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Journal articles on the topic 'LSTM. ESN'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

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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Youssef, Samuel M., MennaAllah Soliman, Mahmood A. Saleh, Mostafa A. Mousa, Mahmoud Elsamanty, and Ahmed G. Radwan. "Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data." Micromachines 13, no. 2 (2022): 216. http://dx.doi.org/10.3390/mi13020216.

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Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA’s tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inp

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Feitosa, Allan Rivalles Souza, Henrique Figuerôa Lacerda, Wellington Pinheiro dos Santos, and Abel Guilhermino da Silva Filho. "Household appliance usage recommendation based on demand forecasting and multiobjective optimization." Research, Society and Development 11, no. 1 (2022): e13411124515. http://dx.doi.org/10.33448/rsd-v11i1.24515.

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Accelerated population growth in the 21st century and increased demand for energy sources, associated with climate change, have resulted in two main challenges: the search for sustainable energy sources and the need to find more efficient ways to use extant sustainable sources. The forecasting module provides an estimate of the future usage of these appliances and it is the source of the recommended module’s suggestion. Time Series Forecasting techniques, such as Autoregressive Integrated Moving Average, LongShort Term Memory (LSTM), Gated Recurrent Units, Echo State Networks (ESN), and Suppo

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Rajamoorthy, Rajasekaran, Hemachandira V. Saraswathi, Jayanthi Devaraj, et al. "A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048." Sustainability 14, no. 3 (2022): 1355. http://dx.doi.org/10.3390/su14031355.

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In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A n

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Bai, Yu-Ting, Wei Jia, Xue-Bo Jin, Ting-Li Su, Jian-Lei Kong, and Zhi-Gang Shi. "Nonstationary Time Series Prediction Based on Deep Echo State Network Tuned by Bayesian Optimization." Mathematics 11, no. 6 (2023): 1503. http://dx.doi.org/10.3390/math11061503.

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The predictions from time series data can help us sense development trends and make scientific decisions in advance. The commonly used forecasting methods with backpropagation consume a lot of computational resources. The deep echo state network (DeepESN) is an advanced prediction method with a deep neural network structure and training algorithm without backpropagation. In this paper, a Bayesian optimization algorithm (BOA) is proposed to optimize DeepESN to address the problem of increasing parameter scale. Firstly, the DeepESN was studied and constructed as the basic prediction model for th

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Isaac Júnior, Marcos Antônio, Murilo Garrett Moura Ferreira dos Santos, Vinícius YujiMendes Da Silva, Thiago Zanetti Albertini, and Adriele Giaretta Biase. "PSVIII-27 Beef Cattle Body Weight Forecast Monitored on Pasture Through Artificial Intelligence." Journal of Animal Science 101, Supplement_3 (2023): 491–92. http://dx.doi.org/10.1093/jas/skad281.582.

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Abstract The use of artificial intelligence through forecasting models for the weight of beef cattle allows decision-making regarding the production chain, which makes it more efficient and respects social, environmental and economic aspects, with ever-increasing predictions. Approaches adopting separately stochastic and deterministic components were adopted. Thus, this work, based on deterministic dynamic systems, aims to forecast the body weight of cattle simultaneously with the variables temperature, atmospheric pressure and global radiation, for the first time being monitored on pasture. D

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Zheng, Guoxiao, Weifang Sun, Hao Zhang, Yuqing Zhou, and Chen Gao. "Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 4 (2021): 612–18. http://dx.doi.org/10.17531/ein.2021.4.3.

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Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro

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K, Manimekalai, and A. Kavitha Dr. "Deep Learning Methods in Classification of Myocardial Infarction by employing ECG Signals." Indian Journal of Science and Technology 13, no. 28 (2020): 2823–32. https://doi.org/10.17485/IJST/v13i28.445.

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Abstract <strong>Background/Objectives:</strong> To automatically classify and detect the Myocardial Infarction using ECG signals.<strong> Methods/Statistical analysis:</strong> Deep Learning algorithms Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) and Enhanced Deep Neural Network(EDN) were implemented. The proposed model EDN, comprises the techniques CNN and LSTM. Vector operations like matrix multiplication and gradient decent were applied to large matrices of data that are executed in parallel with GPU support. Because of parallelism EDN faster the execution

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Widiputra, Harya. "GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic." Emerging Science Journal 5, no. 5 (2021): 619–35. http://dx.doi.org/10.28991/esj-2021-01300.

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The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes t

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Лебедева, А. А., А. А. Гаращенко та Д. Н. Сидоров. "Прогноз пространственно-временной динамики аврорального овала с применением машинного обучения". Информационные и математические технологии в науке и управлении, № 1(37) (12 березня 2025): 25–33. https://doi.org/10.25729/esi.2025.37.1.003.

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Ионосфера – часть атмосферы Земли с высокой концентрацией свободных электронов и ионов. К характерным чертам ионосферы относятся изменчивость и неоднородность. Одной из неоднородностей является так называемый авроральный овал, который определяет диапазон полярного сияния. Распознавание аврорального овала – важная задача для прогнозирования авроральных бурь, так как они влияют на работу систем связи на дальние расстояния, навигацию, связь между спутниками и землей, затрудняя или делая ее невозможной. Таким образом, возникает потребность в обнаружении и прогнозировании перемещения аврорального о

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Tardini, Gabriella Alexander, and Suharjito. "Selection of Modelling for Forecasting Crude Palm Oil Prices Using Deep Learning (GRU & LSTM)." Emerging Science Journal 8, no. 3 (2024): 875–98. http://dx.doi.org/10.28991/esj-2024-08-03-05.

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The unstable crude palm oil (CPO) prices have an impact on assessments of economic growth and environmental sustainability, as well as market strategies, international trade discussions, and consumer pricing expectations for products made from CPO. Therefore, it is crucial to identify the best prediction method to accurately forecast this cost. This research aims to develop an accurate time series data prediction model for crude palm oil prices using GRU and LSTM methods. The study also aims to identify the best-performing model by comparing their performance. This study uses LSTM and GRU meth

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Zulfiqar, M., Kelum A. A. Gamage, M. B. Rasheed, and C. Gould. "Optimised Deep Learning for Time-Critical Load Forecasting Using LSTM and Modified Particle Swarm Optimisation." Energies 17, no. 22 (2024): 5524. http://dx.doi.org/10.3390/en17225524.

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Short-term electric load forecasting is critical for power system planning and operations due to demand fluctuations driven by variable energy resources. While deep learning-based forecasting models have shown strong performance, time-sensitive applications require improvements in both accuracy and convergence speed. To address this, we propose a hybrid model that combines long short-term memory (LSTM) with a modified particle swarm optimisation (mPSO) algorithm. Although LSTM is effective for nonlinear time-series predictions, its computational complexity increases with parameter variations.

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Maseleno, Andino, Miftachul Huda, and Chotirat Ann Ratanamahatana. "An Explainable Deep Learning Approach for Classifying Monkeypox Disease by Leveraging Skin Lesion Image Data." Emerging Science Journal 8, no. 5 (2024): 1875–97. http://dx.doi.org/10.28991/esj-2024-08-05-013.

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According to the World Health Organization's (WHO) external situation report on the multi-country outbreak of Monkeypox in 2023, from 11 countries in Southeast Asia Regions, Thailand recorded the highest reported cases, totaling 461. The ongoing Monkeypox outbreak has raised significant public health concerns due to its rapid spread across several nations. Early detection and diagnosis are imperative for effectively treating and controlling Monkeypox. Given this context, this study aimed to determine the most efficient model for detecting Monkeypox by employing interpretable deep learning tech

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Seo, Yeongung, Seungyoung Park, Myungjin Kim, and Sungbin Lim. "ESS Operation Scheduling Scheme Using LSTM for Peak Demand Reduction." Journal of KIISE 46, no. 11 (2019): 1165–73. http://dx.doi.org/10.5626/jok.2019.46.11.1165.

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Shivanya Shomir Dutta, Aakash Kumar, Amutha S, and R Dhanush. "Enhancing Diabetes Mellitus Prediction: Integrating Hybrid Deep Learning Model with Sampling Techniques." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 1, no. 1 (2024): 29–40. https://doi.org/10.63503/j.ijaimd.2024.8.

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Diabetes, characterized by high blood glucose levels, is a leading cause of liver, eye, kidney, and heart diseases. This study evaluates various deep learning models, combined with machine learning classifiers, for predicting diabetes mellitus using the BRFSS dataset. The dataset's imbalance posed a challenge for binary classification, common in medical diagnostics. To address this, different sampling techniques were tested. Hybrid models combining Convolu-tional Long Short Term Memory (Conv LSTM) networks with traditional classi-fiers were also explored. The Conv LSTM model combined with Adab

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Islam, Umar, Rami Qays Malik, Amnah S. Al-Johani, et al. "A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks." Electronics 11, no. 18 (2022): 2813. http://dx.doi.org/10.3390/electronics11182813.

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The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous fe

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Nafia, Abdellilah, Abdellah Yousfi, and Abdellah Echaoui. "Equity-Market-Neutral Strategy Portfolio Construction Using LSTM-Based Stock Prediction and Selection: An Application to S&P500 Consumer Staples Stocks." International Journal of Financial Studies 11, no. 2 (2023): 57. http://dx.doi.org/10.3390/ijfs11020057.

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In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In this study, we propose a model to build a portfolio according to an equity-market-neutral (EMN) investment strategy. In this portfolio, the selection of stocks comprises two steps: a prediction of the individual returns of stocks using LSTM neural network, followed by a ranking of these stocks according to their predicted returns. The stocks wi

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Lindemann, Benjamin, Nasser Jazdi, and Michael Weyrich. "Detektion von Anomalien zur Qualitätssicherung basierend auf Sequence-to-Sequence LSTM Netzen." at - Automatisierungstechnik 67, no. 12 (2019): 1058–68. http://dx.doi.org/10.1515/auto-2019-0076.

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Zusammenfassung Unvorhersehbare Prozessereignisse und Anomalien sind Treiber erhöhter Ineffizienzen in Form von schwankender Produktqualität. In diesem Beitrag wird ein datengetriebener Ansatz zur Qualitätsoptimierung vorgestellt, auf dessen Basis Anomalien charakterisiert werden, die zur Entwurfszeit des Systems nicht bekannt waren. Es wird eine Netzarchitektur in Form eines Sequence-to-Sequence Netzes mit Long Short-Term Memory (LSTM) Zellen vorgestellt. Dadurch kann vorhergesagt werden, welche Anpassung am Stellgrößenverhalten vorgenommen werden muss, um erwartete Anomalien zu kompensieren.

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Kim, Hye Jin, and Rhee Jung Soo. "Fraud Detection in Financial Transactions Using Advanced Neural Network Techniques." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 33, no. 05 (2025): 573–89. https://doi.org/10.1142/s0218488525400057.

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This paper examines a binary classification issue in fraud detection using several neural network methodologies, including the Synthetic Minority Oversampling Technique (SMOTE) and SMOTE combined with Edited Nearest Neighbours (ENN). This work underscores the pressing need for improved detection approaches due to the rising incidence of fraud in financial institutions. Several configurations were tested on a Feedforward Neural Network (FNN) to tackle class imbalance with class weighting, undersampling, oversampling (SMOTE), and [Formula: see text]. The results demonstrated that [Formula: see t

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Xie, Qiuxia, Yonghui Chen, Qiting Chen, Chunmei Wang, and Yelin Huang. "Correction of ASCAT, ESA–CCI, and SMAP Soil Moisture Products Using the Multi-Source Long Short-Term Memory (MLSTM)." Remote Sensing 17, no. 14 (2025): 2456. https://doi.org/10.3390/rs17142456.

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The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products

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Jiotsop Foze, Wellcome Peujio, Adrian Hernandez-del-Valle, and Francis Magloire Peujio Fozap. "Driving efficiency and sustainability: deep learning-based load forecasting at the substation level." PANORAMA ECONÓMICO 19, no. 39 (2023): 133–48. https://doi.org/10.29201/pe-ipn.v19i39.176.

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This paper presents an investigation into the effectiveness of Long ShortTerm Memory (LSTM) neural networks for forecasting electrical load at a substation level. Electrical load forecasting is a challenging task due to the stochastic nature of time series data, which creates noise and reduces prediction accuracy. To address this issue, we propose a deep learning model based on LSTM recurrent neural networks, which we evaluate using a publicly available 30-minute dataset of real power measurements from individual zone substations in the Ausgrid3 supply area. Our proposed LSTM model with 2 hidd

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Al-Ali, Romel, Khadija Alhumaid, Maha Khalifa, Said A. Salloum, Rima Shishakly, and Mohammed Amin Almaiah. "Analyzing Socio-Academic Factors and Predictive Modeling of Student Performance Using Machine Learning Techniques." Emerging Science Journal 8, no. 4 (2024): 1304–19. http://dx.doi.org/10.28991/esj-2024-08-04-05.

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Understanding the factors that influence student performance is crucial for improving educational outcomes. Thus, this study aims to examine the impact of socio-economic and psychological factors on student performance, less is known about how students' personal attitudes and behaviors across different departments and activities correlate with their academic success. This study employs exploratory data analysis (EDA) to identify trends and relationships within the dataset. Machine learning techniques, such as K-means clustering and Long Short-Term Memory (LSTM) networks, are utilized to model

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Balal, Afshin, Yaser Pakzad Jafarabadi, Ayda Demir, Morris Igene, Michael Giesselmann, and Stephen Bayne. "Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock." Emerging Science Journal 7, no. 4 (2023): 1052–62. http://dx.doi.org/10.28991/esj-2023-07-04-02.

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Solar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the present global energy crisis. However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the output of solar power in or

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Santos, Rolando A., and Brian W. Sloboda. "Macroeconomic Forecasting Examining the COVID-19 Pandemic Using Selected Countries: A Machine Learning LSTM (Long Term Short Term Memory) Approach." European Scientific Journal, ESJ 18, no. 12 (2022): 1. http://dx.doi.org/10.19044/esj.2022.v18n12p1.

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The disease COVID-19 caused by the virus SARS-CoV-2 has initially disrupted the Chinese economy after the first cases were reported in December 2019 in Wuhan city in Hubei province of China. The virus continued to spread throughout the rest of the world. This spread of the virus led to the official designation of the COVID-19 pandemic by the World Health Organization (WHO) in late February 2020, which resulted in the disruption of these economies due to the stringent lockdowns and restrictions in travel disease's evolution. The disruptive economic impact is highly uncertain, making it difficul

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Jieyang, Peng, Wang Dongkun, Andreas Kimmig, Mikhail A. Langovoy, Wang Jiahai, and Jivka Ovtcharova. "Ein hybrides RNN-Modell für die mittel- bis langfristige Vorhersage des Strombedarfs unter Berücksichtigung von Wettereinflüssen." at - Automatisierungstechnik 69, no. 1 (2021): 73–83. http://dx.doi.org/10.1515/auto-2020-0033.

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Zusammenfassung Im täglichen Stadtbetrieb sollte die Stromversorgung unterbrechungsfrei sein, was das moderne Energiemanagement vor Herausforderungen stellt. Die Prognose des Energiebedarfs kann die Strategie des Energiemanagements optimieren und die Energieeffizienz verbessern. Das traditionelle LSTM-Modell, das auf einer Codierungs-Decodierungs-Struktur basiert, codiert alle historischen Informationen als Vektor fester Länge, was zum Informationsverlust führt, wenn der vorhergesagte Wert von den Merkmalen abhängt die weit in der Vergangenheit liegen. Dies ist bei Energieprognosen aufgrund de

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Brenner, Claire, Jonathan Frame, Grey Nearing, and Karsten Schulz. "Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden." Österreichische Wasser- und Abfallwirtschaft 73, no. 7-8 (2021): 295–307. http://dx.doi.org/10.1007/s00506-021-00768-y.

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ZusammenfassungDie Verdunstung ist ein entscheidender Prozess im globalen Wasser‑, Energie- sowie Kohlenstoffkreislauf. Daten zur räumlich-zeitlichen Dynamik der Verdunstung sind daher von großer Bedeutung für Klimamodellierungen, zur Abschätzung der Auswirkungen der Klimakrise sowie nicht zuletzt für die Landwirtschaft.In dieser Arbeit wenden wir zwei Machine- und Deep Learning-Methoden für die Vorhersage der Verdunstung mit täglicher und halbstündlicher Auflösung für Standorte des FLUXNET-Datensatzes an. Das Long Short-Term Memory Netzwerk ist ein rekurrentes neuronales Netzwerk, welchen exp

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Zhang, Shuyu, Mengyi Zhang, Cuimei Bo, and Cunsong Wang. "Industrial Part Faults Prediction for Nonlinearity and Implied Temporal Sequences." Processes 13, no. 2 (2025): 436. https://doi.org/10.3390/pr13020436.

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The ability to preemptively identify potential failures in industrial parts is crucial for minimizing downtime, reducing maintenance costs and ensuring system reliability and safety. However, challenges such as data nonlinearity, temporal dependencies, and imbalanced datasets complicate accurate fault prediction. In this study, we propose a novel combined approach that integrates the Logistic Model Tree Forest (LMT) with Stacked Long Short-Term Memory (LSTM) networks, addressing these challenges effectively. This hybrid method leverages the decision-making capability of the LMT and the tempora

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Bestaeva, Salome. "Rural Tourism Business as an Economic Instrument for the Development of Economically Backward Regions." European Scientific Journal, ESJ 18, no. 29 (2022): 1. http://dx.doi.org/10.19044/esj.2022.v18n29p1.

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The disease COVID-19 caused by the virus SARS-CoV-2 has initially disrupted the Chinese economy after the first cases were reported in December 2019 in Wuhan city in Hubei province of China. The virus continued to spread throughout the rest of the world. This spread of the virus led to the official designation of the COVID-19 pandemic by the World Health Organization (WHO) in late February 2020, which resulted in the disruption of these economies due to the stringent lockdowns and restrictions in travel disease's evolution. The disruptive economic impact is highly uncertain, making it difficul

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Natasya and Abba Suganda Girsang. "Modified EDA and Backtranslation Augmentation in Deep Learning Models for Indonesian Aspect-Based Sentiment Analysis." Emerging Science Journal 7, no. 1 (2022): 256–72. http://dx.doi.org/10.28991/esj-2023-07-01-018.

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In the process of developing a business, aspect-based sentiment analysis (ABSA) could help extract customers' opinions on different aspects of the business from online reviews. Researchers have found great prospective in deep learning approaches to solving ABSA tasks. Furthermore, studies have also explored the implementation of text augmentation, such as Easy Data Augmentation (EDA), to improve the deep learning models’ performance using only simple operations. However, when implementing EDA to ABSA, there will be high chances that the augmented sentences could lose important aspects or senti

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Javed, Khadija, Ren Shengbing, Muhammad Asim, and Mudasir Ahmad Wani. "Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization." Algorithms 17, no. 5 (2024): 175. http://dx.doi.org/10.3390/a17050175.

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Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance

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Huang, Yu, Lichao Yang, and Zuntao Fu. "Reconstructing coupled time series in climate systems using three kinds of machine-learning methods." Earth System Dynamics 11, no. 3 (2020): 835–53. http://dx.doi.org/10.5194/esd-11-835-2020.

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Abstract. Despite the great success of machine learning, its application in climate dynamics has not been well developed. One concern might be how well the trained neural networks could learn a dynamical system and what will be the potential application of this kind of learning. In this paper, three machine-learning methods are used: reservoir computer (RC), backpropagation-based (BP) artificial neural network, and long short-term memory (LSTM) neural network. It shows that the coupling relations or dynamics among variables in linear or nonlinear systems can be inferred by RC and LSTM, which c

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Valdivieso Caraguay, Ángel Leonardo, Juan Pablo Vásconez, Lorena Isabel Barona López, and Marco E. Benalcázar. "Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks." Sensors 23, no. 8 (2023): 3905. http://dx.doi.org/10.3390/s23083905.

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In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals have been of considerable interest in developing human–machine interfaces. Most state-of-the-art HGR approaches are based mainly on supervised machine learning (ML). However, the use of reinforcement learning (RL) techniques to classify EMGs is still a new and open research topic. Methods based on RL have some advantages such as promising classification performance and online learning from the user’s experience. In this work, we propose a user-specific HGR system based on an RL-based agent that

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Paudel, Sagun Babu, Bidur Devkota, and Suresh Timilsina. "Multi-Class Credit Risk Analysis Using Deep Learning." Journal of Engineering and Sciences 2, no. 1 (2023): 82–87. http://dx.doi.org/10.3126/jes2.v2i1.60399.

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Credit risk prediction, reliability, monitoring and effective loan processing are the keys to proper bank decision-making. So, understanding the credit customer during the initial loan processing phase would help the bank prevent future losses. In this regard, this study aims to develop a credit risk evaluation model using deep learning algorithms. The model utilizes a credit risk analysis dataset published in Kaggle. The objective is to build deep learning models for predicting credit risk using real banking datasets published on Kaggle. Firstly, data preprocessing and feature engineering are

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Chen, Min-Rong, Guo-Qiang Zeng, Kang-Di Lu, and Jian Weng. "A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM." IEEE Internet of Things Journal 6, no. 4 (2019): 6997–7010. http://dx.doi.org/10.1109/jiot.2019.2913176.

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Fadli, Faisal, Saib Suwilo, and Muhammad Zarlis. "Model Prediksi Data Besar Distribusi Produk Farmasi: Analisis Kinerja Model Deep Learning." CSRID (Computer Science Research and Its Development Journal) 14, no. 1 (2022): 68. http://dx.doi.org/10.22303/csrid.14.1.2021.79-91.

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<p><em>Seiring dengan berjalannya bisnis perusahaan, masalah dalam penyimpanan dan pengolahan data besar pun akan semakin kompleks. data yang tidak terorganisir dapat menyebabkan perusahaan gagal dalam memaksimalkan strategi penjualan. Salah satu pendekatan untuk memaksimalkan strategi penjualan tersebut adalah dengan peramalan.penelitian ini bertujuan untuk mengurangi tingkat persediaan pelanggan jangka pendek dan membantu dalam menentukan target penjualan yang realistis di masa depan dengan mengusulkan metode pembelajaran mendalam berdasarkan segmentasi pelanggan. Kerangka analis

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Ms. Hemangini Patel, Mrugesh Patel, and Ms. Krinal Savani. "An Optimized XGBoost Framework for Real-Time Credit Card Fraud Detection: Addressing Class Imbalance with Hybrid SMOTE-ENN Resampling." International Journal of Scientific Research in Science and Technology 12, no. 3 (2025): 1129–36. https://doi.org/10.32628/ijsrst25123122.

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Financial fraud, especially in the form of credit card fraud, presents considerable threats to both consumers and businesses, thus requiring sophisticated detection mechanisms. This paper introduces an enhanced framework based on XGBoost to tackle essential issues in fraud detection: class imbalance, the need for real-time processing, and the importance of model interpretability. Using the Kaggle Credit Card Fraud Dataset (which includes 285,807 transactions with a fraud rate of 0.17%), we apply a SMOTE-ENN hybrid resampling method to equalize class distributions and create features (derived f

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Lee, Yao Hui A., and Wen Cheng J. Wei. "Processing and Characterization of La₂O₃-SiO₂-B₂O₃ (LSB) Based Glass-Ceramics for LTCC Application." Key Engineering Materials 280-283 (February 2007): 935–40. http://dx.doi.org/10.4028/www.scientific.net/kem.280-283.935.

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Synthesis of La2O3-SiO2-B2O3 (LSB) based glass-ceramics using glass melting method has een investigated in this study. XRD result showed that some LSB glass systems in this study were ntirely amorphous phases. In addition, TMA results revealed that the LSB/mullite (LSBM) glassceramics ith a mass ratio of 60/40 could be densified at 850oC, which matches the requirements for theLTCC application. Moreover, dispersive behavior of the LSB glass powder with six kinds of commercial ispersants in MEK and toluene solvent had been studied. Furthermore, tape-casting process used for ow-temperature-cofire

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Martín Guerrero, JM, C. Ortiz Moyano, and C. Rodríguez Alonso. "Underwater mucosectomy of a 25 mm IIa-LSTG homogeneous lession at the transverse colon." Revista Andaluza de Patología Digestiva 44, no. 2 (2021): 56–58. http://dx.doi.org/10.37352/2021442.3.

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Alzahrani, Ibrahim R. "Unlocking Potential Score Insights of Sentimental Analysis with a Deep Learning Revolutionizes." Emerging Science Journal 9, no. 1 (2025): 25–44. https://doi.org/10.28991/esj-2025-09-01-03.

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Online hate has emerged as a rapidly growing issue worldwide, often stemming from differences in opinion. It is crucial to use appropriate language and words on social media platforms, as inappropriate communication can negatively impact others. Consequently, detecting hate speech is of significant importance. While manual methods are commonly employed to identify hate and offensive content on social media, they are time-consuming, labor-intensive, and prone to errors. Therefore, AI-based approaches are increasingly being adopted for the effective classification of hate and offensive speech. T

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Jeckson, Jeckson, Hamimi Hamimi, and Ahmad Adhitya Prawira. "Optimalisasi Load Break Switch Motorized Menggunakan Fungsi Sectionalizer Berbasis Scada pada Penyulang Bacan ULP Pulung Kencana." Jurnal Ilmiah Teknik Elektro 2, no. 1 (2021): 14–22. http://dx.doi.org/10.36269/jtr.v2i1.399.

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The medium voltage network of bacan feeders was an air line power network and had a very high potential to experience disruptions impacted on customer outages. The bacan feeder network was divided into sections uisng breaker equipment (Recloser/LSBM) in order to narrow down the area of blackout when a disturbance occurs. The work function of the breaker equipment was not yet optimal, so it can have an impact on the length of time the customer goes out when there was a network disturbance. From the simulation of the existing feeder conditions, the ENS (Energy Not Supplied) results were really h

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Li, Wenbin, Yue Yang, and Stefan Pischinger. "Domain Generalization Using Maximum Mean Discrepancy Loss for Remaining Useful Life Prediction of Lithium-Ion Batteries." Batteries 11, no. 5 (2025): 194. https://doi.org/10.3390/batteries11050194.

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The capacity of Lithium-ion batteries degrades over the time, making accurate prediction of their Remaining Useful Life (RUL) crucial for maintenance and product lifespan design. However, diverse aging mechanisms, changing working conditions and cell-to-cell variation lead to the inhomogeneous cell lifespan and complicated life prediction. In this work, a data-driven algorithm based on stacked Long Short Term Memory (LSTM) encoder–decoders is proposed for RUL prediction. The encoder and upstream decoder form an autoencoder framework for feature extraction. The encoder and the downstream decode

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