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Literatura académica sobre el tema "LSTM ALGORITHM"

Autor: Grafiati

Publicado: 11 de septiembre de 2023

Última modificación: 26 de julio de 2025

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Artículos de revistas sobre el tema "LSTM ALGORITHM"

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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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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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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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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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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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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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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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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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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Tesis sobre el tema "LSTM ALGORITHM"

Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level

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Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of

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Hambarek, Djamel Eddine. "Développement d'une méthodologie d'essais dynamiques appliquée à la mise au point moteur." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0035.

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Les travaux de cette thèse de doctorat s’inscrivent dans le contexte d’évolution desnormes de dépollution des moteurs thermiquescouplée aux exigences de baisse de la consommation des véhicules. La méthodologie développée tente de répondre avec un processus industriel efficace aux exigences d’émissions en roulage réel, dites RDE (Real Driving Emissions). La méthode proposée est basée sur la technique des plans d’expériences dynamiques utilisant les suites à faible discrépance : les résultats d’essais sont utilisés afin d’entraîner un modèle de réseau de neurones type LSTM capable de prédire l’h

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Malina, Ondřej. "Detekce začátku a konce komplexu QRS s využitím hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442595.

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This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the th

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Olsson, Charlie, and David Hurtig. "An approach to evaluate machine learning algorithms for appliance classification." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20217.

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A cheap and powerful solution to lower the electricity usage and making the residents more energy aware in a home is to simply make the residents aware of what appliances that are consuming electricity. Meaning the residents can then take decisions to turn them off in order to save energy. Non-intrusive load monitoring (NILM) is a cost-effective solution to identify different appliances based on their unique load signatures by only measuring the energy consumption at a single sensing point. In this thesis, a low-cost hardware platform is developed with the help of an Arduino to collect consump

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Freberg, Daniel. "Evaluating Statistical MachineLearning and Deep Learning Algorithms for Anomaly Detection in Chat Messages." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235957.

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Automatically detecting anomalies in text is of great interest for surveillance entities as vast amounts of data can be analysed to find suspicious activity. In this thesis, three distinct machine learning algorithms are evaluated as a chat message classifier is being implemented for the purpose of market surveillance. Naive Bayes and Support Vector Machine belong to the statistical class of machine learning algorithms being evaluated in this thesis and both require feature selection, a side objective of the thesis is thus to find a suitable feature selection technique to ensure mentioned algo

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Almqvist, Olof. "A comparative study between algorithms for time series forecasting on customer prediction : An investigation into the performance of ARIMA, RNN, LSTM, TCN and HMM." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16974.

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Time series prediction is one of the main areas of statistics and machine learning. In 2018 the two new algorithms higher order hidden Markov model and temporal convolutional network were proposed and emerged as challengers to the more traditional recurrent neural network and long-short term memory network as well as the autoregressive integrated moving average (ARIMA). In this study most major algorithms together with recent innovations for time series forecasting is trained and evaluated on two datasets from the theme park industry with the aim of predicting future number of visitors. To dev

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Blanco, Martínez Alejandro. "Study and design of classification algorithms for diagnosis and prognosis of failures in wind turbines from SCADA data." Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2018. http://hdl.handle.net/10803/586097.

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Actualmente las operaciones de mantenimiento preventivo de los parques eólicos se soportan sobre técnicas de Machine Learning para reducir los costes de las paradas no planificadas. Por eso se necesita una predicción de fallos con cierta anticipación que funcione sobre los datos de SCADA. Estos datos necesitan ser procesados en distintas etapas descritas en esta tesis, con resultados publicados en cada una de ellas. En una primera fase se limpian los valores extremos (Outliers), indicando cómo deben ser tratados para no eliminar la información sobre los fallos. En una segunda, las distintas va

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Arvidsson, Philip, and Tobias Ånhed. "Sequence-to-sequence learning of financial time series in algorithmic trading." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12602.

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Predicting the behavior of financial markets is largely an unsolved problem. The problem hasbeen approached with many different methods ranging from binary logic, statisticalcalculations and genetic algorithms. In this thesis, the problem is approached with a machinelearning method, namely the Long Short-Term Memory (LSTM) variant of Recurrent NeuralNetworks (RNNs). Recurrent neural networks are artificial neural networks (ANNs)—amachine learning algorithm mimicking the neural processing of the mammalian nervoussystem—specifically designed for time series sequences. The thesis investigates the

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Nitz, Pettersson Hannes, and Samuel Vikström. "VISION-BASED ROBOT CONTROLLER FOR HUMAN-ROBOT INTERACTION USING PREDICTIVE ALGORITHMS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54609.

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The demand for robots to work in environments together with humans is growing. This calls for new requirements on robots systems, such as the need to be perceived as responsive and accurate in human interactions. This thesis explores the possibility of using AI methods to predict the movement of a human and evaluating if that information can assist a robot with human interactions. The AI methods that were used is a Long Short Term Memory(LSTM) network and an artificial neural network(ANN). Both networks were trained on data from a motion capture dataset and on four different prediction times:

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Libros sobre el tema "LSTM ALGORITHM"

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it

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Capítulos de libros sobre el tema "LSTM ALGORITHM"

Minu, R. I., G. Nagarajan, Samarjeet Borah, and Debahuti Mishra. "LSTM-RNN-Based Automatic Music Generation Algorithm." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_30.

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Gupta, Rahul, Anil Kumar Yadav, and Shyama Kant Jha. "Global Horizontal Irradiance Estimation Using Bi-LSTM Algorithm." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8135-9_12.

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Ruban, S., Mohamed Moosa Jabeer, and Ram Shenoy Basti. "Improvisation of Breast Cancer Detection using LSTM Algorithm." In Advances in Computer Science Research. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-250-7_31.

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Lin, Zhaochen, Xinran Zhang, and Fenghua He. "A GNN-LSTM-Based Fleet Formation Recognition Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_702.

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Xiao, Tian, Qingliang Long, Lexi Xu, et al. "5G Construction Efficiency Enhancement Based on LSTM Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9968-0_132.

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Sinhmar, Abhinav, Vinamra Malhotra, R. K. Yadav, and Manoj Kumar. "Spam Detection Using Genetic Algorithm Optimized LSTM Model." In Computer Networks and Inventive Communication Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3728-5_5.

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Wu, Jin, Lei Wang, and Yu Wang. "An Improved CNN-LSTM Model Compression Pruning Algorithm." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_75.

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Niu, Kehan. "A Personalized Recommendation Algorithm based on LSTM Classification." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3210-4_44.

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Rostovski, Jakob, Mohammad Hasan Ahmadilivani, Andrei Krivošei, Alar Kuusik, and Muhammad Mahtab Alam. "Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_17.

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AbstractAnomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device m

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Jing, Niqin. "Human Action Recognition Based on LSTM Neural Network Algorithm." In Application of Big Data, Blockchain, and Internet of Things for Education Informatization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63136-8_18.

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Actas de conferencias sobre el tema "LSTM ALGORITHM"

Yang, Yong, Xiao Wang, Zhihuang Shi, and Junyu Wei. "Recommendation Algorithm Based on LSTM and GCN." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11022309.

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Yu, Lubin, Manshuang She, Jing Ai, et al. "Signal Compression Sensing Algorithm Based on Inception-LSTM." In 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2024. http://dx.doi.org/10.1109/icecai62591.2024.10675265.

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Huang, Xiaofei, Fei Shu, Zhiqiang Peng, Kunsan Zhang, and Wei Ma. "Intrusion Detection Algorithm Based on CNN and LSTM." In 2024 International Conference on HVDC (HVDC). IEEE, 2024. http://dx.doi.org/10.1109/hvdc62448.2024.10723071.

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Sridhar, B., S. Allirani, R. S. Reshmin Shafi, K. R. Rithvik Abinav, and A. Siddharth. "Electric Vehicle Battery Temperature Prediction Using LSTM Algorithm." In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2024. http://dx.doi.org/10.1109/sefet61574.2024.10718084.

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Zhou, Wenye, Wanting Zheng, Weihe Zhang, Hongrui Li, and Zeru Huang. "Research and Application of LSTM Neural Network Algorithm." In 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2024. https://doi.org/10.1109/iciscae62304.2024.10761415.

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Nuo, Zhang. "A LSTM Algorithm for Accounting Financial Investment Forecasting." In 2023 International Conference on Intelligent Computing, Communication & Convergence (ICI3C). IEEE, 2023. http://dx.doi.org/10.1109/ici3c60830.2023.00016.

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Gong, Feixiang, Songsong Chen, Xiangyu Kong, Kun Shi, and Taorong Gong. "Steel load prediction based on SSA-LSTM algorithm." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10990631.

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Niu, Wenshuo, Qi Liu, and Hongyan Han. "Research and Application of Improved LSTM Based Algorithm." In 2025 International Conference on Digital Analysis and Processing, Intelligent Computation (DAPIC). IEEE, 2025. https://doi.org/10.1109/dapic66097.2025.00146.

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Yang, Lei, Jie Ma, and Mengzhao Yao. "Spatio-Temporal CONV-LSTM Traffic Flow Prediction Algorithm." In 2025 6th International Conference on Computer Science, Engineering, and Education (CSEE). IEEE, 2025. https://doi.org/10.1109/csee64583.2025.00035.

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Pang, Yanzhi, Xiang Wang, Shixuan Zhou, et al. "LSTM-based mining of change rules of consumption behavior characteristics." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3040401.

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Informes sobre el tema "LSTM ALGORITHM"

León, Carlos, and Kimmo Soramäki. The Next Generation RTGS: Liquidity Saving Mechanisms as an Overlay Service. FNA, 2024. http://dx.doi.org/10.69701/cfcz133.

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Between 1985 and 2006, a total of 96 central banks implemented Real-time Gross Settlement (RTGS) systems. The adoption of this technology was driven to reduce risks inherent in the then-predominant Deferred Net Settlement (DNS) systems. However, because RTGS systems consume large amounts of liquidity when each payment is settled individually, many RTGS systems (e.g., CHAPS and Target2) implemented Liquidity-Saving Mechanisms (LSMs) of varying complexity, with most deploying variations of the algorithm presented by Morten Bech and Kimmo Soramäki (the co-author) in 2001. Many of these systems ar

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