Long Short-term Memory Recurrent Neural Network Research Articles

Development and Evaluation of Vibration Canceling System Utilizing Macro-Fiber Composites (MFCs) and Long Short-Term Memory (LSTM) Vibration Prediction AI Algorithms for Road Driving Vibrations

This study developed an innovative active vibration canceling (AVC) system designed to mitigate non-periodic vibrations during road driving to enhance passenger comfort. The macro-fiber composite (MFC) used in the system is a smart material that is flexible, soft, lightweight, and applicable in many fields as a dual-purpose sensor and actuator. The target vibrations are road vibration data that were collected while driving on standard urban (Seoul) and highway roads at 40 km/s. To predict and cancel the target vibration accurately before passing it, we modeled the vibration prediction algorithm using a long short-term memory recurrent neural network (LSTM RNN). We regenerated vibrations on Seoul and highway roads at 40 km/s using MFCs and measured the displacements of the measured, predicted, and AVC vibrations of each road condition. To evaluate the vibration, we computed the root mean squared error (RMSE) and compared standard deviation (SD) values. The accuracies of LSTM RNN vibration prediction algorithms are 97.27% and 96.36% on Seoul roads and highway roads, respectively, at 40 km/s. Although the vibration ratio compared with the AVC results are different, there was no difference between the values of the AVC vibrations. According to a previous study and the principle of the AVC system, the target vibrations decrease by canceling the inverse vibration of the MFC actuator.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Elevating sustainability with a multi-renewable hydrogen generation system empowered by machine learning and multi-objective optimization

The global energy landscape is rapidly shifting toward cleaner, lower-carbon electricity generation, necessitating a transition to alternate energy sources. Hydrogen, particularly green hydrogen, looks to be a significant solution for facilitating this transformation, as it is produced by water electrolysis with renewable energy sources such as solar irradiations, wind speed, and biomass residuals. Traditional energy systems are costly and produce energy slowly due to unpredictability in resource supply. To address this challenge, this work provides a novel technique that integrates a multi-renewable energy system using multi objective optimization algorithm to meets the machine learning-based forecasted load model. Several forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Random Forest and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), are assessed for develop the statistical metrics values such as RMSE, MAE, and MAPE. The selected Non-Sorting Moth Flame Optimization (NSMFO) algorithm demonstrates technological prowess in efficiently achieving global optimization, particularly when handling multiple objective functions. This integrated method shows enormous promise in technological, economic, and environmental terms, emphasizing its ability to promote energy sustainability targets.

Prediction of Diffuse Attenuation Coefficient Based on Informer: A Case Study of Hangzhou Bay and Beibu Gulf

Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes the speed at which light decays as it travels through water, obtained from satellite-derived ocean color products can reflect the overall water quality trends. However, current models inadequately explore the complex nonlinear features of Kd, and there are difficulties in achieving accurate long-term predictions and optimal computational efficiency. This study innovatively proposes a model called Remote Sensing-Informer-based Kd Prediction (RSIKP). The proposed RSIKP is characterized by a distinctive Multi-head ProbSparse self-attention mechanism and generative decoding structure. It is designed to comprehensively and accurately capture the long-term variation characteristics of Kd in complex water environments while avoiding error accumulation, which has a significant advantage in multi-dataset experiments due to its high efficiency in long-term prediction. A multi-dataset experiment is conducted at different prediction steps, using 70 datasets corresponding to 70 study areas in Hangzhou Bay and Beibu Gulf. The results show that RSIKP outperforms the five prediction models based on Artificial Neural Networks (ANN, Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Long Short-Term Memory Networks (LSTM)). RSIKP captures the complex influences on Kd more effectively to achieve higher prediction accuracy compared to other models. It shows a mean improvement of 20.6%, 31.1%, and 22.9% on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Particularly notable is its outstanding performance in the long time-series predictions of 60 days. This study develops a cost-effective and accurate method of marine water quality prediction, providing an effective prediction tool for marine water quality management.

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.

Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

Social engineering, on the other hand, presents weaknesses that are difficult to directly quantify in penetration testing. The majority of expert social engineers utilize phishing and adware tactics to convince victims to provide information voluntarily. Social Engineering (SE) in social media has a similar structural layout to regular postings but has a malevolent intrinsic purpose. Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) was used to train a novel SE model to recognize covert SE threats in communications on social networks. The dataset includes a variety of posts, including text, images, and videos. It was compiled over a period of several months and was carefully curated to ensure that it is representative of the types of content that is typically posted on social media. First, by using domain heuristics, the social engineering assaults detection (SEAD) pipeline is intended to weed out social posts with malevolent intent. After tokenizing each social media post into sentences, each post is examined using a sentiment analyzer to determine whether it is a training data normal or an abnormality. Subsequently, an RNN-LSTM model is trained to detect five categories of social engineering assaults, some of which may involve information-gathering signals. Comparing the experimental findings to the ground truth labeled by network experts, the SEA model achieved 0.82 classification precision and 0.79 recall.

Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. Although DuNets have been successfully applied to many linear inverse problems, their performance tends to be impaired by nonlinear problems. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets—the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA, respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems.

Enhancing high-speed digital systems: MVL circuit design with CNTFET and RRAM

Multiple Value Logic (MVL) circuits in high-speed digital systems can represent significantly more information than traditional binary logic circuits. The increasing demand for enhanced data storage capacity has driven the adoption of MVL circuits. However, designing MVL circuits presents challenges, particularly in achieving improved performance aligned with the desired threshold voltage. To address this challenge, harnessing carbon nanotube field-effect transistors (CNTFETs) has been instrumental in designing MVL circuits. CNTFETs offer distinct advantages due to their large resistors. Furthermore, substituting these resistors with Resistive Random Access Memory (RRAM) to implement Ternary logic circuits enhances storage capacity by enabling multiple resistance states within a single cell. This paper presents a novel MVL circuit design that leverages the unique characteristics of CNTFETs and RRAM. Additionally, gate implementation is explored using a Recurrent Neural Network − Long-Short Term Memory (RNN-LSTM) based Tasmanian Devil Optimization algorithm. The practical implementation uses Synopsis HSPICE software with CNTFET technology at 32 nm. A comprehensive performance analysis compares this approach with state-of-the-art solutions regarding delay, area, and power consumption. Significant reductions in area, power consumption, and transistor count are revealed with the proposed approach.

Forecasting the Future Value of a Colombian Investment Fund with LSTM Recurrent Neural Networks (LSTM)

Recurrent neural networks are a tool that is currently used in time series, a widespread use of these networks is the forecasting of future prices in financial time series. One widely used recurrent neural network model is the LSTM (Long Short-Term Memory) model, proposed by Sepp Hochreiter and Jürgen Schmidhuber in their paper called LONG SHORT-TERM MEMORY published in 1997. This model solves the long term memory problem of recurrent neural networks by adding a selective memory cell which acts as a "filter" to choose what kind of information is important to keep and what kind of information is irrelevant and can be discarded. This paper seeks to analyze whether the original LSTM model is relevant to forecast the price of a Colombian mutual fund without taking into account exogenous variables (economic news, macroeconomic and microeconomic variables, underlying assets such as stocks, options, bonds, etc.) that affect its behavior. In this study, three funds with a majority composition of shares in the Colombian market were taken, which have higher volatility levels than public or private bonds.

A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting

Karst spring discharge plays a vital role in understanding karst systems and managing karst groundwater resources. Due to its inherent heterogeneity and complexity, hydrological factors in the karst system often exhibit highly nonlinear and nonstationary characteristics. To overcome this challenge, a novel self-adaptive deep learning architecture is proposed to forecast daily karst spring runoff with precipitation in this study. The newly proposed model effectively integrates discrete wavelet transform (DWT), WaveNet, and long short-term memory (LSTM), enhanced by the attention mechanism and residual connections to learn underlying patterns, mitigate the overfitting risk, focus on the most relevant time steps, and improve the prediction accuracy. The framework is structured into two parts: one part is dedicated to predicting the karst spring discharge, whereas another part primarily predicts the residuals arising from the main predictions. By intergrading the two-part approach, the innovative model can adaptively correct the systematic errors and refine its understanding of some aspects that it might initially struggle with. The newly proposed model and three existing deep learning models including LSTM, GRU and simple RNN models are applied to predict karst spring discharge at Barton Springs, Texas, USA. The results imply that the newly developed DWT-WaveNet-LSTM model can leverage the advantages of various models, adapt to various aspects of the hydrometeorological data, and better capture intrinsic patterns from nonlinear and nonstationary input features. It outperforms other models at all time steps, evidenced by lower RMSE and higher NSE values. The residual analysis is conducted by comparing the predicted and observed spring discharge values. The self-adaptive DWT-WaveNet-LSTM model exhibits residuals that are closely centered around zero with a symmetrical, bell-shaped distribution, indicating superior performance and robust predictions in the karst system due to its effective capture of complex and nonlinear patterns.

Machine learning to predict grains futures prices

AbstractAccurate commodity price forecasts are crucial for stakeholders in agricultural supply chains. They support informed marketing decisions, risk management, and investment strategies. Machine learning methods have significant potential to provide accurate forecasts by maximizing out‐of‐sample accuracy. However, their inherent complexity makes it challenging to understand the appropriate data pre‐processing steps to ensure proper functionality. This study compares the forecasting performance of Long Short‐Term Memory Recurrent Neural Networks (LSTM‐RNNs) with classical econometric time series models for corn futures prices. The study considers various combinations of data pre‐processing techniques, variable clusters, and forecast horizons. Our results indicate that LSTM‐RNNs consistently outperform classical methods, particularly for longer forecast horizons. In particular, our findings demonstrate that LSTM‐RNNs are capable of automatically handling structural breaks, resulting in more accurate forecasts when trained on datasets that include such shocks. However, in our setting, LSTM‐RNNs struggle to deal with seasonality and trend components, necessitating specific data pre‐processing procedures for their removal.

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

Social engineering (SE) presents weaknesses that are difficult to quantify in penetration testing directly. The majority of expert social engineers utilize phishing and adware tactics to convince victims to provide information voluntarily. SE in social media has a similar structural layout to regular postings but has a malevolent intrinsic purpose. Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) was used to train a novel SE model to recognize covert SE threats in communications on social networks. The dataset includes various posts, including text, images, and videos. It was compiled over a period of several months. Then carefully curated to ensure that it is representative of the types of content that are typically posted on social media. First, using domain heuristics, the social engineering assaults detection (SEAD) pipeline is intended to weed out social posts with malevolent intent. After tokenizing each social media post into sentences, each post is examined using a sentiment analyzer to determine whether it is a training data normal or an abnormality. Subsequently, an RNN-LSTM model is trained to detect five categories of social engineering assaults, some of which may involve information-gathering signals. Thus, the proposed SEA model yielded a classification precision of 0.82 and a recall of 0.79.

Neural network and machine learning use cases: Indian bond market predictions

This study examines machine learning techniques to investigate how artificial intelligence (AI) affects predicting future trends in the bond market. The bond market offers a global perspective on capital costs for a business by establishing the fair value of the bond issue, which is based on multiple factors. The asset price market, which has employed machine learning (ML) and deep learning (DL) techniques to address the primary forecasting difficulty, surprisingly plays a significant role in predicting future bond market returns. As an outcome, if this gap can be forecast, it can act as the bond market's data-driven long-term direction and yield additional profits. Daily security-specific data for the 10-to-3-year Indian Treasury Bond (ITB) was gathered from 2013 to 2022 and is available in the global government bonds database. The researchers looked at how well the auto-regressive integrated moving average (ARIMA), linear regression, and deep recurrent neural network-long short-term memory (DLSTM) models could predict bond yields and returns in future bond markets. The empirical results demonstrate that the DLSTM models most fairly predict the price of government bonds over both the short and longer horizons when compared to ARIMA and linear regression.

Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks

In this paper, we investigated and evaluated various machine learning-based approaches for automatically detecting wheezing sounds. We conducted a comprehensive comparison of these proposed systems, assessing their classification performance through metrics such as Sensitivity, Specificity, and Accuracy. The main approach to developing a machine learning-based system for classifying respiratory sounds involved the combination of a technique for extracting features from an unknown input sound with a classification method to determine its belonging class. The characterization techniques used in this study are based on the cepstral analysis, which was extensively employed in the automatic speech recognition field. While MFCC (Mel-Frequency Cepstral Coefficients) feature extraction methods are commonly used in respiratory sounds classification, our study introduces a novelty by employing GFCC (Gammatone-Frequency Cepstral Coefficients) and BFCC (Bark-Frequency Cepstral Coefficients) for this purpose. For the classification task, we employed two types of neural networks: the MLP (Multilayer Perceptron), a feedforward neural network, and a variant of the LSTM (Long Short-Term Memory) recurrent neural network called BiLSTM (Bidirectional LSTM). The proposed classification systems are evaluated using a database consisting of 497 wheezing segments and 915 normal respiratory segments, which are recorded from individuals diagnosticated with asthma and individuals without any respiratory issues, respectively. The highest classification performance was achieved by the BFCC-BiLSTM model, which demonstrated an exceptional accuracy rate of 99.8%.

A novel approach for one-step defect detection and depth estimation using sequenced thermal signal encoding

ABSTRACT Pulsed thermography is a technique of significant interest in non-destructive testing, particularly in defect detection and depth characterisation of composite materials. This study presents an innovative methodology for simultaneously detecting defects and estimating depth using a combination of sequenced thermal signal encoding and a two-dimensional convolution neural network (CNN) model. We compare the results of the proposed method with those obtained from the feed-forward neural network (FFNN), a one-dimensional CNN, and a long short-term memory recurrent neural network (LSTM-RNN). The findings demonstrate that the proposed approach exhibits superior accuracy and robustness compared to the benchmarks.

Application of Artificial Intelligence Technology in Electromechanical Information Security Situation Awareness System

The information security situational awareness system is proposed in this paper to leverage big data and artificial intelligence (AI) to enhance information security situation prediction. Deep learning techniques, specifically the long short-term memory recurrent neural network (LSTM-RNN), predict security situations using complex non-linear and autocorrelation time series data from current and past system conditions. Additionally, the study incorporates the variant gated recurrent unit (GRU) within the LSTM-RNN framework. A comprehensive experimental analysis is conducted, comparing various methods, including LSTM, GRU, and others, to assess and compare their predictive performance. The experimental results reveal that LSTM-RNN demonstrates a commendable level of predictive accuracy on the test dataset, with a mean absolute percentage error (MAPE) of 8.79%, a root mean square error (RMSE) of 0.1107, and a relative root mean square error (RRMSE) of 8.47%. Both LSTM and GRU exhibit exceptional predictive accuracy, with GRU offering a slightly faster training speed due to its simplified architecture and fewer trainable parameters. Overall, this research highlights the potential of AI-based methodologies in constructing robust information security situational awareness systems.

Automatic detection of fake news using recurrent neural network—Long short-term memory

<p>The propagation of deliberate misinformation is gaining significant momentum, especially across different social media platforms. Despite the fact that there are several fact-checking blogs and websites that distinguish between news that is genuine or fake, and regardless of the reality that there is ongoing research being conducted to restrict the propagation of fake news, the issue is still one that needs to be addressed. The most major barrier is a failure to spot monitors and to disclose false news in a reasonable timeframe, both of which are critical components. In this research, a system is proposed that utilizes deep learning model of Recurrent Neural Network—Long Short-Term Memory (RNN-LSTM) in order to put an end to the circulation of misleading information and put a stop to, and expose instances of fake news that are spread through reliable channels. The process of extracting hybrid features from text data, such as Lemmas, Bi-Gram, Tri-Gram, N-gram, Term Frequency Inverse Document Frequency (TF-IDF), part-of-speech, and dependency-based natural language processing features, is developed as a strategy. When compared to other traditional approaches to machine learning classifier, the RNN-LSTM method that was proposed obtains a greater level of accuracy than those other approaches. It achieves an accuracy rate of 99.10% both during training and testing, which is better to the accuracy achieved by common machine learning approaches such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT). In the proposed approach for detecting false news using RNN-LSTM, the three experiments are conducted to acquire, accuracy, precision, recall, and F-score with varying forms of cross validation (5-Fold, 10-Fold and 15-Fold). Based on the findings of the empirical research, the conclusion can be drawn that the RNN-LSTM with ReLu function provides more accurate detection than both the RNN-LSTM (Tan h) function and the RNN-LSTM (sigmoid) function with 15 fold cross validation.<strong></strong></p>

Learning nonlinear integral operators via recurrent neural networks and its application in solving integro-differential equations

In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-differential equations (IDEs). The LSTM-RNN representation of the nonlinear integral operator allows us to turn a system of nonlinear integro-differential equations into a system of ordinary differential equations for which many efficient solvers are available. Furthermore, because the use of LSTM-RNN representation of the nonlinear integral operator in an IDE eliminates the need to perform a numerical integration in each numerical time evolution step, the overall temporal cost of the LSTM-RNN-based IDE solver can be reduced to O(nT) from O(nT2) if a nT-step trajectory is to be computed. We illustrate the efficiency and robustness of this LSTM-RNN-based numerical IDE solver with a model problem. Additionally, we highlight the generalizability of the learned integral operator by applying it to IDEs driven by different external forces. As a practical application, we show how this methodology can effectively solve the Dyson’s equation for quantum many-body systems.

Deep learning-based forecasting modeling of micro gas turbine performance projection: An experimental approach

Performance forecasting of aeroengines is crucial for achieving better operational efficiency, ensuring safety, reducing costs, and minimizing environmental impact in the aviation industry. It enables engineers and researchers to make informed decisions, leading to advancements in technology and the overall evolution of aviation. This study is focused on the effects of flight conditions on the performance of a turbojet, which in consequence affects the environmental aspect of operation. By investigating the relationships between aeroengine efficiency and performance indicators such as thrust, shaft speed, and exhaust gas temperature (EGT), and flight characteristics expressed in terms of environmental and operational conditions, the study seeks to elucidate these connections. The article's significance lies in its successful application of Long Short-Term Memory (LSTM) networks to predict thrust, shaft speed, and EGT variations in turbojet engines under varying flight conditions. Experimental data from a turbojet test bench is processed with deep learning, specifically LSTM recurrent neural networks that are developed based on Matrix Laboratory (MATLAB). The model inputs are free stream air speed, compressor inlet pressure, combustor inlet temperature, combustor inlet pressure, turbine inlet temperature, turbine inlet pressure, nozzle inlet pressure and fuel flow, and the outputs are thrust, shaft speed and EGT. Predicted thrust closely aligns with actual thrust values, though with minor discrepancies. Shaft speed predictions exhibit a similar trend, while EGT predictions showcase a comparable pattern with slight variations. Despite the prediction errors, a thorough evaluation of median values, box plots, and probability density functions confirms that the models effectively capture available information, though discrepancies may arise from measurement inaccuracies and initial engine conditions. These results show that it is possible to accurately predict turbojet performance using LSTM recurrent neural network. This research paves the way for enhanced aeroengine performance prediction, particularly in scenarios requiring off-board or high-performance applications.