Short-term Memory Network Research Articles

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system, with its dynamics closely linked to economic growth. The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets. By examining historical transaction data, latent opportunities for profit can be uncovered, providing valuable insights for both institutional and individual investors to make more informed decisions. This study focuses on analyzing historical transaction data from four banks to predict closing price trends. Various models, including decision trees, random forests, and Long Short-Term Memory (LSTM) networks, are employed to forecast stock price movements. Historical stock transaction data serves as the input for training these models, which are then used to predict upward or downward stock price trends. The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements. The LSTM-based deep neural network model, in particular, demonstrates a commendable level of predictive accuracy. This conclusion is reached following a thorough evaluation of model performance, highlighting the potential of LSTM models in stock market forecasting. The findings offer significant implications for advancing financial forecasting approaches, thereby improving the decision-making capabilities of investors and financial institutions.

Estimation of Hydraulic and Water Quality Parameters Using Long Short-Term Memory in Water Distribution Systems

Predicting essential water quality parameters, such as discharge, pressure, turbidity, temperature, conductivity, residual chlorine, and pH, is crucial for ensuring the safety and efficiency of water supply systems. This study employs long short-term memory (LSTM) networks to address the challenge of capturing temporal dependencies in these complex processes. Our approach, using a robust LSTM-based model, has demonstrated significant predictive accuracy, as evidenced by substantial R-squared values (e.g., 0.86 for discharge and 0.97 for conductivity). These models have proven particularly effective in handling non-linear patterns and time-series data, which are prevalent in water quality metrics. The results indicate the potential for LSTMs not only to enhance the real-time monitoring of water systems but also to aid in the strategic planning and management of water supply systems. This study’s findings can serve as a basis for further research into the integration of AI in environmental engineering, particularly for predictive tasks in complex, dynamic systems.

ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels.

ILMCNet: A Deep Neural Network Model That Uses PLM to Process Features and Employs CRF to Predict Protein Secondary Structure.

Protein secondary structure prediction (PSSP) is a critical task in computational biology, pivotal for understanding protein function and advancing medical diagnostics. Recently, approaches that integrate multiple amino acid sequence features have gained significant attention in PSSP research. We aim to automatically extract additional features represented by evolutionary information from a large number of sequences while simultaneously incorporating positional information for more comprehensive sequence features. Additionally, we consider the interdependence between secondary structures during the prediction stage. To this end, we propose a deep neural network model, ILMCNet, which utilizes a language model and Conditional Random Field (CRF). Protein language models (PLMs) pre-trained on sequences from multiple large databases can provide sequence features that incorporate evolutionary information. ILMCNet uses positional encoding to ensure that the input features include positional information. To better utilize these features, we propose a hybrid network architecture that employs a Transformer Encoder to enhance features and integrates a feature extraction module combining a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM). This design enables deep extraction of localized features while capturing global bidirectional information. In the prediction stage, ILMCNet employs CRF to capture the interdependencies between secondary structures. Experimental results on benchmark datasets such as CB513, TS115, NEW364, CASP11, and CASP12 demonstrate that the prediction performance of our method surpasses that of comparable approaches. This study proposes a new approach to PSSP research and is expected to play an important role in other protein-related research fields, such as protein tertiary structure prediction.

Forecasting trends in food security with real time data

Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives and livelihoods. In this work we present a quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme’s global hunger monitoring system which collects, processes, and displays daily updates on key food security metrics, conflict, weather events, and other drivers of food insecurity. In this study we assessed the performance of various models including Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM) Network, Convolutional Neural Network (CNN), and Reservoir Computing (RC), by comparing their Root Mean Squared Error (RMSE) metrics. Our findings highlight Reservoir Computing as a particularly well-suited model in the field of food security given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity.

Remaining useful life prediction of turbofan engines based on dual attention mechanism guided parallel CNN-LSTM

Abstract Remaining useful life (RUL) prediction is an valuable research task for predictive health management of aero engines, which is crucial to enhance the safety, dependability and economy of the motor. Accurate prediction of remaining engine service life is an important means for the effectively monitoring of engine operating conditions. The forecasting accuracy of turbofan RUL is inadequate with the traditional single-parameter, single-stage mode. To boost the precision of aero-engine RUL anticipation, a novel mode is recommended, based on a parallel convolutional neural network (CNN) with a long and short-term memory neural network (LSTM) and a dual attention mechanism, named PCLD. The degradation information directly from time series sensor data. The advantages of convolutional neural network (CNN) networks and long short-term memory (LSTM) networks in feature mining and time series processing, respectively, are employed to process time series data, which is conducive to preventing the loss of important element in the data. At last, the experimental results on the aero-engine performance recession dataset C-MAPSS demonstrate that the method outperforms the currently popular models with better robustness and higher prediction accuracy.

IDLB-Pred: identification of disordered lipid binding residues in protein sequences using convolutional neural network

Proteins, nucleic acids, and lipids all interact with intrinsically disordered protein areas. Lipid-binding regions are involved in a variety of biological processes as well as a number of human illnesses. The expanding body of experimental evidence for these interactions and the dearth of techniques to anticipate them from the protein sequence serve as driving forces. Although large-scale laboratory techniques are considered to be essential for equipment for studying binding residues, they are time consuming and costly, making it challenging for researchers to predict lipid binding residues. As a result, computational techniques are being looked at as a different strategy to overcome this difficulty. To predict disordered lipid-binding residues (DLBRs), we proposed iDLB-Pred predictor utilizing benchmark dataset to compute feature through extraction techniques to identify relevant patterns and information. Various classification techniques, including deep learning methods such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), were employed for model training. The proposed model, iDLB-Pred, was rigorously validated using metrics such as accuracy, sensitivity, specificity, and Matthew’s correlation coefficient. The results demonstrate the predictor’s exceptional performance, achieving accuracy rates of 81% on an independent dataset and 86% in 10-fold cross-validation.

Predictive classification of pedestrian-vehicle crossing behaviors using a hybrid mountain gazelle optimizer-enhanced Long Short-Term Memory model

ABSTRACT This paper addresses the safety issues of pedestrian-vehicle interactions at unsignalized pedestrian crossings by proposing a Hybrid Mountain Gazelle Optimizer-Long Short-Term Memory (HMGO-LSTM) model. The proposed model combines the Hybrid Mountain Gazelle Optimizer (HMGO) algorithm with a Long Short-Term Memory (LSTM) network, using HMGO as an LSTM hyperparameter optimizer. Real-world datasets of pedestrian and vehicle crossings from Ma’anshan were used to train and evaluate the model. The HMGO-LSTM model was compared with traditional classifiers such as K-Nearest Neighbors (KNN), Random Forest (RF), and Genetic Algorithm-Backpropagation (GA-BP). The results show that the HMGO-LSTM model outperforms these classifiers in predicting pedestrian-vehicle interaction behaviors, achieving higher classification accuracy and F1 score. The model also optimizes safety intervals for crossings, leading to new speed limit recommendations. Overall, the HMGO-LSTM model provides a robust theoretical foundation for managing and designing safer pedestrian and vehicle crossings.

Quality-related fault detection for dynamic process based on quality-driven long short-term memory network and autoencoder

Fault detection consistently plays a crucial role in industrial dynamic processes as it enables timely prevention of production losses. However, since industrial dynamic processes become increasingly coupled and complex, they introduce uneven dynamics within the collected data, posing significant challenges in effectively extracting dynamic features. In addition, it is a tricky business to distinguish whether the fault that occurs is quality-related or not, resulting in unnecessary repairing and large losses. In order to deal with these issues, this paper comes up with a novel fault detection method based on quality-driven long short-term memory and autoencoder (QLSTM-AE). Specifically, an LSTM network is initially employed to extract dynamic features, while quality variables are simultaneously incorporated in parallel to capture quality-related features. Then, a fault detection strategy based on reconstruction error statistic squared prediction error (SPE) and the quality monitoring statistic Hotelling T2 (H2) is designed, which can distinguish various types of faults to realize accurate monitoring for dynamic processes. Finally, several experiments conducted on numerical simulations and the Tennessee Eastman (TE) benchmark process demonstrate the reliability and effectiveness of the proposed QLSTM-AE method, which indicates it has higher accuracy and can separate different faults efficiently compared to some state-of-the-art methods.

Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions.

Enhancing Metaphor Recognition of Literary Works in Applied Artificial Intelligence: A Multi-Level Approach with Bi-LSTM and CNN Fusion

ABSTRACT Understanding metaphorical language is essential for AI to interpret and communicate with humans accurately. However, current methods often struggle with the complexity of metaphors, making it difficult for AI systems to understand human language fully. Recognizing metaphors is challenging because they are frequently ambiguous and depend on context. In this study, we propose a new approach using a combination of Bi-directional Long Short-Term Memory (Bi-LSTM) networks, Convolutional Neural Networks (CNN), and uni-directional LSTM components to create a multi-level model for recognizing metaphors. Our model uses various features, including dependency, semantics, and part-of-speech, to improve its learning ability. Additionally, we introduce a new method for recognizing the emotional context of metaphors using a random walk model to determine the emotional tone of words. Our results show that this model improves performance in recognizing metaphors, enhancing AI’s ability to understand them.

A data-driven prediction for concrete crack propagation path based on deep learning method

Accurate and efficient prediction of concrete crack propagation path facilitates timely maintenance. To avoid errors arising from assumptions in physical models and parameter measurements, a data-driven prediction method for concrete crack propagation path is proposed. The spatio-temporal prediction approach is presented by unifying the conditional Generative Adversarial Network and Long Short-Term Memory Network (UcGLS-Net). The UcGLS-Net has the advantage of enabling temporal (by Long Short-Term Memory Network, LSTM) and spatial information (by the conditional Generative Adversarial Network, cGAN) in the concrete crack propagation process to be treated simultaneously and distinctly thus improving prediction accuracy. Notably, results demonstrate that UcGLS-Net still works in crack propagation path prediction even with unknown aggregate distribution. Moreover, the effects of historical input quantity and prediction intervals on prediction accuracy of the UcGLS-Net are studied and the network framework is optimized. Due to high efficiency, this study provides a new adaptable and real-time prediction method for concrete crack propagation path, offering support for the design, damage prediction, assessment, and maintenance of concrete structures.

A time series algorithm to predict surgery in neonatal necrotizing enterocolitis

BackgroundDetermining the optimal timing of surgical intervention for Neonatal necrotizing enterocolitis (NEC) poses significant challenges. This study develops a predictive model using the long short-term memory network (LSTM) with a focal loss (FL) to identify infants at risk of developing Bell IIB + NEC early and issue timely surgical warnings.MethodsData from 791 neonates diagnosed with NEC are gathered from the Neonatal Intensive Care Unit (NICU), encompassing 35 selected features. Infants are categorized into those requiring surgical intervention (n = 257) and those managed medically (n = 534) based on the Mod-Bell criteria. A fivefold cross-validation approach is employed for training and testing. The LSTM algorithm is utilized to capture and utilize temporal relationships in the dataset, with FL employed as a loss function to address class imbalance. Model performance metrics include precision, recall, F1 score, and average precision (AP).ResultsThe model tested on a real dataset demonstrated high performance. Predicting surgical risk 1 day in advance achieved precision (0.913 ± 0.034), recall (0.841 ± 0.053), F1 score (0.874 ± 0.029), and AP (0.917 ± 0.025). The 2-days-in-advance predictions yielded (0.905 ± 0.036), recall (0.815 ± 0.057), F1 score (0.857 ± 0.035), and AP (0.905 ± 0.029).ConclusionThe LSTM model with FL exhibits high precision and recall in forecasting the need for surgical intervention 1 or 2 days ahead. This predictive capability holds promise for enhancing infants’ outcomes by facilitating timely clinical decisions.

Molecular spectral line data preprocessing container load grouping prediction algorithm based on EMD-LSTM

Unbalanced allocation of container resources in cluster environments is an urgent problem to be solved. Aiming at container load prediction and resource allocation strategy, this paper designs an astronomical data processing container load grouping prediction algorithm based on empirical mode decomposition-long short-term memory network, and proposes an adaptive recommendation value generation algorithm based on predicted load information, which can automatically allocate container computing resources according to the degree of load fluctuation. The load prediction accuracy is verified using simulated data and real astronomical observation data. The experimental results show that the algorithm proposed in this paper has higher prediction accuracy than the triple index method and the single long short-term memory network model. In the real-time preprocessing test of astronomical data, the recommendation value generation algorithm proposed in this paper can effectively improve the utilization efficiency of computing resources compared with the default strategy.

Data fusion method for aircraft surveillance in flight zone based on Trans-Attention

Aiming at the problem of low monitoring accuracy and position jump of single monitoring source of aircraft in the flight zone, a method of aircraft monitoring data fusion based on Transformer and attention mechanism is proposed. Firstly, the encoder structure of Transformer is used to extract features of each monitoring source data respectively, and then weight values are assigned to different monitoring sources through the attention mechanism. Finally, regression calculation is performed through the fully connected network to obtain the final fusion result. The monitoring data of the surface surveillance radar and the broadcast automatic dependent surveillance system are selected as the fusion source, and the multi-point positioning data is used as the true label. The experimental results show that this method effectively reduces the monitoring error of a single monitoring source, and the fusion effect is better than the long short-term memory network, recurrent neural network and extended Kalman filter fusion method based on the attention mechanism, and the mean absolute error is improved by 2.20%、14.32%and respectively 33.94%.

GRU-IKF scene taxiing trajectory prediction model based on attention mechanism

Aircraft taxi trajectory prediction helps solve operational problems such as airport taxiing conflicts and long waiting times, ensuring airport safety while improving service levels and increasing airport throughput. In view of the fact that the performance of machine learning models depends on good data sets, a method for predicting taxi trajectories of aircraft on the ground based on attention mechanism, fusion of gated recurrent unit (GRU) and improved Kalman filter algorithm (IKF) is proposed. Firstly, three independent gated recurrent unit networks are used to capture the motion state and temporal dependency of aircraft at future moments, and the attention mechanism is introduced to enhance the ability to extract data difference features and learn the mapping relationship from input to output; then, it is fused with the improved extended Kalman filter to integrate the output results of the neural network into the state prediction and update process to improve the accuracy of the predicted trajectory sequence. Finally, the effectiveness of the model was verified using the actual taxiing trajectory of aircraft at Lukou Airport. The simulation results show that the model can effectively and accurately predict the taxiing trajectory of aircraft on the surface, with an overall mean square error of about 0.00128. Compared with the single recurrent neural network (RNN), long short-term memory network (LSTM) and GRU model, the RMSE is reduced by 72.9%and respectively 39.9%, 54.7%and the prediction time is 40 ms. It can accurately and quickly predict the taxiing trajectory, which helps to reduce the operating load of the airport surface management system.

Upper limit assessment of GIS equipment failure based on time series model and deep learning algorithm

If a sudden fault of gas-insulated switchgear is not handled in time, it will delay production and cause certain economic losses to the enterprise. Therefore, this study proposes an upper limit assessment algorithm for equipment faults based on time series model and deep learning. The irregular fluctuation components in the time series model are stabilized by using empirical mode decomposition, and the long short-term memory network in the deep learning algorithm is introduced to combine the two. The equipment fault data is obtained by the joint algorithm. After empirical mode decomposition, the intrinsic mode function component of the data signal is obtained, and the sensitive component is extracted from it. The relationship value and mutual information between it and the original data signal are analyzed to complete the fault data feature extraction. In the process of equipment fault upper limit assessment, the parameter optimization and convergence are controlled, and the fault deep learning model is constructed. After the refraction coefficient and reflection coefficient are determined, the upper limit assessment of equipment faults is realized. The proposed method is experimentally tested. The results show that the proposed algorithm has an ideal upper limit assessment result of faults, and the obtained curve has a high degree of fit with the actual result curve. It can be proved that the proposed algorithm can scientifically evaluate the upper limit of equipment faults and has certain application value.

Novel state of charge estimation method of containerized Lithium–Ion battery energy storage system based on deep learning

State of charge (SOC) is a critical indicator for lithium–ion battery energy storage system. However, model-driven SOC estimation is challenging due to the coupling of internal charging and discharging processes, ion diffusion, and chemical reactions in the electrode materials. These factors result in frequent changes in their internal characteristics. Therefore, we devise a novel deep learning SOC estimation method based on the attention mechanism (A) and convolutional neural networks-long short term memory (CNN–LSTM) network model. The CNN layers are employed to extract the critical features of the battery dataset. The LSTM layers are utilized to capture the long-term dependencies in the time series data. The attention mechanism can assign attention weights to different features within the output of the convolution layer. The A–CNN–LSTM method not only amplifies the positive influence of critical knowledge but also provides a global reference. The comparative experiments of three datasets and ablation study are carried out. The root mean square error, mean absolute error, and mean absolute percentage error obtained from the A–CNN–LSTM model decreased by 32.75 %, 45.88 %, and 53.36 %, respectively, compared with the best results obtained from the existing CNN, LSTM, gate recurrent unit (GRU), CNN–LSTM, and CNN–GRU models. The proposed model demonstrates greater stability and consistency than other models, underscoring its efficacy in SOC estimation.

Intelligent algorithmic framework for detection and mitigation of BeiDou spoofing attacks in vehicular ad hoc networks (VANETs)

This research tackles the critical challenge of BeiDou signal spoofing in vehicular ad-hoc networks and addresses significant risks to vehicular safety and traffic management stemming from increased reliance on accurate satellite navigation. The study proposes a novel hybrid machine learning framework that integrates Autoencoders and long short-term memory (LSTM) networks with an advanced cryptographic method, attribute-based encryption, to enhance the detection and mitigation of spoofing attacks. Our methodology leverages both real-time and synthetic navigational data in a comprehensive experimental setup that simulates various spoofing scenarios to test the resilience of the proposed system. The findings demonstrate a significant improvement in the accuracy of spoofing detection and the robustness of mitigation strategies by ensuring the integrity and reliability of navigational data. This investigation enhances the existing body of knowledge by demonstrating the effectiveness of integrating machine learning with cryptographic techniques to secure VANETs. Ultimately, it effectively paves the way for future research into adaptive security mechanisms that can dynamically respond to evolving cyber threats.

Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks

Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks