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Journal articles on the topic 'Memory (LSTM)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 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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Ding, Xianghua, Jingnan Wang, Yiqi Liu, and Uk Jung. "Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory Autoencoder Network." Applied Sciences 15, no. 5 (2025): 2861. https://doi.org/10.3390/app15052861.

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“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role in domains such as industrial manufacturing, financial transactions, and other related domains. In the context of Industry 4.0, the proliferation of sensors has resulted in a massive influx of time series data, making the anomaly detection of such multivariate time series data a pop

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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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Karyadi, Yadi. "Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (2022): 671–84. http://dx.doi.org/10.35957/jatisi.v9i1.1588.

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Kualitas udara menjadi salah satu masalah utama di kota besar. Salah satu cara pengendalian kualitas udara adalah dengan cara memprediksi beberapa parameter utama dengan menggunakan algoritma deep learning. Penelitian ini menggunakan metoda deep learning yang merupakan bagian dari Recurrent Neural network yaitu Long Short Term Memory, Bidirectional Long Short Term Memory, dan Gated Recurrent Unit yang diterapkan pada permasalahan memprediksi data time series kualitas udara dengan parameter suhu, kelembaban, particular matter PM10, dan Indeks Standar Pencemar Udara (ISPU). Dari hasil pengujian

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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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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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Fu, Kun, Yang Li, Wenkai Zhang, Hongfeng Yu, and Xian Sun. "Boosting Memory with a Persistent Memory Mechanism for Remote Sensing Image Captioning." Remote Sensing 12, no. 11 (2020): 1874. http://dx.doi.org/10.3390/rs12111874.

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The encoder–decoder framework has been widely used in the remote sensing image captioning task. When we need to extract remote sensing images containing specific characteristics from the described sentences for research, rich sentences can improve the final extraction results. However, the Long Short-Term Memory (LSTM) network used in decoders still loses some information in the picture over time when the generated caption is long. In this paper, we present a new model component named the Persistent Memory Mechanism (PMM), which can expand the information storage capacity of LSTM with an exter

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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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Putera Khano, Muhammad Nazhif Abda, Dewi Retno Sari Saputro, Sutanto Sutanto, and Antoni Wibowo. "SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 4 (2023): 2235–42. http://dx.doi.org/10.30598/barekengvol17iss4pp2235-2242.

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Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of

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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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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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Roy, Sanjiban Sekhar, Ali Ismail Awad, Lamesgen Adugnaw Amare, Mabrie Tesfaye Erkihun, and Mohd Anas. "Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models." Future Internet 14, no. 11 (2022): 340. http://dx.doi.org/10.3390/fi14110340.

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In today’s world, phishing attacks are gradually increasing, resulting in individuals losing valuables, assets, personal information, etc., to unauthorized parties. In phishing, attackers craft malicious websites disguised as well-known, legitimate sites and send them to individuals to steal personal information and other related private details. Therefore, an efficient and accurate method is required to determine whether a website is malicious. Numerous methods have been proposed for detecting malicious uniform resource locators (URLs) using deep learning, machine learning, and other approach

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Becerra Muriel, Cristian. "Forecasting the Future Value of a Colombian Investment Fund with LSTM Recurrent Neural Networks (LSTM)." System Analysis & Mathematical Modeling 6, no. 1 (2024): 78–88. http://dx.doi.org/10.17150/2713-1734.2024.6(1).78-88.

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Recurrent neural networks are a tool that is currently used in time series, a widespread use of these networks is the forecasting of future prices in financial time series. One widely used recurrent neural network model is the LSTM (Long Short-Term Memory) model, proposed by Sepp Hochreiter and Jürgen Schmidhuber in their paper called LONG SHORT-TERM MEMORY published in 1997. This model solves the long term memory problem of recurrent neural networks by adding a selective memory cell which acts as a "filter" to choose what kind of information is important to keep and what kind of information i

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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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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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Raut, Supriya. "Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30115.

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The objective of this research is to develop a Deep Learning model to forecast the stock price, by using the variant of Long Short-Term Memory. This model predicts the close price of the stock for the future selected date, choosing as inputs the following data: open, high, low, adj close and close prices. This model shows a comparative analysis between three different LSTM networks: Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (Stacked LSTM), and Stacked Bi-directional Long Short-Term Memory (Stacked Bidirectional LSTM) concluding which one is the best and implementing the mod

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Fernando, Yeremias. "Analisis Sentimen pada Ulasan Konsumen Ayam Goreng Nelongso di Google Maps Menggunakan Metode Bidirectional Long Short-Term Memory (Bi-LSTM)." Jurnal Ilmiah Mahasiswa Sains Unisma Malang 3, no. 1 (2025): 65. https://doi.org/10.33474/jimsum.v3i1.26592.

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This study analyzes consumer review sentiments of Ayam Goreng Nelongso on Google Maps using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. The data used consists of 4,450 reviews, with varying training and testing data ratios ranging from 90:10 to 10:90. The evaluation results show that the Bi-LSTM model performs excellently, with an average accuracy of 98.33%, precision of 99.44%, recall of 99.44%, and F1-score of 99.44%. These findings demonstrate that Bi-LSTM can reliably and consistently identify positive, negative, and neutral sentiments in consumer review data, providing valu

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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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Yudi Widhiyasana, Transmissia Semiawan, Ilham Gibran Achmad Mudzakir, and Muhammad Randi Noor. "Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 10, no. 4 (2021): 354–61. http://dx.doi.org/10.22146/jnteti.v10i4.2438.

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Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan

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Chawngthu, Zoremsanga, and Hussain Jamal. "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. https://doi.org/10.17485/IJST/v17i18.2505.

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Abstract <strong>Objectives:</strong> 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. <strong>Methods:</strong> 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

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Chen, Nuo. "Exploring the development and application of LSTM variants." Applied and Computational Engineering 53, no. 1 (2024): 103–7. http://dx.doi.org/10.54254/2755-2721/53/20241288.

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Long Short-Term Memory (LSTM) is receiving increasing attention as the development of deep learning technology. The gate structure of LSTM enhances long-term memory, forming its superior capacity to complete tasks that challenge traditional RNN. However, considering the wide variety of applications, a comprehensive understanding of the development and application of the model, which is vital for future research, is comparatively lacking. Therefore, this paper is produced with the hope of offering an overview of the development of LSTM. It shows the process of development from RNN to LSTM and e

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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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Pan, Yu, Jing Xu, Maolin Wang, et al. "Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4683–90. http://dx.doi.org/10.1609/aaai.v33i01.33014683.

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Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs very difficult. To address this challenge, we pro

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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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Wang, Hao. "Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models." ITM Web of Conferences 70 (2025): 04008. https://doi.org/10.1051/itmconf/20257004008.

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Accurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions. In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of improving accuracy remains. This research introduces an innovative approach to predicting stock prices, employing two sophisticated models: Long Short-Tenn Memory (LSTM) and Bidirectional Long Short-Tenn Memory (Bi-LSTM) networks. Through rigorous analysis, the research demonstrates that, with proper

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Zou, Hangtao, and Shibing Zhou. "Deep Multi-View Clustering Optimized by Long Short-Term Memory Network." Symmetry 17, no. 2 (2025): 161. https://doi.org/10.3390/sym17020161.

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Long short-term memory (LSTM) networks have shown great promise in sequential data analysis, especially in time-series and natural language processing. However, their potential for multi-view clustering has been largely underexplored. In this paper, we introduce a novel approach called deep multi-view clustering optimized by long short-term memory network (DMVC-LSTM), which leverages the sequential modeling capability of LSTM to effectively integrate multi-view data. By capturing complex interdependencies and nonlinear relationships between views, DMVC-LSTM improves clustering accuracy and rob

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Zhang, Xiaoyu, Yongqing Li, Song Gao, and Peng Ren. "Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory." Journal of Marine Science and Engineering 9, no. 5 (2021): 514. http://dx.doi.org/10.3390/jmse9050514.

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This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical predict

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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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Liu, Guohua, Mirco Migliavacca, Christian Reimers, et al. "DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology." Geoscientific Model Development 17, no. 17 (2024): 6683–701. http://dx.doi.org/10.5194/gmd-17-6683-2024.

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Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation develop

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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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Fajar Abdillah, Moh, and Kusnawi Kusnawi. "Comparative Analysis of Long Short-Term Memory Architecture for Text Classification." ILKOM Jurnal Ilmiah 15, no. 3 (2023): 455–64. http://dx.doi.org/10.33096/ilkom.v15i3.1906.455-464.

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Text classification which is a part of NLP is a grouping of objects in the form of text based on certain characteristics that show similarities between one document and another. One of methods used in text classification is LSTM. The performance of the LSTM method itself is influenced by several things such as datasets, architecture, and tools used to classify text. On this occasion, researchers analyse the effect of the number of layers in the LSTM architecture on the performance generated by the LSTM method. This research uses IMDB movie reviews data with a total of 50,000 data. The data con

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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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Zhang, Jinyuan, Yan Feng, Jiaxuan Zhang, and Yijun Li. "The Short Time Prediction of the Dst Index Based on the Long-Short Time Memory and Empirical Mode Decomposition–Long-Short Time Memory Models." Applied Sciences 13, no. 21 (2023): 11824. http://dx.doi.org/10.3390/app132111824.

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The Dst index is the geomagnetic storm index used to measure the energy level of geomagnetic storms, and the prediction of this index is of great significance for geomagnetic storm studies and solar activities. In contrast to traditional numerical modeling techniques, machine learning, which emerged decades ago based on rapidly developing computer hardware and software and artificial intelligence methods, has been unprecedentedly developed in geophysics, especially solar–terrestrial space physics. This study uses two machine learning models, the LSTM (Long-Short Time Memory, LSTM) and EMD-LSTM

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Chen, Yiqing, Zongzhu Chen, Kang Li, et al. "Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory." Processes 11, no. 10 (2023): 3011. http://dx.doi.org/10.3390/pr11103011.

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Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently i

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Riyadi, Willy, and Jasmir Jasmir. "Performance Prediction of Airport Traffic Using LSTM and CNN-LSTM Models." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22, no. 3 (2023): 627–38. http://dx.doi.org/10.30812/matrik.v22i3.3032.

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During the COVID-19 pandemic, airports faced a significant drop in passenger numbers, impacting the vital hub of the aircraft transportation industry. This study aimed to evaluate whether Long Short-Term Memory Network (LSTM) and Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) offer more accurate predictions for airport traffic during the COVID-19 pandemic from March to December 2020. The studies involved data filtering, applying min-max scaling, and dividing the dataset into 80% training and 20% testing sets. Parameter adjustment was performed with different optimizer

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Alamri, Nawaf Mohammad H., Michael Packianather, and Samuel Bigot. "Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm." Applied Sciences 13, no. 4 (2023): 2536. http://dx.doi.org/10.3390/app13042536.

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Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. This paper attempts to overcome this problem by optimizing LSTM parameters using the Bees Algorithm (BA), which is a nature-inspired algorithm that mimics the foraging behavior of honey bees. In particular, it was used to optimize the adjustment factors of the learning rate in the forget, input, and output gates, in addition to cell candidate, in bo

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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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Lu, Hongwei. "LSTM-Based Stock Price Prediction." Frontiers in Computing and Intelligent Systems 4, no. 2 (2023): 68–71. http://dx.doi.org/10.54097/fcis.v4i2.10208.

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Predicting stock prices is a job that researchers and analysts have been working on for many years. Investors have shown great interest in this area so that they can better manage their assets. Accurately predicting changes in stock prices in the market can generate huge economic benefits. In view of the high noise, nonlinearity and non-stationarity of stock price data, which makes it very difficult to accurately predict the stock price, this paper intends to use the long-short-term memory network (Long Short-Term Memory, LSTM) Recurrent Neural Network (RNN) architecture to establish A model t

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Liu, Zhen, An-Ran Zhao, and Si-Lu Liu. "Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model." Applied Sciences 14, no. 21 (2024): 9735. http://dx.doi.org/10.3390/app14219735.

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Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wol

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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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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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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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Snahashis, Kanrar, Chand Giri Nimai, Adak Debjyoti, Paul Suman, Ghosh Saikat, and Das Shreya. "LSTM Models: A Comprehensive Analysis and Applications." Advancement in Image Processing and Pattern Recognition 6, no. 1 (2023): 44–53. https://doi.org/10.5281/zenodo.7861337.

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<em>Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that is designed to handle the problem of vanishing gradients in traditional RNNs. LSTM models are widely used in a variety of applications such as speech recognition, natural language processing, and image captioning, among others. The architecture of an LSTM model consists of a memory cell, three gates (input, forget, and output), and an output. The memory cell is responsible for maintaining information over time, while the gates regulate the flow of information into and out of the memory cell. The forget gate dete

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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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Vaseekaran S, Pragadeeswaran S, and Mrs S Janani. "Brain Tumour Prediction Using Temporal Memory." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 02 (2025): 235–39. https://doi.org/10.47392/irjaeh.2025.0033.

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Brain tumor prediction plays a critical role in advancing early diagnosis and effective treatment planning, directly impacting patient survival rates. Traditional methods for detecting brain tumors involve extensive image processing and manual feature extraction, which can be time-consuming and prone to errors. Recent advancements in deep learning have introduced neural networks, specifically Long Short-Term Memory (LSTM) networks, as effective tools for handling the sequential nature of medical imaging data. This study presents an approach leveraging LSTM-based models for brain tumor predicti

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Wang, Yonggang, Zhida Li, and Nannan Zhang. "A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas." Sensors 24, no. 7 (2024): 2340. http://dx.doi.org/10.3390/s24072340.

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As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas’s oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing

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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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Nauman, Muhammad Asif, Mahlaqa Saeed, Oumaima Saidani, et al. "IoT and Ensemble Long-Short-Term-Memory-Based Evapotranspiration Forecasting for Riyadh." Sensors 23, no. 17 (2023): 7583. http://dx.doi.org/10.3390/s23177583.

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Evapotranspiration (ET) is the fundamental component of efficient water resource management. Accurate forecasting of ET is essential for efficient water utilization in agriculture. ET forecasting is a complex process due to the requirements of large meteorological variables. The recommended approach is based on the Internet of Things (IoT) and an ensemble-learning-based approach for meteorological data collection and ET forecasting with limited meteorological conditions. IoT is part of the recommended approach to collect real-time data on meteorological variables. The daily maximum temperature

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Han, Shipeng, Zhen Meng, Xingcheng Zhang, and Yuepeng Yan. "Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions." Micromachines 12, no. 2 (2021): 214. http://dx.doi.org/10.3390/mi12020214.

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Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neur

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