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Relevant bibliographies by topics / LSTM. ESN

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Academic literature on the topic 'LSTM. ESN'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "LSTM. ESN"

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Chattopadhyay, Ashesh, Pedram Hassanzadeh, and Devika Subramanian. "Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network." Nonlinear Processes in Geophysics 27, no. 3 (2020): 373–89. http://dx.doi.org/10.5194/npg-27-373-2020.

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Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fa

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Mirza, Sami F., and Abdulbasit K. Al-Talabani. "Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 2 (2021): 1–9. http://dx.doi.org/10.14500/aro.10827.

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Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a

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Pei, Jiaxin, and Jian Wang. "Multisensor Prognostic of RUL Based on EMD-ESN." Mathematical Problems in Engineering 2020 (November 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6639171.

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This paper presents a prognostic method for RUL (remaining useful life) prediction based on EMD (empirical mode decomposition)-ESN (echo state network). The combination method adopts EMD to decompose the multisensor time series into a bunch of IMFs (intrinsic mode functions), which are then predicted by ESNs, and the outputs of each ESN are summarized to obtain the final prediction value. The EMD can decompose the original data into simpler portions and during the decomposition process, much noise is filtered out and the subsequent prediction is much easier. The ESN is a relatively new type of

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Chen, Xiaojuan, Haiyang Zhang, and Hongwu Qin. "Lowering Nitrogen Oxide Emissions in a Coal-Powered 1000-MW Boiler." Journal of Sensors 2021 (August 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/9958972.

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Burning of coal in power plants produces excessive nitrogen oxide (NOx) emissions, which endanger people’s health. Proven and effective methods are highly needed to reduce NOx emissions. This paper constructs an echo state network (ESN) model of the interaction between NOx emissions and the operational parameters in terms of real historical data. The grey wolf optimization (GWO) algorithm is employed to improve the ESN model accuracy. The operational parameters are subsequently optimized via the GWO algorithm to finally cut down the NOx emissions. The experimental results show that the ESN mod

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Sheng, Hui, Min Liu, Jiyong Hu, Ping Li, Yali Peng, and Yugen Yi. "LA-ESN: A Novel Method for Time Series Classification." Information 14, no. 2 (2023): 67. http://dx.doi.org/10.3390/info14020067.

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Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods have become mainstream. Echo State Networks (ESN) and Convolutional Neural Networks (CNN) are commonly utilized as deep neural network methods in TSC research. However, ESN and CNN can only extract local dependencies relations of time series, resulting in long-term temporal data dependence needing to be more challenging to capture. As a result, a

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Li, Xin, Fengrong Bi, Lipeng Zhang, Xiao Yang, and Guichang Zhang. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer." Energies 15, no. 3 (2022): 1205. http://dx.doi.org/10.3390/en15031205.

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This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MV

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Zandi, Iman, Ali Jafari, and Aynaz Lotfata. "Enhancing PM2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms." Urban Science 9, no. 5 (2025): 138. https://doi.org/10.3390/urbansci9050138.

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Air pollution presents significant risks to both human health and the environment. This study uses air pollution and meteorological data to develop an effective deep learning model for hourly PM2.5 concentration predictions in Tehran, Iran. This study evaluates efficient metaheuristic algorithms for optimizing deep learning model hyperparameters to improve the accuracy of PM2.5 concentration predictions. The optimal feature set was selected using the Variance Inflation Factor (VIF) and the Boruta-XGBoost methods, which indicated the elimination of NO, NO2, and NOx. Boruta-XGBoost highlighted P

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Bonci, Andrea, Renat Kermenov, Lorenzo Longarini, et al. "An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge." Machines 12, no. 10 (2024): 743. http://dx.doi.org/10.3390/machines12100743.

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Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artifici

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Tian, Ye, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, and Qian Zhu. "Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting." Water 10, no. 11 (2018): 1655. http://dx.doi.org/10.3390/w10111655.

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This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimate

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Batu, Barın,. "Investigating Performance of ESN’s in Forecasting Financial Metrics When Compared To Traditional RNN Types." International Journal of Social Science and Economic Research 09, no. 06 (2024): 1950–82. http://dx.doi.org/10.46609/ijsser.2024.v09i06.023.

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This research investigates the performance of Echo State Networks (ESN) in forecasting financial metrics and compares their effectiveness against traditional recurrent neural network (RNN) architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), as well as Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models. By analyzing datasets sourced from Yahoo Finance for various financial indices, exchange-traded funds and stocks over five years, this study examines the accuracy, and structural simplicity of ESNs in predicting close prices, daily vo

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Dissertations / Theses on the topic "LSTM. ESN"

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ALIBERTI, ALESSANDRO. "Machine learning techniques to forecast non-linear trends in smart environments." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2846613.

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Baker, Ryan. "IMAGING AND ANALYSIS OF LARVAL ZEBRAFISH GUT MOTILITY, AND AUTOMATED TOOLS FOR 3D MICROSCOPY." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23133.

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Nearly all individual members of the animal kingdom have gastrointestinal tracts which feature unique cellular compositions, geometries, and temporal dynamics. These guts are distinct enough from one another, even across siblings or even across the same individual at different points in space and time, that defining meaningful scientific representations of those features is difficult. Studying these guts is also innately challenging as it requires accessing to the insides of the enclosed 3D volumes. The work presented here describes tools and methodologies designed to address these difficulti

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Elkandari, Bader M. H. M. "Excimer laser surface melting treatment on 7075-T6 aluminium alloy for improved corrosion resistance." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/excimer-laser-surface-melting-treatment-on-7075t6-aluminium-alloy-for-improved-corrosion-resistance(c2da3b82-eeb5-4eae-a1dc-e4aefba18c62).html.

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High strength 7xxx aluminium alloys are used extensively in the aerospace industry because the alloys offer excellent mechanical properties. Unfortunately, the alloys can suffer localised corrosion due to the presence of large intermetallic particles at the alloy surface that are aligned in the rolling direction. Laser surface melting (LSM) techniques offer the potential to reduce and/or to eliminate the intermetallic phases from the surface of the alloy without affecting the alloy matrix.The present study concerns the application of LSM using an excimer laser to enhance the corrosion resistan

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Books on the topic "LSTM. ESN"

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Lorenzana Molina, Daniela, and Miroslava Cruz-Aldrete. Xochicalco / Xochelkalhme. Universidad Autónoma del Estado de Morelos, 2020. http://dx.doi.org/10.30973/2020/xochicalco-xochelkalhme.

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Esta obra, dirigida al público infantil, es la propuesta de un grupo de profesionales provenientes de diversas disciplinas, entre ellas la lingüística y la literatura, para hacer partícipes a los usuarios de la lengua de señas mexicana (LSM), o de una lengua indígena, de la riqueza cultural de México, en particular, de su grandioso pasado prehispánico. Este propósito se concreta en este libro, que nos presenta una narración, en español y náhuatl, acompañada de cuidadas ilustraciones, y cuyo escenario es el sitio arqueológico de Xochicalco.

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Creation, Eabign. Ern�hrungstagebuch : Ein Ganz Pers�nliches Tagebuch F�r Die Ern�hrung Zum Eintragen Von Mahlzeiten: Fr�hst�ck, Mittagessen, Abendessen, Zwischenmahlzeiten - Damit Beh�lst du Die Kontrolle �ber Dein Essverhalten. Independently Published, 2019.

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Book chapters on the topic "LSTM. ESN"

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Aykent, Baris. "LSTM-Based Electronic Stability Control (ESC)." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85689-1_9.

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Beiche, Hans-Peter. "Lst-1 — ein wissensbasiertes System zur Durchführung und Berechnung des Lohnsteuerjahresausgleichs." In 3. Österreichische Artificial-Intelligence-Tagung. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-46620-5_9.

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Zahidi, Youssra, Yassine Al-Amrani, and Yacine El Younoussi. "LSTM Algorithm with FastText Word Embedding Model for Evaluating Arabic Sentiment Analysis." In Transformación digital en la educación: innovaciones y desafíos desde los campus virtuales. United Academic Journals (UA Journals), 2024. https://doi.org/10.54988/uaj.000027.006.

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With the rise of social networks, online users increasingly express their sentiments on various topics. Sentiment Analysis (SA), an essential area in Natural Language Processing (NLP), aims to identify the polarity of senti-ment and derive insights from public opinions. The Arabic language poses significant challenges for SA because of its diverse dialects, complex morphology, and syntax. Neural Networks (NN) models in Deep Learning (DL) are highly effective for sentiment classification in many tasks, especially in the education sector thanks to their advanced abilities to analyze textual data

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S., Immaculate Joy, and Kanagamalliga S. "Advancements in Remote Heart Monitoring: Wearable Technology and AI-based Approaches for Cardiovascular Disease Detection." In AI in the Social and Business World: A Comprehensive Approach. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256864124010006.

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In the era of precision medicine and individualized approaches, remote monitoring and control of heart function have emerged as critical components of patient evaluation and management. The integration of consumer-grade software and hardware devices for health monitoring has gained popularity as technological advancements become increasingly integrated into daily life. The cardiology community must adapt to the demands of distant and decentralized care, as highlighted during the COVID-19 pandemic. Wearable technology, such as vital sign monitors, holds significant potential for monitoring hear

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Wolter, K., A. Albers, and M. Adrian. "Entwicklung und Validierung eines LSTM-basierten Modells zur Vorhersage der Stator-Temperaturen in Elektromotoren – Ein datengetriebener Ansatz unter dynamischen Lastbedingungen." In SIMVEC. VDI Verlag, 2024. http://dx.doi.org/10.51202/9783181024454-367.

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Conference papers on the topic "LSTM. ESN"

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Gill, Taimur Shahzad, and Syed Ibrahim Zahid. "Time Series Forecasting of KSE-100 Index Using a Hybrid ESN-LSTM Model." In 2024 International Conference on Robotics and Automation in Industry (ICRAI). IEEE, 2024. https://doi.org/10.1109/icrai62391.2024.10894346.

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Olivera-Guerra, Luis, Catherine Ottlé, Nina Raoult, and Philippe Peylin. "Improving Orchidee Simulations of Surface Energy Fluxes Assimilating the ESA-CCI LST Product." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642547.

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TAN, YAN-KE, YU-LING WANG, YI-QING NI, QI-LIN ZHANG, and YOU-WU WANG. "LARGE-SPAN BRIDGE STRAIN RECONSTRUCTION BASED ON BIDIRECTIONAL LSTM AND ESN." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/37062.

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Partly missing and anomalous of the data collected from structural health monitoring (SHM) systems are inevitable due to the failure of sensor and data acquisition equipment, which lead to misjudgment of the target structure state. The data integrity demands for guaranty using reconstruction algorithms before signal processing. Recurrent neural networks (RNN) has been proved effective of reconstruction issue by learning from the historical and future signal segments. The gated RNN represented by long short-term memory (LSTM) networks and reservoir computing represented by echo state networks (

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Qazani, Mohammad Reza Chalak, Farzin Tabarsinezhad, Houshyar Asadi, et al. "A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945220.

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BHIH, Mouad, Zouhair Elamrani Abou Elassad, Mohammed AMEKSA, Abdelhakim El Boustani, Othmane EL Meslouhi, and Dauha Elamrani Abou Elassad. "Optimizing Smart Home Energy Forecasting: Unveiling Superiority among RBFNN, ESN, and LSTM." In 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2024. http://dx.doi.org/10.1109/iraset60544.2024.10548233.

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Romualdo, Hudson de P., Luciano de S. Fraga, Paulo F. da Conceição, Flávio Geraldo C. Rocha, and Kleber V. Cardoso. "Otimização da associação entre estações base e equipamentos de usuário com auxílio de Aprendizado Federado para suporte a Realidade Aumentada Móvel." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sbrc.2024.1448.

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Aplicações imersivas, como Realidade Aumentada Móvel (MAR), dependem de suporte adequado da infraestrutura de comunicação para atender às expectativas dos usuários. Neste trabalho, investigamos o problema de associação de usuários de MAR a estações base que operam em sub-6 GHz e em ondas milimétricas. O objetivo é maximizar a imersão de múltiplos usuários que concorrem pelos recursos de comunicação, minimizando a latência de subida (uplink) e maximizando a vazão de subida e descida (downlink e uplink). Prever a posição e orientação de cada usuário pode contribuir de maneira significativa nesse

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Han, Guoqing, Xin Lu, He Zhang, Xianfu Sui, Biao Wang, and Kegang Ling. "ESP Wells Dynamic Survival Analysis and Lifespan Prediction Using Machine Learning Algorithms." In SPE Annual Technical Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/221041-ms.

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Abstract The electric submersible pump (ESP) is the primary artificial lift method for offshore oil wells. Due to the high operation and maintenance costs, it is crucial to avoid operations and conditions that negatively impact ESP wells' lifespan and seek ways to prolong their run life. However, the lifespan of ESP is affected by many factors, and it isn't easy to quantify the effects of these factors by conventional methods. To improve the lifespan of ESP and identify critical factors affecting it, this study thoroughly utilizes the wealth of downhole and wellhead measurement parameters avai

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Melo, R. A. Lastra, D. J. Worth, and S. Swaffield. "Assessment of Real-Time ESP Failure Prediction Using Digital Twin, Machine Learning and Damage Modelling." In SPE Gulf Coast Section - Electric Submersible Pumps Symposium. SPE, 2023. http://dx.doi.org/10.2118/214725-ms.

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Abstract In the presented research, an assessment was conducted of how machine learning techniques, in combination with physics-based damage modelling, could be capitalized on to help move towards the goal of developing an effective and automated real-time ESP failure prediction system. A prediction model was developed that has the capability of predicting two groups of failure mechanisms. One group consisted of failures related to pump wear and scale plugging and the other group consisted of failures related to increased drag (rotational resistance) in either the motor or the pump. The model

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Abdalla, Ramez, Denis Nikolaev, David Gönzi, Roman Manasipov, Andreas Schweiger, and Michael Stundner. "Deep Insight into Electrical Submersible Pump Maintenance: A Predictive Approach with Deep Learning." In SPE Offshore Europe Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215596-ms.

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Abstract Artificial lift systems play a crucial role in the oil and gas industry by maintaining or enhancing production rates through the conversion of kinetic energy into hydraulic pressure. However, identifying abnormal performance and preventing failures remains a major challenge. Failures occur when key parameters deviate from safe operating conditions, leading to downtime and loss of production volumes. To address this challenge, the objective is to establish repeatable frameworks for constructing predictive models through the utilization of deep learning algorithms. These models provide

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Zhao, Jin, Ting Zhang, Keke Ji, and Xin Sun. "Data analysis method based on LSTM is applied to enterprise ESG performance prediction." In ASENS 2024: International Conference on Algorithms, Software Engineering, and Network Security. ACM, 2024. http://dx.doi.org/10.1145/3677182.3677205.

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Reports on the topic "LSTM. ESN"

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Palmer, Jennifer, and Diane Duclos. Considérations Clés : Surveillance à Base Communautaire dans le Domaine de la Santé Publique. Institute of Development Studies, 2023. http://dx.doi.org/10.19088/sshap.2023.014.

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Les récentes épidémies et pandémies à grande échelle ont démontré l’importance d’engager les communautés en tant que partenaires pour prévenir, détecter et répondre aux situations d’urgence sanitaire. La surveillance à base communautaire (SBC), qui s’appuie sur les communautés pour communiquer des informations en matière de santé publique, peut constituer un élément essentiel pour la mise en œuvre d’interventions efficaces, inclusives et responsables dans les situations d’urgence humanitaire et de santé publique, ainsi que dans le cadre de la lutte à long terme contre les maladies. Cette note

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Palmer, Jennifer, and Diane Duclos. Key Considerations: Community-Based Surveillance in Public Health. Institute of Development Studies, 2023. http://dx.doi.org/10.19088/sshap.2023.010.

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Recent large-scale epidemics and pandemics have demonstrated the importance of engaging communities as partners in preventing, detecting and responding to public health emergencies. Community-based surveillance (CBS), which relies on communities to report public health information, can be an important part of effective, inclusive and accountable responses to humanitarian and public health emergencies, as well as long-term disease control. This brief offers key considerations for CBS programming to guide policymakers, public health officials, civil society organisations, health workers, researc

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Hrynick, Tabitha, and Megan Schmidt-Sane. Note d’Orientation sur l’Engagement Communautaire Concernant la Riposte Contre la Flambée Epidémique de Choléra dans la Région Afrique de l’Est et Australe. Institute of Development Studies, 2023. http://dx.doi.org/10.19088/sshap.2023.008.

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Les flambées épidémiques de choléra s’intensifient dans la région Afrique de l’Est et australe (ESAR) depuis janvier 2023, avec une transmission généralisée et étendue au Malawi et au Mozambique, ainsi que des flambées épidémiques signalées en Tanzanie, en Afrique du Sud, au Zimbabwe, au Burundi et en Zambie.1 Il existe un risque de propagation accrue causée par les effets du cyclone Freddy, qui a frappé Madagascar, le Malawi et le Mozambique en mars 2023. Les flambées épidémiques se poursuivent en Somalie, en Éthiopie, au Kenya et au Soudan du Sud, où les pays sont confrontés à la sécheresse

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