CNN-LSTM Network Research Articles

Stock Price Prediction and Investment Simulation - Based on SSA-CNN-LSTM Method

Stock price fluctuations, which are time-series in nature. At the same time, based on the machine learning Long Short-Term Memory Network (LSTM) has excellent processing ability in predicting long time series, this paper proposes a stock price prediction method using CNN-LSTM optimized for LSTM hyperparameters using Sparrow algorithm. The data used in this study covers a total of 3403 trading days from March 1, 2010 to March 29, 2024 and about 40 technical indicator factors were selected. CNN model was firstly used to obtain features in the data. And later, CNN-LSTM network using SSA for parameter optimization uses the extracted features for stock price prediction and simulated trading based on the predicted up and down signals. The experimental results show that the SSA-CNN-LSTM network is able to provide better prediction accuracy than the CNN-LSTM network for price derivation. This prediction method not only provides a better op-eration idea for actual trading, but also provides practical experience for scholars to study time se-ries in finance.

Drone Detection and Recognition Based on Fiber-Optic EFPI Acoustic Sensor and CNN-LSTM Network Model

Drone Detection and Recognition Based on Fiber-Optic EFPI Acoustic Sensor and CNN-LSTM Network Model

UWB indoor localization method based on neural network multi-classification for NLOS distance correction

It is well known that ultra-wideband (UWB) is widely used in building indoor positioning systems (IPS) because of its unique advantages. However, compared with the line-of-sight environment (LOS), UWB localization on none-line-of-sight (NLOS) channels has certain limitations, which will reduce the UWB ranging accuracy and location reliability in indoor environment. In this paper, a neural network (NN)-enhanced UWB positioning method is proposed. It can improve positioning performance by using the received channel impulse response (CIR) and UWB raw ranging data to classify the channel conditions and predict the distance. By training CNN-LSTM and MLP neural networks, the proposed method can alleviate the deterioration of localization performance caused by NLOS. The experimental results showed that the average NLOS recognition accuracy of five different obstacles including wooden doors, concrete walls, metal shelves, human body and glass windows reaches up to 92.36 %. In addition, the average root mean square error (RMSE) between the predicted distance and the true distance was 0.3123 m. The indoor positioning test was carried out by weighted least squares (WLS) and the average positioning error under three trajectories was 0.1223 m, which improved the performance by 83.56 % compared with the original UWB positioning system, thus proving its ability to reduce positioning degradation.

A novel multi-channel CNN-LSTM and transformer-based network for diesel engine misfire diagnosis under different noise conditions

A novel multi-channel CNN-LSTM and transformer-based network for diesel engine misfire diagnosis under different noise conditions

A hybrid approach to detecting Parkinson's disease using spectrogram and deep learning CNN-LSTM network

A hybrid approach to detecting Parkinson's disease using spectrogram and deep learning CNN-LSTM network

DACLnet: A Dual-Attention-Mechanism CNN-LSTM Network for the Accurate Prediction of Nonlinear InSAR Deformation

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data.

Particle Filter Enhanced by CNN-LSTM for Localization of Autonomous Robot in Indoor Environment

Particle filters play a crucial role in indoor localization tasks where GPS or similar sensors are unavailable, owing to their adaptability to dynamic environments and contribution to precise navigation. However, despite their effectiveness in non-Gaussian and non-linear settings, particle filters face challenges such as issues with particle initialization and recovery from scenarios like robot kidnapping. These limitations impede the complete autonomy of robots, a critical aspect of their functionality. This research presents a novel solution that integrates particle filters with a CNN-LSTM network to address these challenges. Leveraging the time-series image processing capabilities of CNN-LSTMs, this architecture aids in particle filter initialization and facilitates recovery from challenging situations. The integration enhances the overall performance and autonomy of robots, making them more efficient in indoor navigation tasks.

Schlieren imaging and video classification of alphabet pronunciations: exploiting phonetic flows for speech recognition and speech therapy

Speech is a highly coordinated process that requires precise control over vocal tract morphology/motion to produce intelligible sounds while simultaneously generating unique exhaled flow patterns. The schlieren imaging technique visualizes airflows with subtle density variations. It is hypothesized that speech flows captured by schlieren, when analyzed using a hybrid of convolutional neural network (CNN) and long short-term memory (LSTM) network, can recognize alphabet pronunciations, thus facilitating automatic speech recognition and speech disorder therapy. This study evaluates the feasibility of using a CNN-based video classification network to differentiate speech flows corresponding to the first four alphabets: /A/, /B/, /C/, and /D/. A schlieren optical system was developed, and the speech flows of alphabet pronunciations were recorded for two participants at an acquisition rate of 60 frames per second. A total of 640 video clips, each lasting 1 s, were utilized to train and test a hybrid CNN-LSTM network. Acoustic analyses of the recorded sounds were conducted to understand the phonetic differences among the four alphabets. The hybrid CNN-LSTM network was trained separately on four datasets of varying sizes (i.e., 20, 30, 40, 50 videos per alphabet), all achieving over 95% accuracy in classifying videos of the same participant. However, the network’s performance declined when tested on speech flows from a different participant, with accuracy dropping to around 44%, indicating significant inter-participant variability in alphabet pronunciation. Retraining the network with videos from both participants improved accuracy to 93% on the second participant. Analysis of misclassified videos indicated that factors such as low video quality and disproportional head size affected accuracy. These results highlight the potential of CNN-assisted speech recognition and speech therapy using articulation flows, although challenges remain in expanding the alphabet set and participant cohort.

CNN-LSTM Networks Based Sand and Dust Storms Monitoring Model Using FY-4A Satellite Data

CNN-LSTM Networks Based Sand and Dust Storms Monitoring Model Using FY-4A Satellite Data

Crop Classification with Attention Based BI-LSTM and Temporal Convolution Neural Network Combination for Remote Sensing Breizhcrop Time Series Data

In the modern era, remote sensing data has become increasingly useful for determining land use and coverage requirements. Remote sensing data can be used for a variety of purposes, including the classification of crops. It is possible to aggregate remote sensing data for a specific area over time in order to obtain a more complete picture based on the time series of this data. One example of these types of data is the Breizhcrop dataset, which was collected using satellite images acquired by Sentinel 2 over a period of time. This study aims to investigate a neural network based on attention mechanisms using the BI-LSTM layer in conjunction with Temporal-CNN for the classification of crops. The aim of the research is to find a model for corps classification in image-based time series. In line with this goal, in addition to finding features over time, the presented model also needs to produce high-accuracy features at each time step to increase classification. Utilizing the designed neural network, we seek to find local features with the attention mechanism and general features with a second layer. This neural network was validated on the BreizhCrop dataset and we conclude that it performs better than alternative approaches. The proposed method has been compared with Temporal CNN, Star RNN, and Vanilla LSTM networks and it has obtained better results than the mentioned neural networks. Taking advantage of these local and global features that extract with developed model obtained a high accuracy rate of 82%.

Failure Prediction of Coal Mine Equipment Braking System Based on Digital Twin Models

The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach.

Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems.

With the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized. A key factor constraining the clinical application of rehabilitation robots is how to realize pattern recognition of human movement intentions by using the surface electromyography (sEMG) sensors to ensure unhindered human-robot interaction. A multilayer CNN-LSTM prediction network incorporating the self-attention mechanism (SAM) is proposed, in this paper, which can extract and learn the periodic and trend characteristics of the sEMG signals, and realize the accurate autoregressive prediction of the human motion information. Firstly, the multilayer CNN-LSTM network utilizes the CNN layer for initial feature extraction of data, and the LSTM network is used to improve the enhancement of the historical time-series features. Then, the SAM is used to improve the global feature extraction performance and parallel computation speed of the network. In comparison with existing test is carried out using actual data from five healthy subjects as well as a clinical hemiplegic patient to verify the superiority and practicality of the proposed algorithm. The results show that most of the model's prediction R > 0.9 for different motion states of healthy subjects; in the experiments oriented to the motion characteristics of patient subjects, the angle prediction results of R > 0.99 for the untrained data on the affected side, which proves that our proposed model also has a better effect on the angle prediction of the affected side. The main contribution of this paper is to realize continuous motion estimation of ankle joint for healthy and hemiplegic individuals under non-ideal conditions (weak sEMG signals, muscle fatigue, high muscle tension, etc.), which improves the pattern recognition accuracy and robustness of the sEMG sensor-based system.

Decoding Human Emotions Through Multimodal Analysis Using Deep Learning

In the evolving landscape of emotion recognition, this study addresses the inherent shortcomings in existing technology. Recognizing the imperative role emotions play in human interaction, the need for a more nuanced and accurate multimodal emotion recognition system is evident. This research aims to refine multimodal emotion recognition by addressing existing challenges. The problem statement revolves around enhancing the accuracy and depth of emotion recognition through innovative methodologies. Leveraging Convolutional Neural Networks (CNN), Attention layers, and Bi-LSTM networks, the study utilizes a comprehensive approach. Data fusion involves combining audio extracted from video data, followed by fusion combinations of audio and text. The methodology incorporates SoftMax classifiers for feature recognition. The fusion of audio, text, and video data, coupled with the innovative use of CNN-LSTM and Attention layer networks, contributes to this success. The discussion interprets these results, highlighting the efficacy of the proposed approach in addressing the challenges of multimodal emotion recognition. The outcomes signify a substantial advancement in the field, providing a more comprehensive understanding of emotional expressions.

Deep learning approach for accurate and stable recognition of driver's lateral intentions using naturalistic driving data

Accurate and stable recognition of a driver's lateral intention is a crucial prerequisite for the proper functioning of advanced driver-assistance systems (ADAS). Existing studies usually rely on auxiliary sensor signals, such as cameras and eye trackers; however, this reliance poses challenges in applying these methods to vehicles lacking such auxiliary sensors. Furthermore, existing studies have not fully leveraged the inherent temporal dependence of lateral intentions, leading to difficulties in avoiding erroneous recognition interruptions. Thus, this study proposes a deep-learning-based lateral intention recognition method to achieve accurate and stable recognition of lateral intention using onboard sensor signals. First, a real vehicle is used to collect a vast amount of driving data, and thus guarantee the robustness and practicality of the recognition model. Subsequently, vehicle trajectories are extracted, and a trajectory clustering method is used to label lateral intentions of the driving data; these intention labels and a feature selection algorithm are utilized to select the most representative recognition features. Therefore, a lateral driving intention recognition model is constructed using double convolutional neural networks with a long short-term memory layer (CNN-LSTM). This network architecture can fully utilize the temporal dependence of lateral intentions. Finally, the recognition performance of the designed double CNN-LSTM networks is validated using the existing driving data and real-world vehicle tests. The results indicate that the double CNN-LSTM networks can achieve stable recognition of lateral intention in real-time and the accuracy reaches 98.64% in the experiment.

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.

The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN.

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

Fast and Automatic Parametric Model Construction of Antenna Structures Using CNN–LSTM Networks

Fast and Automatic Parametric Model Construction of Antenna Structures Using CNN–LSTM Networks

Bearing remaining useful life prediction with an improved CNN-LSTM network using an artificial gorilla troop optimization algorithm

To address the problem of reliance on a priori knowledge and difficult hyperparameter selection in feature fusion. The effect of different convolutional kernel sizes and filters on feature fusion is investigated firstly, based on which an artificial Gorilla Troops Optimizer (GTO) enhanced Convolutional Long-Short Term Memory Neural Network (CNN-LSTM) method for bearing lifetime prediction is suggested. The GTO algorithm was used to optimize hyperparameters such as the convolutional kernel size of CNN-LSTM, filters and pooling layer size, batch size, number of hidden layer neurons, and rate of learning with the goal of minimizing the mean squared error of the remaining useful life (RUL) prediction. From the optimized CNN-LSTM network analyze the monitored performance degradation data, construct health indicators (HI) reflecting bearing degradation, and build the remaining bearing life prediction model. Typical life cycle data has been used for the validation of the proposed method. The results indicate that the health indicators have better trending and robustness, and leading to smaller errors in life prediction outcomes.

Forecasting solar irradiation using convolutional long short-term memory and feature selection of data from neighboring locations

The capacity of renewable energy systems, such as photovoltaic farms, has been steadily increasing owing to the continuous depletion of fossil-based energy sources. Information that can be used to optimize the use of renewable energy is required. This work proposes a novel method that uses data from neighboring locations to perform 24 h-ahead solar GHI forecasting. The approach is helpful in supporting the operation of photovoltaic farms, minimizing the need to collect new data on the target location. This work proposes the Convolutional Long Short-term Memory (ConvLSTM) model to maximize the performance using a feature-rich spatiotemporal dataset. The ConvLSTM model uses Hadamard convolutions in its input-to-state transition and state-to-state transition, allowing it to retain better information. The feature selection methods are used to ensure that only the critical subsets of the dataset are used to develop the model. The subsets identified by the feature selection methods are used on Vector Autoregressive, Support Vector Regression, Multilayer Perceptron, Convolutional Neural Network (CNN), CNN-LSTM and ConvLSTM networks. The winning method is compared to related studies using Stacked-LSTM with transfer-learning, attention-based LSTM and Krill-Herd Algorithm – Support vector Regression (KHA-SVR). The resulting coefficients of determination and root-mean-squared errors were statistically evaluated and compared. Ultimately, the proposed ConvLSTM model performed consistently better across all the seasons and feature subsets.

Dynamic prediction of steam consumption in beer production process based on Attention Mechanism CNN-BiLSTM

During the normal production process of the brewery, the steam consumption can be accurately predicted, and the boiler steam output can be planned to achieve a balance between steam production and use. In order to improve the accuracy of steam consumption prediction, this paper proposes a dynamic prediction method of steam consumption based on attention mechanism-convolution-bidirectional long short-term memory neural network (CNN-BiLSTM). In this paper, the real-time monitoring data of the brewery energy management system is selected as the experimental data for analysis, and the CNN-BiLSTM network model based on attention mechanism is compared with the results of CNN net-work, LSTM network, BiLSTM network and CNN-BiLSTM network prediction. The experimental results show that the mean absolute error, root mean square error and R-Square of the model are 0.0329, 0.0449 and 97.5%, which are better than the other four models, and can predict the steam consumption of brewery more accurately.