Journal articles on the topic 'CNN AND LSTM NETWORKS'
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Garcia, Carlos Iturrino, Francesco Grasso, Antonio Luchetta, Maria Cristina Piccirilli, Libero Paolucci, and Giacomo Talluri. "A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM." Applied Sciences 10, no. 19 (September 27, 2020): 6755. http://dx.doi.org/10.3390/app10196755.
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The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.
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Xu-Nan Tan, Xu-Nan Tan. "Human Activity Recognition Based on CNN and LSTM." 電腦學刊 34, no. 3 (June 2023): 221–35. http://dx.doi.org/10.53106/199115992023063403016.
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<p>Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject’s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.</p> <p> </p>
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Liu, Tianyuan, Jinsong Bao, Junliang Wang, and Yiming Zhang. "A Hybrid CNN–LSTM Algorithm for Online Defect Recognition of CO2 Welding." Sensors 18, no. 12 (December 10, 2018): 4369. http://dx.doi.org/10.3390/s18124369.
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At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks.
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Geng, Yue, Lingling Su, Yunhong Jia, and Ce Han. "Seismic Events Prediction Using Deep Temporal Convolution Networks." Journal of Electrical and Computer Engineering 2019 (April 2, 2019): 1–14. http://dx.doi.org/10.1155/2019/7343784.
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Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.
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Banda, Anish. "Image Captioning using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2666–69. http://dx.doi.org/10.22214/ijraset.2021.37846.
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Abstract: In the model we proposed, we examine the deep neural networks-based image caption generation technique. We give image as input to the model, the technique give output in three different forms i.e., sentence in three different languages describing the image, mp3 audio file and an image file is also generated. In this model, we use the techniques of both computer vision and natural language processing. We are aiming to develop a model using the techniques of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to build a model to generate a Caption. Target image is compared with the training images, we have a large dataset containing the training images, this is done by convolutional neural network. This model generates a decent description utilizing the trained data. To extract features from images we need encoder, we use CNN as encoder. To decode the description of image generated we use LSTM. To evaluate the accuracy of generated caption we use BLEU metric algorithm. It grades the quality of content generated. Performance is calculated by the standard calculation matrices. Keywords: CNN, RNN, LSTM, BLEU score, encoder, decoder, captions, image description.
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Reddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy, and Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.
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Abstract: The objective of the project is to generate caption of an image. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. With the advancement in Deep learning techniques and availability of huge datasets and computer power, we can build models that can generate captions for an image. This is what we have implemented in this Python based project where we have used the deep learning techniques of CNN (Convolutional Neural Networks) and LSTM (Long short term memory) which is a type of RNN (Recurrent Neural Network) together so that using computer vision computer can recognize the context of an image and display it in natural language like English. Gray Scale Image captioning can give captions for both monochrome and color images. Keywords: Image, Caption, Convolutional Neural Networks, Long Short Term Memory, Recurrent Neural Network
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Zhang, Jilin, Lishi Ye, and Yongzeng Lai. "Stock Price Prediction Using CNN-BiLSTM-Attention Model." Mathematics 11, no. 9 (April 23, 2023): 1985. http://dx.doi.org/10.3390/math11091985.
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Accurate stock price prediction has an important role in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine-learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but also has its limitations. In this paper, a CNN-BiLSTM-Attention-based model is proposed to boost the accuracy of predicting stock prices and indices. First, the temporal features of sequence data are extracted using a convolutional neural network (CNN) and bi-directional long and short-term memory (BiLSTM) network. Then, an attention mechanism is introduced to fit weight assignments to the information features automatically; and finally, the final prediction results are output through the dense layer. The proposed method was first used to predict the price of the Chinese stock index—the CSI300 index and was found to be more accurate than any of the other three methods—LSTM, CNN-LSTM, CNN-LSTM-Attention. In order to investigate whether the proposed model is robustly effective in predicting stock indices, three other stock indices in China and eight international stock indices were selected to test, and the robust effectiveness of the CNN-BiLSTM-Attention model in predicting stock prices was confirmed. Comparing this method with the LSTM, CNN-LSTM, and CNN-LSTM-Attention models, it is found that the accuracy of stock price prediction is highest using the CNN-BiLSTM-Attention model in almost all cases.
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Yang, Xingyu, and Zhongrong Zhang. "A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China." Water 14, no. 15 (July 31, 2022): 2377. http://dx.doi.org/10.3390/w14152377.
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In this study, a deep learning model is proposed to predict groundwater levels. The model is able to accurately complete the prediction task even when the data utilized are insufficient. The hybrid model that we have developed, CNN-LSTM-ML, uses a combined network structure of convolutional neural networks (CNN) and long short-term memory (LSTM) network to extract the time dependence of groundwater level on meteorological factors, and uses a meta-learning algorithm framework to ensure the network’s performance under sample conditions. The study predicts groundwater levels from 66 observation wells in the middle and lower reaches of the Heihe River in arid regions and compares them with other data-driven models. Experiments show that the CNN-LSTM-ML model outperforms other models in terms of prediction accuracy in both the short term (1 month) and long term (12 months). Under the condition that the training data are reduced by 50%, the MAE of the proposed model is 33.6% lower than that of LSTM. The results of ablation experiments show that CNN-LSTM-ML is 26.5% better than the RMSE of the original CNN-LSTM structure. The model provides an effective method for groundwater level prediction and contributes to the sustainable management of water resources in arid regions.
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Sridhar, C., and Aniruddha Kanhe. "Performance Comparison of Various Neural Networks for Speech Recognition." Journal of Physics: Conference Series 2466, no. 1 (March 1, 2023): 012008. http://dx.doi.org/10.1088/1742-6596/2466/1/012008.
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Abstract Speech recognition is a method where an audio signal is translated into text, words, or commands and also tells how the speech is recognized. Recently, many deep learning models have been adopted for automatic speech recognition and proved more effective than traditional machine learning methods like Artificial Neural Networks(ANN). This work examines the efficient learning architectures of features by different deep neural networks. In this paper, five neural network models, namely, CNN, LSTM, Bi-LSTM, GRU, and CONV-LSTM, for the comparative study. We trained the networks using Audio MNIST dataset for three different iterations and evaluated them based on performance metrics. Experimentally, CNN and Conv-LSTM network model consistently offers the best performance based on MFCC Features.
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Xu, Lingfeng, Xiang Chen, Shuai Cao, Xu Zhang, and Xun Chen. "Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation." Sensors 18, no. 10 (September 25, 2018): 3226. http://dx.doi.org/10.3390/s18103226.
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To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.
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Kłosowski, Grzegorz, Anna Hoła, Tomasz Rymarczyk, Mariusz Mazurek, Konrad Niderla, and Magdalena Rzemieniak. "Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection." Energies 16, no. 4 (February 11, 2023): 1818. http://dx.doi.org/10.3390/en16041818.
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Wet foundations and walls of buildings significantly increase the energy consumption of buildings, and the drying of walls is one of the priority activities as part of thermal modernization, along with the insulation of the facades. This article discusses the research findings of detecting moisture decomposition within building walls utilizing electrical impedance tomography (EIT) and deep learning techniques. In particular, the focus was on algorithmic models whose task is transforming voltage measurements into spatial EIT images. Two homogeneous deep learning networks were used: CNN (Convolutional Neural Network) and LSTM (Long-Short Term Memory). In addition, a new heterogeneous (hybrid) network was built with LSTM and CNN layers. Based on the reference reconstructions’ simulation data, three separate neural network algorithmic models: CNN, LSTM, and the hybrid model (CNN+LSTM), were trained. Then, based on popular measures such as mean square error or correlation coefficient, the quality of the models was assessed with the reference images. The obtained research results showed that hybrid deep neural networks have great potential for solving the tomographic inverse problem. Furthermore, it has been proven that the proper joining of CNN and LSTM layers can improve the effect of EIT reconstructions.
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Nguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi, and Yong-Hwa Kim. "NLOS Identification in WLANs Using Deep LSTM with CNN Features." Sensors 18, no. 11 (November 20, 2018): 4057. http://dx.doi.org/10.3390/s18114057.
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Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.
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Bilgera, Christian, Akifumi Yamamoto, Maki Sawano, Haruka Matsukura, and Hiroshi Ishida. "Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments." Sensors 18, no. 12 (December 18, 2018): 4484. http://dx.doi.org/10.3390/s18124484.
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Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks.
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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 (April 15, 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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Kumar, M. Pranay. "Image Captioning Generator Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2847–51. http://dx.doi.org/10.22214/ijraset.2022.44502.
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Abstract: The project's goal is to come up with a caption for an image. Photograph captioning is the process of making a description for an image. It necessitates an understanding of the important things, their characteristics, and the relationships between them.an image's objects Deep learning techniques have progressed, and as a result, we can create models that can predict the future thanks to the availability of large datasets and computing power. Create captions for a picture This is what we've done in Python-based research in which we applied CNN's deep learning algorithm (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) are two types of neural networks. Combining different types of RNNs (Recurrent Neural Networks) so that computer vision can be used A computer can recognize the context of a picture and present it in the appropriate context
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Ben Ismail, Mohamed Maher. "Insult detection using a partitional CNN-LSTM model." Computer Science and Information Technologies 1, no. 2 (July 1, 2020): 84–92. http://dx.doi.org/10.11591/csit.v1i2.p84-92.
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Recently, deep learning has been coupled with notice- able advances in Natural Language Processing related research. In this work, we propose a general framework to detect verbal offense in social networks comments. We introduce a partitional CNN-LSTM architecture in order to automatically recognize ver- bal offense patterns in social network comments. Specifically, we use a partitional CNN along with a LSTM model to map the social network comments into two predefined classes. In particular, rather than considering a whole document/comments as input as performed using typical CNN, we partition the comments into parts in order to capture and weight the locally relevant information in each partition. The resulting local information is then sequentially exploited across partitions using LSTM for verbal offense detection. The combination of the partitional CNN and LSTM yields the integration of the local within comments information and the long distance correlation across comments. The proposed approach was assessed using real dataset, and the obtained results proved that our solution outperforms existing relevant solutions.
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He, Yijuan, Jidong Lv, Hongjie Liu, and Tao Tang. "Toward the Trajectory Predictor for Automatic Train Operation System Using CNN–LSTM Network." Actuators 11, no. 9 (August 31, 2022): 247. http://dx.doi.org/10.3390/act11090247.
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The accurate trajectory of the train ahead with more dynamic behaviour, such as train position, speed, acceleration, etc., is the critical issue of virtual coupling for future railways, which can drastically reduce their headways and increase line capacity. This paper presents an integrated convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model for the task of trajectory prediction. A CNN–LSTM hybrid algorithm has been proposed. The model employs CNN and LSTM to extract the spatial dimension feature of the trajectory and the long-term dependencies of train trajectory data, respectively. The proposed CNN–LSTM model has superiority in achieving collaborative data mining on spatiotemporal measurement data to simultaneously learn spatial and temporal features from phasor measurement unit data. Therefore, the high-precision prediction of the train trajectory prediction is achieved based on the sufficient fusion of the above features. We use real automatic train operation (ATO) collected data for experiments and compare the proposed method with recurrent neural networks (RNN), recurrent neural networks (GRU), LSTM, and stateful-LSTM models on the same data sets. Experimental results show that the prediction performance of long-term trajectories is satisfyingly accurate. The root mean square error (RMSE) error can be reduced to less than 0.21 m, and the hit rate achieves 93% when the time horizon increases to 4S, respectively.
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Ming, Ye, Hu Qian, and Liu Guangyuan. "CNN-LSTM Facial Expression Recognition Method Fused with Two-Layer Attention Mechanism." Computational Intelligence and Neuroscience 2022 (October 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/7450637.
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When exploring facial expression recognition methods, it is found that existing algorithms make insufficient use of information about the key parts that express emotion. For this problem, on the basis of a convolutional neural network and long short-term memory (CNN-LSTM), we propose a facial expression recognition method that incorporates an attention mechanism (CNN-ALSTM). Compared with the general CNN-LSTM algorithm, it can mine the information of important regions more effectively. Furthermore, a CNN-LSTM facial expression recognition method incorporating a two-layer attention mechanism (ACNN-ALSTM) is proposed. We conducted comparative experiments on Fer2013 and processed CK + datasets with CNN-ALSTM, ACNN-ALSTM, patch based ACNN (pACNN), Facial expression recognition with attention net (FERAtt), and other networks. The results show that the proposed ACNN-ALSTM hybrid neural network model is superior to related work in expression recognition.
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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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K A, Shirien, Neethu George, and Surekha Mariam Varghese. "Descriptive Answer Script Grading System using CNN-BiLSTM Network." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 139–44. http://dx.doi.org/10.35940/ijrte.e5212.019521.
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Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.
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Shen, Qianqiao, Guiyong Wang, Yuhua Wang, Boshun Zeng, Xuan Yu, and Shuchao He. "Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network." Energies 16, no. 14 (July 13, 2023): 5347. http://dx.doi.org/10.3390/en16145347.
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In order to address the challenge of accurately predicting nitrogen oxide (NOx) emission from diesel engines in transient operation using traditional neural network models, this study proposes a NOx emission forecasting model based on a hybrid neural network architecture combining the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The objective is to enhance calibration efficiency and reduce diesel engine emissions. The proposed model utilizes data collected under the thermal cycle according to the world harmonized transient cycle (WHTC) emission test standard for training and verifying the prediction model. The CNN is employed to extract features from the training data, while LSTM networks are used to fit the data, resulting in the precise prediction of training NOx emissions from diesel engines. Experimental verification was conducted and the results demonstrate that the fitting coefficient (R2) of the CNN-LSTM network model in predicting transient NOx emissions from diesel engines is 0.977 with a root mean square error of 33.495. Compared to predictions made by a single LSTM neural network, CNN neural network predictions, and back-propagation (BP) neural network predictions, the root mean square error (RMSE) decreases by 35.6%, 50.8%, and 62.9%, respectively, while the fitting degree R2 increases by 2.5%, 4.4%, and 6.6%. These results demonstrate that the CNN-LSTM network prediction model has higher accuracy, good convergence, and robustness.
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Yao, Ruizhe, Ning Wang, Zhihui Liu, Peng Chen, and Xianjun Sheng. "Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach." Sensors 21, no. 2 (January 18, 2021): 626. http://dx.doi.org/10.3390/s21020626.
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Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abnormal information in the AMI network, so they are an important means by which to solve network intrusion. However, the existing methods exhibit a poor ability to detect intrusions in AMI, because they cannot comprehensively consider the temporal and global characteristics of intrusion information. To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a cross-layer; the CNN component recognizes regional features to obtain global features, while the LSTM component obtain periodic features by memory function. The two types of features are aggregated to obtain comprehensive features with multi-domain characteristics, which can more accurately identify intrusion information in AMI. Experiments based on the KDD Cup 99 and NSL-KDD datasets demonstrate that the proposed cross-layer feature-fusion CNN-LSTM model is superior to other existing methods.
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Zhang, Chun-Xiang, Shu-Yang Pang, Xue-Yao Gao, Jia-Qi Lu, and Bo Yu. "Attention Neural Network for Biomedical Word Sense Disambiguation." Discrete Dynamics in Nature and Society 2022 (January 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/6182058.
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In order to improve the disambiguation accuracy of biomedical words, this paper proposes a disambiguation method based on the attention neural network. The biomedical word is viewed as the center. Morphology, part of speech, and semantic information from 4 adjacent lexical units are extracted as disambiguation features. The attention layer is used to generate a feature matrix. Average asymmetric convolutional neural networks (Av-ACNN) and bidirectional long short-term memory (Bi-LSTM) networks are utilized to extract features. The softmax function is applied to determine the semantic category of the biomedical word. At the same time, CNN, LSTM, and Bi-LSTM are applied to biomedical WSD. MSH corpus is adopted to optimize CNN, LSTM, Bi-LSTM, and the proposed method and testify their disambiguation performance. Experimental results show that the average disambiguation accuracy of the proposed method is improved compared with CNN, LSTM, and Bi-LSTM. The average disambiguation accuracy of the proposed method achieves 91.38%.
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Wei, Jun, Fan Yang, Xiao-Chen Ren, and Silin Zou. "A Short-Term Prediction Model of PM2.5 Concentration Based on Deep Learning and Mode Decomposition Methods." Applied Sciences 11, no. 15 (July 27, 2021): 6915. http://dx.doi.org/10.3390/app11156915.
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Based on a set of deep learning and mode decomposition methods, a short-term prediction model for PM2.5 concentration for Beijing city is established in this paper. An ensemble empirical mode decomposition (EEMD) algorithm is first used to decompose the original PM2.5 timeseries to several high- to low-frequency intrinsic mode functions (IMFs). Each IMF component is then trained and predicted by a combination of three neural networks: back propagation network (BP), long short-term memory network (LSTM), and a hybrid network of a convolutional neural network (CNN) + LSTM. The results showed that both BP and LSTM are able to fit the low-frequency IMFs very well, and the total prediction errors of the summation of all IMFs are remarkably reduced from 21 g/m3 in the single BP model to 4.8 g/m3 in the EEMD + BP model. Spatial information from 143 stations surrounding Beijing city is extracted by CNN, which is then used to train the CNN+LSTM. It is found that, under extreme weather conditions of PM2.5 <35 g/m3 and PM2.5 >150 g/m3, the prediction errors of the CNN + LSTM model are improved by ~30% compared to the single LSTM model. However, the prediction of the very high-frequency IMF mode (IMF-1) remains a challenge for all neural networks, which might be due to microphysical turbulences and chaotic processes that cannot be resolved by the above-mentioned neural networks based on variable–variable relationship.
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Alshingiti, Zainab, Rabeah Alaqel, Jalal Al-Muhtadi, Qazi Emad Ul Haq, Kashif Saleem, and Muhammad Hamza Faheem. "A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN." Electronics 12, no. 1 (January 3, 2023): 232. http://dx.doi.org/10.3390/electronics12010232.
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In terms of the Internet and communication, security is the fundamental challenging aspect. There are numerous ways to harm the security of internet users; the most common is phishing, which is a type of attack that aims to steal or misuse a user’s personal information, including account information, identity, passwords, and credit card details. Phishers gather information about the users through mimicking original websites that are indistinguishable to the eye. Sensitive information about the users may be accessed and they might be subject to financial harm or identity theft. Therefore, there is a strong need to develop a system that efficiently detects phishing websites. Three distinct deep learning-based techniques are proposed in this paper to identify phishing websites, including long short-term memory (LSTM) and convolutional neural network (CNN) for comparison, and lastly an LSTM–CNN-based approach. Experimental findings demonstrate the accuracy of the suggested techniques, i.e., 99.2%, 97.6%, and 96.8% for CNN, LSTM–CNN, and LSTM, respectively. The proposed phishing detection method demonstrated by the CNN-based system is superior.
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Mou, Hanlin, and Junsheng Yu. "CNN-LSTM Prediction Method for Blood Pressure Based on Pulse Wave." Electronics 10, no. 14 (July 13, 2021): 1664. http://dx.doi.org/10.3390/electronics10141664.
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Cardiovascular disease (CVD), which seriously threatens human health, can be prevented by blood pressure (BP) measurement. However, convenient and accurate BP measurement is a vital problem. Although the easily-collected pulse wave (PW)-based methods make it possible to monitor BP at all times and places, the current methods still require professional knowledge to process the medical data. In this paper, we combine the advantages of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to propose a CNN-LSTM BP prediction method based on PW data. In detailed, CNN first extract features from PW data, and then the features are input into LSTM for further training. The numerical results based on real-life data sets show that the proposed method can achieve high predicted accuracy of BP while saving training time. As a result, CNN-LSTM can achieve convenient BP monitoring in daily health.
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Wang, Changyuan, Ting Yan, and Hongbo Jia. "Spatial-Temporal Feature Representation Learning for Facial Fatigue Detection." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 12 (August 27, 2018): 1856018. http://dx.doi.org/10.1142/s0218001418560189.
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In order to reduce the serious problems caused by the operators’ fatigue, we propose a novel network model Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) — for fatigue detection in the inter-frame images of video sequences, which mainly consists of CNN and LSTM network. Firstly, in order to improve the accuracy of the deep network structure, the Viola–Jones detection algorithm and the Kernelized Correlation Filter (KCF) tracking algorithm are used in the face detection to normalize the size of the inter-frame images of video sequences. Secondly, we use the CNN and the LSTM network to detect the fatigue state in real time and efficiently. The fatigue-related facial features are extracted by the CNN. Then, the temporal symptoms of the whole fatigue process can be extracted by LSTM networks, the input data which is the facial feature vector can be obtained by the CNN. Thirdly, we train and test the network in a step-by-step approach. Finally, we experiment with the proposed network model. The experimental results demonstrate that the network structure can effectively detect the fatigue state, and the overall accuracy rate can rise to 82.8%.
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Sun, Jiaqi, Jiarong Wang, Zhicheng Hao, Ming Zhu, Haijiang Sun, Ming Wei, and Kun Dong. "AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM." Remote Sensing 14, no. 13 (July 4, 2022): 3221. http://dx.doi.org/10.3390/rs14133221.
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Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.
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Ashraf, Mohsin, Fazeel Abid, Ikram Ud Din, Jawad Rasheed, Mirsat Yesiltepe, Sook Fern Yeo, and Merve T. Ersoy. "A Hybrid CNN and RNN Variant Model for Music Classification." Applied Sciences 13, no. 3 (January 22, 2023): 1476. http://dx.doi.org/10.3390/app13031476.
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Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify unique patterns but are still unable to determine the original music characteristics. Comparatively, music classification using deep learning models has been shown to be dynamic and effective. Among the many neural networks, the combination of a convolutional neural network (CNN) and variants of a recurrent neural network (RNN) has not been significantly considered. Additionally, addressing the flaws in the particular neural network classification model, this paper proposes a hybrid architecture of CNN and variants of RNN such as long short-term memory (LSTM), Bi-LSTM, gated recurrent unit (GRU), and Bi-GRU. We also compared the performance based on Mel-spectrogram and Mel-frequency cepstral coefficient (MFCC) features. Empirically, the proposed hybrid architecture of CNN and Bi-GRU using Mel-spectrogram achieved the best accuracy at 89.30%, whereas the hybridization of CNN and LSTM using MFCC achieved the best accuracy at 76.40%.
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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 (August 2, 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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Kim, Tae-Young, and Sung-Bae Cho. "Predicting residential energy consumption using CNN-LSTM neural networks." Energy 182 (September 2019): 72–81. http://dx.doi.org/10.1016/j.energy.2019.05.230.
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Li, Shuyan, Zhixiang Chen, Xiu Li, Jiwen Lu, and Jie Zhou. "Unsupervised Variational Video Hashing With 1D-CNN-LSTM Networks." IEEE Transactions on Multimedia 22, no. 6 (June 2020): 1542–54. http://dx.doi.org/10.1109/tmm.2019.2946096.
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Sperandio Nascimento, Erick Giovani, Júnia Ortiz, Adhvan Novais Furtado, and Diego Frias. "Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks." PLOS ONE 18, no. 4 (April 6, 2023): e0282621. http://dx.doi.org/10.1371/journal.pone.0282621.
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This work aims to compare deep learning models designed to predict daily number of cases and deaths caused by COVID-19 for 183 countries, using a daily basis time series, in addition to a feature augmentation strategy based on Discrete Wavelet Transform (DWT). The following deep learning architectures were compared using two different feature sets with and without DWT: (1) a homogeneous architecture containing multiple LSTM (Long-Short Term Memory) layers and (2) a hybrid architecture combining multiple CNN (Convolutional Neural Network) layers and multiple LSTM layers. Therefore, four deep learning models were evaluated: (1) LSTM, (2) CNN + LSTM, (3) DWT + LSTM and (4) DWT + CNN + LSTM. Their performances were quantitatively assessed using the metrics: Mean Absolute Error (MAE), Normalized Mean Squared Error (NMSE), Pearson R, and Factor of 2. The models were designed to predict the daily evolution of the two main epidemic variables up to 30 days ahead. After a fine-tuning procedure for hyperparameters optimization of each model, the results show a statistically significant difference between the models’ performances both for the prediction of deaths and confirmed cases (p-value<0.001). Based on NMSE values, significant differences were observed between LSTM and CNN+LSTM, indicating that convolutional layers added to LSTM networks made the model more accurate. The use of wavelet coefficients as additional features (DWT+CNN+LSTM) achieved equivalent results to CNN+LSTM model, which demonstrates the potential of wavelets application for optimizing models, since this allows training with a smaller time series data.
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Zhang, Yilin. "Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors." Mathematical Problems in Engineering 2022 (May 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/2871889.
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Accurate power load forecasting is essential for power grid operation and dispatching. To further improve the accuracy of power load forecasting, this study proposes a new power load forecasting method. Firstly, correlation coefficients of influential variables are calculated for feature selection. Secondly, the form of input data is changed to adjust for autocorrelated errors. Thirdly, data features are extracted by convolutional neural networks (CNN) to construct feature vectors. Finally, the feature vectors are input into long short-term memory (LSTM) network for training to obtain prediction results. Moreover, for solving the problem that network hyperparameters are difficult to set, the simulated annealing particle swarm optimization (SAPSO) algorithm is used to optimize the hyperparameters. Experiments show that the prediction accuracy of the proposed model is higher compared with LSTM, CNN-LSTM, and other models.
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Zhang, Chen, Qingxu Li, and Xue Cheng. "Text Sentiment Classification Based on Feature Fusion." Revue d'Intelligence Artificielle 34, no. 4 (September 30, 2020): 515–20. http://dx.doi.org/10.18280/ria.340418.
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The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was employed to obtain the global text features containing contextual semantics. After that, the features extracted by the two neural networks (NNs) were fused, and processed by Softmax classifier for text sentiment classification. To verify its performance, the CNN_BiLSTM was compared with single NNs like CNN and LSTM, as well as other deep learning (DL) NNs through experiments. The experimental results show that the proposed parallel hybrid model outperformed the contrastive methods in F1-score and accuracy. Therefore, our model can solve text sentiment classification tasks effectively, and boast better practical value than other NNs.
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Alam, Md Shahinur, Ki-Chul Kwon, Shariar Md Imtiaz, Md Biddut Hossain, Bong-Gyun Kang, and Nam Kim. "TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network." Human Behavior and Emerging Technologies 2022 (September 24, 2022): 1–13. http://dx.doi.org/10.1155/2022/6063779.
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Air-writing is a growing research topic in the field of gesture-based writing systems. This research proposes a unified, lightweight, and general-purpose deep learning algorithm for a trajectory-based air-writing recognition network (TARNet). We combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network. The architecture and applications of CNN and LSTM networks differ. LSTM is good for time series prediction yet time-consuming; on the other hand, CNN is superior in feature generation but comparatively faster. In this network, the CNN and LSTM serve as a feature generator and a recognizer, optimizing the time and accuracy, respectively. The TARNet utilizes 1-dimensional separable convolution in the first part to obtain local contextual features from low-level data (trajectories). The second part employs the recurrent algorithm to acquire the dependency of high-level output. Four publicly available air-writing digit (RealSense trajectory digit), character (RealSense trajectory character), smart-band, and Abas datasets were employed to verify the accuracy. Both the normalized and nonnormalized conditions were considered. The use of normalized data required longer training times but provided better accuracy. The test time was the same as those for nonnormalized data. The accuracy for RTD, RTC, smart-band, and Abas datasets were 99.63%, 98.74%, 95.62%, and 99.92%, respectively.
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Barua, Arnab, Daniel Fuller, Sumayyah Musa, and Xianta Jiang. "Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition." Biosensors 12, no. 7 (July 21, 2022): 549. http://dx.doi.org/10.3390/bios12070549.
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Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridization (CNN-LSTM), have been implemented. However, the sensor orientation problem poses challenges in HAR, and the length of windows as inputs for the deep neural networks has mostly been adopted arbitrarily. This paper explores the effect of window lengths with orientation invariant heuristic features on the performance of 1D-CNN-LSTM in recognizing six human activities; sitting, lying, walking and running at three different speeds using data from an accelerometer sensor encapsulated into a smartphone. Forty-two participants performed the six mentioned activities by keeping smartphones in their pants pockets with arbitrary orientation. We conducted an inter-participant evaluation using 1D-CNN-LSTM architecture. We found that the average accuracy of the classifier was saturated to 80 ± 8.07% for window lengths greater than 65 using only four selected simple orientation invariant heuristic features. In addition, precision, recall and F1-measure in recognizing stationary activities such as sitting and lying decreased with increment of window length, whereas we encountered an increment in recognizing the non-stationary activities.
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Du, Wenjun, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu, and Tuo Sun. "Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion." Journal of Advanced Transportation 2021 (July 9, 2021): 1–16. http://dx.doi.org/10.1155/2021/9512501.
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Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).
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Jing, Xin, Jungang Luo, Shangyao Zhang, and Na Wei. "Runoff forecasting model based on variational mode decomposition and artificial neural networks." Mathematical Biosciences and Engineering 19, no. 2 (2021): 1633–48. http://dx.doi.org/10.3934/mbe.2022076.
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<abstract> <p>Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecasting. However, most of the models simulate each decomposition sub-signals separately without considering the potential correlation information. A neoteric hybrid runoff forecasting model based on variational mode decomposition (VMD), convolution neural networks (CNN), and long short-term memory (LSTM) called VMD-CNN-LSTM, is proposed to improve the runoff forecasting performance further. The two-dimensional matrix containing both the time delay and correlation information among sub-signals decomposing by VMD is firstly applied to the CNN. The feature of the input matrix is then extracted by CNN and delivered to LSTM with more potential information. The experiment performed on monthly runoff data investigated from Huaxian and Xianyang hydrological stations at Wei River, China, demonstrates the VMD-superiority of CNN-LSTM to the baseline models, and robustness and stability of the forecasting of the VMD-CNN-LSTM for different leading times.</p> </abstract>
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Liu, Kun, Yong Liu, Shuo Ji, Chi Gao, Shizhong Zhang, and Jun Fu. "A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors." Sensors 23, no. 13 (June 26, 2023): 5905. http://dx.doi.org/10.3390/s23135905.
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Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is proposed. In the LSTM-CNN model, the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features A wireless sensor system including six inertial measurement units (IMU) fixed on the six positions of the lower limbs was developed. The difference in the gait recognition performance of the LSTM-CNN model was estimated using different groups of input data collected by seven different IMU grouping methods. Four phases in a complete gait were considered in this paper including the supporting phase with the right hill strike (SU-RHS), left leg swimming phase (SW-L), the supporting phase with the left hill strike (SU-LHS), and right leg swimming phase (SW-R). The results show that the best performance of the model in gait recognition appeared based on the group of data from all the six IMUs, with the recognition precision and macro-F1 unto 95.03% and 95.29%, respectively. At the same time, the best phase recognition accuracy for SU-RHS and SW-R appeared and up to 96.49% and 95.64%, respectively. The results also showed the best phase recognition accuracy (97.22%) for SW-L was acquired based on the group of data from four IMUs located at the left and right thighs and shanks. Comparably, the best phase recognition accuracy (97.86%) for SU-LHS was acquired based on the group of data from four IMUs located at left and right shanks and feet. Ulteriorly, a novel gait recognition method based on Data Pre-Filtering Long Short-Term Memory and Convolutional Neural Network (DPF-LSTM-CNN) model was proposed and its performance for gait phase recognition was evaluated. The experiment results showed that the recognition accuracy reached 97.21%, which was the highest compared to Deep convolutional neural networks (DCNN) and CNN-LSTM.
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Arif, Sheeraz, Jing Wang, Tehseen Ul Hassan, and Zesong Fei. "3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition." Future Internet 11, no. 2 (February 13, 2019): 42. http://dx.doi.org/10.3390/fi11020042.
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Human activity recognition is an active field of research in computer vision with numerous applications. Recently, deep convolutional networks and recurrent neural networks (RNN) have received increasing attention in multimedia studies, and have yielded state-of-the-art results. In this research work, we propose a new framework which intelligently combines 3D-CNN and LSTM networks. First, we integrate discriminative information from a video into a map called a ‘motion map’ by using a deep 3-dimensional convolutional network (C3D). A motion map and the next video frame can be integrated into a new motion map, and this technique can be trained by increasing the training video length iteratively; then, the final acquired network can be used for generating the motion map of the whole video. Next, a linear weighted fusion scheme is used to fuse the network feature maps into spatio-temporal features. Finally, we use a Long-Short-Term-Memory (LSTM) encoder-decoder for final predictions. This method is simple to implement and retains discriminative and dynamic information. The improved results on benchmark public datasets prove the effectiveness and practicability of the proposed method.
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Sun, Tuo, Chenwei Yang, Ke Han, Wanjing Ma, and Fan Zhang. "Bidirectional Spatial–Temporal Network for Traffic Prediction with Multisource Data." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (July 5, 2020): 78–89. http://dx.doi.org/10.1177/0361198120927393.
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Urban traffic congestion has an obvious spatial and temporal relationship and is relevant to real traffic conditions. Traffic speed is a significant parameter for reflecting congestion of road networks, which is feasible to predict. Traditional traffic forecasting methods have poor accuracy for complex urban road networks, and do not take into account weather and other multisource data. This paper proposes a convolutional neural network (CNN)-based bidirectional spatial–temporal network (CNN-BDSTN) using traffic speed and weather data by crawling electric map information. In CNN-BDSTN, the spatial dependence of traffic network is captured by CNN to compose the time-series input dataset. Bidirectional long short-term memory (LSTM) is introduced to train the convolutional time-series dataset. Compared with linear regression, autoregressive integrated moving average, extreme gradient boosting, LSTM, and CNN-LSTM, CNN-BDSTN presents its ability of spatial and temporal extension and achieves more accurately predicted results. In this case study, traffic speed data of 155 roads and weather information in Urumqi, Xinjiang, People’s Republic of China, with 1-min interval for 5 months are tested by CNN-BDSTN. The experiment results show that the accuracy of CNN-BDSTN with input of weather information is better than the scenario of no weather information, and the average predicted error is less than 5%.
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Livieris, Ioannis E., Niki Kiriakidou, Stavros Stavroyiannis, and Panagiotis Pintelas. "An Advanced CNN-LSTM Model for Cryptocurrency Forecasting." Electronics 10, no. 3 (January 26, 2021): 287. http://dx.doi.org/10.3390/electronics10030287.
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Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks.
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Zhou, Xiu, Xutao Wu, Pei Ding, Xiuguang Li, Ninghui He, Guozhi Zhang, and Xiaoxing Zhang. "Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm." Energies 13, no. 1 (December 20, 2019): 61. http://dx.doi.org/10.3390/en13010061.
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In view of the fact that the statistical feature quantity of traditional partial discharge (PD) pattern recognition relies on expert experience and lacks certain generalization, this paper develops PD pattern recognition based on the convolutional neural network (cnn) and long-term short-term memory network (lstm). Firstly, we constructed the cnn-lstm PD pattern recognition model, which combines the advantages of cnn in mining local spatial information of the PD spectrum and the advantages of lstm in mining the PD spectrum time series feature information. Then, the transformer PD UHF (Ultra High Frequency) experiment was carried out. The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.
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Du, Shaohui, Zhenghan Chen, Haoyan Wu, Yihong Tang, and YuanQing Li. "Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks." Complexity 2021 (July 2, 2021): 1–9. http://dx.doi.org/10.1155/2021/5196190.
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In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks. First, according to the time stamp information of the dataset, the interaction records of each user are sorted by the closest time. Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction. For image recommendation, two LSTM neural networks are established, which accept these feature vectors as input, respectively. The compressed output of the two sub-ESTM neural networks is used as the input of another LSTM neural network. The multilayer regression algorithm is adopted to randomly sample some network nodes to obtain the cognitive information of the nodes sampled in the entire network, predict the relationship between all nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction. The experiments show that proposed LSTM model together with CNN feature vectors can outperform other algorithms.
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Lu, Wenxing, Haidong Rui, Changyong Liang, Li Jiang, Shuping Zhao, and Keqing Li. "A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots." Entropy 22, no. 3 (February 25, 2020): 261. http://dx.doi.org/10.3390/e22030261.
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Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.
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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.
Full textAbstract:
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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Chuang, Chia-Chun, Chien-Ching Lee, Chia-Hong Yeng, Edmund-Cheung So, and Yeou-Jiunn Chen. "Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation." Applied Sciences 11, no. 24 (December 17, 2021): 12019. http://dx.doi.org/10.3390/app112412019.
Full textAbstract:
Monitoring people’s blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard.
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Lu, Yi-Xiang, Xiao-Bo Jin, Dong-Jie Liu, Xin-Chang Zhang, and Guang-Gang Geng. "Anomaly Detection Using Multiscale C-LSTM for Univariate Time-Series." Security and Communication Networks 2023 (January 23, 2023): 1–12. http://dx.doi.org/10.1155/2023/6597623.
Full textAbstract:
Computers generate network traffic data when people go online, and devices generate sensor data when they communicate with each other. When events such as network intrusion or equipment failure occur, the corresponding time-series will show abnormal trends. By detecting these time-series, anomalous events can be detected instantly, ensuring the security of network communication. However, existing time-series anomaly detection methods are difficult to deal with sequences with different degrees of correlation in complex scenes. In this paper, we propose three multiscale C-LSTM deep learning models to efficiently detect abnormal time-series: independent multiscale C-LSTM (IMC-LSTM), where each LSTM has an independent scale CNN; combined multiscale C-LSTM (CMC-LSTM), that is, the output of multiple scales of CNN is combined as an LSTM input; and shared multiscale C-LSTM (SMC-LSTM), that is, the output of multiple scales of CNN shares an LSTM model. Comparative experiments on multiple data sets show that the proposed three models have achieved excellent performance on the famous Yahoo Webscope S5 dataset and Numenta Anomaly Benchmark dataset, even better than the existing C-LSTM based latest model.
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Fu, Lei, Qizhi Tang, Peng Gao, Jingzhou Xin, and Jianting Zhou. "Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network." Algorithms 14, no. 6 (June 8, 2021): 180. http://dx.doi.org/10.3390/a14060180.
Full textAbstract:
The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.