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1
Noh, Seol-Hyun. "Analysis of Gradient Vanishing of RNNs and Performance Comparison." Information 12, no. 11 (2021): 442. http://dx.doi.org/10.3390/info12110442.
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A recurrent neural network (RNN) combines variable-length input data with a hidden state that depends on previous time steps to generate output data. RNNs have been widely used in time-series data analysis, and various RNN algorithms have been proposed, such as the standard RNN, long short-term memory (LSTM), and gated recurrent units (GRUs). In particular, it has been experimentally proven that LSTM and GRU have higher validation accuracy and prediction accuracy than the standard RNN. The learning ability is a measure of the effectiveness of gradient of error information that would be backpropagated. This study provided a theoretical and experimental basis for the result that LSTM and GRU have more efficient gradient descent than the standard RNN by analyzing and experimenting the gradient vanishing of the standard RNN, LSTM, and GRU. As a result, LSTM and GRU are robust to the degradation of gradient descent even when LSTM and GRU learn long-range input data, which means that the learning ability of LSTM and GRU is greater than standard RNN when learning long-range input data. Therefore, LSTM and GRU have higher validation accuracy and prediction accuracy than the standard RNN. In addition, it was verified whether the experimental results of river-level prediction models, solar power generation prediction models, and speech signal models using the standard RNN, LSTM, and GRUs are consistent with the analysis results of gradient vanishing.
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Alam, Muhammad S., AKM B. Hossain, and Farhan B. Mohamed. "Performance Evaluation of Recurrent Neural Networks Applied to Indoor Camera Localization." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (2022): 116–24. http://dx.doi.org/10.46338/ijetae0822_15.
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Researchers in robotics and computer vision are experimenting with the image-based localization of indoor cameras. Implementation of indoor camera localization problems using a Convolutional neural network (CNN) or Recurrent neural network (RNN) is more challenging from a large image dataset because of the internal structure of CNN or RNN. We can choose a preferable CNN or RNN variant based on the problem type and size of the dataset. CNN is the most flexible method for implementing indoor localization problems. Despite CNN's suitability for hyper-parameter selection, it requires a lot of training images to achieve high accuracy. In addition, overfitting leads to a decrease in accuracy. Introduce RNN, which accurately keeps input images in internal memory to solve these problems. Longshort-term memory (LSTM), Bi-directional LSTM (BiLSTM), and Gated recurrent unit (GRU) are three variants of RNN. We may choose the most appropriate RNN variation based on the problem type and dataset. In this study, we can recommend which variant is effective for training more speedily and which variant produces more accurate results. Vanishing gradient issues also affect RNNs, making it difficult to learn more data. Overcome the gradient vanishing problem by utilizing LSTM. The BiLSTM is an advanced version of the LSTM and is capable of higher performance than the LSTM. A more advanced RNN variant is GRU which is computationally more efficient than an LSTM. In this study, we explore a variety of recurring units for localizing indoor cameras. Our focus is on more powerful recurrent units like LSTM, BiLSTM, and GRU. Using the Microsoft 7-Scenes and InteriorNet datasets, we evaluate the performance of LSTM, BiLSTM, and GRU. Our experiment has shown that the BiLSTM is more efficient in accuracy than the LSTM and GRU. We also observed that the GRU is faster than LSTM and BiLSTM
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Muhuri, Pramita Sree, Prosenjit Chatterjee, Xiaohong Yuan, Kaushik Roy, and Albert Esterline. "Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks." Information 11, no. 5 (2020): 243. http://dx.doi.org/10.3390/info11050243.
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An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves intrusion detection. The performance of the IDS was analyzed by calculating the accuracy, recall, precision, f-score, and confusion matrix. The NSL-KDD dataset was used to analyze the performances of the classifiers. An LSTM-RNN was used to classify the NSL-KDD datasets into binary (normal and abnormal) and multi-class (Normal, DoS, Probing, U2R, and R2L) sets. The results indicate that applying the GA increases the classification accuracy of LSTM-RNN in both binary and multi-class classification. The results of the LSTM-RNN classifier were also compared with the results using a support vector machine (SVM) and random forest (RF). For multi-class classification, the classification accuracy of LSTM-RNN with the GA model is much higher than SVM and RF. For binary classification, the classification accuracy of LSTM-RNN is similar to that of RF and higher than that of SVM.
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Wang, Yiyang, Wenchuan Wang, Hongfei Zang, and Dongmei Xu. "Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin." Water 15, no. 22 (2023): 3928. http://dx.doi.org/10.3390/w15223928.
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The long short-term memory network (LSTM) model alleviates the gradient vanishing or exploding problem of the recurrent neural network (RNN) model with gated unit architecture. It has been applied to flood forecasting work. However, flood data have the characteristic of unidirectional sequence transmission, and the gated unit architecture of the LSTM model establishes connections across different time steps which may not capture the physical mechanisms or be easily interpreted for this kind of data. Therefore, this paper investigates whether the gated unit architecture has a positive impact and whether LSTM is still better than RNN in flood forecasting work. We establish LSTM and RNN models, analyze the structural differences and impacts of the two models in transmitting flood data, and compare their performance in flood forecasting work. We also apply hyperparameter optimization and attention mechanism coupling techniques to improve the models, and establish an RNN model for optimizing hyperparameters using BOA (BOA-RNN), an LSTM model for optimizing hyperparameters using BOA (BOA-LSTM), an RNN model with MHAM in the hidden layer (MHAM-RNN), and an LSTM model with MHAM in the hidden layer (MHAM-LSTM) using the Bayesian optimization algorithm (BOA) and the multi-head attention mechanism (MHAM), respectively, to further examine the effects of RNN and LSTM as the underlying models and of cross-time scale bridging for flood forecasting. We use the measured flood process data of LouDe and HuaYuankou stations in the Yellow River basin to evaluate the models. The results show that compared with the LSTM model, under the 1 h forecast period of the LouDe station, the RNN model with the same structure and hyperparameters improves the four performance indicators of the Nash–Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency coefficient (KGE), the mean absolute error (MAE), and the root mean square error (RMSE) by 1.72%, 4.43%, 35.52% and 25.34%, respectively, and the model performance of the HuaYuankou station also improves significantly. In addition, under different situations, the RNN model outperforms the LSTM model in most cases. The experimental results suggest that the simple internal structure of the RNN model is more suitable for flood forecasting work, while the cross-time bridging methods such as gated unit architecture may not match well with the flood propagation process and may have a negative impact on the flood forecasting accuracy. Overall, the paper analyzes the impact of model architecture on flood forecasting from multiple perspectives and provides a reference for subsequent flood forecasting modeling.
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Zaheer, Shahzad, Nadeem Anjum, Saddam Hussain, et al. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model." Mathematics 11, no. 3 (2023): 590. http://dx.doi.org/10.3390/math11030590.
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Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.
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Liu, Di, Qingyuan Xia, Changhui Jiang, Chaochen Wang, and Yuming Bo. "A LSTM-RNN-Assisted Vector Tracking Loop for Signal Outage Bridging." International Journal of Aerospace Engineering 2020 (August 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/2975489.
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Global Navigation Satellite System (GNSS) has been the most popular tool for providing positioning, navigation, and timing (PNT) information. Some methods have been developed for enhancing the GNSS performance in signal challenging environments (urban canyon, dense foliage, signal blockage, multipath, and none-line-of-sight signals). Vector Tracking Loop (VTL) was recognized as the most promising and prospective one among these technologies, since VTL realized mutual aiding between channels. However, momentary signal blockage from part of the tracking channels affected the VTL operation and the navigation solution estimation. Moreover, insufficient available satellites employed would lead to the navigation solution errors diverging quickly over time. Short-time or temporary signal blockage was common in urban areas. Aiming to improve the VTL performance during the signal outage, in this paper, the deep learning method was employed for assisting the VTL navigation solution estimation; more specifically, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) was employed to aid the VTL navigation filter (navigation filter was usually a Kalman filter). LSTM-RNN obtained excellent performance in time-series data processing; therefore, in this paper, the LSTM-RNN was employed to predict the navigation filter innovative sequence values during the signal outage, and then, the predicted innovative values were employed to aid the navigation filter for navigation solution estimation. The LSTM-RNN was well trained while the signal was normal, and the past innovative sequence was employed as the input of the LSTM-RNN. A simulation was designed and conducted based on an open-source Matlab GNSS software receiver; a dynamic trajectory with several temporary signal outages was designed for testing the proposed method. Compared with the conventional VTL, the LSTM-RNN-assisted VTL could keep the horizontal positioning errors within 50 meters during a signal outage. Also, conventional Support Vector Machine (SVM) and radial basis function neural network (RBF-NN) were compared with the LSTM-RNN method; LSTM-RNN-assisted VTL could maintain the positioning errors less than 20 meters during the outages, which demonstrated LSTM-RNN was superior to the SVM and RBF-NN in these applications.
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Lee, Ju-Hyung, and Jun-Ki Hong. "Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations." Electronics 12, no. 23 (2023): 4778. http://dx.doi.org/10.3390/electronics12234778.
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We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, the prediction performances of six RNN techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gate recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional-GRU (Bi-GRU), are analyzed comparatively. In order to assess the prediction accuracy of these RNN techniques in predicting concatenated vibrations, both normal and abnormal vibration data are collected from the motors connected to the drone’s propellers. Consequently, a concatenated vibration dataset is generated by combining 50% of normal vibration data with 50% of abnormal vibration data. This dataset is then used to compare and analyze vibration prediction performance and simulation runtime across the six RNN techniques. The goal of this analysis is to comparatively analyze the performances of the six RNN techniques for vibration prediction. According to the simulation results, it is observed that Attn.-LSTM and Attn.-GRU, incorporating the attention mechanism technique to focus on information highly relevant to the prediction target through unidirectional learning, demonstrate the most promising predictive performance among the six RNN techniques. This implies that employing the attention mechanism enhances the concentration of relevant information, resulting in superior predictive accuracy compared to the other RNN techniques.
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Musyoka, Gabriel, Antony Waititu, and Herbert Imboga. "Credit Risk Modelling Using RNN-LSTM Hybrid Model for Digital Financial Institutions." International Journal of Statistical Distributions and Applications 10, no. 2 (2024): 16–24. http://dx.doi.org/10.11648/j.ijsd.20241002.11.
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In response to the rapidly evolving financial market and the escalating concern surrounding credit risk in digital financial institutions, this project addresses the urgency for accurate credit risk prediction models. Traditional methods such as Neural network models, kernel-based virtual machines, Z-score, and Logit (logistic regression model) have all been used, but their results have proven less than satisfactory. The project focuses on developing a credit scoring model specifically tailored for digital financial institutions, by leveraging a hybrid model that combines long short-term memory (LSTM) networks with recurrent neural networks (RNN). This innovative approach capitalizes on the strengths of the Long-Short Term Memory (LSTM) for long-term predictions and Recurrent Neural Network (RNN) for its recurrent neural network capabilities. A key component of the approach is feature selection, which entails extracting a subset of pertinent features from the credit risk data using RNN in order to help classify loan applications. The researcher chose to use data from Kaggle to study and compare the efficacy of different models. The findings reveal that the RNN-LSTM hybrid model outperforms other RNNs, LSTMs, and traditional models. Specifically, the hybrid model demonstrated distinct advantages, showcasing higher accuracy and a superior Area Under the Curve (AUC) compared to individual RNN and LSTM models. While RNN and LSTM models exhibited slightly lower accuracy individually, their combination in the hybrid model proved to be the optimal choice. In summary, the RNN-LSTM hybrid model developed stands out as the most effective solution for predicting credit risk in digital financial institutions, surpassing the performance of standalone RNN and LSTM models as well as traditional methodologies. This research contributes valuable insights for banks, regulators, and investors seeking robust credit risk assessment tools in the dynamic landscape of digital finance.
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Ahmad, M. Haroon, Ali Saeed, M. Usman Bhatti, Naveed Hussain, Muhammad Farhat Ullah, and Mehmood Anwar. "Next Word Prediction for Urdu using Deep Learning Techniques." VFAST Transactions on Software Engineering 13, no. 1 (2025): 49–59. https://doi.org/10.21015/vtse.v13i1.2044.
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A language model for next-word prediction is a probabilistic representation of a natural language that utilizes text corpora to generate word probabilities. These models play a crucial role in text generation, machine translation, and question-answering applications. The focus of this study is to develop an improved algorithm for next-word prediction in Urdu. The study implements deep learning models, including RNN, LSTM, and Bi-LSTM, on a subset of the Ur-Mono Urdu corpus containing 3,000 and 5,000 sentences. To prepare the data for experimentation, tokenization and stemming data cleaning techniques are applied. The study achieved an accuracy of 87% using the RNN model on the first 3,000 sentences of the Ur-Mono dataset and 84% accuracy using the RNN model on the first 5,000 sentences of the Ur-Mono dataset. In conclusion, it can be stated that when the corpus size is small, the RNN outperforms both the LSTM and BiLSTM. However, as the corpus size increases, the Bi-LSTM exhibits superior performance compared to both RNN and LSTM.
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Ali, Murtadha. "Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization." Wasit Journal of Computer and Mathematics Science 3, no. 4 (2024): 1–14. https://doi.org/10.31185/wjcms.264.
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While this dependence on interconnected computer networks and the web requires robust cybersecurity. Cyber threats have been met with solutions like Intrusion Detection Systems (IDS). IDS are commonly rule-based, and very often use either signature-based or heuristic approaches to detect intrusions. Therefore, for such we recommend an IDS that merges the Grey Wolf Optimization (GWO) algorithm and Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM). RNN-LSTM to Handle Dynamic Network data, but not provided enough complain details in model training. Based on the behavior of grey wolf, an optimization technique GWO is implemented for intrusion detection to enhance accuracy and minimize false alarm in RNN-LSTM. Preprocess and segment network data with creating RNN-LSTM model for considering the dependence of our dataset Our approach improves the IDS performance by optimizing hyperparameters such as hidden layers, units, learning rates using GWO. The architecture of this RNN-LSTM with GWO IDS provides capable and responsive intrusion detection, training on previous data to be able to detect new threats. Made for network security by combining deep learning and optimization, tests reached 99.5% accurate. The research advances IDSs, addressing the limitations of traditional systems, and underscores the potential of AI and optimization in complex network security. This study demonstrates the promise of RNN-LSTM and GWO for creating robust, adaptive intrusion detection systems in intricate network environments.
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Chen, Nuo. "Exploring the development and application of LSTM variants." Applied and Computational Engineering 53, no. 1 (2024): 103–7. http://dx.doi.org/10.54254/2755-2721/53/20241288.
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Long Short-Term Memory (LSTM) is receiving increasing attention as the development of deep learning technology. The gate structure of LSTM enhances long-term memory, forming its superior capacity to complete tasks that challenge traditional RNN. However, considering the wide variety of applications, a comprehensive understanding of the development and application of the model, which is vital for future research, is comparatively lacking. Therefore, this paper is produced with the hope of offering an overview of the development of LSTM. It shows the process of development from RNN to LSTM and explains the aim and necessity of LSTMs birth. After that it introduces the structure of LSTM, analyses its advantages over RNN, and discusses the application of some popular LSTM variants, such as peephole LSTM, bidirectional LSTM, and GRU. Hopefully, this work can provide a more profound knowledge of LSTM's benefits and potential, identifying worthwhile avenues or fields of future research.
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Ali Hadi Abdulwahid. "IoT-Based Hybrid Fuzzy LSTM-RNN for Secure Disease Prediction in Healthcare EHRs." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 339–56. https://doi.org/10.52783/jisem.v10i36s.6438.
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The integration of Fuzzy Logic and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) is employed to handle healthcare data, leading to a significant improvement in the prediction of unknown disease outcomes and notably enhancing reliability and accuracy. In this research, we propose an integrated IoT-based healthcare data management system with Fuzzy Long Short-Term Memory Recurrent Neural Network (IF-LSTM-RNN) for disease prediction and diagnosis. Our approach includes gathering data via IoT devices, preprocessing through min-max normalization, and utilizing IF-LSTM-RNN for predictions. Clinical data is first collected and preprocessed, from which the health outcomes of patients are then predicted through IF-LSTM-RNN. The anticipated data is securely stored in Electronic Health Record (EHR) systems, making it more secure and providing accurate predictions. To evaluate the performance of the proposed system, we applied it to a dataset comprising glucose concentrations from 12,612 data points of five monitored subjects with diabetes. The IF-LSTM-RNN outperformed traditional techniques (Random Forest, Support Vector Machine, and K-Nearest Neighbors) with an accuracy of 99.62%, precision of 98.71%, recall of 97.91%, an F1-score of 98.64%, sensitivity of 98.95%, and specificity of 97.88%. The IF-LSTM-RNN also achieved a correct classification rate of 99.37% with an execution time of approximately 1.28 seconds. The results demonstrate that the proposed framework offers a viable solution for secure and effective healthcare data management and prediction in IoT environments.
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Zaware, Sarika, Bhavana Kanawade, Shilpa Pimpalkar, Aher Chetan, Anuja Phapale, and Sumedha Zaware. "Crude Oil Cost Forecasting using Variants of Recurrent Neural Network." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 438–45. http://dx.doi.org/10.17762/ijritcc.v11i9s.7454.
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Crude oil cost plays very important role in the country’s economic growth. It is having close impact on economical stability of nation. Because of these reasons it is very important to have accurate oil forecasting system. Due to impact of different factors oil cost data is highly nonlinear and in fluctuated manner. Performing prediction on those data using data driven approaches is very complex task which require lots of preprocessing of data. Working on such a non-stationary data is very difficult. This research proposes recurrent neural network (RNN) based approaches such as simple RNN, deep RNN and RNN with LSTM. To compare performance of RNN variants this research has also implemented Naive forecast and Sequential ANN methods. Performance of all these models are evaluated based on root mean square error(RMSE), mean absolute error(MAE) and mean absolute percentage error(MAPE). The experimental result shows that RNN with LSTM is more accurate compare to all other models. Accuracy of LSTM is more than 96% for the dataset of U.S. Energy Information administration from March 1983 to June 2022. On the basis of experimental result, we come to the conclusion that RNN with LSTM is best suitable for time series data which is highly nonlinear.
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Subha, J., and S. Saudia. "Precipitation forecast using RNN variants by analyzing Optimizers and Hyperparameters for Time-series based Climatological Data." International journal of electrical and computer engineering systems 15, no. 3 (2024): 261–74. http://dx.doi.org/10.32985/ijeces.15.3.5.
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Flood is a significant problem in many regions of the world for the catastrophic damage it causes to both property and human lives; excessive precipitation being the major cause. The AI technologies, Deep Learning Neural Networks and Machine Learning algorithms attempt realistic solutions to numerous disaster management challenges. This paper works on RNN- based rainfall/ precipitation forecasting models by investigating the performances of various Recurrent Neural Network (RNN) architectures, Bidirectional RNN (BRNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and ensemble models such as BRNN-GRU, BRNN-LSTM, LSTM-GRU, BRNN-LSTM-GRU using NASAPOWER datasets of Andhra Pradesh (AP) and Tamil Nadu (TN) in India. The different stages in the workflow of the methodology are Data collection, Data pre-processing, Data splitting, Defining hyperparameters, Model building and Performance evaluation. Experiments for identifying improved optimizers and hyperparameters for the time-series climatological data are investigated for accurate precipitation forecast. The metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Root Mean Squared Logarithmic Error (RMSLE) values are used to compare the precipitation predictions of different models. The RNN variants and ensemble models, BRNN, LSTM, GRU, BRNN-GRU, BRNN-LSTM, LSTM-GRU, BRNN-LSTM-GRU produce predictions with RMSLE values of 2.448, 0.555, 0.255, 1.305, 1.383, 0.364, 1.740 for AP and 1.735, 0.663, 0.152, 0.889, 1.118, 0.379, 1.328 for TN respectively. The best performing RNN model, GRU when ensembled with the existing statistical model SARIMA produces an RMSLE value of 0.754 and 1.677 respectively for AP and TN.
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Zhang, Yujie. "Using LSTM neural network to generate music." Theoretical and Natural Science 19, no. 1 (2023): 111–15. http://dx.doi.org/10.54254/2753-8818/19/20230512.
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Music generation is a cutting edge and useful research filed, which is helpful for artists to compose novel melodies as well as revealing potential patterns of music. Recurrent neural network (RNN) is a member of the neural network family, which is commonly used for processing sequential data. It can deal with sequential changes in data compared to normal neural networks. Long short-term memory (LSTM) aims at improving the conventional RNN. It is designed to alleviate the deficiencies of gradient disappearance and gradient explosion that possibly happened in RNN during training. In simple terms, LSTM is superior at grasping long term information than normal RNN. It can record the information that requires to be recorded for a long time and abandon these unimportant features. Unlike RNN, which have merely one way of stacking long-term information. It's quite useful for tasks that require long range dependence. In this work the effectiveness of the LSTM is validated on the music generation task.
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Mai, Yunhao. "Deep Learning Based Player Identification Via Behavioral Characteristics." Highlights in Science, Engineering and Technology 61 (July 30, 2023): 266–71. http://dx.doi.org/10.54097/hset.v61i.10732.
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Behavioral recognition in game is a fundamental topic in data analysis. With the emerging of deep learning-based method, more and more artificial neural networks are proposed to achieve higher accuracy and robustness. This paper presents a comparison between CNN and RNN-LSTM for player identification based on player behavioral characteristics in a simple game environment. The goal is to detect potential manual cheating in competitions by recognizing unidentified players (cheaters) from their behavioral patterns. We implement a basic game, record player behaviors, and then compare the accuracy of outputs from the CNN and RNN-LSTM models. The conclusion is summarized as follow. CNN obtains 87.5% accuracy and RNN-LSTM achieves 92.3% accuracy in the simulated data. Our results indicate that the RNN-LSTM model outperforms the CNN model in terms of accuracy, making it a more suitable choice for player identification in this contextt.
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Shi, Yue, Xuanhui Li, Jianwei Ao, Keju Liu, Yuan Li, and Hui Cheng. "Novel Classification of Inclusion Defects in Glass Fiber-Reinforced Polymer Based on THz-TDS and One-Dimensional Neural Network Sequential Models." Photonics 12, no. 3 (2025): 250. https://doi.org/10.3390/photonics12030250.
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Fiber-reinforced composites, such as glass fiber-reinforced polymer (GFRP), are widely used across industries but are susceptible to inclusion defects during manufacturing. Detecting and classifying these defects is crucial for ensuring material integrity. This study classifies four common inclusion defects—metal, peel ply, release paper, and PTFE film—in GFRP using terahertz technology and machine learning. Two GFRP sheets with inclusion defects at different depths were fabricated. Terahertz time-domain signals were acquired, and a cross-correlation-based deconvolution algorithm extracted impulse responses. LSTM-RNN, Bi-LSTM RNN, and 1D-CNN models were trained and tested on time-domain, frequency-domain, and impulse response signals. The defect-free region exhibited the highest classification accuracy. Bi-LSTM RNN achieved the best recall and macro F1-score, followed by 1D-CNN, while LSTM-RNN performed worse. Training with impulse response signals improved classification while maintaining accuracy.
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Wu, Yijun, and Yonghong Qin. "Machine translation of English speech: Comparison of multiple algorithms." Journal of Intelligent Systems 31, no. 1 (2022): 159–67. http://dx.doi.org/10.1515/jisys-2022-0005.
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Abstract In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length.
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Zhang, Mei. "Analysis of Intelligent English Chunk Recognition based on Knowledge Corpus." Annals of Emerging Technologies in Computing 6, no. 3 (2022): 11–18. http://dx.doi.org/10.33166/aetic.2022.03.002.
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Chunks play an important role in applied linguistics, such as Teaching English as a Second Language (TESL) and Computer-Aided Translation (CAT). Although corpora have already been widely used in the areas mentioned above, annotation and recognition of chunks are mainly done manually. Computer- and linguistic-based chunk recognition is significant in natural language processing (NLP). This paper briefly introduced the intelligent recognition of English chunks and applied the Recurrent Neural Network (RNN) to recognise chunks. To strengthen the RNN, it was improved by Long Short Term Memory (LSTM) for recognising English chunk. The LSTM-RNN was compared with support vector machine (SVM) and RNN in simulation experiments. The results suggested that the performance of the LSTM-RNN was always the highest when dealing with English texts, no matter whether it was trained using a general corpus or a corpus of specialised domain knowledge.
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Arafat, M. Y., M. J. Hossain, and Li Li. "Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis." Energies 18, no. 6 (2025): 1535. https://doi.org/10.3390/en18061535.
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This paper presents advanced frameworks for microgrid predictive maintenance by performing a comprehensive correlative analysis of advanced recurrent neural network (RNN) architectures, i.e., RNNs, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) for photovoltaic (PV) based DC microgrids (MGs). Key contributions of this analysis are development of advanced architectures based on RNN, GRU and LSTM, their correlative performance analysis, and integrating adaptive threshold technique with the algorithms to detect faulty operations of inverters which is indispensable for ensuring the reliability and sustainability of distributed energy resources (DERs) in modern MG systems. The proposed models are trained and evaluated with a dataset of diverse real-world operational scenarios and environmental conditions. Moreover, the performances of those advanced models have been compared with the conventional RNN-based techniques. The achieved decremental MAE scores from 12.102 (advanced RNN) to 10.182 (advanced GRU) to 8.263 (advanced LSTM) and incremental R2 scores from 0.941 (advanced RNN) to 0.958 (advanced GRU), and finally to 0.971 (advanced LSTM) demonstrate strong predictive capabilities of all, while the proposed advanced LSTM method outperforming other counterparts. This study can contribute to the emerging technology for predictive maintenance of MGs and provide significant insights into the modeling and performance of RNN architectures for improving fault detection in MG systems. The findings can have noteworthy implications to enhance the efficiency and resilience in MG systems, thereby evolving the renewable energy technologies in power sector and contributing to the sustainable and greener energy landscape.
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Yang, Yun, Zongtao Duan, and Mark Tehranipoor. "Identify a Spoofing Attack on an In-Vehicle CAN Bus Based on the Deep Features of an ECU Fingerprint Signal." Smart Cities 3, no. 1 (2020): 17–30. http://dx.doi.org/10.3390/smartcities3010002.
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An in-vehicle controller area network (CAN) bus is vulnerable because of increased sharing among modern autonomous vehicles and the weak protocol design principle. Spoofing attacks on a CAN bus can be difficult to detect and have the potential to enable devastating attacks. To effectively identify spoofing attacks, we propose the authentication of sender identities using a recurrent neural network with long short-term memory units (RNN-LSTM) based on the features of a fingerprint signal. We also present a way to generate the analog fingerprint signals of electronic control units (ECUs) to train the proposed RNN-LSTM classifier. The proposed RNN-LSTM model is accelerated on embedded Field-Programmable Gate Arrays (FPGA) to allow for real-time detection despite high computational complexity. A comparison of experimental results with the latest studies demonstrates the capability of the proposed RNN-LSTM model and its potential as a solution to in-vehicle CAN bus security.
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Bukhsh, Madiha, Muhammad Saqib Ali, Abdullah Alourani, et al. "Long Short-Term Memory Recurrent Neural Network Approach for Approximating Roots (Eigen Values) of Transcendental Equation of Cantilever Beam." Applied Sciences 13, no. 5 (2023): 2887. http://dx.doi.org/10.3390/app13052887.
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In this study, the natural frequencies and roots (Eigenvalues) of the transcendental equation in a cantilever steel beam for transverse vibration with clamped free (CF) boundary conditions are estimated using a long short-term memory-recurrent neural network (LSTM-RNN) approach. The finite element method (FEM) package ANSYS is used for dynamic analysis and, with the aid of simulated results, the Euler–Bernoulli beam theory is adopted for the generation of sample datasets. Then, a deep neural network (DNN)-based LSTM-RNN technique is implemented to approximate the roots of the transcendental equation. Datasets are mainly based on the cantilever beam geometry characteristics used for training and testing the proposed LSTM-RNN network. Furthermore, an algorithm using MATLAB platform for numerical solutions is used to cross-validate the dataset results. The network performance is evaluated using the mean square error (MSE) and mean absolute error (MAE). Finally, the numerical and simulated results are compared using the LSTM-RNN methodology to demonstrate the network validity.
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Liang, Xinyue. "Stock Market Prediction with RNN-LSTM and GA-LSTM." SHS Web of Conferences 196 (2024): 02006. http://dx.doi.org/10.1051/shsconf/202419602006.
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The stock price reflects various factors such as the rate of economic growth, inflation, overall economy, trade balance, and monetary system, all of which impact the stock market as a whole. Investors often find the principle of stock price trends unclear because of the many important variables involved. When creating an investment strategy or determining the timing for buying or selling stocks, forecasting stock market trends plays a critical role. It is difficult to estimate the value of the stock market due to the non-linear and dynamic nature of the stock index. Numerous studies using deep learning techniques have been successful in making such predictions. The Long Short Term Memory (LSTM) has become popular for predicting stock market prices. This paper thoroughly examines methods for predicting stock market performance using RNN-LSTM and GA-LSTM, provides explanations of these methods, and performs a comparative analysis. We will discuss future directions and outline the significance of using RNN-LSTM and GA-LSTM for forecasting stock market trends, based on the papers we have reviewed.
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Alkahfi, Cahya, Anang Kurnia, and Asep Saefuddin. "Perbandingan Kinerja Model Berbasis RNN pada Peramalan Data Ekonomi dan Keuangan Indonesia." MALCOM: Indonesian Journal of Machine Learning and Computer Science 4, no. 4 (2024): 1235–43. http://dx.doi.org/10.57152/malcom.v4i4.1415.
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Peramalan deret waktu merupakan salah satu elemen kunci dalam analisis ekonomi dan keuangan. memungkinkan pemangku kepentingan untuk membuat perkiraan terhadap berbagai indikator ekonomi sebelum data resmi dirilis. Dalam konteks ini, model pembelajaran mesin seperti Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) menunjukkan potensi yang menjanjikan dalam memprediksi data deret waktu. Sejumlah penelitian juga menegaskan bahwa LSTM dan GRU mampu mengungguli kinerja RNN. Kedua model tersebut memiliki mekanisme untuk mengatasi masalah vanishing gradient yang sering ditemui pada model RNN konvensional. Penelitian ini menitikberatkan untuk menguji kinerja ketiga model tersebut pada data-data yang ada di Indonesia. Agar hasil lebih komprehensif, penelitian ini akan menguji model pada tiga jenis data yang berbeda meliputi IHSG, nilai ekspor dan PDB. Hasil penelitian ini mengindikasikan bahwa secara keseluruhan, model GRU menunjukkan kinerja terbaik, diikuti oleh model LSTM yang juga kompetitif dibandingkan RNN. Selain akurasi, model GRU dan LSTM juga menonjol dalam hal stabilitas kinerja, ditandai dengan simpangan baku yang relatif kecil jika dibandingkan dengan RNN. Keunggulan ini menjadi semakin signifikan terutama saat diterapkan pada model PDB dimana hanya tersedia untuk periode waktu yang pendek.
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Chattopadhyay, Ashesh, Pedram Hassanzadeh, and Devika Subramanian. "Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network." Nonlinear Processes in Geophysics 27, no. 3 (2020): 373–89. http://dx.doi.org/10.5194/npg-27-373-2020.
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Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.
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Joshi, Dinesh, Rijan Bhakta Kayastha, Kundan Lal Shrestha, and Rakesh Kayastha. "A hybrid approach to enhance streamflow simulation in data-constrained Himalayan basins: combining the Glacio-hydrological Degree-day Model and recurrent neural networks." Proceedings of IAHS 387 (November 18, 2024): 17–24. http://dx.doi.org/10.5194/piahs-387-17-2024.
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Abstract. The Glacio-hydrological Degree-day Model (GDM) is a distributed model, but it is prone to uncertainties due to its conceptual nature, parameter estimation, and limited data in the Himalayan basins. To enhance accuracy without sacrificing interpretability, we propose a hybrid model approach that combines GDM with recurrent neural networks (RNNs), hereafter referred to as GDM–RNN. Three RNN types – a simple RNN model, a gated recurrent unit (GRU) model, and a long short-term memory (LSTM) model – are integrated with GDM. Rather than directly predicting streamflow, RNNs forecast GDM's residual errors. We assessed performance across different data availability scenarios, with promising results. Under limited-data conditions (1 year of data), GDM–RNN models (GDM–simple RNN, GDM–LSTM, and GDM–GRU) outperformed standalone GDM and machine learning models. Compared with GDM's respective Nash–Sutcliffe efficiency (NSE), R2, and percent bias (PBIAS) values of 0.80, 0.63, and −4.78, the corresponding values for the GDM–simple RNN were 0.85, 0.82, and −6.21; for GDM–LSTM, they were 0.86, 0.79, and −6.37; and for GDM–GRU, they were 0.85, 0.8, and −5.64. Machine learning models yielded similar results, with the simple RNN at 0.81, 0.7, and −16.6; LSTM at 0.79, 0.65, and −21.42; and GRU at 0.82, 0.75, and −12.29, respectively. Our study highlights the potential of machine learning with respect to enhancing streamflow predictions in data-scarce Himalayan basins while preserving physical streamflow mechanisms.
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Yan, Lei, Yuting Zhu, and Haiyan Wang. "Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil." Discrete Dynamics in Nature and Society 2021 (October 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/1566093.
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Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. First, we use principal component analysis (PCA), multidimensional scale (MDS), and locally linear embedding (LLE) methods to reduce the dimensions of the data. Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. From the analysis and comparison of the prediction results, we find that reducing the dimension of the data can improve the accuracy of the model and the applicability of RNN and LSTM models. In addition, the LLE-RNN/LSTM models can most successfully capture the nonlinear characteristics of crude oil prices. When the moving window size is twenty, that is, when crude oil price data are lagging by almost a month, each model can minimize its error, and the LLE-RNN /LSTM models have the best robustness.
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Liu, Kang, Longyun Kang, and Di Xie. "Online State of Health Estimation of Lithium-Ion Batteries Based on Charging Process and Long Short-Term Memory Recurrent Neural Network." Batteries 9, no. 2 (2023): 94. http://dx.doi.org/10.3390/batteries9020094.
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Accurate state of health (SOH) estimation is critical to the operation, maintenance, and replacement of lithium-ion batteries (LIBs), which have penetrated almost every aspect of our life. This paper introduces a new approach to accurately estimate the SOH for rechargeable lithium-ion batteries based on the corresponding charging process and long short-term memory recurrent neural network (LSTM-RNN). In order to learn the mapping function without employing battery models and filtering techniques, the LSTM-RNN is initially fed into the health indicators (HIs) extracted from the charging process and trained to encode the dependencies of the related data sequence. Subsequently, the trained LSTM-RNN can properly estimate online SOHs of LIBs using extracted HIs. We experiment on two public datasets for model construction, validation, and comparison. Conclusively, the trained LSTM-RNN achieves an overall root mean square error (RMSE) lower than 1% on the cases with the same discharging current rate and an RMSE of 1.1198% above 80% SOH on another testing case that underwent a different discharging current rate.
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H, Irani. "Netflix Stock Price Trend Prediction Using Recurrent Neural Network." Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi 8, no. 2 (2022): 97. http://dx.doi.org/10.24014/coreit.v8i2.16599.
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Abstract— Stocks are investments that have dynamic movements. Stock price changes move every day even hourly. With very fast changes, stock prices require predictions to be able to determine stock market projections. Predictions are used to reduce risk when making transactions. In this study, predictions of stock price trends were made using the Recurrent Neural Network (RNN). The approach taken is to perform a time series analysis using the RNN variance, namely Long Short Term Memory (LSTM). Hyperparameter construction in the LSTM model testing simulation can estimate stock prices with maximum percentage accuracy. The results showed that the prediction model produced a loss function of 0.0012 and a training time of 73 m/step. The evaluation was carried out with the RMSE which resulted in a score of 17.13325. Predictions are obtained after doing machine learning using 1239 data. The RMSE and LSTM models are calculated by changing the number of epochs, the variation between the predicted stock price and the current stock price. Computations are carried out using a stock market dataset that includes open, high, low, close, adj prices, closes, and volumes. The main objective of this study is to determine the extent to which the LSTM algorithm anticipates stock market prices with better accuracy. Code can be seen at iranihoeronis/RNN-LSTM (github.com) Keywords— Stock Prediction, Time Series, Recurrent Neural Network (RNN), Long Short Term Memory (LSTM).
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Nakhaei, Mahdi, Hossein Zanjanian, Pouria Nakhaei, et al. "Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin." Water 16, no. 2 (2024): 208. http://dx.doi.org/10.3390/w16020208.
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Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting.
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Wang, Yung-Chung, Yi-Chun Houng, Han-Xuan Chen, and Shu-Ming Tseng. "Network Anomaly Intrusion Detection Based on Deep Learning Approach." Sensors 23, no. 4 (2023): 2171. http://dx.doi.org/10.3390/s23042171.
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The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, most experiments have used old datasets, so they could not reflect the latest attack information. In this paper, a current state of the CSE-CIC-IDS2018 dataset and standard evaluation metrics has been employed to evaluate the proposed mechanism. After preprocessing the dataset, six models—deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN + RNN and CNN + LSTM—were constructed to judge whether network traffic comprised a malicious attack. In addition, multi-classification experiments were conducted to sort traffic into benign traffic and six categories of malicious attacks: BruteForce, Denial-of-service (DoS), Web Attacks, Infiltration, Botnet, and Distributed denial-of-service (DDoS). Each model showed a high accuracy in various experiments, and their multi-class classification accuracy were above 98%. Compared with the intrusion detection system (IDS) of other papers, the proposed model effectively improves the detection performance. Moreover, the inference time for the combinations of CNN + RNN and CNN + LSTM is longer than that of the individual DNN, RNN and CNN. Therefore, the DNN, RNN and CNN are better than CNN + RNN and CNN + LSTM for considering the implementation of the algorithm in the IDS device.
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Wu, Sung-Heng, Usman Tariq, Ranjit Joy, Muhammad Arif Mahmood, Asad Waqar Malik, and Frank Liou. "A Robust Recurrent Neural Networks-Based Surrogate Model for Thermal History and Melt Pool Characteristics in Directed Energy Deposition." Materials 17, no. 17 (2024): 4363. http://dx.doi.org/10.3390/ma17174363.
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In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups.
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Dr. Madhur Jain, Shilpi Jain, and Ankit Gupta. "Decoding Stocks Patterns Using LSTM." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 306–10. http://dx.doi.org/10.32628/cseit2410328.
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Decoding stocks is extensively utilized in the financial sector by numerous organizations. It is volatile in nature, so it’s tough to predict the prices of stock. Numerous methodologies exist for tackling this task, including logistic regression, support vector machines (SVM), autoregressive conditional heteroskedasticity (ARCH) models, recurrent neural network (RNN), convolutional neural networks (CNN), backpropagation, Naïve Bayes, among others. Among these, Long Short-Term Memory (LSTM) stands out as particularly adept at handling time series data. The primary aim is to discern prevailing market trends and achieve accurate stock price forecasts. Leveraging LSTM and RNN , we strive for error free stock price predictions, with promising results.
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Liu, Xiangying, Zhiqiang Li, Zhuwei Tang, Xiang Zhang, and Hongxia Wang. "Application of Artificial Intelligence Technology in Electromechanical Information Security Situation Awareness System." Scalable Computing: Practice and Experience 25, no. 1 (2024): 127–36. http://dx.doi.org/10.12694/scpe.v25i1.2280.
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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.
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Akil, Ibnu, and Indra Chaidir. "Prediksi Harga Saham Twitter Dengan Long Short-Term Memory Recurrent Neural Network." INTI Nusa Mandiri 17, no. 1 (2022): 1–7. http://dx.doi.org/10.33480/inti.v17i1.3277.
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Abstract— Today the trading business has become a trend to get money easily without having to work hard as long as you have capital. To get maximum results and avoid losses, it is necessary to have expertise in predicting the ups and downs of the stock market value. The purpose of this research is to utilize machine learning technology to predict the fluctuation of stock value by using the Long Short-Term Memory RNN model. From the results of this study, it was found that LSTM+RNN is suitable for use in single-step models.
 Keywords: stock price, machine learning, recurrent neural network, lstm
 Abstrak—Dewasa ini bisnis trading menjadi suatu trend untuk mendapatkan uang dengan mudah tanpa harus bekerja keras asalkan memiliki modal. Untuk mendapatkah hasil yang maksimal dan menghindari kerugian maka diperlukan keahlian di dalam memprediksi naik turunya nilai bursa saham. Tujuan dari penelitian ini adalah memanfaatkan teknologi machine learning untuk memprediksi naik turunya nilai saham dengan menggunakan model Long Short-Term Memory RNN. Dari hasil penelitian ini didapatkan bahwa LSTM+RNN cocok untuk digunakan pada model single-step.
 Kata kunci: harga saham, machine learning, recurrent neural network, lstm
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Yu, Dian, and Shouqian Sun. "A Systematic Exploration of Deep Neural Networks for EDA-Based Emotion Recognition." Information 11, no. 4 (2020): 212. http://dx.doi.org/10.3390/info11040212.
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Subject-independent emotion recognition based on physiological signals has become a research hotspot. Previous research has proved that electrodermal activity (EDA) signals are an effective data resource for emotion recognition. Benefiting from their great representation ability, an increasing number of deep neural networks have been applied for emotion recognition, and they can be classified as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a combination of these (CNN+RNN). However, there has been no systematic research on the predictive power and configurations of different deep neural networks in this task. In this work, we systematically explore the configurations and performances of three adapted deep neural networks: ResNet, LSTM, and hybrid ResNet-LSTM. Our experiments use the subject-independent method to evaluate the three-class classification on the MAHNOB dataset. The results prove that the CNN model (ResNet) reaches a better accuracy and F1 score than the RNN model (LSTM) and the CNN+RNN model (hybrid ResNet-LSTM). Extensive comparisons also reveal that our three deep neural networks with EDA data outperform previous models with handcraft features on emotion recognition, which proves the great potential of the end-to-end DNN method.
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Fazira, Rara, Dimas Yudistira, and Lailan Sofinah Harahap. "Evaluasi Kinerja Model RNN & LSTM untuk Prediksi Magnitude Gempa di Indonesia." Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer 2, no. 6 (2024): 62–75. http://dx.doi.org/10.61132/mars.v2i6.498.
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Indonesia di kawasan Cincin Api Pasifik, yang dikenal memiliki aktivitas seismik yang sangat tinggi dengan ribuan gempa bumi yang terjadi setiap tahunnya. Penelitian ini bertujuan untuk menganalisis kinerja Recurrent Neural Network (RNN) dan Long Short-Term Memory (LSTM) dalam memprediksi magnitudo gempa bumi menggunakan data historis yang diambil dari Kaggle. Data tersebut mencakup rentang waktu dari November 2008 hingga September 2022, yang telah melalui proses normalisasi serta perpecahan menjadi data pelatihan dan pengujian. Model evaluasi kinerja dilakukan dengan menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Pada uji coba pertama, LSTM menunjukkan performa terbaik dengan nilai MAE 0.6226 dan RMSE 0.7731 pada data pengujian, lebih baik dibandingkan RNN yang mencatatkan MAE 0.6271 dan RMSE 0.7831. Sebaliknya, pada uji coba kedua, RNN unggul dengan nilai MAE 0.5583 dan RMSE 0.7008, sementara LSTM memiliki MAE 0.5822 dan RMSE 0.7132. Hasil ini menunjukkan bahwa LSTM lebih cocok untuk menangani pola data temporal yang kompleks, sedangkan RNN lebih andal pada dataset dengan pola yang lebih sederhana. Penelitian ini diharapkan dapat menjadi pijakan dalam pengembangan sistem prediktif untuk mitigasi risiko bencana gempa bumi di Indonesia.
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Prasad, Bathaloori Reddy. "Classification of Analyzed Text in Speech Recognition Using RNN-LSTM in Comparison with Convolutional Neural Network to Improve Precision for Identification of Keywords." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1097–108. http://dx.doi.org/10.47059/revistageintec.v11i2.1739.
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Aim: Text classification is a method to classify the features from language translation in speech recognition from English to Telugu using a recurrent neural network- long short term memory (RNN-LSTM) comparison with convolutional neural network (CNN). Materials and Methods: Accuracy and precision are performed with dataset alexa and english-telugu of size 8166 sentences. Classification of language translation is performed by the recurrent neural network where a number of the samples (N=62) and convolutional neural network were a number of samples (N=62) techniques, the algorithm RNN implies speech recognition that can be compared with convolutional is the second technique. Results and Discussion: RNN-LSTM from the dataset speech recognition, feature Telugu_id produce accuracy 93% and precision 68.04% which can be comparatively higher than CNN accuracy 66.11%, precision 61.90%. It shows a statistical significance as 0.007 from Independent Sample T-test. Conclusion: The RNN-LSTM performs better in finding accuracy and precision when compared to CNN.
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Meng, Jiaqi, Chengbo Li, Jin Tao, et al. "RNN-LSTM-Based Model Predictive Control for a Corn-to-Sugar Process." Processes 11, no. 4 (2023): 1080. http://dx.doi.org/10.3390/pr11041080.
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The corn-to-sugar process is difficult to control automatically because of the complex physical and chemical phenomena involved. Because the RNN-LSTN model has been shown to handle long-term time dependencies well, this article focused on the design of a model predictive control system based on this machine learning model. Based on the historical data, we first reduced the input variable dimension through data preprocessing, data dimension reduction, sensitivity analysis, etc., and then the RNN-LSTM model, with these identified key sites as inputs, and the dextrose equivalent value as the output, was constructed. Then, through model predictive control using the locally linearized RNN-LSTM as the predictive model, the objective value of the dextrose equivalent was successfully controlled at the target value by our simulation study, in different situations of setpoint changes and disturbances. This showed the potential of applying RNN-LSTM-Based model predictive control in a corn-to-sugar process.
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Lee, Ju-Hyung, and Jun-Ki Hong. "Comparative Performance Analysis of Vibration Prediction Using RNN Techniques." Electronics 11, no. 21 (2022): 3619. http://dx.doi.org/10.3390/electronics11213619.
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Drones are increasingly used in several industries, including rescue, firefighting, and agriculture. If the motor connected to a drone’s propeller is damaged, there is a risk of a drone crash. Therefore, to prevent such incidents, an accurate and quick prediction tool of the motor vibrations in drones is required. In this study, normal and abnormal vibration data were collected from the motor connected to the propeller of a drone. The period and amplitude of the vibrations are consistent in normal vibrations, whereas they are irregular in abnormal vibrations. The collected vibration data were used to train six recurrent neural network (RNN) techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gated recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional GRU (Bi-GRU). Then, the simulation runtime it took for each RNN technique to predict the vibrations and the accuracy of the predicted vibrations were analyzed to compare the performances of the RNN model. Based on the simulation results, the Attn.-LSTM and Attn.-GRU techniques, incorporating the attention mechanism, had the best efficiency compared to the conventional LSTM and GRU techniques, respectively. The attention mechanism calculates the similarity between the input value and the to-be-predicted value in advance and reflects the similarity in the prediction.
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Putera Khano, Muhammad Nazhif Abda, Dewi Retno Sari Saputro, Sutanto Sutanto, and Antoni Wibowo. "SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 4 (2023): 2235–42. http://dx.doi.org/10.30598/barekengvol17iss4pp2235-2242.
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Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. GRU is an algorithm that is used to make each recurrent unit able to record adaptively at different time scales. Meanwhile, LSTM is a network architecture with the advantage of learning long-term dependencies on data. LSTM can remember long-term memory information, learn long-sequential data, and form information relation data in LTM. The combination of LSTM and GRU aims to overcome RNN’s weakness in LTM. The LSTM-GRU is combined by adding GRU to the data generated from LSTM. The combination of LSTM and GRU creates a better performance algorithm for addressing the LTM problem.
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КОБИЛІН, Олег, Ірина ВЕЧІРСЬКА та Анатолій АФАНАСЬЄВ. "АНАЛІЗ ІСНУЮЧИХ МОДЕЛЕЙ ГЛИБИННОГО НАВЧАННЯ В ЗАДАЧАХ ОБРОБКИ ПРИРОДНОЇ МОВИ". Information Technology: Computer Science, Software Engineering and Cyber Security, № 3 (6 грудня 2024): 63–76. https://doi.org/10.32782/it/2024-3-7.
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Обробка природної мови (NLP) є однією з найактуальніших галузей штучного інтелекту, що охоплює широкий спектр завдань, таких як аналіз емоцій, машинний переклад, розпізнавання мовлення та інші. Мета роботи: Метою цього дослідження є всебічний аналіз продуктивності моделей глибинного навчання, включаючи рекурентні нейронні мережі (RNN), мережі довготривалої короткочасної пам’яті (LSTM) та керовані рекурентні блоки (GRU), у задачах NLP. Особлива увага приділяється ефективності цих моделей у завданнях аналізу емоцій. Методологія: Дослідження включає кілька етапів: збір та попередню обробку даних, реалізацію та навчання моделей RNN, LSTM і GRU на вибраних наборах даних, оцінку їхньої продуктивності за допомогою таких показників, як точність, пригадування та F1-score, а також аналіз ресурсних вимог моделей, особливо в умовах обмежених обчислювальних ресурсів. Крім того, у роботі проводиться порівняльний аналіз моделей за показниками їхньої масштабованості при роботі з великими обсягами даних. Наукова новизна: Дане дослідження пропонує детальний порівняльний аналіз ефективності RNN, LSTM та GRU в різних задачах NLP, з акцентом на їхній здатності обробляти послідовні дані та враховувати довготривалі залежності. Проведений аналіз виявляє, яка з моделей є найбільш ефективною в конкретних умовах, залежно від доступних ресурсів і специфіки даних. Висновки: В результаті дослідження було встановлено, що GRU показала найвищу продуктивність в аналізі емоцій, перевершуючи RNN і LSTM за точністю, пригадуванням і F1-score. LSTM виявилася оптимальною для роботи з великими обсягами даних, демонструючи високу ефективність і точність. RNN, хоча і забезпечує швидке навчання на невеликих наборах даних, поступається іншим моделям у точності, що робить її менш придатною для складних задач NLP. Отримані результати містять цінну інформацію для дослідників і практиків, які займаються застосуванням моделей глибинного навчання у задачах NLP.
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Gunarto, Dzaki Mahadika, Siti Sa’adah, and Dody Qori Utama. "Predicting Cryptocurrency Price Using RNN and LSTM Method." Jurnal Sisfokom (Sistem Informasi dan Komputer) 12, no. 1 (2023): 1–8. http://dx.doi.org/10.32736/sisfokom.v12i1.1554.
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Cryptocurrency price prediction is a crucial task for financial investors as it helps determine appropriate investment strategies and mitigate risk. In recent years, deep learning methods have shown promise in predicting time-series data, making them a viable approach for cryptocurrency price prediction. In this study, we compare the effectiveness of two deep learning techniques, the Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM), in predicting the prices of Bitcoin and Ethereum. Results of this research show that the LSTM method outperformed the RNN method, obtaining lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for predicting both cryptocurrencies. Bitcoin and Ethereum. Specifically, the LSTM model had a RMSE of 0.061 and MAPE of 5.66% for predicting Bitcoin, and a RMSE of 0.036 and MAPE of 4.58% for predicting Ethereum. In this research, we found that the LSTM model is a more effective method for predicting cryptocurrency prices than the RNN model.
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Bartouli, Monia, Imen Hagui, Amina Msolli, Abdelhamid Helali, and Fredj Hassen. "Smart Grid Load Forecasting Models Using Recurrent Neural Network and Long Short-Term Memory." Jordan Journal of Electrical Engineering 11, no. 1 (2025): 1. http://dx.doi.org/10.5455/jjee.204-1703066445.
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This paper presents two methods for managing electrical energy consumption and demand, with the objective of developing reliable and accurate forecasting models for smart electrical network energy consumption and optimization. The first method utilizes a recurrent neural network (RNN), while the second employs long short-term memory (LSTM) techniques. This approach builds upon previous studies that have explored the use of machine learning models for energy forecasting, but often with limited performance or the inability to capture long-term dependencies in the data. The study utilizes the Global Energy Forecast 2012 database - for the period from 2004 to 2008, with a focus on electricity consumption - to validate the performance of the proposed models. The R-squared (R2) score is used as the primary evaluation metric, with the LSTM model achieving a remarkable 90% R2 score, outperforming the RNN model's 80% R2 score. This is a significant improvement over previous studies, which have typically reported R2 scores in the range of 70-80% for energy forecasting models. Furthermore, the LSTM model demonstrates superior error rate performance, with a Mean Squared Error (MSE) of 4.345%, compared to the RNN model's 16.644% MSE. This highlights the ability of LSTM models to capture long-term dependencies in the data, which is crucial for accurate energy consumption forecasting, a limitation often observed in traditional RNN-based approaches. The findings of this study highlight the superior performance of the LSTM-based approach in accurately predicting energy consumption in smart grids, a crucial aspect for optimizing energy management and distribution. This contribution is particularly significant, as it showcases the advantages of LSTM models over traditional RNN techniques in the context of energy forecasting, providing valuable insights for researchers and practitioners in the field of smart grid optimization, where accurate forecasting is essential for efficient energy management and distribution.
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Brilliansyah, Krisna Taufik, and Unit Three Kartini. "Peramalan Jangka Sangat Pendek Daya Listrik PLTS On Grid Rumah Tinggal Menggunakan Metode Recurrent Neural Network Long Short Term Memory (RNN-LSTM) Berdasarkan Data Meteorologi." JURNAL TEKNIK ELEKTRO 12, no. 1 (2023): 60–66. http://dx.doi.org/10.26740/jte.v12n1.p60-66.
Full textAbstract:
Pembangkit Listrik Tenaga Surya yang terhubung dengan jaringan PLN atau on grid dalam rumah tinggal berfungsi sebagai cadangan energi atau bahkan menjadi energi utama listrik pada rumah tinggal. Produksi daya listrik PLTS ini dipengaruhi oleh data meteorologi. Permalan daya pembangkitan listrik PLTS on grid berguna untuk mengetahui daya listrik yang diproduksi. Pada penelitian ini menggunakan metode peramalan Recurrent Neural Network Long Short Term Memory. Tujuan penelitian ini adalah untuk memanfaatkan data meteorologi dan model peramalan RNN-LSTM untuk memprediksi daya listrik dalam jangka sangat pendek. Hasil dari penelitian ini model peramalan pada data uji sudah cukup mengikuti pola daya listrik aktual dan menunjukan nilai akurasi peramalan MSE 0,0139 dan MAPE 31,87%. Dapat disimpulkan bahwa metode RNN-LSTM memiliki intrepetasi peramalan dengan predikat layak. 
 Kata Kunci: PLTS on grid, Peramalan, RNN-LSTM.
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Gupta, Aayush Kumar, and Sheenu Rizvi. "Study of Language Models: Evolution & Limitations." Journal of Management and Service Science (JMSS) 2, no. 1 (2022): 1–7. http://dx.doi.org/10.54060/jmss/002.01.006.
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We have come far from the days when rule-based language models used to be the predominant thing in the market. Machine Learning came into play and changed the Language Model industry. In this paper, we will look at how RNN did a much better task for generating output based on its previous results and then how LSTM fulfilled the memory requirement for RNN. Also, we will take a look at how Transformer is much better than RNN combined with LSTM, which is the state-of-the-art language model on which the two best natural processing models like BERT and GPT3.
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Kumar, Naresh, Jatin Bindra, Rajat Sharma, and Deepali Gupta. "Air Pollution Prediction Using Recurrent Neural Network, Long Short-Term Memory and Hybrid of Convolutional Neural Network and Long Short-Term Memory Models." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4580–84. http://dx.doi.org/10.1166/jctn.2020.9283.
Full textAbstract:
Air pollution prediction was not an easy task few years back. With the increasing computation power and wide availability of the datasets, air pollution prediction problem is solved to some extend. Inspired by the deep learning models, in this paper three techniques for air pollution prediction have been proposed. The models used includes recurrent neural network (RNN), Long short-term memory (LSTM) and a hybrid combination of Convolutional neural network (CNN) and LSTM models. These models are tested by comparing MSE loss on air pollution test of Belgium. The validation loss on RNN is 0.0045, LSTM is 0.00441 and CNN and LSTM is 0.0049. The loss on testing dataset for these models are 0.00088, 0.00441 and 0.0049 respectively.
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Dharmalingam, M., and G.D Praveenkumar. "Hierarchical Image Classification on Bayesian Cascade Neural Learning." Innovative Computing and Communication: An International Journal 1, no. 3 (2020): 1–6. https://doi.org/10.5281/zenodo.4743651.
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The performance of image classification on Bayesian cascade neural learning techniques using in coarse and fine layer in LSTM. Recurrent Neural Network (RNN) system experience from Vanishing Gradient (VG) issues. The Gradients needs to proliferate down through numerous layers of the Recurrent Neural Network (RNN).So we integrate the LSTM do not go through from vanishing gradient problem that forward layer. It support different number of layers in Convolutional Neural Network (CNN) is designed for image classification. The Long Short Term Memory (LSTM) processed with Bayesian Cascade Neural Learning (BCNL) with CNN of GoogleNet framework to designed the hierarchical image classification. The elasticity of LSTM model to computed hierarchical label on standard dataset of CIFAR-100.
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Bahadori, Nazanin, Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, et al. "Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset." Forests 14, no. 7 (2023): 1325. http://dx.doi.org/10.3390/f14071325.
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Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.
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Dutta, Shawni, Jyotsna Kumar Mandal, Tai Hoon Kim, and Samir Kumar Bandyopadhyay. "Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN." Applied Computer Systems 25, no. 2 (2020): 163–71. http://dx.doi.org/10.2478/acss-2020-0018.
Full textAbstract:
Abstract Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining the possibility of being affected by breast cancer. The proposed model is compared against other baseline classifiers such as stacked simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that the stacked GRU-LSTM-BRNN model yields better classification performance for predictions related to breast cancer disease.