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

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

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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Bhalke, D. G., Daideep Bhingarde, Siddhi Deshmukh, and Digvijay Dhere. "Stock Price Prediction Using Long Short Term Memory." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (2022): 271–73. http://dx.doi.org/10.18090/samriddhi.v14spli02.12.

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Stock market price prediction is difficult and complex task. Prediction in stock market is very complex and unstable Process. Stock Price are most of the time tend to follow patterns those are more or less regular in stock price curve. Machine Learning techniques use different predictive models and algorithms to predict and automate things to reduce human effort. This research paper focuses on the use of Long Short Term Memory (LSTM) to predict the future stock market company price of stock using each day closing price analysis. LSTM is very helpful in sequential data models. In this paper LST

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Andi, Tri, and Candra Juni Cahya Kusuma. "Stock price forecasting using Long Short Term Memory." Internet of Things and Artificial Intelligence Journal 5, no. 1 (2025): 187–93. https://doi.org/10.31763/iota.v5i1.900.

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The objective of this research is to develop a solution for predicting BRI stock prices using Long Short-Term Memory (LSTM) models. The LSTM model was selected for its capacity to process extensive time series data and discern latent temporal patterns. In this study, a BRI stock dataset obtained from Yahoo Finance is employed for the training and testing of an LSTM model. The evaluation results demonstrate that the LSTM model exhibits excellent predictive performance, with a mean absolute percentage error (MAPE) of 1.58768% and a root mean square error (RMSE) of 81.88216%. The Google test resu

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Zhou, Chenze. "Long Short-term Memory Applied on Amazon's Stock Prediction." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 71–76. http://dx.doi.org/10.54097/hset.v34i.5380.

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More and more investors are paying attention to how to use data mining technology into stock investing decisions as a result of the introduction of big data and the quick expansion of financial markets. Machine learning can automatically apply complex mathematical calculations to big data repeatedly and faster. The machine model can analyze all the factors and indicators affecting stock price and achieve high efficiency. Based on the Amazon stock price published on Kaggle, this paper adopts the Long Short-term Memory (LSTM) method for model training. The Keras package in the Python program is

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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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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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Mukhlis, Mukhlis, Aziz Kustiyo, and Aries Suharso. "Peramalan Produksi Pertanian Menggunakan Model Long Short-Term Memory." BINA INSANI ICT JOURNAL 8, no. 1 (2021): 22. http://dx.doi.org/10.51211/biict.v8i1.1492.

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Abstrak: Masalah yang timbul dalam peramalan hasil produksi pertanian antara lain adalah sulit untuk mendapatkan data yang lengkap dari variabel-variabel yang mempengaruhi hasil pertanian dalam jangka panjang. Kondisi ini akan semakin sulit ketika peramalan mencakup wilayah yang cukup luas. Akibatnya, variabel-variabel tersebut harus diinterpolasi sehingga akan menyebabkan bias terhadap hasil peramalan. (1) Mengetahui gambaran meta analisis penelitian peramalan produk pertanian menggunakan Long Short Term Memory (LSTM), (2) Mengetahui penelitian meta analisis cakupan wilayah, komoditi dan peri

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Rezza, Muhammad, M. Ismail Yusuf, and Redi Ratiandi Yacoub. "Prediksi Radiasi Surya Menggunakan Metode Long Short-Term Memory." ILKOMNIKA: Journal of Computer Science and Applied Informatics 6, no. 1 (2024): 33–44. http://dx.doi.org/10.28926/ilkomnika.v6i1.571.

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Penelitian ini memfokuskan pada optimalisasi pemanfaatan energi matahari di Kalimantan Barat, sebuah wilayah yang kaya akan sumber daya matahari, dengan total pembangkit listrik tenaga surya (PLTS) mencapai 1.58 MW. Untuk memprediksi potensi energi matahari, penelitian menggunakan metode jaringan syaraf tiruan Long Short-Term Memory (LSTM) dengan data yang diperoleh dari data logger yang merekam tegangan, arus, dan daya keluaran panel surya selama 57 hari dengan interval 1-2 detik, menghasilkan 4.294.273 data. Dalam pengolahan data, 80% digunakan untuk pelatihan dan sisanya untuk pengujian mod

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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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Xiao, Hongfei. "Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting." PLOS One 20, no. 6 (2025): e0322737. https://doi.org/10.1371/journal.pone.0322737.

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LSTM (Long Short-Term Memory Network) is currently extensively utilized for forecasting financial time series, primarily due to its distinct advantages in separating the long-term from the short-term memory information within a sequence. However, the experimental results presented in this paper indicate that LSTM may struggle to clearly differentiate between these two types of information. To overcome this limitation, we propose the ARMA-RNN-LSTM Hybrid Model, aimed at enhancing the separation between the long-term and short-term memory information on top of LSTM framework. The experiment in t

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Wimbassa, Muhamad Dwirizqy, Taswiyah Marsyah Noor, Salma Yasara, Vannesha Vannesha, Tubagus Muhammad Arsyah, and Abdiansah Abdiansah. "Emotional Text Detection dengan Long Short Term Memory (LSTM)." Format : Jurnal Ilmiah Teknik Informatika 12, no. 2 (2023): 158. http://dx.doi.org/10.22441/format.2023.v12.i2.009.

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Emotional Text Detection is a technique in natural language processing that aims to identify the emotions contained in conversations or text messages. The LSTM (Long Short-Term Memory) method is one of the techniques used in natural language processing to model and predict sequential data. In this study, we propose the use of the LSTM method for emotion detection in conversation. The dataset used is a conversational dataset that contains positive, negative, and neutral emotions. We process datasets using data pre-processing techniques such as tokenization, data cleansing and one-hot encoding.

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Sushanth, P. "Automatic Text Summarization using Long Short-Term Memory (LSTM)." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6237–42. https://doi.org/10.22214/ijraset.2025.71560.

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Text summarization is the process of automatically generating a shorter version of a given text while retaining its important information. Long Short-Term Memory (LSTM) is a type of recurrent neural network that is commonly used in natural language processing tasks such as text summarization. LSTM networks have a memory component that allows them to remember important information from the input text, which enables them to generate a more concise and relevant summary of the original text. LSTM networks can be trained on a large corpus of text data, and they can be fine-tuned for specific applic

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Liu, Chen. "Long short-term memory (LSTM)-based news classification model." PLOS ONE 19, no. 5 (2024): e0301835. http://dx.doi.org/10.1371/journal.pone.0301835.

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In this study, we used unidirectional and bidirectional long short-term memory (LSTM) deep learning networks for Chinese news classification and characterized the effects of contextual information on text classification, achieving a high level of accuracy. A Chinese glossary was created using jieba—a word segmentation tool—stop-word removal, and word frequency analysis. Next, word2vec was used to map the processed words into word vectors, creating a convenient lookup table for word vectors that could be used as feature inputs for the LSTM model. A bidirectional LSTM (BiLSTM) network was used f

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Amgad, Muneer, Faizan Ali Rao, Almaghthawi Ahmed, Mohd Taib Shakirah, Alghamdi Amal, and Abdulwasea Abdullah Ghaleb Ebrahim. "Short term residential load forecasting using long short-term memory recurrent neural network." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5589–99. https://doi.org/10.11591/ijece.v12i5.pp5589-5599.

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Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecast

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Gui, Tao, Qi Zhang, Lujun Zhao, et al. "Long Short-Term Memory with Dynamic Skip Connections." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6481–88. http://dx.doi.org/10.1609/aaai.v33i01.33016481.

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In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions bas

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Faulina, Ria, Nuramaliyah Nuramaliyah, and Emeylia Safitri. "Air Temperature Prediction System Using Long Short-Term Memory Algorithm." Rekayasa 17, no. 3 (2024): 463–73. https://doi.org/10.21107/rekayasa.v17i3.28229.

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Air temperature is a highly essential parameter in weather forecasting methods and a critical variable for predicting future weather patterns. An accurate temperature prediction system can assist individuals and organizations in preparing for activities heavily influenced by weather conditions. Therefore, developing a precise temperature prediction model requires a reliable and effective algorithm. In this study, the Long Short-Term Memory (LSTM) algorithm, a type of artificial neural network (Recurrent Neural Network - RNN), is implemented with time series data decomposition for variable inpu

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Aditi, Aditi, Aditi Sharma, Preetish Kakkar, Daya Nand, Arvind R. Yadav, and Gaurav Kumar Ameta. "Gated Recurrent Fusion in Long Short-Term Memory Fusion." Fusion: Practice and Applications 19, no. 1 (2025): 50–56. https://doi.org/10.54216/fpa.190105.

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Fusion techniques on enhancing the efficiency of Long Short-Term Memory (LSTM) networks are dominating across a variety of domains. To handle sequential data while integrating from various sources is often challenging using LSTM techniques. Fusion methods that integrate different models enhances LSTM’ ability to handle complex correlations in the data. This paper examines early, late and hybrid fusion techniques. The study provides fusion approaches to enhance LSTM networks to efficiently handle complex multimodal data across self-navigating models. The findings reveal that the hybrid fusion t

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He, Jialuo. "Stock price prediction with long short-term memory." Applied and Computational Engineering 4, no. 1 (2023): 127–33. http://dx.doi.org/10.54254/2755-2721/4/20230428.

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Stock forecasting aims to predict future stock prices based on past price changes in the market, playing an essential role in the field of financial transactions. However, since the stock market is highly uncertain, stock prediction is complex and challenging. This paper uses the long short-term memory (LSTM) model to predict the stock market and compares it with the current stock prediction algorithm. Firstly, we preprocessed the raw dataset and normalized data into the range from 0 to 1. Secondly, we introduced the LSTM model and improved its performance by tuning four parameters: learning r

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Muneer, Amgad, Rao Faizan Ali, Ahmed Almaghthawi, Shakirah Mohd Taib, Amal Alghamdi, and Ebrahim Abdulwasea Abdullah Ghaleb. "Short term residential load forecasting using long short-term memory recurrent neural network." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5589. http://dx.doi.org/10.11591/ijece.v12i5.pp5589-5599.

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<span>Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditio

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Hu, Sile, Wenbin Cai, Jun Liu, Hao Shi, and Jiawei Yu. "Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network." Volume 31, Issue 3 31, no. 3 (2024): 151–66. http://dx.doi.org/10.20532/cit.2023.1005730.

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Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processe

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Kumar, Naresh, Jatin Bindra, Rajat Sharma, and Deepali Gupta. "Air Pollution Prediction Using Recurrent Neural Network, Long Short-Term Memory and Hybrid of Convolutional Neural Network and Long Short-Term Memory Models." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4580–84. http://dx.doi.org/10.1166/jctn.2020.9283.

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Air pollution prediction was not an easy task few years back. With the increasing computation power and wide availability of the datasets, air pollution prediction problem is solved to some extend. Inspired by the deep learning models, in this paper three techniques for air pollution prediction have been proposed. The models used includes recurrent neural network (RNN), Long short-term memory (LSTM) and a hybrid combination of Convolutional neural network (CNN) and LSTM models. These models are tested by comparing MSE loss on air pollution test of Belgium. The validation loss on RNN is 0.0045,

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Shankar, Sonali, P. Vigneswara Ilavarasan, Sushil Punia, and Surya Prakash Singh. "Forecasting container throughput with long short-term memory networks." Industrial Management & Data Systems 120, no. 3 (2019): 425–41. http://dx.doi.org/10.1108/imds-07-2019-0370.

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Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analys

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Salman, Umar, Shafiqur Rehman, Basit Alawode, and Luai Alhems. "Short term prediction of wind speed based on long-short term memory networks." FME Transactions 49, no. 3 (2021): 643–52. http://dx.doi.org/10.5937/fme2103643s.

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Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent

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Son, Hojae, Anand Paul, and Gwanggil Jeon. "Country Information Based on Long-Term Short-Term Memory (LSTM)." International Journal of Engineering & Technology 7, no. 4.44 (2018): 47. http://dx.doi.org/10.14419/ijet.v7i4.44.26861.

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Social platform such as Facebook, Twitter and Instagram generates tremendous data these days. Researchers make use of these data to extract meaningful information and predict future. Especially twitter is the platform people can share their thought briefly on a certain topic and it provides real-time streaming data API (Application Programming Interface) for filtering data for a purpose. Over time a country has changed its interest in other countries. People can get a benefit to see a tendency of interest as well as prediction result from twitter streaming data. Capturing twitter data flow is

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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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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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Izzadiana, Helma Syifa, Herlina Napitupulu, and Firdaniza Firdaniza. "Peramalan Data Univariat Menggunakan Metode Long Short Term Memory." SisInfo : Jurnal Sistem Informasi dan Informatika 5, no. 2 (2023): 29–39. http://dx.doi.org/10.37278/sisinfo.v5i2.669.

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Peramalan data univariat mengacu pada kegiatan meramalkan nilai pada data dengan satu variabel independen yang mungkin muncul di masa depan berdasarkan nilai-nilai yang ada di masa lalu. Penelitian ini bertujuan untuk memperoleh model yang dibangun menggunakan pendekatan deep learning jenis supervised learning yaitu metode Long Short Term Memory (LSTM) yang diterapkan pada data univariat. Metode LSTM merupakan pengembangan dari metode Recurrent Neural Network (RNN) dengan menambahkan 3 gate yang mampu memilih informasi yang dibutuhkan untuk pelatihan sel sehingga mampu mengurangi kemungkinan e

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Abhinandan, Kalita. "In-depth understanding of LSTM and its recent advances in lung disease diagnosis." World Journal of Advanced Research and Reviews 14, no. 3 (2022): 517–22. https://doi.org/10.5281/zenodo.7731979.

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Of late, long short-term memory (LSTM) has proven its worth in medical diagnosis. Hence, there is a need to explore this special version of recurrent neural network (RNN), which can learn long-term dependencies. LSTM addresses the short-term memory problem of basic RNNs. In this paper, an in-depth study of LSTM is done with the help of a few real-life examples. Some of the recent advances of LSTM in COVID-19 and other lung disease diagnoses have also been discussed. 

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Arifin, Samsul, AndyanKalmer Wijaya, Rinda Nariswari, et al. "Long Short-Term Memory (LSTM): Trends and Future Research Potential." International Journal of Emerging Technology and Advanced Engineering 13, no. 5 (2023): 24–34. http://dx.doi.org/10.46338/ijetae0523_04.

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-One of the most widely used machine learning methods, Long Short-Term Memory (LSTM), is particularly useful for time series prediction. In this study, we carried out a bibliometric analysis against publications about LSTMs to identify trends and contributions of researchers in the development of machine learning technology. We collect bibliometric data from the Scopus database and use the bibliometric analysis method to analyze trends and contributions of researchers in publications about LSTM. Results of the bibliometric analysis show that LSTM is a lot used in related machine learning appli

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Awad, Asmaa Ahmed, Ahmed Fouad Ali, and Tarek Gaber. "An improved long short term memory network for intrusion detection." PLOS ONE 18, no. 8 (2023): e0284795. http://dx.doi.org/10.1371/journal.pone.0284795.

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Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to ach

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Eko, Sediyono, Ngudi Wahyuni Sri, and Sembiring Irwan. "Optimizing the long short-term memory algorithm to improve the accuracy of infectious diseases prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2893–903. https://doi.org/10.11591/ijai.v13.i3.pp2893-2903.

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This study discusses the implementation of the proposed optimized long short term memory (LSTM) to predict the number of infectious disease cases that spread in Central Java, Indonesia. The proposed model is developed by optimizing the output layer, which affects the output value of the cell state. This study used cases of four infectious diseases in Indonesia's Central Java Province, namely COVID-19, dengue, diarrhea, and hepatitis A. This model was compared to basic LSTM and MinMax schaler LSTM improvement to see the difference in the accuracy of each disease. The results showed a significan

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Bhandarkar, Tanvi, Vardaan K, Nikhil Satish, S. Sridhar, R. Sivakumar, and Snehasish Ghosh. "Earthquake trend prediction using long short-term memory RNN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1304–12. https://doi.org/10.11591/ijece.v9i2.pp1304-1312.

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The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN

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Song, Tianyu, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou, and Jinggang Chu. "Flash Flood Forecasting Based on Long Short-Term Memory Networks." Water 12, no. 1 (2019): 109. http://dx.doi.org/10.3390/w12010109.

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Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory

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Xu, Wei, Yanan Jiang, Xiaoli Zhang, Yi Li, Run Zhang, and Guangtao Fu. "Using long short-term memory networks for river flow prediction." Hydrology Research 51, no. 6 (2020): 1358–76. http://dx.doi.org/10.2166/nh.2020.026.

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Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of

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Bhandarkar, Tanvi, Vardaan K, Nikhil Satish, S. Sridhar, R. Sivakumar, and Snehasish Ghosh. "Earthquake trend prediction using long short-term memory RNN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1304. http://dx.doi.org/10.11591/ijece.v9i2.pp1304-1312.

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<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Netw

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Guenoukpati, Agbassou, Akuété Pierre Agbessi, Adekunlé Akim Salami, and Yawo Amen Bakpo. "Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting." Energies 17, no. 19 (2024): 4914. http://dx.doi.org/10.3390/en17194914.

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To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various

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Wang, Jianyong, Lei Zhang, Yuanyuan Chen, and Zhang Yi. "A New Delay Connection for Long Short-Term Memory Networks." International Journal of Neural Systems 28, no. 06 (2018): 1750061. http://dx.doi.org/10.1142/s0129065717500617.

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Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is a

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Yang, Tianyi, Quanming Zhao, and Yifan Meng. "Ultra-short-term Photovoltaic Power Prediction Based on Multi-head ProbSparse Self-attention and Long Short-term Memory." Journal of Physics: Conference Series 2558, no. 1 (2023): 012007. http://dx.doi.org/10.1088/1742-6596/2558/1/012007.

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Abstract To provide accurate predictions of photovoltaic (PV) power generation, an MHPSA-LSTM ultra-short-term multipoint PV power prediction model combining Multi-head ProbSparse self-attention (MHPSA) and long short-term memory (LSTM) network is posited. The MHPSA is first used to capture information dependencies at a distance. Secondly, the LSTM is used to enhance the local correlation. At last, a pooling layer is added after LSTM to reduce the parameters of the fully-connected layer and alleviate overfitting, thus improving the prediction accuracy. The MHPSA-LSTM model is validated on a PV

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Wundari, Farhan, Muhammad Nathan Asy Syaiba Amien, and Dida Haiman Irtsa. "Identifying Fake News Using Long-Short Term Memory Model." Journal of Dinda : Data Science, Information Technology, and Data Analytics 4, no. 1 (2024): 28–34. http://dx.doi.org/10.20895/dinda.v4i1.1424.

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Designed to deceive readers and manipulate public opinion, fake news can be created for a variety of reasons ranging from political propaganda to generating revenue through clickbait. Another significant challenge in combating fake news is the difficult balance between curbing misinformation and preserving free speech, though some argue for stricter regulations to control the spread of fake news. Thus, the purpose of this study is to identify fake news using Long-Short Term Memory (LSTM). LSTM models are often used to analyze the linguistic features of news articles or social media posts. The

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Sugiartawan, Putu, Agus Aan Jiwa Permana, and Paholo Iman Prakoso. "Forecasting Kunjungan Wisatawan Dengan Long Short Term Memory (LSTM)." Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) 1, no. 1 (2018): 43–52. http://dx.doi.org/10.33173/jsikti.5.

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Bali is one of the favorite tourist attractions in Indonesia, where the number of foreign tourists visiting Bali is around 4 million over 2015 (Dispar Bali). The number of tourists visiting is spread in various regions and tourist attractions that are located in Bali. Although tourist visits to Bali can be said to be large, the visit was not evenly distributed, there were significant fluctuations in tourist visits. Forecasting or forecasting techniques can find out the pattern of tourist visits. Forecasting technique aims to predict the previous data pattern so that the next data pattern

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Lv, Liujia, Weijian Kong, Jie Qi, and Jue Zhang. "An improved long short-term memory neural network for stock forecast." MATEC Web of Conferences 232 (2018): 01024. http://dx.doi.org/10.1051/matecconf/201823201024.

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This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in th

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