORT-TERM MEMORY FOR FORECASTING COAL PRICE." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 1 (2025): 245–58. https://doi.org/10.30598/barekengvol19iss1pp245-258.

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Coal remains vital for global energy despite recent demand fluctuations due to the COVID-19 pandemic and geopolitical tensions. The International Energy Agency (IEA) projected a decline in global coal demand starting in early 2024, driven by increasing renewable energy adoption. As one of the top coal exporters, Indonesia must adjust to these changes. This study aims to forecast future coal prices using historical data from Indonesia's Ministry of Energy and Mineral Resources (KESDM), applying and comparing Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models. While BiLSTM has

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23

Wong, C. W. "Two circuits to convert short-term memory into long-term memory." Medical Hypotheses 49, no. 5 (1997): 375–78. http://dx.doi.org/10.1016/s0306-9877(97)90082-7.

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24

Tudor, Bounegru. "10.5281/zenodo.7664270." Journal of Educational Theory and Practice DIDACTICA PRO... 23, no. 1 (137) (2023): 20–23. https://doi.org/10.5281/zenodo.7664278.

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The article presents the mechanisms of knowledge formation, from stimuli perception to their registration in longterm memory. The author also offers a new perspective on the quality of knowledge, bringing to the fore a complex classification of knowledge and arguing his position on training future qualified and competent specialists.

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25

Nguyen, Sang Thi Thanh, and Bao Duy Tran. "Long Short-Term Memory Based Movie Recommendation." Science & Technology Development Journal - Engineering and Technology 3, SI1 (2020): SI1—SI9. http://dx.doi.org/10.32508/stdjet.v3isi1.540.

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Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or he

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26

Abadie, Marlène, and Valérie Camos. "False memory at short and long term." Journal of Experimental Psychology: General 148, no. 8 (2019): 1312–34. http://dx.doi.org/10.1037/xge0000526.

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27

Wöllmer, Martin, Björn Schuller, and Gerhard Rigoll. "Keyword spotting exploiting Long Short-Term Memory." Speech Communication 55, no. 2 (2013): 252–65. http://dx.doi.org/10.1016/j.specom.2012.08.006.

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28

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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29

Puteri, Dian Islamiaty. "Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 11, no. 1 (2023): 35–43. http://dx.doi.org/10.34312/euler.v11i1.19791.

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The development of the stock market in Indonesia is currently growing quite rapidly. This can be seen based on the number of investors who have increased every year. In 2011, sharia stocks were launched for the first time in Indonesia, and it can be seen that the price of the stock is not always stable or can experience increases or decreases. For investors, a strategy is needed to predict stock prices in order to make the right decisions in investing. In this study, stock prediction was carried out using the Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) metho

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30

Sai Rahul, S. "Data-Driven Approach for SOC Estimation of Battery using Long-Short Term Memory Network." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 481–83. http://dx.doi.org/10.21275/sr221207081939.

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31

Sai Swaroop Reddy, Venkata. "Predicting Soccer Match Outcomes Using Deep Learning: A Long Short-Term Memory (LSTM) Approach." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 1454–58. https://doi.org/10.21275/sr22108120231.

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32

Prasanna, A. Shalini Divya, and C. Beulah Christalin Latha. "Bi-Lingual Machine Translation Approach using Long Short–Term Memory Model for Asian Languages." Indian Journal Of Science And Technology 16, no. 18 (2023): 1357–64. http://dx.doi.org/10.17485/ijst/v16i18.176.

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33

Morimoto, Bruce H., and Daniel E. Koshland. "Short‐term and long‐term memory in single cells." FASEB Journal 5, no. 7 (1991): 2061–67. http://dx.doi.org/10.1096/fasebj.5.7.2010059.

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34

Labusov, M. V. "SHORT-TERM FINANCIAL TIME SERIES ANALYSIS WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3, no. 4 (2021): 165–77. http://dx.doi.org/10.36871/ek.up.p.r.2021.04.03.023.

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The process of creating a long short-term memory neural network for high-frequency financial time series analyzing and forecasting is considered in the article. The research base is compiled in the beginning. Further the estimation of long short-term memory neural network parameters is carried out on the learning subsamples. The forecast of future returns signs is made for the horizon of 90 minutes with the estimated neural network. In conclusion the trading strategy is formulated.

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35

Fukuda, K., and E. K. Vogel. "Visual short term memory also gates long term memory without explicit retrieval." Journal of Vision 11, no. 11 (2011): 1275. http://dx.doi.org/10.1167/11.11.1275.

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36

Chang, Ting, Sung-Hyun Jo, and Wei Lu. "Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor." ACS Nano 5, no. 9 (2011): 7669–76. http://dx.doi.org/10.1021/nn202983n.

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37

Fukuda, K., and E. K. Vogel. "Visual short term memory serves as a gateway to long term memory." Journal of Vision 10, no. 7 (2010): 730. http://dx.doi.org/10.1167/10.7.730.

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38

Majerus, Steve, and Martial Linden. "Long-term memory effects on verbal short-term memory: A replication study." British Journal of Developmental Psychology 21, no. 2 (2003): 303–10. http://dx.doi.org/10.1348/026151003765264101.

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39

Takeyama, E., M. Takenoshita, S. Nishimura, and I. Yoshiya. "THE TIME NEEDED TO CONSOLIDATE SHORT-TERM MEMORY TO LONG-TERM MEMORY." Anesthesiology 89, Supplement (1998): 354A. http://dx.doi.org/10.1097/00000542-199809070-00046.

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40

Nairne, James S., and Ian Neath. "Long-term memory span." Behavioral and Brain Sciences 24, no. 1 (2001): 134–35. http://dx.doi.org/10.1017/s0140525x01433929.

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Cowan assumes that chunk-based capacity limits are synonymous with the essence of a “specialized STM mechanism.” In a single experiment, we measured the capacity, or span, of long-term memory and found that it, too, corresponds roughly to the magical number 4. The results imply that a chunk-based capacity limit is not a signature characteristic of remembering over the short-term.

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41

Majerus, Steve, and Arnaud D’Argembeau. "Verbal short-term memory reflects the organization of long-term memory: Further evidence from short-term memory for emotional words." Journal of Memory and Language 64, no. 2 (2011): 181–97. http://dx.doi.org/10.1016/j.jml.2010.10.003.

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42

Vallar, Giuseppe. "The short-term/long-term memory distinction: Back to the past?" Behavioral and Brain Sciences 26, no. 6 (2003): 757–58. http://dx.doi.org/10.1017/s0140525x03520167.

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The view that short-term memory should be conceived of as being a process based on the activation of long-term memory is inconsistent with neuropsychological evidence. Data from brain-damaged patients, showing specific patterns of impairment, are compatible with a vision of memory as a multiple-component system, whose different aspects, in neurologically unimpaired subjects, show a high degree of interaction.

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XUE, Zelong, and Yang XUE. "Multi Long-Short Term Memory Models for Short Term Traffic Flow Prediction." IEICE Transactions on Information and Systems E101.D, no. 12 (2018): 3272–75. http://dx.doi.org/10.1587/transinf.2018edl8087.

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44

Botzer, Dina, Silvia Markovich, and Abraham J. Susswein. "Multiple Memory Processes Following Training That a Food Is Inedible in Aplysia." Learning & Memory 5, no. 3 (1998): 204–19. http://dx.doi.org/10.1101/lm.5.3.204.

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In many organisms, memory after training can be separated into a number of processes. We now report that separable memory processes are also initiated by a training procedure affectingAplysia feeding behavior, a model system for examining the neural mechanisms underlying the regulation of a complex behavior. Four distinct memory process were identified: (1) a very short-term memory that declines within 15 min, (2) a short-term memory that persists for 0.5–1.0 hr, (3) an intermediate-term memory, observed 4 hr after training, and (4) a long-term memory that is seen only after a 12- to 24-hr del

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Ruchkin, Daniel S., Jordan Grafman, Katherine Cameron, and Rita S. Berndt. "Working memory retention systems: A state of activated long-term memory." Behavioral and Brain Sciences 26, no. 6 (2003): 709–28. http://dx.doi.org/10.1017/s0140525x03000165.

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High temporal resolution event-related brain potential and electroencephalographic coherence studies of the neural substrate of short-term storage in working memory indicate that the sustained coactivation of both prefrontal cortex and the posterior cortical systems that participate in the initial perception and comprehension of the retained information are involved in its storage. These studies further show that short-term storage mechanisms involve an increase in neural synchrony between prefrontal cortex and posterior cortex and the enhanced activation of long-term memory representations of

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46

Alvarez, G., T. Konkle, T. Brady, J. Gill, and A. Oliva. "Comparing the fidelity of perception, short-term memory, and long-term memory: Evidence for highly detailed long-term memory representations." Journal of Vision 9, no. 8 (2010): 584. http://dx.doi.org/10.1167/9.8.584.

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47

Mamajanov, M. M., and B. B. Turayev. "High Hearing Memory Of Schoolchildren And Students And Factors Affecting It." American Journal of Applied sciences 02, no. 11 (2020): 88–95. http://dx.doi.org/10.37547/tajas/volume02issue11-17.

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Non-regular mental and physical activity of schoolchildren and students, excessive use of mobile phones, harmful habits (smoking and snuff), iodine deficiency, poor diet, lack of sleep are the main reasons for memory loss decrease is observed. In our experiments, long-term memory volume was up to 37% and short-term memory volume was up to 51% in 6th grade students (13-year-olds); In 8th grade students (15-year-olds), long-term memory was up to 83% and short-term memory was up to 61%; In 10th grade students (17-year-olds), long-term memory volume was up to 83% and short-term memory volume was u

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48

Prashanthi, M., M. Chandra Mohan, N. Pothanna, and G. Ramesh Chandra. "Software Defect Prediction Using Fuzzy Entropy Based Short Term Memory." Indian Journal Of Science And Technology 18, Sp1 (2025): 1–8. https://doi.org/10.17485/ijst/v18si1.icamada1.

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Objective: To detect software defects with minimum computational complexity, the Fuzzy Entropy Based Short Term Memory (FESTM) model is proposed. Methods: The FESTM is a combination of collaborative entropies of fuzzy entropy, condition entropy as well as clustering entropy, and long short-term memory with two parallel layers (BiLSTM) where multi-entropy deals in decision-making with uncertain or imprecise data and BiLSTM increases the performance by enabling additional training to the model due to the passing of input data twice to the LSTM Layers. Findings: The comparative analysis of the mo

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49

Sahoo, Biswaranjan, and Shiv K. Sharma. "Sulfotransferase activity contributes to long-term potentiation and long-term memory." Learning & Memory 29, no. 6 (2022): 155–59. http://dx.doi.org/10.1101/lm.053538.121.

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A critical role of protein modifications such as phosphorylation and acetylation in synaptic plasticity and memory is well documented. Tyrosine sulfation plays important roles in several biological processes. However, its role in synaptic plasticity and memory is not well understood. Here, we show that sulfation contributes to long-term potentiation (LTP) in the hippocampal slices. In addition, inhibition of sulfation impairs long-term memory in a spatial memory task without affecting acquisition or short-term memory. Furthermore, LTP-inducing stimulus enhances protein tyrosine sulfation. Thes

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50

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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