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Liang, Bushun, Siye Wang, Yeqin Huang, Yiling Liu, and Linpeng Ma. "F-LSTM: FPGA-Based Heterogeneous Computing Framework for Deploying LSTM-Based Algorithms." Electronics 12, no. 5 (February 26, 2023): 1139. http://dx.doi.org/10.3390/electronics12051139.
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Long Short-Term Memory (LSTM) networks have been widely used to solve sequence modeling problems. For researchers, using LSTM networks as the core and combining it with pre-processing and post-processing to build complete algorithms is a general solution for solving sequence problems. As an ideal hardware platform for LSTM network inference, Field Programmable Gate Array (FPGA) with low power consumption and low latency characteristics can accelerate the execution of algorithms. However, implementing LSTM networks on FPGA requires specialized hardware and software knowledge and optimization skills, which is a challenge for researchers. To reduce the difficulty of deploying LSTM networks on FPGAs, we propose F-LSTM, an FPGA-based framework for heterogeneous computing. With the help of F-LSTM, researchers can quickly deploy LSTM-based algorithms to heterogeneous computing platforms. FPGA in the platform will automatically take up the computation of the LSTM network in the algorithm. At the same time, the CPU will perform the pre-processing and post-processing in the algorithm. To better design the algorithm, compress the model, and deploy the algorithm, we also propose a framework based on F-LSTM. The framework also integrates Pytorch to increase usability. Experimental results on sentiment analysis tasks show that deploying algorithms to the F-LSTM hardware platform can achieve a 1.8× performance improvement and a 5.4× energy efficiency improvement compared to GPU. Experimental results also validate the need to build heterogeneous computing systems. In conclusion, our work reduces the difficulty of deploying LSTM on FPGAs while guaranteeing algorithm performance compared to traditional work.
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Hong, Juan, and Wende Tian. "Prediction in Catalytic Cracking Process Based on Swarm Intelligence Algorithm Optimization of LSTM." Processes 11, no. 5 (May 11, 2023): 1454. http://dx.doi.org/10.3390/pr11051454.
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Deep learning can realize the approximation of complex functions by learning deep nonlinear network structures, characterizing the distributed representation of input data, and demonstrating the powerful ability to learn the essential features of data sets from a small number of sample sets. A long short-term memory network (LSTM) is a deep learning neural network often used in research, which can effectively extract the dependency relationship between time series data. The LSTM model has many problems such as excessive reliance on empirical settings for network parameters, as well as low model accuracy and weak generalization ability caused by human parameter settings. Optimizing LSTM through swarm intelligence algorithms (SIA-LSTM) can effectively solve these problems. Group behavior has complex behavioral patterns, which makes swarm intelligence algorithms exhibit strong information exchange capabilities. The particle swarm optimization algorithm (PSO) and cuckoo search (CS) algorithm are two excellent algorithms in swarm intelligent optimization. The PSO algorithm has the advantage of being a simple algorithm with fast convergence speed, fewer requirements on optimization function, and easy implementation. The CS algorithm also has these advantages, using the simulation of the parasitic reproduction behavior of cuckoo birds during their breeding period. The SIM-LSTM model is constructed in this paper, and some hyperparameters of LSTM are optimized by using the PSO algorithm and CS algorithm with a wide search range and fast convergence speed. The optimal parameter set of an LSTM is found. The SIM-LSTM model achieves high prediction accuracy. In the prediction of the main control variables in the catalytic cracking process, the predictive performance of the SIM-LSTM model is greatly improved.
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Khataei Maragheh, Hamed, Farhad Soleimanian Gharehchopogh, Kambiz Majidzadeh, and Amin Babazadeh Sangar. "A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification." Mathematics 10, no. 3 (February 2, 2022): 488. http://dx.doi.org/10.3390/math10030488.
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An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.
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Alamri, Nawaf Mohammad H., Michael Packianather, and Samuel Bigot. "Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm." Applied Sciences 13, no. 4 (February 16, 2023): 2536. http://dx.doi.org/10.3390/app13042536.
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Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. This paper attempts to overcome this problem by optimizing LSTM parameters using the Bees Algorithm (BA), which is a nature-inspired algorithm that mimics the foraging behavior of honey bees. In particular, it was used to optimize the adjustment factors of the learning rate in the forget, input, and output gates, in addition to cell candidate, in both forward and backward sides. Furthermore, the BA was used to optimize the learning rate factor in the fully connected layer. In this study, artificial porosity images were used for testing the algorithms; since the input data were images, a Convolutional Neural Network (CNN) was added in order to extract the features in the images to feed into the LSTM for predicting the percentage of porosity in the sequential layers of artificial porosity images that mimic real CT scan images of products manufactured by the Selective Laser Melting (SLM) process. Applying a Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) yielded a porosity prediction accuracy of 93.17%. Although using Bayesian Optimization (BO) to optimize the LSTM parameters mentioned previously did not improve the performance of the LSTM, as the prediction accuracy was 93%, adding the BA to optimize the same LSTM parameters did improve its performance in predicting the porosity, with an accuracy of 95.17% where a hybrid Bees Algorithm Convolutional Neural Network Long Short-Term Memory (BA-CNN-LSTM) was used. Furthermore, the hybrid BA-CNN-LSTM algorithm was capable of dealing with classification problems as well. This was shown by applying it to Electrocardiogram (ECG) benchmark images, which improved the test set classification accuracy, which was 92.50% for the CNN-LSTM algorithm and 95% for both the BO-CNN-LSTM and BA-CNN-LSTM algorithms. In addition, the turbofan engine degradation simulation numerical dataset was used to predict the Remaining Useful Life (RUL) of the engines using the LSTM network. A CNN was not needed in this case, as there was no feature extraction for the images. However, adding the BA to optimize the LSTM parameters improved the prediction accuracy in the testing set for the LSTM and BO-LSTM, which increased from 74% to 77% for the hybrid BA-LSTM algorithm.
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Abubaker, Shaikh Shoieb, and Syed Rouf Farid. "Stock Market Prediction Using LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 3178–84. http://dx.doi.org/10.22214/ijraset.2022.42039.
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Abstract: Different machine learning algorithms are discussed in this literature review. These algorithms can be used for predicting the stock market. The prediction of the stock market is one of the challenging tasks that must have to be handled.In this paper, it is discussed how the machine learning algorithms can be used for predicting the stock value. Different attributes are identified that can be used for training the algorithm for this purpose. Some of the other factors are also discussed that can have an effect on the stock value. Keywords: Machine learning, stock market prediction, literature review, artificial neural network, support vector machine, genetic algorithm, investment decision, RNN, LSTM.
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Fang, Wei, Jinguang Jiang, Shuangqiu Lu, Yilin Gong, Yifeng Tao, Yanan Tang, Peihui Yan, Haiyong Luo, and Jingnan Liu. "A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages." Remote Sensing 12, no. 2 (January 10, 2020): 256. http://dx.doi.org/10.3390/rs12020256.
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Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.
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Li, Hailin, Zhizhou Zhao, and Xue Du. "Research and Application of Deformation Prediction Model for Deep Foundation Pit Based on LSTM." Wireless Communications and Mobile Computing 2022 (July 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/9407999.
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Deep foundation pit is a door with a long history, but it has new disciplines; in this paper, firstly, the modeling method and process of LSTM (long short-term memory) network are discussed in detail, then the optimization algorithm used in the model is described in detail, and the parameter selection methods such as initial learning rate, activation function, and iteration number related to LSTM network training are introduced in detail. LSTM network is used to process the deformation data of deep foundation pit, and random gradient descent, momentum, Nesterov, RMSProp, AdaGmd, and Adam algorithms are selected in the same example for modeling prediction and comparison. Two examples of horizontal displacement prediction of pile and vertical displacement prediction of column in deep foundation pit show that the LSTM network model established by different optimization algorithms has different prediction accuracy, and the LSTM network model established by Adam optimization algorithm has the highest accuracy, which proves that the selection of optimization algorithm plays an important role in LSTM and also verifies the feasibility of LSTM network in the data processing and prediction of deep foundation pit deformation.
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Qin, Wanting, Jun Tang, Cong Lu, and Songyang Lao. "Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example." Computational Geosciences 25, no. 3 (March 5, 2021): 1005–23. http://dx.doi.org/10.1007/s10596-021-10037-2.
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AbstractTrajectory data can objectively reflect the moving law of moving objects. Therefore, trajectory prediction has high application value. Hurricanes often cause incalculable losses of life and property, trajectory prediction can be an effective means to mitigate damage caused by hurricanes. With the popularization and wide application of artificial intelligence technology, from the perspective of machine learning, this paper trains a trajectory prediction model through historical trajectory data based on a long short-term memory (LSTM) network. An improved LSTM (ILSTM) trajectory prediction algorithm that improves the prediction of the simple LSTM is proposed, and the Kalman filter is used to filter the prediction results of the improved LSTM algorithm, which is called LSTM-KF. Through simulation experiments of Atlantic hurricane data from 1851 to 2016, compared to other LSTM and ILSTM algorithms, it is found that the LSTM-KF trajectory prediction algorithm has the lowest prediction error and the best prediction effect.
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Ulum, Dinar Syahid Nur, and Abba Suganda Girsang. "Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction." International Journal of Innovative Research and Scientific Studies 5, no. 2 (April 29, 2022): 121–33. http://dx.doi.org/10.53894/ijirss.v5i2.415.
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Producing the best possible predictive result from long-short term memory (LSTM) requires hyperparameters to be tuned by a data scientist or researcher. A metaheuristic algorithm was used to optimize hyperparameter tuning and reduce the computational complexity to improve the manual process. Symbiotic organism search (SOS), which was introduced in 2014, is an algorithm that simulates the symbiotic interactions that organisms use to survive in an ecosystem. SOS offers an advantage over other metaheuristic algorithms in that it has fewer parameters, allowing it to avoid parameter determination errors and produce suboptimal solutions. SOS was used to optimize hyperparameter tuning in LSTM for stock prediction. The stock prices were time-series data, and LSTM has proven to be a popular method for time-series forecasting. This research employed the Indonesia composite index dataset and assessed it using root mean square error (RMSE) as a key indicator and the fitness function for the metaheuristic approach. Genetic algorithm (GA) and particle swarm optimization (PSO) were used as benchmarking algorithms in this research. The hybrid SOS-LSTM model outperformed GA-LSTM and PSO-LSTM with an RMSE of 78.799, compared to the GA-LSTM model with an RMSE of 142.663 and the PSO-LSTM model with an RMSE of 529.170.
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Chen, Wantong, Hailong Wu, and Shiyu Ren. "CM-LSTM Based Spectrum Sensing." Sensors 22, no. 6 (March 16, 2022): 2286. http://dx.doi.org/10.3390/s22062286.
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This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm.
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Yang, Fan, Kewen Xia, Shurui Fan, and Zhiwei Zhang. "Equalization Optimizer-Based LSTM Application in Reservoir Identification." Computational Intelligence and Neuroscience 2022 (September 9, 2022): 1–20. http://dx.doi.org/10.1155/2022/7372984.
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In recent years, the use of long short-term memory (LSTM) has made significant contributions to various fields and the use of intelligent optimization algorithms combined with LSTM is also one of the best ways to improve model shortcomings and increase classification accuracy. Reservoir identification is a key and difficult point in the process of logging, so using LSTM to identify the reservoir is very important. To improve the logging reservoir identification accuracy of LSTM, an improved equalization optimizer algorithm (TAFEO) is proposed in this paper to optimize the number of neurons and various parameters of LSTM. The TAFEO algorithm mainly employs tent chaotic mapping to enhance the population diversity of the algorithm, convergence factor is introduced to better balance the local and global search, and then, a premature disturbance strategy is employed to overcome the shortcomings of local minima. The optimization performance of the TAFEO algorithm is tested with 16 benchmark test functions and Wilcoxon rank-sum test for optimization results. The improved algorithm is superior to many intelligent optimization algorithms in accuracy and convergence speed and has good robustness. The receiver operating characteristic (ROC) curve is used to evaluate the performance of the optimized LSTM model. Through the simulation and comparison of UCI datasets, the results show that the performance of the LSTM model based on TAFEO has been significantly improved, and the maximum area under the ROC curve value can get 99.43%. In practical logging applications, LSTM based on an equalization optimizer is effective in well-logging reservoir identification, the highest recognition accuracy can get 95.01%, and the accuracy of reservoir identification is better than other existing identification methods.
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Wójcikowski, Marek. "Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices." Sensors 22, no. 1 (December 27, 2021): 164. http://dx.doi.org/10.3390/s22010164.
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This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.
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Park, Kang Yun, and Yong Sang Lee. "Deep Learning Algorithm Exploration for Automated Korean essay Scoring." Korean Society for Educational Evaluation 35, no. 3 (September 30, 2022): 465–88. http://dx.doi.org/10.31158/jeev.2022.35.3.465.
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This study was carried out for the purpose of searching for the optimal algorithm for automated scoring system of Korean essay through the comparison of deep learning-based learning models. For this purpose, in this study, deep learning algorithms such as Recurrent Neural Network (RNN), Long-Short-Term-Memory (LSTM), and Gated-Recurrent-Unit (GRU) algorithms were compared. The performance of each algorithm was evaluated based on classification accuracy, precision, recall, and F1. The empirical results showed that the LSTM and GRU algorithm-based models performed better than RNN. Although there is no significant difference in model performance between LSTM and GRU, the GRU algorithm was found to be more efficient in terms of the time required to train the model, so it could be considered to be the optimal algorithm for automated scoring if the machine leanring time is critical.
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Zhen, Tao, Lei Yan, and Peng Yuan. "Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm." Algorithms 12, no. 12 (November 26, 2019): 253. http://dx.doi.org/10.3390/a12120253.
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Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different walking speeds and different test subjects. During the identification process, the acceleration signals obtained from the acceleration sensors were normalized to ensure that the different features had the same scale. Principal components analysis (PCA) was used to reduce the data dimensionality and the processed data were used to create the input feature vector of the LSTM-DNN algorithm. Finally, the data set was classified using the Softmax classifier in the full connection layer. Different algorithms were applied to the gait phase detection of multiple male and female subjects. The experimental results showed that the gait-phase recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8% and 92%, respectively, which is better than the other three algorithms and also verifies the effectiveness of the LSTM-DNN algorithm in practice.
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Li, Xueguang, and Menchita F. Dumlao. "SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm." Frontiers in Computing and Intelligent Systems 4, no. 3 (July 20, 2023): 71–77. http://dx.doi.org/10.54097/fcis.v4i3.11146.
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New energy vehicles are developing rapidly in the world, China and Europe are vigorously promoting new energy vehicles. The State of Charge (SOC) is circumscribed as the remaining charge of the lithium battery (Li-ion), that indicates the driving range of a pure electric vehicle. Additionally, it is the basis for SOH and fault state prediction. Nevertheless, the SOC is incapable of measuring directly. In this paper, an LSTM-Attention-R network framework is proposed. The LSTM algorithm is accustomed to present the timing information and past state information of the lithium battery data. The Attention algorithm is used to extract the global information of features and solve the problem of long-term dependency. To ensure the diversity of feature extraction, the Attention algorithm in this paper uses multi-headed self-attentiveness. The CACLE dataset from the University of Maryland is used in this paper. Through the training of the model and the comparison, it is concluded that the LSTM-Attention-R algorithm networks proposed in this article can predict the value of SOC well. Meanwhile, this paper compares the LSTM-Attention-R algorithm with the LSTM algorithm, and also compares the LSTM-Attention-R algorithm with the Attention algorithm. Finally, it is concluded that the accomplishment of the network framework contrived is superior to the performance of these two algorithms alone. Finally, the algorithm has good engineering practice implications. The algorithm proposed provides a better research direction for future parameter prediction in the field of lithium batteries. It has a better theoretical significance.
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Awad, Asmaa Ahmed, Ahmed Fouad Ali, and Tarek Gaber. "An improved long short term memory network for intrusion detection." PLOS ONE 18, no. 8 (August 1, 2023): e0284795. http://dx.doi.org/10.1371/journal.pone.0284795.
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Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.
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Yang, Yifang. "Application of LSTM Neural Network Technology Embedded in English Intelligent Translation." Computational Intelligence and Neuroscience 2022 (September 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/1085577.
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With the rapid development of computer technology, the loss of long-distance information in the transmission process is a prominent problem faced by English machine translation. The self-attention mechanism is combined with convolutional neural network (CNN) and long-term and short-term memory network (LSTM). An English intelligent translation model based on LSTM-SA is proposed, and the performance of this model is compared with other deep neural network models. The study adds SA to the LSTM neural network model and constructs the English translation model of LSTM-SA attention embedding. Compared with other deep learning algorithms such as 3RNN and GRU, the LSTM-SA neural network algorithm has faster convergence speed and lower loss value, and the loss value is finally stable at about 8.6. Under the three values of adaptability, the accuracy of LSTM-SA neural network structure is higher than that of LSTM, and when the adaptability is 1, the accuracy of LSTM-SA neural network improved the fastest, with an accuracy of nearly 20%. Compared with other deep learning algorithms, the LSTM-SA neural network algorithm has a better translation level map under the three hidden layers. The proposed LSTM-SA model can better carry out English intelligent translation, enhance the representation of source language context information, and improve the performance and quality of English machine translation model.
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Kundu, Ripan Kumar, Akhlaqur Rahman, and Shuva Paul. "A Study on Sensor System Latency in VR Motion Sickness." Journal of Sensor and Actuator Networks 10, no. 3 (August 6, 2021): 53. http://dx.doi.org/10.3390/jsan10030053.
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One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error.
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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 (January 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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Song, Lijun, Peiyu Xu, Xing He, Yunlong Li, Jiajie Hou, and Haoyu Feng. "Improved LSTM Neural Network-Assisted Combined Vehicle-Mounted GNSS/SINS Navigation and Positioning Algorithm." Electronics 12, no. 17 (September 4, 2023): 3726. http://dx.doi.org/10.3390/electronics12173726.
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Aiming at the problem of the combined navigation system of on-board GNSS (global navigation satellite system)/SINS (strapdown inertial navigation system), the accuracy of the combined navigation system decreases due to the dispersion of the SINS over time and under the condition of No GNSS signals. An improved LSTM (long short-term memory) neural network in No GNSS signal conditions is proposed to assist the combination of navigation data and the positioning algorithm. When the GNSS signal is normal input, the current on-board combination of the navigation module’s output sensor data information is used for training to improve the LSTM algorithm and to establish the incremental output of the GNSS position of the mapping of the different weights. In No GNSS signal conditions, using the improved LSTM algorithm can improve the combination of navigation and positioning algorithms. Under No GNSS signal conditions, the improved LSTM training model is used to predict the dynamics of SINS information component data. Under No GNSS signal conditions, the combined navigation filtering design is completed, and the error correction of SINS navigation and positioning information is carried out to obtain a more accurate combination of navigation and positioning system accuracy. It can be seen through the actual test experiment using a sports car in the two trajectories under the conditions of No GNSS signals that the proposed algorithm can be compared with the LSTM algorithm. In testing road sections, the proposed algorithm, when compared with the LSTM algorithm to obtain the northward position that the mean square errors were improved by 55.63% and 76.64%, and the eastward position mean square errors were improved by 43.42% and 54.67%. In a straight-line trajectory, improving the effect’s navigation and positioning accuracy and reliability is significant.
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Wu, Jizhou, Hongmin Zhang, and Xuanhao Gao. "Radar High-Resolution Range Profile Target Recognition by the Dual Parallel Sequence Network Model." International Journal of Antennas and Propagation 2021 (December 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/4699373.
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Using traditional neural network algorithms to adapt to high-resolution range profile (HRRP) target recognition is a complex problem in the current radar target recognition field. Under the premise of in-depth analysis of the long short-term memory (LSTM) network structure and algorithm, this study uses an attention model to extract data from the sequence. We build a dual parallel sequence network model for rapid classification and recognition and to effectively improve the initial LSTM network structure while reducing network layers. Through demonstration by designing control experiments, the target recognition performance of HRRP is demonstrated. The experimental results show that the bidirectional long short-term memory (BiLSTM) algorithm has obvious advantages over the template matching method and initial LSTM networks. The improved BiLSTM algorithm proposed in this study has significantly improved the radar HRRP target recognition accuracy, which enhanced the effectiveness of the improved algorithm.
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Lv, Liujia, Weijian Kong, Jie Qi, and Jue Zhang. "An improved long short-term memory neural network for stock forecast." MATEC Web of Conferences 232 (2018): 01024. http://dx.doi.org/10.1051/matecconf/201823201024.
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This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in the last two years. Compared with typical algorithms by simulation, we find the LSTM has better performance in reliability and adaptability, and the improved PSO-LSTM algorithm has better accuracy.
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ÇETİNER, Halit. "Recurrent Neural Network Based Model Development for Energy Consumption Forecasting." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 11, no. 3 (September 30, 2022): 759–69. http://dx.doi.org/10.17798/bitlisfen.1077393.
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The world population is increasing day by day. As a result, limited resources are decreasing day by day. On the other hand, the amount of energy needed is constantly increasing. In this sense, decision makers must accurately estimate the amount of energy that society will require in the coming years and make plans accordingly. These plans are of critical importance for the peace and welfare of society. Based on the energy consumption values of Germany, it is aimed at estimating the energy consumption values with the GRU, LSTM, and proposed hybrid LSTM-GRU methods, which are among the popular RNN algorithms in the literature. The estimation performances of LSTM and GRU algorithms were obtained for MSE, RMSE, MAPE, MAE, and R2 values as 0.0014, 0.0369, 6.35, 0.0292, 0.9703 and 0.0017, 0.0375, 6.60, 0.0298, 0.9650, respectively. The performance of the proposed hybrid LSTM-GRU method, which is another RNN-based algorithm used in the study, was obtained as 0.0013, 0.0358, 5.89, 0.0275, and 0.9720 for MSE, RMSE, MAPE, MAE and R2 values, respectively. Although all three methods gave similar results, the training times of the proposed hybrid LSTM-GRU and LSTM algorithms took 7.50 and 6.58 minutes, respectively, but it took 4.87 minutes for the GRU algorithm. As can be understood from this value, it has been determined that it is possible to obtain similar values by sacrificing a very small amount of prediction performance in cases with time limitations.
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Nguyen-Da, Thao, Yi-Min Li, Chi-Lu Peng, Ming-Yuan Cho, and Phuong Nguyen-Thanh. "Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces." Sustainability 15, no. 9 (April 25, 2023): 7179. http://dx.doi.org/10.3390/su15097179.
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The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic limitation on foreign travel. This research proposes a hybrid algorithm, combined convolution neural network (CNN) and long short-term memory (LSTM), to accurately predict the tourism demand in Vietnam and some provinces. The number of new COVID-19 cases worldwide and in Vietnam is considered a promising feature in predicting algorithms, which is novel in this research. The Pearson matrix, which evaluates the correlation between selected features and target variables, is computed to select the most appropriate input parameters. The architecture of the hybrid CNN–LSTM is optimized by utilizing hyperparameter fine-tuning, which improves the prediction accuracy and efficiency of the proposed algorithm. Moreover, the proposed CNN–LSTM outperformed other traditional approaches, including the backpropagation neural network (BPNN), CNN, recurrent neural network (RNN), gated recurrent unit (GRU), and LSTM algorithms, by deploying the K-fold cross-validation methodology. The developed algorithm could be utilized as the baseline strategy for resource planning, which could efficiently maximize and deeply utilize the available resource in Vietnam.
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Chen, Keqiao. "APSO-LSTM: An Improved LSTM Neural Network Model Based on APSO Algorithm." Journal of Physics: Conference Series 1651 (November 2020): 012151. http://dx.doi.org/10.1088/1742-6596/1651/1/012151.
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Sudhakaran, P., Subbiah Swaminathan, D. Yuvaraj, and S. Shanmuga Priya. "Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 05 (April 7, 2020): 150. http://dx.doi.org/10.3991/ijim.v14i05.13361.
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Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms.
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Fitzhan, Adriel Lazaro, and Antoni Wibowo. "Long Short-Term Memory Network Hyperparameter Optimization using Hybrid Algorithm GA-PSO on LQ45 Stock Prediction." International Journal of Emerging Technology and Advanced Engineering 13, no. 2 (February 4, 2023): 57–64. http://dx.doi.org/10.46338/ijetae0223_08.
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Stock is a good investment tool, keeping money from inflation, and very trendy to earn a living nowadays by becoming a trader. There is always a risk, especially when trading, because stocks can fluctuate easily depending on the company. One of the data science capabilities, prediction modeling, can help lower the risk by predicting the stock price movement. This research proposed a prediction sequential data model, an optimized hyperparameter LSTM Network using hybrid GA-PSO (LSTM-GA-PSO). Hybrid GA-PSO aims to overcome the GA problem in terms of slow execution time and PSO that tend to be trapped in the local optimum. With the characteristics of both algorithms, the hybrid algorithm can solve each other algorithms downside. The low fluctuation stock of the Indonesian Index LQ45 dataset will be used to train and test the model and compare the proposed model with LSTM-GA and LSTM-PSO. Experiment results show that the hybrid LSTM-GA-PSO has a promising performance. Hybrid GA-PSO improved 18.18% of its time execution to GA and 29.07% accuracy to PSO.
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Chen, Ping, JianYi Zhong, and YueChao Zhu. "Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching." Computational Intelligence and Neuroscience 2022 (January 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/5755327.
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The question answering link in the traditional teaching method is analyzed to optimize the shortcomings and deficiencies of the existing question-and-answer (Q&A) machines and solve the problems of financial students’ difficulty in answering questions. Firstly, the difficulties and needs of students in answering questions are understood. Secondly, the traditional algorithm principle by the Q&A system is introduced and analyzed, and the problems and defects existing in the traditional Q&A system are summarized. On this basis, deep learning algorithms are introduced, the long short-term memory (LSTM) neural network and convolutional neural network (CNN) are combined, and a Q&A system by long short-term memory-convolutional neural network (LSTM-CNN) is proposed, the gated recurrent unit (GRU) attention mechanism is introduced, and the algorithm is optimized. Finally, the design experiments to determine the nearest parameters of the neural network algorithm and verify the effectiveness of the algorithm are carried out. The results show that for the LSTM-CNN, the effect is the best when dropout = 0.5. After introducing the attention mechanism optimization, the effect is the best when dropout = 0.6. The test results of the comparison between the recommended algorithm and the traditional Q&A model algorithm show that the LSTM-CNN algorithm maintains the ability of the LSTM algorithm to arrange information in chronological order. After being combined with the CNN algorithm, the language features of the sentence can be extracted more deeply, the semantic feature information can be captured more accurately from the sentence, and better performance can be maintained when processing more complex sentences. The introduction of a BANet can simultaneously obtain the past and future information so that the algorithm can more appropriately combine it with the context to retrieve the semantic features, and the effectiveness of the model has been greatly improved. The research results have played an optimizing role in improving the Q&A effect of finance and economics teaching and provided a reference for research in related fields.
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Lan, Pu, Kewen Xia, Yongke Pan, and Shurui Fan. "An Improved Equilibrium Optimizer Algorithm and Its Application in LSTM Neural Network." Symmetry 13, no. 9 (September 15, 2021): 1706. http://dx.doi.org/10.3390/sym13091706.
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An improved equilibrium optimizer (EO) algorithm is proposed in this paper to address premature and slow convergence. Firstly, a highly stochastic chaotic mechanism is adopted to initialize the population for range expansion. Secondly, the capability to conduct global search to jump out of local optima is enhanced by assigning adaptive weights and setting adaptive convergence factors. In addition 25 classical benchmark functions are used to validate the algorithm. As revealed by the analysis of the accuracy, speed, and stability of convergence, the IEO algorithm proposed in this paper significantly outperforms other meta-heuristic algorithms. In practice, the distribution is asymmetric because most logging data are unlabeled. Traditional classification models have difficulty in accurately predicting the location of oil layer. In this paper, the oil layers related to oil exploration are predicted using long short-term memory (LSTM) networks. Due to the large amount of data used, however, it is difficult to adjust the parameters. For this reason, an improved equilibrium optimizer algorithm (IEO) is applied to optimize the parameters of LSTM for improved performance, while the effective IEO-LSTM is applied for oil layer prediction. As indicated by the results, the proposed model outperforms the current popular optimization algorithms including particle swarm algorithm PSO and genetic algorithm GA in terms of accuracy, absolute error, root mean square error and mean absolute error.
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Lin, Xiaoyu, Hang Yu, Meng Wang, Chaoen Li, Zi Wang, and Yin Tang. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method." Energies 14, no. 16 (August 6, 2021): 4785. http://dx.doi.org/10.3390/en14164785.
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Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power consumption, respectively and discusses the influence of weather parameters and schedule parameters on the prediction accuracy. The results demonstrate that using the LSTM algorithm to accurately predict the electricity consumption of air conditioners is more challenging than predicting lighting electricity consumption. To improve the prediction accuracy of air conditioning power consumption, two parameters, relative humidity, and scheduling, must be added to the prediction model.
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Hamayel, Mohammad J., and Amani Yousef Owda. "A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms." AI 2, no. 4 (October 13, 2021): 477–96. http://dx.doi.org/10.3390/ai2040030.
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Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.
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Suneetha Rani R, Sri Vinithri Chowdary D, Uday Kiran C H, Deekshith K, and Alekhya V. "Bitcoin price prediction based on linear regression and lstm." South Asian Journal of Engineering and Technology 12, no. 3 (July 11, 2022): 87–95. http://dx.doi.org/10.26524/sajet.2022.12.44.
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Forecasting can be used in many fields such as crypto currency prediction, financial entities, supermarkets etc. We get the time series date which we use to feed the data into the algorithm is given by Y finance with this we get refreshed data every day. The stock market prediction or forecasting helps customers and brokers get a brief view of how the market behaves for the coming years. Many models are currently in use Like Regression techniques, Long Short-Term Memory algorithm etc. FB Prophet is proven to perform better than most other Algorithms with better accuracy. From the proposed research and references we have determined Facebook's Prophet algorithm as our forecasting algorithm because it is predicting at better accuracy, low error rate, handles messy data, doesn’t bother for null values and better fitting.
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Yang, Yufan, Chunlei Wei, Fan Yang, Tianyi Lu, Langfeng Zhu, and Jun Wei. "A Machine Learning-Based Correction Method for High-Frequency Surface Wave Radar Current Measurements." Applied Sciences 12, no. 24 (December 17, 2022): 12980. http://dx.doi.org/10.3390/app122412980.
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An algorithm based on a long short-term memory (LSTM) network is proposed to reduce errors from high-frequency surface wave radar current measurements. In traditional inversion algorithms, the radar velocities are derived from electromagnetic echo signals, with no constraints imposed by physical oceanographic processes. In this study, sea surface winds and tides are included in the LSTM algorithm to improve radar data. These physical factors provide the LSTM network with more oceanic information by which to constrain and improve its training efficiency. The results show that the domain-averaged root-mean-square errors of the radar-derived velocities are reduced from 0.22 to 0.09 m/s for the whole radar observation area. The overall correlation coefficient increases from 0.37 to 0.88. To provide a practical strategy for future field work, we conduct a set of sensitivity experiments, showing that the LSTM network based on one single point can be applied to other data points within a sub-domain.
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Kwon, Semin, Bo-Seung Kim, and Junhong Park. "Active Noise Reduction with Filtered Least-Mean-Square Algorithm Improved by Long Short-Term Memory Models for Radiation Noise of Diesel Engine." Applied Sciences 12, no. 20 (October 12, 2022): 10248. http://dx.doi.org/10.3390/app122010248.
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This study presents an active noise control (ANC) algorithm using long short-term memory (LSTM) layers as a type of recurrent neural network. The filtered least-mean-square (FxLMS) algorithm is a widely used ANC algorithm, where the noise in a target area is reduced through a control signal generated from an adaptive filter. Artificial intelligence can enhance the reduction performance of ANC for specific applications. An LSTM is an artificial neural network for recognizing patterns in arbitrarily long sequence data. In this study, an ANC controller consisting of LSTM layers based on deep neural networks was designed for predicting a reference noise signal, which was used to generate the control signal to minimize the noise residue. The structure of the LSTM neural networks and procedure for training the LSTM controller for the ANC were determined. Simulations were conducted to compare the convergence time and performances of the ANC with the LSTM controller and those with a conventional FxLMS algorithm. The noise source adopted sounds from a single-cylinder diesel engine, while reference noises selected were single harmonics, superposed harmonics, and impulsive signals generated from the diesel engine. The characteristics of each algorithm were examined through a Fourier transform analysis of the ANC results. The simulation results demonstrated that the proposed ANC method with LSTM layers showed outstanding noise reduction capabilities in narrowband, broadband, and impulsive noise environments, without high computational cost and complexity relative to the conventional FxLMS algorithm.
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Chen, Ningyan. "Visual recognition and prediction analysis of China’s real estate index and stock trend based on CNN-LSTM algorithm optimized by neural networks." PLOS ONE 18, no. 2 (February 24, 2023): e0282159. http://dx.doi.org/10.1371/journal.pone.0282159.
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Today, with the rapid growth of Internet technology, the changing trend of real estate finance has brought great an impact on the progress of the social economy. In order to explore the visual identification (VI) effect of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) algorithm based on neural network optimization on China’s real estate index and stock trend, in this study, artificial neural network (ANN) algorithm is introduced to predict its trend. Firstly, LSTM algorithm can effectively solve the problem of vanishing gradient, which is suitable for dealing with the problems related to time series. Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. Through the performance verification of the model, the results reveal that the CNN-LSTM optimization algorithm has a more accurate prediction effect, the prediction accuracy is 90.55%, and the prediction time is only 52.05s. At the same time, the significance advantage of CNN-LSTM algorithm is verified by statistical method, which can provide experimental reference for intelligent VI and prediction of trend of China real estate index and property company stocks.
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Ren, Jinyan. "Pop Music Trend and Image Analysis Based on Big Data Technology." Computational Intelligence and Neuroscience 2021 (December 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/4700630.
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With people’s pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer’s songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.
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Wang, Shengwei, Ping Li, Hao Ji, Yulin Zhan, and Honghong Li. "Prediction of air particulate matter in Beijing, China, based on the improved particle swarm optimization algorithm and long short-term memory neural network." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1869–85. http://dx.doi.org/10.3233/jifs-210603.
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Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution.
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Hou, Dali, and Mingjia Cao. "A hybrid deep learning model approach for performance index prediction of mechanical equipment." Measurement Science and Technology 33, no. 10 (July 13, 2022): 105108. http://dx.doi.org/10.1088/1361-6501/ac769d.
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Abstract To forecast the health status of mechanical equipment in industrial production, fault diagnosis systems need a fast and accurate algorithm to forecast the important performance indexes of mechanical equipment. According to the characteristics of time series, a composite variable wavelet transform, deep autoencoder and long short-term memory (CWD-LSTM) hybrid neural network forecast algorithm is proposed to carry out one-step forecast experiments on air compressor datasets. As one of the important indexes reflecting the performance of the air compressor, loading time is usually a parameter that the fault diagnosis system needs to forecast and analyze. The experimental results show that compared with the original neural network and other similar algorithms, the CWD-LSTM algorithm has obvious advantages in forecasting the loading time under a variety of detection indexes. More importantly, CWD-LSTM does not require a high update frequency of the neural network, and manufacturers do not need a frequent training model to ensure the reliability of forecast.
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Xiao, Junyan. "Stock Prediction using LSTM model." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 74–79. http://dx.doi.org/10.54254/2755-2721/8/20230084.
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With the development of the times, investors are increasingly in demand for stock price forecasting. However, stock price fluctuations are full of uncertainty, making traditional machine learning algorithms more erroneous in long-term forecasting. Based on the LSTM model, this paper uses Tushare to obtain the historical price of stocks, and the optimal structure and best training parameters of the LSTM model in stock price prediction are determined experimentally. The prediction accuracy of the LSTM model was evaluated by MAE, and the best result was 69.15, which achieved accurate prediction of stock prices. Compared with the traditional SVR model and the ARMA model, the prediction results of LSTM are more in line with the actual value, and the prediction accuracy of the algorithm is higher.
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Shi, Yongsheng, Jiarui Ren, Mengzhuo Shi, Jin Li, and Kai Zhang. "Battery life prediction method based on DE-GWO-LSTM." Journal of Physics: Conference Series 2076, no. 1 (November 1, 2021): 012105. http://dx.doi.org/10.1088/1742-6596/2076/1/012105.
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Abstract Aiming at the problem of inaccurate prediction results of lithium-ion battery life, a lithium-ion battery life prediction model based on hybrid algorithm is designed. The position of grey wolf algorithm is updated by differential evolution algorithm, which improves the population diversity and avoids premature stagnation of the algorithm. The GWO-LSTM model and DE-GWO-LSTM model are compared and analyzed by using NASA data. The proposed DE-GWO-LSTM can well conduct global search and local search, and improve the prediction performance to a certain extent.
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He, Yawen, Weirong Li, Zhenzhen Dong, Tianyang Zhang, Qianqian Shi, Linjun Wang, Lei Wu, et al. "Lithologic Identification of Complex Reservoir Based on PSO-LSTM-FCN Algorithm." Energies 16, no. 5 (February 22, 2023): 2135. http://dx.doi.org/10.3390/en16052135.
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Reservoir lithology identification is the basis for the exploration and development of complex lithological reservoirs. Efficient processing of well-logging data is the key to lithology identification. However, reservoir lithology identification through well-logging is still a challenge with conventional machine learning methods, such as Convolutional Neural Networks (CNN), and Long Short-term Memory (LSTM). To address this issue, a fully connected network (FCN) and LSTM were coupled for predicting reservoir lithology. The proposed algorithm (LSTM-FCN) is composed of two sections. One section uses FCN to extract the spatial properties, the other one captures feature selections by LSTM. Well-logging data from Hugoton Field is used to evaluate the performance. In this study, well-logging data, including Gamma-ray (GR), Resistivity (ILD_log10), Neutron-density porosity difference (DeltaPHI), Average neutron-density porosity(PHIND), and (Photoelectric effect) PE, are used for training and identifying lithology. For comparison, seven conventional methods are also proposed and trained, such as support vector machines (SVM), and random forest classifiers (RFC). The accuracy results indicate that the proposed architecture obtains better performance. After that, particle swarm optimization (PSO) is proposed to optimize hyper-parameters of LSTM-FCN. The investigation indicates the proposed PSO-LSTM-FCN model can enhance the performance of machine learning algorithms on identify the lithology of complex reservoirs.
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Luo, Yulan. "Soybean Futures Price Prediction Based on CNN-LSTM Model of Bayesian Optimization Algorithm." Highlights in Business, Economics and Management 16 (August 2, 2023): 6–17. http://dx.doi.org/10.54097/hbem.v16i.10419.
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In recent years, the complex international environment and economic situation have made soybean futures prices increasingly unstable, which is not conducive to financial stability. Therefore, this paper uses a BO-CNN-LSTM model to accurately predict soybean futures prices and to manage price fluctuations for investors and governments. Firstly, LSTM network is employed to predict soybean futures prices using the local features extracted by CNN network. In addition, CNN-LSTM hyperparameters are optimally solved using Bayesian optimization algorithms. Finally, the constructed model is compared with BP neural network, LSTM model and CNN-LSTM model. This paper selects the basic daily data of the soybean futures contract No.1 of Dalian Commodity Exchange from 2014 to 2021 for research. According to the results, CNN-LSTM models based on Bayesian optimization algorithms perform best. Compared with the basic CNN-LSTM model, MAPE increased by 44.17%, RMSE increased by 24.61%, MAE increased by 41.48%, and R2 increased by 0.06%, which demonstrates Bayesian optimization's superiority.
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Wang, Ju, Leifeng Zhang, Sanqiang Yang, Shaoning Lian, Peng Wang, Lei Yu, and Zhenyu Yang. "Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction." Electronic Research Archive 31, no. 6 (2023): 3435–52. http://dx.doi.org/10.3934/era.2023174.
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<abstract> <p>In order to effectively control and predict the settlement deformation of the surrounding ground surface caused by deep foundation excavation, the deep foundation pit project of Baoding City Automobile Technology Industrial Park is explored as an example. The initial population approach of the whale algorithm (WOA) is optimized using Cubic mapping, while the weights of the shrinkage envelope mechanism are adjusted to avoid the algorithm falling into local minima, the improved whale algorithm (IWOA) is proposed. Meanwhile, 10 benchmark test functions are selected to simulate the performance of IWOA, and the advantages of IWOA in learning efficiency and convergence speed are verified. The IWOA-LSTM deep foundation excavation deformation prediction model is established by optimizing the input weights and hidden layer thresholds in the deep long short-term memory (LSTM) neural network using the improved whale algorithm. The IWOA-LSTM prediction model is compared with LSTM, WOA-optimized LSTM (WOA-LSTM) and traditional machine learning, the results show that the final prediction score of the IWOA-LSTM prediction model is higher than the score of other models, and the prediction accuracy is better than that of traditional machine learning.</p> </abstract>
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Tseng, Po-He, Núria Armengol Urpi, Mikhail Lebedev, and Miguel Nicolelis. "Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network." Neural Computation 31, no. 6 (June 2019): 1085–113. http://dx.doi.org/10.1162/neco_a_01189.
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Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large ( N = 134–402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.
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Diwan, Sinan, and Yanash Azwin Mohmad. "Credit Card Fraud Detection Using LSTM Algorithm." Wasit Journal of Computer and Mathematics Science 1, no. 3 (October 1, 2022): 39–53. http://dx.doi.org/10.31185/wjcm.60.
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With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.
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Nurhaida, Ida, Handrie Noprisson, Vina Ayumi, Hong Wei, Erwin Dwika Putra, Marissa Utami, and Hadiguna Setiawan. "Implementation of Deep Learning Predictor (LSTM) Algorithm for Human Mobility Prediction." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 18 (November 10, 2020): 132. http://dx.doi.org/10.3991/ijim.v14i18.16867.
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The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction result accuration, however, only few researchers have addressed the issue of<em> </em>human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result,<em> </em>the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of <em>epoch</em> and <em>batch_size</em> is 100 and 20 respectively.
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Alkahtani, Hasan, Theyazn H. H. Aldhyani, and Mohammed Al-Yaari. "Adaptive Anomaly Detection Framework Model Objects in Cyberspace." Applied Bionics and Biomechanics 2020 (December 9, 2020): 1–14. http://dx.doi.org/10.1155/2020/6660489.
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Telecommunication has registered strong and rapid growth in the past decade. Accordingly, the monitoring of computers and networks is too complicated for network administrators. Hence, network security represents one of the biggest serious challenges that can be faced by network security communities. Taking into consideration the fact that e-banking, e-commerce, and business data will be shared on the computer network, these data may face a threat from intrusion. The purpose of this research is to propose a methodology that will lead to a high level and sustainable protection against cyberattacks. In particular, an adaptive anomaly detection framework model was developed using deep and machine learning algorithms to manage automatically-configured application-level firewalls. The standard network datasets were used to evaluate the proposed model which is designed for improving the cybersecurity system. The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks. The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. The system was used to classify DoS and DDoS attacks in four stand datasets namely KDD cup 199, NSL-KDD, ISCX, and ICI-ID2017. The empirical results indicate that the deep learning based on the LSTM-RNN algorithm has obtained the highest accuracy. The proposed system based on the LSTM-RNN algorithm produced the highest testing accuracy rate of 99.51% and 99.91% with respect to KDD Cup’99, NSL-KDD, ISCX, and ICI-Id2017 datasets, respectively. A comparative result analysis between the machine learning algorithms, namely SVM and KNN, and the deep learning algorithms based on the LSTM-RNN model is presented. Finally, it is concluded that the LSTM-RNN model is efficient and effective to improve the cybersecurity system for detecting anomaly-based cybersecurity.
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Bai, Chenyao. "AGA-LSTM: An Optimized LSTM Neural Network Model Based on Adaptive Genetic Algorithm." Journal of Physics: Conference Series 1570 (June 2020): 012011. http://dx.doi.org/10.1088/1742-6596/1570/1/012011.
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Xing, Jiarui, and Huilin Li. "Study about Football Action Recognition Method Based on Deep Learning and Improved Dynamic Time Warping Algorithm." Mobile Information Systems 2022 (April 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/3861620.
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Artificial intelligence recognition of human actions has been used in various fields. This article is based on deep learning and improved dynamic time regularization algorithms to study football action postures. This paper proposes a hierarchical recurrent network for understanding team sports activities in image and position sequences. In the hierarchical model, this article integrates the proposed multiple human-centered features on the time series based on LSTM output. In order to realize this scheme, the holding state is introduced as one of the external controllable states in LSTM, and the hierarchical LSTM is extended to include the integration mechanism. Test outcomes demonstrate those adequacies of the recommended framework, which includes progressive LSTM human-centred benefits. In this study, the improvement of the reference model in the two-stream LSTM-based method is shown. Specifically, by combining human-centered features and meta-information (e.g., location data) into the postfusion framework proposed in the article, the article also proves that the action categories have increased, and the observations enhanced the robustness of fluctuations in the number of football players. The experimental data shows that 67.89% of the postures of football players through this algorithm can be recognized by the improved dynamic time warping algorithm.
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Pan, Jeng-Shyang, Pei Hu, Tien-Szu Pan, and Shu-Chuan Chu. "Improved Equilibrium Optimizer for Short-Term Traffic Flow Prediction." Journal of Database Management 34, no. 1 (April 21, 2023): 1–20. http://dx.doi.org/10.4018/jdm.321758.
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Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.