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Artículos de revistas sobre el tema "LSTM ALGORITHM"

Autor: Grafiati

Publicado: 11 de septiembre de 2023

Última modificación: 26 de julio de 2025

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1

Liang, Bushun, Siye Wang, Yeqin Huang, Yiling Liu, and Linpeng Ma. "F-LSTM: FPGA-Based Heterogeneous Computing Framework for Deploying LSTM-Based Algorithms." Electronics 12, no. 5 (2023): 1139. http://dx.doi.org/10.3390/electronics12051139.

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Long Short-Term Memory (LSTM) networks have been widely used to solve sequence modeling problems. For researchers, using LSTM networks as the core and combining it with pre-processing and post-processing to build complete algorithms is a general solution for solving sequence problems. As an ideal hardware platform for LSTM network inference, Field Programmable Gate Array (FPGA) with low power consumption and low latency characteristics can accelerate the execution of algorithms. However, implementing LSTM networks on FPGA requires specialized hardware and software knowledge and optimization sk

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2

Hong, Juan, and Wende Tian. "Prediction in Catalytic Cracking Process Based on Swarm Intelligence Algorithm Optimization of LSTM." Processes 11, no. 5 (2023): 1454. http://dx.doi.org/10.3390/pr11051454.

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Deep learning can realize the approximation of complex functions by learning deep nonlinear network structures, characterizing the distributed representation of input data, and demonstrating the powerful ability to learn the essential features of data sets from a small number of sample sets. A long short-term memory network (LSTM) is a deep learning neural network often used in research, which can effectively extract the dependency relationship between time series data. The LSTM model has many problems such as excessive reliance on empirical settings for network parameters, as well as low mode

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Khataei Maragheh, Hamed, Farhad Soleimanian Gharehchopogh, Kambiz Majidzadeh, and Amin Babazadeh Sangar. "A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification." Mathematics 10, no. 3 (2022): 488. http://dx.doi.org/10.3390/math10030488.

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An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the

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4

Shang, Xiaofeng. "A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP." Journal of Applied Mathematics 2023 (October 25, 2023): 1–13. http://dx.doi.org/10.1155/2023/4573352.

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Flexible job-shop scheduling problem (FJSP) is a new research hotspot in the field of production scheduling. To solve the multiobjective FJSP problem, the production of flexible job shop can run normally and quickly. This research takes into account various characteristics of FJSP problems, such as the need to ensure the continuity and stability of processing, the existence of multiple objectives in the whole process, and the constant complexity of changes. It starts with deep learning neural networks and genetic algorithms. Long short-term memory (LSTM) and convolutional neural networks (CNN)

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5

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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6

Abubaker, Shaikh Shoieb, and Syed Rouf Farid. "Stock Market Prediction Using LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 3178–84. http://dx.doi.org/10.22214/ijraset.2022.42039.

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Abstract: Different machine learning algorithms are discussed in this literature review. These algorithms can be used for predicting the stock market. The prediction of the stock market is one of the challenging tasks that must have to be handled.In this paper, it is discussed how the machine learning algorithms can be used for predicting the stock value. Different attributes are identified that can be used for training the algorithm for this purpose. Some of the other factors are also discussed that can have an effect on the stock value. Keywords: Machine learning, stock market prediction, li

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7

N. Laxmi, Et al. "Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1077–86. http://dx.doi.org/10.17762/ijritcc.v11i10.8627.

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This paper addresses the problem of predicting insulin dosage in diabetes patients using the PSO-LSTM, COA-LSTM, and LOA-LSTM algorithms. Accurate insulin dosage prediction is crucial in effectively managing Diabetes and maintaining blood glucose levels within the desired range. The study proposes a novel approach that combines particle swarm optimization (PSO) with the long short-term memory (LSTM) model. PSO is used to optimize the LSTM's parameters, enhancing its prediction capabilities specifically for insulin dosage. Additionally, two other techniques, COA-LSTM and LOA-LSTM, are introduce

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8

Fang, Wei, Jinguang Jiang, Shuangqiu Lu, et al. "A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages." Remote Sensing 12, no. 2 (2020): 256. http://dx.doi.org/10.3390/rs12020256.

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Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynam

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9

Li, Hailin, Zhizhou Zhao, and Xue Du. "Research and Application of Deformation Prediction Model for Deep Foundation Pit Based on LSTM." Wireless Communications and Mobile Computing 2022 (July 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/9407999.

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Deep foundation pit is a door with a long history, but it has new disciplines; in this paper, firstly, the modeling method and process of LSTM (long short-term memory) network are discussed in detail, then the optimization algorithm used in the model is described in detail, and the parameter selection methods such as initial learning rate, activation function, and iteration number related to LSTM network training are introduced in detail. LSTM network is used to process the deformation data of deep foundation pit, and random gradient descent, momentum, Nesterov, RMSProp, AdaGmd, and Adam algor

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10

Qin, Wanting, Jun Tang, Cong Lu, and Songyang Lao. "Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example." Computational Geosciences 25, no. 3 (2021): 1005–23. http://dx.doi.org/10.1007/s10596-021-10037-2.

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AbstractTrajectory data can objectively reflect the moving law of moving objects. Therefore, trajectory prediction has high application value. Hurricanes often cause incalculable losses of life and property, trajectory prediction can be an effective means to mitigate damage caused by hurricanes. With the popularization and wide application of artificial intelligence technology, from the perspective of machine learning, this paper trains a trajectory prediction model through historical trajectory data based on a long short-term memory (LSTM) network. An improved LSTM (ILSTM) trajectory predicti

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11

Ulum, Dinar Syahid Nur, and Abba Suganda Girsang. "Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction." International Journal of Innovative Research and Scientific Studies 5, no. 2 (2022): 121–33. http://dx.doi.org/10.53894/ijirss.v5i2.415.

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Producing the best possible predictive result from long-short term memory (LSTM) requires hyperparameters to be tuned by a data scientist or researcher. A metaheuristic algorithm was used to optimize hyperparameter tuning and reduce the computational complexity to improve the manual process. Symbiotic organism search (SOS), which was introduced in 2014, is an algorithm that simulates the symbiotic interactions that organisms use to survive in an ecosystem. SOS offers an advantage over other metaheuristic algorithms in that it has fewer parameters, allowing it to avoid parameter determination e

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12

Chen, Wantong, Hailong Wu, and Shiyu Ren. "CM-LSTM Based Spectrum Sensing." Sensors 22, no. 6 (2022): 2286. http://dx.doi.org/10.3390/s22062286.

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This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the

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Yang, Fan, Kewen Xia, Shurui Fan, and Zhiwei Zhang. "Equalization Optimizer-Based LSTM Application in Reservoir Identification." Computational Intelligence and Neuroscience 2022 (September 9, 2022): 1–20. http://dx.doi.org/10.1155/2022/7372984.

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In recent years, the use of long short-term memory (LSTM) has made significant contributions to various fields and the use of intelligent optimization algorithms combined with LSTM is also one of the best ways to improve model shortcomings and increase classification accuracy. Reservoir identification is a key and difficult point in the process of logging, so using LSTM to identify the reservoir is very important. To improve the logging reservoir identification accuracy of LSTM, an improved equalization optimizer algorithm (TAFEO) is proposed in this paper to optimize the number of neurons and

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14

Wójcikowski, Marek. "Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices." Sensors 22, no. 1 (2021): 164. http://dx.doi.org/10.3390/s22010164.

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This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including

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15

Park, Kang Yun, and Yong Sang Lee. "Deep Learning Algorithm Exploration for Automated Korean essay Scoring." Korean Society for Educational Evaluation 35, no. 3 (2022): 465–88. http://dx.doi.org/10.31158/jeev.2022.35.3.465.

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This study was carried out for the purpose of searching for the optimal algorithm for automated scoring system of Korean essay through the comparison of deep learning-based learning models. For this purpose, in this study, deep learning algorithms such as Recurrent Neural Network (RNN), Long-Short-Term-Memory (LSTM), and Gated-Recurrent-Unit (GRU) algorithms were compared. The performance of each algorithm was evaluated based on classification accuracy, precision, recall, and F1. The empirical results showed that the LSTM and GRU algorithm-based models performed better than RNN. Although there

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Zhen, Tao, Lei Yan, and Peng Yuan. "Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm." Algorithms 12, no. 12 (2019): 253. http://dx.doi.org/10.3390/a12120253.

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Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different wa

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17

Li, Xueguang, and Menchita F. Dumlao. "SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm." Frontiers in Computing and Intelligent Systems 4, no. 3 (2023): 71–77. http://dx.doi.org/10.54097/fcis.v4i3.11146.

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New energy vehicles are developing rapidly in the world, China and Europe are vigorously promoting new energy vehicles. The State of Charge (SOC) is circumscribed as the remaining charge of the lithium battery (Li-ion), that indicates the driving range of a pure electric vehicle. Additionally, it is the basis for SOH and fault state prediction. Nevertheless, the SOC is incapable of measuring directly. In this paper, an LSTM-Attention-R network framework is proposed. The LSTM algorithm is accustomed to present the timing information and past state information of the lithium battery data. The At

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18

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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19

Yang, Yifang. "Application of LSTM Neural Network Technology Embedded in English Intelligent Translation." Computational Intelligence and Neuroscience 2022 (September 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/1085577.

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With the rapid development of computer technology, the loss of long-distance information in the transmission process is a prominent problem faced by English machine translation. The self-attention mechanism is combined with convolutional neural network (CNN) and long-term and short-term memory network (LSTM). An English intelligent translation model based on LSTM-SA is proposed, and the performance of this model is compared with other deep neural network models. The study adds SA to the LSTM neural network model and constructs the English translation model of LSTM-SA attention embedding. Compa

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20

Wu, Yijun, and Yonghong Qin. "Machine translation of English speech: Comparison of multiple algorithms." Journal of Intelligent Systems 31, no. 1 (2022): 159–67. http://dx.doi.org/10.1515/jisys-2022-0005.

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Abstract In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neura

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Kundu, Ripan Kumar, Akhlaqur Rahman, and Shuva Paul. "A Study on Sensor System Latency in VR Motion Sickness." Journal of Sensor and Actuator Networks 10, no. 3 (2021): 53. http://dx.doi.org/10.3390/jsan10030053.

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One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in V

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Ayuningtyas, Puji, Siti Khomsah, and Sudianto Sudianto. "Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU." JISKA (Jurnal Informatika Sunan Kalijaga) 9, no. 3 (2024): 217–29. http://dx.doi.org/10.14421/jiska.2024.9.3.217-229.

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In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling

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Yang, Zhengcai, Zhengjun Wu, Yilin Wang, and Haoran Wu. "Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction." World Electric Vehicle Journal 15, no. 4 (2024): 173. http://dx.doi.org/10.3390/wevj15040173.

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Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an algorithm that leverages the deep deterministic policy gradient (DDPG) reinforcement learning, integrated with a long short-term memory (LSTM) trajectory prediction model, termed as LSTM-DDPG. In the proposed LSTM-DDPG model, the LSTM state module transforms the observed values from the

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Han, Lijia, Xiaohong Wang, Yin Yu, and Duan Wang. "Power Load Forecast Based on CS-LSTM Neural Network." Mathematics 12, no. 9 (2024): 1402. http://dx.doi.org/10.3390/math12091402.

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Load forecast is the foundation of power system operation and planning. The forecast results can guide the power system economic dispatch and security analysis. In order to improve the accuracy of load forecast, this paper proposes a forecasting model based on the combination of the cuckoo search (CS) algorithm and the long short-term memory (LSTM) neural network. Load data are specific data with time series characteristics and periodicity, and the LSTM algorithm can control the information added or discarded through the forgetting gate, so as to realize the function of forgetting or memorizin

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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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Song, Lijun, Peiyu Xu, Xing He, Yunlong Li, Jiajie Hou, and Haoyu Feng. "Improved LSTM Neural Network-Assisted Combined Vehicle-Mounted GNSS/SINS Navigation and Positioning Algorithm." Electronics 12, no. 17 (2023): 3726. http://dx.doi.org/10.3390/electronics12173726.

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Aiming at the problem of the combined navigation system of on-board GNSS (global navigation satellite system)/SINS (strapdown inertial navigation system), the accuracy of the combined navigation system decreases due to the dispersion of the SINS over time and under the condition of No GNSS signals. An improved LSTM (long short-term memory) neural network in No GNSS signal conditions is proposed to assist the combination of navigation data and the positioning algorithm. When the GNSS signal is normal input, the current on-board combination of the navigation module’s output sensor data informati

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Cui, Chen, and Jian Chen. "Industrial Process Modeling Method Using Arima-ACNN-LSTM Coupling Algorithm." Journal of Physics: Conference Series 2890, no. 1 (2024): 012038. http://dx.doi.org/10.1088/1742-6596/2890/1/012038.

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Abstract Considering the difficulty to built mechanisms models for complex industrial processes, this paper proposes a data-driven modelling method. This method includes a process for processing real industrial data and an improved deep learning algorithm. The data processing flow completes the basic preparation work for the raw data, and the improved algorithm calculates the model based on this. The improved algorithm is Arima-ACNN-LSTM coupling algorithm, which can enhance the original LSTM algorithm by adopting CNN and Attention mechanisms. The experimental results show that, the accuracy o

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Zhu, Mingfu, Yaxing Liu, Panke Qin, et al. "Improving long short-term memory (LSTM) networks for arbitrage spread forecasting: integrating cuckoo and zebra algorithms in chaotic mapping space for enhanced accuracy." PeerJ Computer Science 10 (December 12, 2024): e2552. https://doi.org/10.7717/peerj-cs.2552.

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Long short-term memory (LSTM) networks, widely used for financial time series forecasting, face challenges in arbitrage spread prediction, especially in hyperparameter tuning for large datasets. These issues affect model complexity and adaptability to market dynamics. Existing heuristic algorithms for LSTM often struggle to capture the complex dynamics of futures spread data, limiting prediction accuracy. We propose an integrated Cuckoo and Zebra Algorithms-optimised LSTM (ICS-LSTM) network for arbitrage spread prediction. This method replaces the Lévy flight in the Cuckoo algorithm with the Z

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Gou, Liming, Jian Zhang, Lihao Wen, and Yu Fan. "State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China." Sustainability 16, no. 10 (2024): 4099. http://dx.doi.org/10.3390/su16104099.

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The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that

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Wu, Jizhou, Hongmin Zhang, and Xuanhao Gao. "Radar High-Resolution Range Profile Target Recognition by the Dual Parallel Sequence Network Model." International Journal of Antennas and Propagation 2021 (December 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/4699373.

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Using traditional neural network algorithms to adapt to high-resolution range profile (HRRP) target recognition is a complex problem in the current radar target recognition field. Under the premise of in-depth analysis of the long short-term memory (LSTM) network structure and algorithm, this study uses an attention model to extract data from the sequence. We build a dual parallel sequence network model for rapid classification and recognition and to effectively improve the initial LSTM network structure while reducing network layers. Through demonstration by designing control experiments, the

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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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Akhter, Jamila, Noman Naseer, Hammad Nazeer, Haroon Khan, and Peyman Mirtaheri. "Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer Interface Application." Sensors 24, no. 10 (2024): 3040. http://dx.doi.org/10.3390/s24103040.

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Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an

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Pu, Long, and Chun Wang. "Combined State-of-Charge Estimation Method for Lithium-Ion Batteries Using Long Short-Term Memory Network and Unscented Kalman Filter." Energies 18, no. 5 (2025): 1106. https://doi.org/10.3390/en18051106.

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The state of charge (SOC) of lithium-ion batteries (LIBs) is a pivotal metric within the battery management system (BMS) of electric vehicles (EVs). An accurate SOC is crucial to ensuring both the safety and the operational efficiency of a battery. The unscented Kalman filter (UKF) is a classic and commonly used method among the various SOC estimation algorithms. However, an unscented transform (UT) utilized in the algorithm struggles to completely simulate the probability density function of actual data. Additionally, inaccuracies in the identification of battery model parameters can lead to

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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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Vaish, Rohan Kumar. "Stock Price Prediction Using LSTM Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34831.

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ock price prediction is a challenging task due to the volatile and non-linear nature of the financial markets. This paper explores the application of the Long Short-Term Memory (LSTM) neural network, a type of Recurrent Neural Network (RNN), for predicting stock prices. The study demonstrates the effectiveness of LSTM in capturing the temporal dependencies in stock price data, comparing its performance with traditional models.

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Nguyen-Da, Thao, Yi-Min Li, Chi-Lu Peng, Ming-Yuan Cho, and Phuong Nguyen-Thanh. "Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces." Sustainability 15, no. 9 (2023): 7179. http://dx.doi.org/10.3390/su15097179.

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The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic limitation on foreign travel. This research proposes a hybrid algorithm, combined convolution neural network (CNN) and long short-term memory (LSTM), to accurately predict the tourism demand in Vietnam and some provinces. The number of new COVID-19 cases worldwide and in Vietnam is considered a promising feature in predicting algorithms, which is nov

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ÇETİNER, Halit. "Recurrent Neural Network Based Model Development for Energy Consumption Forecasting." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 11, no. 3 (2022): 759–69. http://dx.doi.org/10.17798/bitlisfen.1077393.

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The world population is increasing day by day. As a result, limited resources are decreasing day by day. On the other hand, the amount of energy needed is constantly increasing. In this sense, decision makers must accurately estimate the amount of energy that society will require in the coming years and make plans accordingly. These plans are of critical importance for the peace and welfare of society. Based on the energy consumption values of Germany, it is aimed at estimating the energy consumption values with the GRU, LSTM, and proposed hybrid LSTM-GRU methods, which are among the popular R

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Chen, Jiayu, Lisang Liu, Kaiqi Guo, Shurui Liu, and Dongwei He. "Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models." Applied Sciences 14, no. 14 (2024): 5966. http://dx.doi.org/10.3390/app14145966.

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Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecas

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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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Lan, Pu, Kewen Xia, Yongke Pan, and Shurui Fan. "An Improved Equilibrium Optimizer Algorithm and Its Application in LSTM Neural Network." Symmetry 13, no. 9 (2021): 1706. http://dx.doi.org/10.3390/sym13091706.

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An improved equilibrium optimizer (EO) algorithm is proposed in this paper to address premature and slow convergence. Firstly, a highly stochastic chaotic mechanism is adopted to initialize the population for range expansion. Secondly, the capability to conduct global search to jump out of local optima is enhanced by assigning adaptive weights and setting adaptive convergence factors. In addition 25 classical benchmark functions are used to validate the algorithm. As revealed by the analysis of the accuracy, speed, and stability of convergence, the IEO algorithm proposed in this paper signific

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41

Wang, Yizhou, Yishuo Meng, Jiaxing Wang, and Chen Yang. "LSTM-CRP: Algorithm-Hardware Co-Design and Implementation of Cache Replacement Policy Using Long Short-Term Memory." Big Data and Cognitive Computing 8, no. 10 (2024): 140. http://dx.doi.org/10.3390/bdcc8100140.

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As deep learning has produced dramatic breakthroughs in many areas, it has motivated emerging studies on the combination between neural networks and cache replacement algorithms. However, deep learning is a poor fit for performing cache replacement in hardware implementation because its neural network models are impractically large and slow. Many studies have tried to use the guidance of the Belady algorithm to speed up the prediction of cache replacement. But it is still impractical to accurately predict the characteristics of future access addresses, introducing inaccuracy in the discriminat

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42

Chen, Ping, JianYi Zhong, and YueChao Zhu. "Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching." Computational Intelligence and Neuroscience 2022 (January 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/5755327.

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The question answering link in the traditional teaching method is analyzed to optimize the shortcomings and deficiencies of the existing question-and-answer (Q&A) machines and solve the problems of financial students’ difficulty in answering questions. Firstly, the difficulties and needs of students in answering questions are understood. Secondly, the traditional algorithm principle by the Q&A system is introduced and analyzed, and the problems and defects existing in the traditional Q&A system are summarized. On this basis, deep learning algorithms are introduced, the long short-t

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Ratković, Kruna, Nataša Kovač, and Marko Simeunović. "Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro." Applied Sciences 13, no. 18 (2023): 10152. http://dx.doi.org/10.3390/app131810152.

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Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter confi

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44

Wang, Weisheng, Yongkang Hao, Xiaozhen Zheng, et al. "Runoff Prediction for Hydrological Applications Using an INFO-Optimized Deep Learning Model." Processes 12, no. 8 (2024): 1776. http://dx.doi.org/10.3390/pr12081776.

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Runoff prediction is essential in water resource management, environmental protection, and agricultural development. Due to the large randomness, high non-stationarity, and low prediction accuracy of nonlinear effects of the traditional model, this study proposes a runoff prediction model based on the improved vector weighted average algorithm (INFO) to optimize the convolutional neural network (CNN)-bidirectional long short-term memory (Bi-LSTM)-Attention mechanism. First, the historical data are analyzed and normalized. Secondly, CNN combined with Attention is used to extract the depth local

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45

Jana, Radha Krishna, Dharmpal Singh, Saikat Maity, and Hrithik Paul. "A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets." Indian Journal Of Science And Technology 17, no. 7 (2024): 610–16. http://dx.doi.org/10.17485/ijst/v17i7.3017.

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Objectives: The objective of this study is to introduce a hybrid model for analyzing the people sentiment on covid-19 tweets. Methods: We used a total no. of 27,500 datasets, 70% of the data sets for training and reserved the other 30% for testing. Due to this separation 19,250 samples are used for training, the remaining 8,250 were used to evaluate the accuracy of the test. This paper proposes a technique for sentiment analysis that integrates deep learning, genetic algorithms (GA), and social media sentiment. For more accuracy and performance, we here suggested a hybrid genetic algorithm-bas

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46

Sudhakaran, P., Subbiah Swaminathan, D. Yuvaraj, and S. Shanmuga Priya. "Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 05 (2020): 150. http://dx.doi.org/10.3991/ijim.v14i05.13361.

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Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud

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47

Zhao, Ziquan, and Jing Bai. "Ultra-Short-Term Wind Power Forecasting Based on the MSADBO-LSTM Model." Energies 17, no. 22 (2024): 5689. http://dx.doi.org/10.3390/en17225689.

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To address the challenges of the strong randomness and intermittency of wind power generation that affect wind power grid integration, power system scheduling, and the safe and stable operation of the system, an improved Dung Beetle Optimization Algorithm (MSADBO) is proposed to optimize the hyperparameters of the Long Short-Term Memory neural network (LSTM) for ultra-short-term wind power forecasting. By applying Bernoulli mapping for population initialization, the model’s sensitivity to wind power fluctuations is reduced, which accelerates the algorithm’s convergence speed. Incorporating an

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48

Suneetha Rani R, Sri Vinithri Chowdary D, Uday Kiran C H, Deekshith K, and Alekhya V. "Bitcoin price prediction based on linear regression and lstm." South Asian Journal of Engineering and Technology 12, no. 3 (2022): 87–95. http://dx.doi.org/10.26524/sajet.2022.12.44.

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   Forecasting can be used in many fields such as crypto currency prediction, financial entities, supermarkets etc. We get the time series date which we use to feed the data into the algorithm is given by Y finance with this we get refreshed data every day. The stock market prediction or forecasting helps customers and brokers get a brief view of how the market behaves for the coming years. Many models are currently in use Like Regression techniques, Long Short-Term Memory algorithm etc. FB Prophet is proven to perform better than most other Algorithms with better accuracy. F

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Lin, Xiaoyu, Hang Yu, Meng Wang, Chaoen Li, Zi Wang, and Yin Tang. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method." Energies 14, no. 16 (2021): 4785. http://dx.doi.org/10.3390/en14164785.

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Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power

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50

Sediyono, Eko, Sri Ngudi Wahyuni, and Irwan Sembiring. "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. http://dx.doi.org/10.11591/ijai.v13.i3.pp2893-2903.

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<p><span lang="EN-US">This study discusses the implementation of the proposed optimized</span><span lang="EN-US">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 diff

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